diff --git a/.Rbuildignore b/.Rbuildignore
new file mode 100644
index 0000000..d19bff8
--- /dev/null
+++ b/.Rbuildignore
@@ -0,0 +1,13 @@
+^.*\.Rproj$
+^\.Rproj\.user$
+^\.github$
+^LICENSE\.md$
+^CODE_OF_CONDUCT\.md$
+^CONTRIBUTING\.md$
+^README\.Rmd$
+^cran-comments\.md$
+^CRAN-SUBMISSION$
+^_pkgdown\.yml$
+^docs$
+^vignettes/CustomizeThePlot\.Rmd$
+^vignettes/TimeAndMemory\.Rmd$
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
new file mode 100644
index 0000000..ae0c779
--- /dev/null
+++ b/.github/workflows/R-CMD-check.yaml
@@ -0,0 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, dev]
+  pull_request:
+    branches: [main, dev]
+  workflow_dispatch:
+
+name: R-CMD-check
+
+jobs:
+  R-CMD-check:
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: macos-latest, r: 'release'}
+          - {os: windows-latest, r: 'release'}
+          - {os: ubuntu-latest, r: 'release'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::rcmdcheck
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
diff --git a/.gitignore b/.gitignore
index 5cb39df..16e98ef 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,4 @@
 inst/doc
 .Rhistory
-.Rbuildignore
 .Rproj*
 .Rproj.user
diff --git a/DESCRIPTION b/DESCRIPTION
index 42d38d4..0b29213 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,12 +1,18 @@
 Package: ggcoverage
 Type: Package
 Title: Visualize Genome/Protein Coverage with Various Annotations
-Version: 1.2.0
-Authors@R: 
+Version: 1.3.0
+Authors@R: c(
     person(given = "Yabing",
            family = "Song",
            role = c("aut", "cre"),
-           email = "songyb0519@gmail.com")
+           email = "songyb0519@gmail.com"),
+    person(given = "Michael",
+           family = "Jahn", 
+           email = "jahn@mpusp.mpg.de",
+           role = "aut",
+           comment = c(ORCID = "0000-0002-3913-153X"))
+  )
 Maintainer: Yabing Song <songyb0519@gmail.com>
 Description: The goal of 'ggcoverage' is to simplify the process of visualizing genome/protein coverage. It contains functions to 
     load data from BAM, BigWig, BedGraph or txt/xlsx files, create genome/protein coverage plot, add various annotations to 
@@ -14,7 +20,7 @@ Description: The goal of 'ggcoverage' is to simplify the process of visualizing
     peak annotation, contact map annotation, link annotation and peotein feature annotation.
 License: MIT + file LICENSE
 Encoding: UTF-8
-RoxygenNote: 7.3.0
+RoxygenNote: 7.3.1
 URL: https://showteeth.github.io/ggcoverage/, https://github.com/showteeth/ggcoverage
 BugReports: https://github.com/showteeth/ggcoverage/issues
 biocViews:
@@ -50,7 +56,7 @@ Imports:
     BiocParallel,
     openxlsx,
     stringr,
-    ggpp
+    gridExtra
 Suggests: 
     rmarkdown,
     knitr,
diff --git a/LICENSE b/LICENSE
index ea8d657..afa75e4 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,2 +1,2 @@
-YEAR: 2022
-COPYRIGHT HOLDER: ggtrack authors
+YEAR: 2022-2024
+COPYRIGHT HOLDER: ggcoverage authors
diff --git a/LICENSE.md b/LICENSE.md
index 71fd20e..9c73e33 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2022 ggcoverage authors
+Copyright (c) 2022-2024 ggcoverage authors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/NAMESPACE b/NAMESPACE
index 43633a7..9a7fb32 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -73,11 +73,14 @@ importFrom(RColorBrewer,brewer.pal)
 importFrom(Rsamtools,ScanBamParam)
 importFrom(Rsamtools,indexBam)
 importFrom(S4Vectors,"values<-")
+importFrom(dplyr,all_of)
 importFrom(dplyr,arrange)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
+importFrom(dplyr,mutate)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
+importFrom(dplyr,summarize)
 importFrom(ggbio,layout_karyogram)
 importFrom(ggforce,geom_bezier)
 importFrom(ggh4x,elem_list_rect)
@@ -90,6 +93,7 @@ importFrom(ggplot2,aes_string)
 importFrom(ggplot2,annotate)
 importFrom(ggplot2,arrow)
 importFrom(ggplot2,coord_cartesian)
+importFrom(ggplot2,cut_width)
 importFrom(ggplot2,element_blank)
 importFrom(ggplot2,element_rect)
 importFrom(ggplot2,element_text)
@@ -118,10 +122,11 @@ importFrom(ggplot2,scale_y_continuous)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_classic)
 importFrom(ggplot2,unit)
-importFrom(ggpp,annotate)
 importFrom(ggrepel,geom_text_repel)
 importFrom(grDevices,col2rgb)
 importFrom(grDevices,colorRampPalette)
+importFrom(gridExtra,tableGrob)
+importFrom(gridExtra,ttheme_default)
 importFrom(magrittr,"%>%")
 importFrom(methods,extends)
 importFrom(openxlsx,read.xlsx)
diff --git a/NEWS.md b/NEWS.md
index cee56c5..c80ec28 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,15 @@
+# ggcoverage 1.3.0
+## Major changes
+* Refactored `LoadTrack` function.
+* Added GH actions workflow to run automatic R CMD CHECK
+
+## Minor changes
+* Handle binning for bam files regardless if done via `bamCoverage` or `GenomicAlignments`.
+* Various small linting fixes.
+* Refactored `ggcoverage.Rmd` and `README.Rmd` in order to remove all lint errors, adhere to R code style
+
+-------------
+
 # ggcoverage 1.2.0
 ## Major changes
 * Support protein coverage and annotation plot (`ggprotein`, `geom_protein`, `geom_feature`).
diff --git a/R/LoadTrack.R b/R/LoadTrack.R
index 69b4031..1e63f33 100644
--- a/R/LoadTrack.R
+++ b/R/LoadTrack.R
@@ -3,25 +3,26 @@
 #' @param track.file Track file, when \code{track.folder} is not NULL, determined by \code{track.folder}.
 #' @param track.folder Track file folder. Default: NULL.
 #' @param format Track file format, chosen from bam, wig, bw(bigwig), bedgraph(bedGraph) and txt.
-#' @param region Region used to create coverage plot, eg: chr14:21,677,306-21,737,601 or chr14:21,677,306.
-#' Default: "chr14:21,677,306-21,737,601"
+#' @param region Region to extract coverage for, eg: chr14:21,677,306-21,737,601 or chr14:21,677,306.
+#'   Default: NULL, coverage is extracted from the first annotated chromosome/sequence.
 #' @param extend Extend length of \code{region}. Default: 2000.
 #' @param gtf.gr Granges object of GTF, created with \code{\link{import.gff}}. Default: NULL.
 #' @param gene.name The name of gene. Default: HNRNPC.
 #' @param gene.name.type Gene name type (filed of \code{gtf.gr}), chosen from gene_name and gene_id.
-#' Default: gene_name.
+#'   Default: gene_name.
 #' @param meta.info Track file metadata. The columns should be: SampleName (\code{track.file} without suffix),
-#' Type (sample with replicates information), Group (sample group). when \code{meta.file} is not NULL,
-#' determined by \code{meta.file}.Default: NULL.
+#'   Type (sample with replicates information), Group (sample group). when \code{meta.file} is not NULL,
+#'   determined by \code{meta.file}.Default: NULL.
 #' @param meta.file File contains track file metadata. Default: "".
 #' @param bamcoverage.path The path to \code{bamCoverage}, used when \code{format} is bam. Default: NULL (auto-detect).
 #' @param norm.method Methods to normalize the number of reads per bin, chosen from "RPKM", "CPM", "BPM", "RPGC", "None".
-#' Default: RPKM.
+#'   Default: RPKM.
 #' @param single.nuc Logical value, whether to visualize at single nucleotide level. Default: FALSE.
 #' @param single.nuc.region Region for \code{single.nuc}. Default: NULL
-#' @param bin.size Size of the bins, in bases. Default: 50.
+#' @param bin.size Size of the bins, in bases. Default: 10. Only used for BAM files, ignored for Wig, Bigwig, etc.
+#'   Set to NULL to turn binning off.
 #' @param bc.extra.para Extra parameters for \code{bamCoverage}, eg: "--effectiveGenomeSize 2700000000 --ignoreForNormalization chrX"
-#' @param n.cores The number of cores to be used for this job. Default:1.
+#' @param n.cores The number of cores to be used for this job. Default: 1.
 #'
 #' @return A dataframe.
 #' @importFrom rtracklayer import
@@ -31,28 +32,41 @@
 #' @importFrom GenomicRanges GRanges
 #' @importFrom IRanges IRanges subsetByOverlaps
 #' @importFrom magrittr %>%
-#' @importFrom dplyr select filter
+#' @importFrom dplyr select filter mutate all_of group_by summarize
 #' @importFrom BiocParallel register MulticoreParam bplapply
+#' @importFrom ggplot2 cut_width
 #' @export
 #'
 #' @examples
 #' library(ggcoverage)
 #' library(utils)
+#'
 #' meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
 #' sample.meta <- utils::read.csv(meta.file)
+#'
 #' # track folder
 #' track.folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")
+#'
 #' # load bigwig file
 #' track.df <- LoadTrackFile(
-#'   track.folder = track.folder, format = "bw", region = "chr14:21,677,306-21,737,601",
-#'   extend = 2000, meta.info = sample.meta
+#'   track.folder = track.folder,
+#'   format = "bw",
+#'   region = "chr14:21,677,306-21,737,601",
+#'   extend = 2000,
+#'   meta.info = sample.meta
 #' )
-LoadTrackFile <- function(track.file, track.folder = NULL, format = c("bam", "wig", "bw", "bedgraph", "txt"),
-                          region = "chr14:21,677,306-21,737,601", extend = 2000,
-                          gtf.gr = NULL, gene.name = "HNRNPC", gene.name.type = c("gene_name", "gene_id"),
-                          meta.info = NULL, meta.file = "",
-                          bamcoverage.path = NULL, norm.method = c("RPKM", "CPM", "BPM", "RPGC", "None"),
-                          single.nuc = FALSE, single.nuc.region = NULL, bin.size = 10, bc.extra.para = NULL, n.cores = 1) {
+LoadTrackFile <- function(
+  track.file, track.folder = NULL,
+  format = c("bam", "wig", "bw", "bedgraph", "txt"),
+  region = NULL, extend = 2000,
+  gtf.gr = NULL, gene.name = "HNRNPC",
+  gene.name.type = c("gene_name", "gene_id"),
+  meta.info = NULL, meta.file = "",
+  bamcoverage.path = NULL,
+  norm.method = c("RPKM", "CPM", "BPM", "RPGC", "None"),
+  single.nuc = FALSE, single.nuc.region = NULL,
+  bin.size = 10, bc.extra.para = NULL, n.cores = 1
+) {
   # check parameters
   format <- match.arg(arg = format)
   gene.name.type <- match.arg(arg = gene.name.type)
@@ -63,216 +77,115 @@ LoadTrackFile <- function(track.file, track.folder = NULL, format = c("bam", "wi
     track.file <- list.files(path = track.folder, full.names = TRUE, pattern = paste0(format, "$"))
   }
 
+  # get genomic region if supplied, else it is guessed from input
+  if (is.null(region)) {
+    message("No 'region' specified; extracting coverage for an example range\n(<=100,000 bases, first annotated sequence)")
+    if (format == "bam") {
+      seqnames <- Rsamtools::scanBamHeader(track.file[1]) %>%
+        lapply(function(x) x$targets) %>%
+        unname %>%
+        unlist
+      gr <- GenomicRanges::GRanges(
+        seqnames = names(seqnames[1]),
+        IRanges(start = 1, end = min(100000, seqnames[1]))
+      )
+    } else if (format %in% c("wig", "bw", "bedgraph")) {
+      gr <- range(rtracklayer::import(track.file[1]))
+      seqnames <- as.character(seqnames(gr))
+      if (GenomicRanges::width(gr) <= 100000) {
+        gr <- GenomicRanges::resize(gr, width = 100000)
+      }
+    }
+    message(paste0("Coverage extracted from sequence/chromosome: ", names(seqnames[1])))
+  } else {
+    gr <- PrepareRegion(
+      region = region,
+      gtf.gr = gtf.gr,
+      gene.name = gene.name,
+      gene.name.type = gene.name.type,
+      extend = extend
+    )
+  }
+
   # get track dataframe
   if (format %in% c("wig", "bw", "bedgraph")) {
     if (single.nuc) {
-      stop("To visualize single nucleotide, please use bam file!")
+      stop("To visualize single nucleotide resolution, please use bam file!")
     } else {
-      # get used gr
-      gr <- PrepareRegion(region = region, gtf.gr = gtf.gr, gene.name = gene.name, gene.name.type = gene.name.type, extend = extend)
       if (is.null(n.cores) || n.cores == 1) {
-        # read track file
-        track.list <- lapply(track.file, function(x) {
-          # get basename
-          track.file.base <- basename(x)
-          # import wig, bigwig and bedgraph file
-          single.track.df <- as.data.frame(rtracklayer::import(x, which = gr))
-          single.track.df$TrackFile <- track.file.base
-          return(single.track.df)
-        })
+        track.list <- lapply(track.file, import_bw, gr)
       } else {
-        # register
         BiocParallel::register(BiocParallel::MulticoreParam(workers = n.cores), default = TRUE)
-        # read track file
-        track.list <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = function(x) {
-          # get basename
-          track.file.base <- basename(x)
-          # import wig, bigwig and bedgraph file
-          single.track.df <- as.data.frame(rtracklayer::import(x, which = gr))
-          single.track.df$TrackFile <- track.file.base
-          return(single.track.df)
-        })
+        track.list <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = import_bw, gr)
       }
     }
   } else if (format == "bam") {
     # create index
     if (is.null(n.cores) || n.cores == 1) {
-      for (bam in track.file) {
-        bam.index.file <- paste(bam, "bai", sep = ".")
-        if (!file.exists(bam.index.file)) {
-          message("Create index file for: ", basename(bam))
-          Rsamtools::indexBam(bam)
-        }
-      }
+      lapply(track.file, index_bam)
     } else {
-      # register
       BiocParallel::register(BiocParallel::MulticoreParam(workers = n.cores), default = TRUE)
-      index.flag <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = function(x) {
-        bam.index.file <- paste(x, "bai", sep = ".")
-        if (!file.exists(bam.index.file)) {
-          message("Create index file for: ", basename(x))
-          Rsamtools::indexBam(x)
-        }
-      })
+      BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = index_bam)
     }
     if (single.nuc) {
-      if (!is.null(single.nuc.region)) {
-        single.nuc.region <- gsub(pattern = ",", replacement = "", x = single.nuc.region)
-        single.nuc.region.chr <- unlist(strsplit(x = single.nuc.region, split = ":"))[1]
-        single.nuc.region.se <- unlist(strsplit(x = single.nuc.region, split = ":"))[2]
-        single.nuc.region.start <- unlist(strsplit(x = single.nuc.region.se, split = "-"))[1]
-        single.nuc.region.end <- unlist(strsplit(x = single.nuc.region.se, split = "-"))[2]
-        # load
         if (is.null(n.cores) || n.cores == 1) {
-          track.list <- lapply(track.file, function(x) {
-            single.track.df <- GenomicAlignments::alphabetFrequencyFromBam(x, param = single.nuc.region, baseOnly = TRUE) %>% as.data.frame()
-            single.track.df <- single.track.df[, c("A", "G", "C", "T")]
-            single.track.df$score <- rowSums(single.track.df)
-            single.track.df$seqnames <- single.nuc.region.chr
-            single.track.df$start <- single.nuc.region.start:single.nuc.region.end
-            single.track.df$end <- single.track.df$start + 1
-            single.track.df$width <- 1
-            single.track.df$strand <- "*"
-            single.track.df <- single.track.df %>% dplyr::select(-c("A", "G", "C", "T"))
-            # get basename
-            track.file.base <- basename(x)
-            single.track.df$TrackFile <- track.file.base
-            single.track.df <- single.track.df[c(
-              "seqnames", "start", "end", "width",
-              "strand", "score", "TrackFile"
-            )]
-            return(single.track.df)
-          })
+          track.list <- lapply(
+            track.file,
+            single_nuc_cov,
+            single.nuc.region
+          )
         } else {
-          # register
-          BiocParallel::register(BiocParallel::MulticoreParam(workers = n.cores), default = TRUE)
-          track.list <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = function(x) {
-            single.track.df <- GenomicAlignments::alphabetFrequencyFromBam(x, param = single.nuc.region, baseOnly = TRUE) %>% as.data.frame()
-            single.track.df <- single.track.df[, c("A", "G", "C", "T")]
-            single.track.df$score <- rowSums(single.track.df)
-            single.track.df$seqnames <- single.nuc.region.chr
-            single.track.df$start <- single.nuc.region.start:single.nuc.region.end
-            single.track.df$end <- single.track.df$start + 1
-            single.track.df$width <- 1
-            single.track.df$strand <- "*"
-            single.track.df <- single.track.df %>% dplyr::select(-c("A", "G", "C", "T"))
-            # get basename
-            track.file.base <- basename(x)
-            single.track.df$TrackFile <- track.file.base
-            single.track.df <- single.track.df[c(
-              "seqnames", "start", "end", "width",
-              "strand", "score", "TrackFile"
-            )]
-            return(single.track.df)
-          })
+          track.list <- BiocParallel::bplapply(
+            track.file,
+            BPPARAM = BiocParallel::MulticoreParam(),
+            FUN = single_nuc_cov,
+            single.nuc.region
+          )
         }
-      } else {
-        stop("Please provide region for visualizing single nucleotide!")
-      }
     } else {
-      # get used gr
-      gr <- PrepareRegion(region = region, gtf.gr = gtf.gr, gene.name = gene.name, gene.name.type = gene.name.type, extend = extend)
       if (norm.method == "None") {
-        message("Calculate coverage with GenomicAlignments when norm.method is None!")
+        message("Calculating coverage with GenomicAlignments when 'norm.method = None'")
         if (is.null(n.cores) || n.cores == 1) {
-          track.list <- lapply(track.file, function(x) {
-            # get basename
-            track.file.base <- basename(x)
-            # load track
-            param <- Rsamtools::ScanBamParam(which = gr)
-            ga <- GenomicAlignments::readGAlignments(x, param = param)
-            ga.cov <- GenomicAlignments::coverage(ga)
-            ga.cov.gr <- GenomicRanges::GRanges(ga.cov)
-            ga.cov.df <- IRanges::subsetByOverlaps(ga.cov.gr, gr) %>% as.data.frame()
-            # valid the region
-            gr.df <- as.data.frame(gr)
-            ga.cov.df[1, "start"] <- gr.df[1, "start"]
-            ga.cov.df[nrow(ga.cov.df), "end"] <- gr.df[1, "end"]
-            # add track file
-            ga.cov.df$TrackFile <- track.file.base
-            return(ga.cov.df)
-          })
+          track.list <- lapply(
+            track.file, import_bam_ga, gr, bin.size
+          )
         } else {
-          # register
-          BiocParallel::register(BiocParallel::MulticoreParam(workers = n.cores), default = TRUE)
-          track.list <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = function(x) {
-            # get basename
-            track.file.base <- basename(x)
-            # load track
-            param <- Rsamtools::ScanBamParam(which = gr)
-            ga <- GenomicAlignments::readGAlignments(x, param = param)
-            ga.cov <- GenomicAlignments::coverage(ga)
-            ga.cov.gr <- GenomicRanges::GRanges(ga.cov)
-            ga.cov.df <- IRanges::subsetByOverlaps(ga.cov.gr, gr) %>% as.data.frame()
-            # valid the region
-            gr.df <- as.data.frame(gr)
-            ga.cov.df[1, "start"] <- gr.df[1, "start"]
-            ga.cov.df[nrow(ga.cov.df), "end"] <- gr.df[1, "end"]
-            # add track file
-            ga.cov.df$TrackFile <- track.file.base
-            return(ga.cov.df)
-          })
+          track.list <- BiocParallel::bplapply(
+            track.file,
+            BPPARAM = BiocParallel::MulticoreParam(),
+            FUN = import_bam_ga, gr, bin.size
+          )
         }
       } else {
-        message("Calculate coverage with bamCoverage when norm.method is not None!")
+        message("Calculate coverage with bamCoverage when 'norm.method != None'")
         # require deeptools
         if (is.null(bamcoverage.path)) {
           bamcoverage.path <- Sys.which("bamCoverage")
           if (bamcoverage.path == "") {
-            stop("Can not find bamCoverage automatically, please specify the path!")
+            stop("Can not find bamCoverage automatically, please specify 'bamcoverage.path'")
           }
         } else {
           bamcoverage.path <- bamcoverage.path
         }
-        # read track file
         if (is.null(n.cores) || n.cores == 1) {
-          track.list <- lapply(track.file, function(x) {
-            # get basename
-            track.file.base <- basename(x)
-            # bigwig file
-            out.bw.file <- tempfile(fileext = c(".bw"))
-            # prepare bamCoverage cmd
-            bamcoverage.cmd <- paste(
-              bamcoverage.path, "-b", x, "-o", out.bw.file,
-              "--binSize", bin.size, "--normalizeUsing", norm.method, bc.extra.para
-            )
-            # run command
-            message(paste("Calling bamCoverage: ", bamcoverage.cmd))
-            bamcoverage.status <- system(bamcoverage.cmd, intern = TRUE)
-            bamcoverage.status.code <- attr(bamcoverage.status, "status")
-            if (!is.null(bamcoverage.status.code)) {
-              stop("Run bamCoverage error!")
-            }
-            # import wig, bigwig and bedgraph file
-            single.track.df <- as.data.frame(rtracklayer::import(out.bw.file, which = gr))
-            single.track.df$TrackFile <- track.file.base
-            return(single.track.df)
-          })
+          track.list <- lapply(
+            track.file,
+            bam_coverage,
+            bamcoverage.path,
+            bin.size,
+            norm.method,
+            bc.extra.para, gr
+          )
         } else {
-          # register
-          BiocParallel::register(BiocParallel::MulticoreParam(workers = n.cores), default = TRUE)
-          track.list <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = function(x) {
-            # get basename
-            track.file.base <- basename(x)
-            # bigwig file
-            out.bw.file <- tempfile(fileext = c(".bw"))
-            # prepare bamCoverage cmd
-            bamcoverage.cmd <- paste(
-              bamcoverage.path, "-b", x, "-o", out.bw.file,
-              "--binSize", bin.size, "--normalizeUsing", norm.method, bc.extra.para
-            )
-            # run command
-            message(paste("Calling bamCoverage: ", bamcoverage.cmd))
-            bamcoverage.status <- system(bamcoverage.cmd, intern = TRUE)
-            bamcoverage.status.code <- attr(bamcoverage.status, "status")
-            if (!is.null(bamcoverage.status.code)) {
-              stop("Run bamCoverage error!")
-            }
-            # import wig, bigwig and bedgraph file
-            single.track.df <- as.data.frame(rtracklayer::import(out.bw.file, which = gr))
-            single.track.df$TrackFile <- track.file.base
-            return(single.track.df)
-          })
+          track.list <- BiocParallel::bplapply(
+            track.file,
+            BPPARAM = BiocParallel::MulticoreParam(),
+            FUN = bam_coverage,
+            bamcoverage.path,
+            bin.size,
+            norm.method,
+            bc.extra.para, gr
+          )
         }
       }
     }
@@ -282,34 +195,15 @@ LoadTrackFile <- function(track.file, track.folder = NULL, format = c("bam", "wi
     } else {
       # read track file
       if (is.null(n.cores) || n.cores == 1) {
-        track.list <- lapply(track.file, function(x) {
-          # get basename
-          track.file.base <- basename(x)
-          # import wig, bigwig and bedgraph file
-          single.track.df <- utils::read.table(track.file, header = TRUE)
-          single.track.df$TrackFile <- track.file.base
-          return(single.track.df)
-        })
+        track.list <- lapply(track.file, import_txt)
       } else {
-        # register
         BiocParallel::register(BiocParallel::MulticoreParam(workers = n.cores), default = TRUE)
-        track.list <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = function(x) {
-          # get basename
-          track.file.base <- basename(x)
-          # import wig, bigwig and bedgraph file
-          single.track.df <- utils::read.table(track.file, header = TRUE)
-          single.track.df$TrackFile <- track.file.base
-          return(single.track.df)
-        })
+        track.list <- BiocParallel::bplapply(track.file, BPPARAM = BiocParallel::MulticoreParam(), FUN = import_txt)
       }
     }
   }
   # get track dataframe
   track.df <- do.call(rbind, track.list)
-  # remove width and strand
-  if (all(c("width", "strand") %in% colnames(track.df))) {
-    track.df <- track.df %>% dplyr::select(-c(width, strand))
-  }
 
   # get metadata
   if (file.exists(meta.file)) {
@@ -317,7 +211,7 @@ LoadTrackFile <- function(track.file, track.folder = NULL, format = c("bam", "wi
   } else if (!is.null(meta.info)) {
     meta.info.used <- meta.info
   } else {
-    message("Sample without metadata!")
+    message("No metadata provided, returning coverage as is.")
     meta.info.used <- NULL
   }
 
@@ -340,6 +234,117 @@ LoadTrackFile <- function(track.file, track.folder = NULL, format = c("bam", "wi
       gene.name = gene.name, gene.name.type = gene.name.type
     )
   }
-
+  # return final df
   return(track.df)
 }
+
+import_bw <- function(x, gr) {
+  single.track.df <- as.data.frame(rtracklayer::import(x, which = gr))
+  single.track.df$TrackFile <- basename(x)
+  return(single.track.df)
+}
+
+import_txt <- function(x) {
+  single.track.df <- utils::read.table(x, header = TRUE)
+  single.track.df$TrackFile <- basename(x)
+  return(single.track.df)
+}
+
+import_bam_ga <- function(x, gr, bin.size) {
+  # get basename
+  track.file.base <- basename(x)
+  # load track
+  param <- Rsamtools::ScanBamParam(which = gr)
+  ga <- GenomicAlignments::readGAlignments(x, param = param)
+  ga.cov <- GenomicAlignments::coverage(ga)
+  ga.cov.gr <- GenomicRanges::GRanges(ga.cov)
+  ga.cov.df <- IRanges::subsetByOverlaps(ga.cov.gr, gr) %>%
+    as.data.frame()
+  # valid the region
+  gr.df <- as.data.frame(gr)
+  ga.cov.df[1, "start"] <- gr.df[1, "start"]
+  ga.cov.df[nrow(ga.cov.df), "end"] <- gr.df[1, "end"]
+  # optional binning
+  ga.cov.df <- bin_coverage(ga.cov.df, bin.size)
+  # add track file
+  ga.cov.df$TrackFile <- track.file.base
+  return(ga.cov.df)
+}
+
+index_bam <- function(x) {
+  bam.index.file <- paste(x, "bai", sep = ".")
+  if (!file.exists(bam.index.file)) {
+    message("Create index file for: ", basename(x))
+    Rsamtools::indexBam(x)
+  }
+}
+
+single_nuc_cov <- function(x, single.nuc.region) {
+  if (!is.null(single.nuc.region)) {
+    single.nuc.region <- gsub(pattern = ",", replacement = "", x = single.nuc.region)
+    single.nuc.region.chr <- unlist(strsplit(x = single.nuc.region, split = ":"))[1]
+    single.nuc.region.se <- unlist(strsplit(x = single.nuc.region, split = ":"))[2]
+    single.nuc.region.start <- unlist(strsplit(x = single.nuc.region.se, split = "-"))[1]
+    single.nuc.region.end <- unlist(strsplit(x = single.nuc.region.se, split = "-"))[2]
+  } else {
+    stop("Please provide region for visualizing single nucleotide!")
+  }
+  single.track.df <- GenomicAlignments::alphabetFrequencyFromBam(x, param = single.nuc.region, baseOnly = TRUE) %>%
+    as.data.frame()
+  single.track.df <- single.track.df[, c("A", "G", "C", "T")]
+  single.track.df$score <- rowSums(single.track.df)
+  single.track.df$seqnames <- single.nuc.region.chr
+  single.track.df$start <- single.nuc.region.start:single.nuc.region.end
+  single.track.df$end <- single.track.df$start + 1
+  single.track.df$width <- 1
+  single.track.df$strand <- "*"
+  single.track.df <- single.track.df %>% dplyr::select(-c("A", "G", "C", "T"))
+  # get basename
+  track.file.base <- basename(x)
+  single.track.df$TrackFile <- track.file.base
+  single.track.df <- single.track.df[c(
+    "seqnames", "start", "end", "width",
+    "strand", "score", "TrackFile"
+  )]
+  return(single.track.df)
+}
+
+bam_coverage <- function(
+  x, bamcoverage.path, bin.size, norm.method, bc.extra.para, gr
+) {
+  # bigwig file
+  out.bw.file <- tempfile(fileext = c(".bw"))
+  # prepare bamCoverage cmd
+  bamcoverage.cmd <- paste(
+    bamcoverage.path, "-b", x, "-o", out.bw.file,
+    "--binSize", bin.size, "--normalizeUsing", norm.method, bc.extra.para
+  )
+  # run command
+  message(paste("Calling bamCoverage: ", bamcoverage.cmd))
+  bamcoverage.status <- system(bamcoverage.cmd, intern = TRUE)
+  bamcoverage.status.code <- attr(bamcoverage.status, "status")
+  if (!is.null(bamcoverage.status.code)) {
+    stop("Run bamCoverage error.")
+  }
+  # import wig, bigwig and bedgraph file
+  single.track.df <- as.data.frame(rtracklayer::import(out.bw.file, which = gr))
+  single.track.df$TrackFile <- basename(x)
+  return(single.track.df)
+}
+
+bin_coverage <- function(df, bin.size = 10) {
+  if (!is.null(bin.size) && is.numeric(bin.size)) {
+    binned_df <- df %>%
+      dplyr::mutate(
+        bin = ggplot2::cut_width(start, width = bin.size, center = bin.size / 2, labels = FALSE) * bin.size
+      ) %>%
+      dplyr::group_by(.data$seqnames, .data$bin, .data$strand) %>%
+      dplyr::summarize(score = mean(.data$score, na.rm = TRUE), .groups = "drop") %>%
+      dplyr::mutate(start = .data$bin - (min(.data$bin) - 1), end = .data$bin, width = bin.size) %>%
+      dplyr::select(dplyr::all_of(c("seqnames", "start", "end", "width", "strand", "score"))) %>%
+      as.data.frame()
+    return(binned_df)
+  } else {
+    return(df)
+  }
+}
diff --git a/R/geom_base.R b/R/geom_base.R
index ea83762..c044913 100644
--- a/R/geom_base.R
+++ b/R/geom_base.R
@@ -183,16 +183,16 @@ ggplot_add.base <- function(object, plot, object_name) {
   colnames(pos.nuc.freq.long) <- c("Chr", "Pos", "Ref", "Total", "Base", "Freq")
   # get position with alt
   alt.pos <- pos.nuc.freq.long %>%
-    dplyr::filter(Ref == Base & Total != Freq) %>%
-    dplyr::pull(Pos) %>%
+    dplyr::filter(.data$Ref == .data$Base & .data$Total != .data$Freq) %>%
+    dplyr::pull(.data$Pos) %>%
     unique()
-  alt.pos.nuc.freq.long <- pos.nuc.freq.long %>% dplyr::filter(Pos %in% c(alt.pos))
+  alt.pos.nuc.freq.long <- pos.nuc.freq.long %>% dplyr::filter(.data$Pos %in% c(alt.pos))
   # get position without alt
   ref.pos <- pos.nuc.freq.long %>%
-    dplyr::filter(Ref == Base & Total == Freq) %>%
-    dplyr::pull(Pos) %>%
+    dplyr::filter(.data$Ref == .data$Base & .data$Total == .data$Freq) %>%
+    dplyr::pull(.data$Pos) %>%
     unique()
-  ref.pos.nuc.freq.long <- pos.nuc.freq.long %>% dplyr::filter(Pos %in% c(ref.pos))
+  ref.pos.nuc.freq.long <- pos.nuc.freq.long %>% dplyr::filter(.data$Pos %in% c(ref.pos))
   # create label offset
   pos.nuc.freq$Offset <- nuc.offset
   # add guide line
diff --git a/R/geom_feature.R b/R/geom_feature.R
index 85fd801..2f4db9f 100644
--- a/R/geom_feature.R
+++ b/R/geom_feature.R
@@ -17,14 +17,29 @@
 #'
 #' @examples
 #' # library(ggcoverage)
-#' # coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-#' # fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+#' # coverage.file <- system.file(
+#' #   "extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage"
+#' # )
+#' # fasta.file <- system.file(
+#' #   "extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage"
+#' # )
 #' # protein.id = "sp|P02769|ALBU_BOVIN"
-#' # protein.coverage = ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
-#' # feature.df = data.frame(ProteinID = protein.id, start = c(1, 19, 25), end = c(18, 24, 607),
-#' #                         Type = c("Signal", "Propeptide", "Chain"))
+#' # protein.coverage = ggprotein(
+#' #   coverage.file = coverage.file,
+#' #   fasta.file = fasta.file,
+#' #   protein.id = protein.id
+#' # )
+#' # feature.df = data.frame(
+#' #   ProteinID = protein.id,
+#' #   start = c(1, 19, 25),
+#' #   end = c(18, 24, 607),
+#' #   Type = c("Signal", "Propeptide", "Chain")
+#' # )
 #' # protein.coverage +
-#' #    geom_feature(feature.df = feature.df, feature.color = c("#4d81be","#173b5e","#6a521d"))
+#' #   geom_feature(
+#' #     feature.df = feature.df,
+#' #     feature.color = c("#4d81be","#173b5e","#6a521d")
+#' #   )
 geom_feature <- function(feature.file = NULL, feature.df = NULL, feature.color = "black", feature.size = 5,
                          plot.space = 0.1, plot.height = 0.1) {
   structure(list(
diff --git a/R/geom_protein.R b/R/geom_protein.R
index 6d8afb0..3613769 100644
--- a/R/geom_protein.R
+++ b/R/geom_protein.R
@@ -14,7 +14,7 @@
 #' @param show.table Logical value, whether to show coverage summary table. Default: TRUE.
 #' @param table.position The position of the coverage summary table, choose from right_top, left_top, left_bottom, right_bottom.
 #' Default: right_top.
-#' @param table.size The font size of coverage summary table. Default: 4.
+#' @param table.size The font size of coverage summary table. Default: 12.
 #' @param table.color The font color of coverage summary table. Default: black.
 #' @param range.size The label size of range text, used when \code{range.position} is in. Default: 3.
 #' @param range.position The position of y axis range, chosen from in (move y axis in the plot) and
@@ -30,22 +30,26 @@
 #' @importFrom GenomicRanges reduce GRanges setdiff
 #' @importFrom IRanges IRanges
 #' @importFrom ggplot2 ggplot geom_rect geom_text aes aes_string
-#' @importFrom ggpp annotate
 #' @importFrom scales scientific
+#' @importFrom gridExtra ttheme_default tableGrob
 #' @export
 #'
 #' @examples
 #' library(ggplot2)
 #' library(ggcoverage)
-#' coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-#' fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+#' coverage.file <- system.file(
+#'   "extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage"
+#' )
+#' fasta.file <- system.file(
+#'   "extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage"
+#' )
 #' protein.id = "sp|P02769|ALBU_BOVIN"
 #' ggplot() +
 #'   geom_protein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
 geom_protein <- function(coverage.file, fasta.file, protein.id, XCorr.threshold = 2,
                          confidence = "High", contaminant = NULL, remove.na = TRUE,
                          color = "grey", mark.bare = TRUE, mark.color = "red", mark.alpha = 0.5,
-                         show.table = TRUE, table.position = c("right_top", "left_top", "left_bottom", "right_bottom"),
+                         show.table = TRUE, table.position = c("top_right", "top_left", "bottom_right", "bottom_left"),
                          table.size = 4, table.color = "black", range.size = 3, range.position = c("in", "out")) {
   # check parameters
   table.position <- match.arg(arg = table.position)
@@ -165,22 +169,37 @@ geom_protein <- function(coverage.file, fasta.file, protein.id, XCorr.threshold
   # summary table
   if (show.table) {
     # table position
-    if (table.position == "left_top") {
-      table.x <- 0
-      table.y <- max(coverage.final[, "abundance"])
-    } else if (table.position == "right_top") {
-      table.x <- nchar(aa.seq.used)
-      table.y <- max(coverage.final[, "abundance"])
-    } else if (table.position == "left_bottom") {
-      table.x <- 0
-      table.y <- 0
-    } else if (table.position == "right_bottom") {
-      table.x <- nchar(aa.seq.used)
-      table.y <- 0
+
+    if (table.position == "top_left") {
+      table_xmin <- 0
+      table_xmax <- nchar(aa.seq.used) * 0.5
+      table_ymin <- max(coverage.final[, "abundance"]) * 0.5
+      table_ymax <- max(coverage.final[, "abundance"]) * 1.0
+    } else if (table.position == "top_right") {
+      table_xmin <- nchar(aa.seq.used) * 0.5
+      table_xmax <- nchar(aa.seq.used) * 1.0
+      table_ymin <- max(coverage.final[, "abundance"]) * 0.5
+      table_ymax <- max(coverage.final[, "abundance"]) * 1.0
+    } else if (table.position == "bottom_left") {
+      table_xmin <- 0
+      table_xmax <- nchar(aa.seq.used) * 0.5
+      table_ymin <- 0
+      table_ymax <- max(coverage.final[, "abundance"]) * 0.5
+    } else if (table.position == "bottom_right") {
+      table_xmin <- nchar(aa.seq.used) * 0.5
+      table_xmax <- nchar(aa.seq.used) * 1.0
+      table_ymin <- 0
+      table_ymax <- max(coverage.final[, "abundance"]) * 0.5
     }
-    summary.table <- ggpp::annotate(
-      geom = "table", label = list(coverage.summary), x = table.x, y = table.y,
-      color = table.color, size = table.size
+    table_theme <- gridExtra::ttheme_default(
+      base_size = table.size, base_colour = table.color
+    )
+    summary.table <- ggplot2::annotation_custom(
+      grob = gridExtra::tableGrob(
+        d = coverage.summary,
+        theme = table_theme),
+      xmin = table_xmin, xmax = table_xmax,
+      ymin = table_ymin, ymax = table_ymax
     )
     plot.ele <- append(plot.ele, summary.table)
   }
diff --git a/R/ggprotein.R b/R/ggprotein.R
index d0fc2e9..f0388d2 100644
--- a/R/ggprotein.R
+++ b/R/ggprotein.R
@@ -21,31 +21,28 @@
 #' out (normal y axis). Default: in.
 #'
 #' @return A ggplot2 object.
-#' @importFrom openxlsx read.xlsx
-#' @importFrom magrittr %>%
-#' @importFrom dplyr filter group_by summarise arrange
-#' @importFrom rlang .data
-#' @importFrom Biostrings readAAStringSet
-#' @importFrom stringr str_locate
-#' @importFrom GenomicRanges reduce GRanges setdiff
-#' @importFrom IRanges IRanges
-#' @importFrom ggplot2 ggplot geom_rect geom_text aes aes_string scale_x_continuous theme_classic theme
-#' element_blank annotate rel scale_y_continuous expansion
-#' @importFrom ggpp annotate
-#' @importFrom scales scientific
 #' @export
 #'
 #' @examples
-#' # library(ggcoverage)
-#' # coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-#' # fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
-#' # protein.id = "sp|P02769|ALBU_BOVIN"
-#' # ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
+#' library(ggcoverage)
+#' coverage.file <- system.file(
+#'   "extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage"
+#' )
+#' fasta.file <- system.file(
+#'   "extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage"
+#' )
+#' protein.id = "sp|P02769|ALBU_BOVIN"
+#'
+#' ggprotein(
+#'   coverage.file = coverage.file,
+#'   fasta.file = fasta.file,
+#'   protein.id = protein.id
+#' )
 ggprotein <- function(coverage.file, fasta.file, protein.id, XCorr.threshold = 2,
                       confidence = "High", contaminant = NULL, remove.na = TRUE,
                       color = "grey", mark.bare = TRUE, mark.color = "red", mark.alpha = 0.5,
-                      show.table = TRUE, table.position = c("right_top", "left_top", "left_bottom", "right_bottom"),
-                      table.size = 4, table.color = "black", range.size = 3, range.position = c("in", "out"), plot.space = 0.2) {
+                      show.table = TRUE, table.position = c("top_right", "top_left", "bottom_left", "bottom_right"),
+                      table.size = 10, table.color = "black", range.size = 3, range.position = c("in", "out")) {
   # check parameters
   table.position <- match.arg(arg = table.position)
   range.position <- match.arg(arg = range.position)
diff --git a/R/utils.R b/R/utils.R
index 1920618..148c73c 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -1,5 +1,5 @@
 # prepare GR
-PrepareRegion <- function(region = "chr14:21,677,306-21,737,601",
+PrepareRegion <- function(region = NULL,
                           gtf.gr = NULL, gene.name = "HNRNPC", gene.name.type = c("gene_name", "gene_id"),
                           extend = 2000) {
   # check parameters
@@ -126,7 +126,7 @@ GetGeneGroup <- function(gene.gr, fc = "queryHits", sc = "subjectHits", overlap.
 GetGeneGroupTight <- function(gene.gr, overlap.gene.gap = 1) {
   # convert to dataframe
   gene.gr.df <- as.data.frame(gene.gr)
-  gene.gr.df$ID <- 1:nrow(gene.gr.df)
+  gene.gr.df$ID <- seq_len(nrow(gene.gr.df))
   # split to group
   group.flag <- 1
   group.list <- list()
@@ -142,12 +142,12 @@ GetGeneGroupTight <- function(gene.gr, overlap.gene.gap = 1) {
       }
     }
     group.list[[paste0("G", group.flag)]] <- vec
-    gene.gr.df <- gene.gr.df %>% dplyr::filter(!ID %in% vec)
+    gene.gr.df <- gene.gr.df %>% dplyr::filter(!.data$ID %in% vec)
     group.flag <- group.flag + 1
   }
   # get group index
   group.index <- c()
-  for (g in 1:length(group.list)) {
+  for (g in seq_along(group.list)) {
     g.index <- 1 + overlap.gene.gap * (g - 1)
     g.index.vec <- rep(g.index, length(group.list[[g]]))
     names(g.index.vec) <- group.list[[g]]
@@ -322,7 +322,7 @@ getIdeogram <- function(genome, subchr = NULL, cytobands = TRUE) {
 # used in geom_base
 PrepareRect <- function(df, y.center = -0.2) {
   valid.df <- df[df$aa == "B" | df$anno != "", ]
-  rect.li <- lapply(1:nrow(valid.df), function(x) {
+  rect.li <- lapply(seq_len(nrow(valid.df)), function(x) {
     row.info <- valid.df[x, ]
     if (row.info$aa == "B") {
       c(row.info$Pos - 0.5, row.info$Pos + 0.5, row.info$aa)
diff --git a/README.Rmd b/README.Rmd
index fa790d4..7c2049b 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -10,7 +10,8 @@ knitr::opts_chunk$set(
   comment = "#>",
   fig.path = "man/figures/README-",
   out.width = "100%",
-  dpi=60
+  dpi = 60,
+  crop = NULL
 )
 ```
 
@@ -19,11 +20,15 @@ knitr::opts_chunk$set(
 <img src = "man/figures/ggcoverage.png" align = "right" width = "200"/>
 
 [![CRAN](https://www.r-pkg.org/badges/version/ggcoverage?color=orange)](https://cran.r-project.org/package=ggcoverage)
+[![R-CMD-check](https://github.com/showteeth/ggcoverage/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/showteeth/ggcoverage/actions/workflows/R-CMD-check.yaml)
+![GitHub issues](https://img.shields.io/github/issues/showteeth/ggcoverage)
+![GitHub last commit](https://img.shields.io/github/last-commit/showteeth/ggcoverage)
 ![License](https://img.shields.io/badge/license-MIT-green)
 [![CODE_SIZE](https://img.shields.io/github/languages/code-size/showteeth/ggcoverage.svg)](https://github.com/showteeth/ggcoverage)
 
 ## Introduction
-  The goal of `ggcoverage` is simplify the process of visualizing omics coverage. It contains three main parts:
+
+The goal of `ggcoverage` is simplify the process of visualizing omics coverage. It contains three main parts:
 
 * **Load the data**: `ggcoverage` can load BAM, BigWig (.bw), BedGraph, txt/xlsx files from various omics data, including WGS, RNA-seq, ChIP-seq, ATAC-seq, proteomics, et al.
 * **Create omics coverage plot**
@@ -43,21 +48,22 @@ knitr::opts_chunk$set(
 
 
 ## Installation
+
 `ggcoverage` is an R package distributed as part of the [CRAN](https://cran.r-project.org/).
-To install the package, start R and enter:
+To install the package, start R and enter one of the following commands:
   
 ```{r install, eval=FALSE}
-# install via CRAN
+# install via CRAN (not yet available)
 install.packages("ggcoverage")
 
-# install via Github
-# install.package("remotes")   #In case you have not installed it.
+# OR install via Github
+install.package("remotes")
 remotes::install_github("showteeth/ggcoverage")
 ```
 
 In general, it is **recommended** to install from [Github repository](https://github.com/showteeth/ggcoverage) (update more timely).
 
-Once `ggcoverage` is installed, it can be loaded by the following command.
+Once `ggcoverage` is installed, it can be loaded (together with other libraries) like this:
 
 ```{r library, message=FALSE, warning=FALSE}
 library("rtracklayer")
@@ -66,6 +72,7 @@ library("ggpattern")
 ```
 
 ## Manual
+
 `ggcoverage` provides two [vignettes](https://showteeth.github.io/ggcoverage/): 
 
 * **detailed manual**: step-by-step usage
@@ -73,251 +80,372 @@ library("ggpattern")
 
 
 ## RNA-seq data
+
 ### Load the data
+
 The RNA-seq data used here are from [Transcription profiling by high throughput sequencing of HNRNPC knockdown and control HeLa cells](https://bioconductor.org/packages/release/data/experiment/html/RNAseqData.HNRNPC.bam.chr14.html), we select four sample to use as example: ERR127307_chr14, ERR127306_chr14, ERR127303_chr14, ERR127302_chr14, and all bam files are converted to bigwig file with [deeptools](https://deeptools.readthedocs.io/en/develop/).
 
 Load metadata:
+
 ```{r load_metadata}
 # load metadata
-meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
-sample.meta = read.csv(meta.file)
-sample.meta
+meta_file <-
+  system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
+sample_meta <- read.csv(meta_file)
+sample_meta
 ```
 
 Load track files:
+
 ```{r load_track}
 # track folder
-track.folder = system.file("extdata", "RNA-seq", package = "ggcoverage")
+track_folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")
 # load bigwig file
-track.df = LoadTrackFile(track.folder = track.folder, format = "bw",
-                         region = "chr14:21,677,306-21,737,601", extend = 2000,
-                         meta.info = sample.meta)
+track_df <- LoadTrackFile(
+  track.folder = track_folder,
+  format = "bw",
+  region = "chr14:21,677,306-21,737,601",
+  extend = 2000,
+  meta.info = sample_meta
+)
 # check data
-head(track.df)
+head(track_df)
 ```
 
 Prepare mark region:
+
 ```{r prepare_mark}
 # create mark region
-mark.region=data.frame(start=c(21678900,21732001,21737590),
-                       end=c(21679900,21732400,21737650),
-                       label=c("M1", "M2", "M3"))
+mark_region <- data.frame(
+  start = c(21678900, 21732001, 21737590),
+  end = c(21679900, 21732400, 21737650),
+  label = c("M1", "M2", "M3")
+)
 # check data
-mark.region
+mark_region
 ```
 
 ### Load GTF
+
 To add **gene annotation**, the gtf file should contain **gene_type** and **gene_name** attributes in **column 9**; to add **transcript annotation**, the gtf file should contain **transcript_name** attribute in **column 9**.
+
 ```{r load_gtf}
-gtf.file = system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
-gtf.gr = rtracklayer::import.gff(con = gtf.file, format = 'gtf')
+gtf_file <-
+  system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
+gtf_gr <- rtracklayer::import.gff(con = gtf_file, format = "gtf")
 ```
 
 ### Basic coverage
+
 The basic coverage plot has **two types**: 
 
 * **facet**: Create subplot for every track (specified by `facet.key`). This is default.
 * **joint**: Visualize all tracks in a single plot.
 
 #### joint view
+
 Create line plot for **every sample** (`facet.key = "Type"`) and color by **every sample** (`group.key = "Type"`):
+
 ```{r basic_coverage_joint, warning=FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df,
-                            plot.type = "joint", facet.key = "Type", group.key = "Type",
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "joint",
+  facet.key = "Type",
+  group.key = "Type",
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 Create **group average line plot** (sample is indicated by `facet.key = "Type"`, group is indicated by `group.key = "Group"`):
+
 ```{r basic_coverage_joint_avg, warning=FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df,
-                            plot.type = "joint", facet.key = "Type", group.key = "Group", 
-                            joint.avg = TRUE,
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "joint",
+  facet.key = "Type",
+  group.key = "Group",
+  joint.avg = TRUE,
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
-#### facet view
+#### Facet view
+
 ```{r basic_coverage, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 #### Custom Y-axis style
+
 **Change the Y-axis scale label in/out of plot region with `range.position`**:
+
 ```{r basic_coverage_2, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "in")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "in"
+)
+
+basic_coverage
 ```
 
 **Shared/Free Y-axis scale with `facet.y.scale`**:
+
 ```{r basic_coverage_3, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "in", 
-                            facet.y.scale = "fixed")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "in",
+  facet.y.scale = "fixed"
+)
+
+basic_coverage
 ```
 
 ### Add gene annotation
+
 ```{r gene_coverage, warning=FALSE, fig.height = 8, fig.width = 12, fig.align = "center"}
-basic.coverage + 
-  geom_gene(gtf.gr=gtf.gr)
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr)
 ```
 
 
 ### Add transcript annotation
+
 **In "loose" stype (default style; each transcript occupies one line)**:
+
 ```{r transcript_coverage, warning=FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5)
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5)
 ```
 
 **In "tight" style (place non-overlap transcripts in one line)**:
+
 ```{r transcript_coverage_tight, warning=FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr, overlap.style = "tight", label.vjust = 1.5)
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr,
+                  overlap.style = "tight",
+                  label.vjust = 1.5)
 ```
 
 ### Add ideogram
+
 ```{r ideogram_coverage_1, warning=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
-basic.coverage +
-  geom_gene(gtf.gr=gtf.gr) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
 ```{r ideogram_coverage_2, warning=FALSE, fig.height = 14, fig.width = 12, fig.align = "center"}
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
 ## DNA-seq data
+
 ### CNV
-#### Example 1 
+
+#### Example 1
+
 ##### Load the data
+
 The DNA-seq data used here are from [Copy number work flow](http://bioconductor.org/help/course-materials/2014/SeattleOct2014/B02.2.3_CopyNumber.html), we select tumor sample, and get bin counts with `cn.mops::getReadCountsFromBAM` with `WL` 1000.
 
 ```{r load_bin_counts}
 # prepare metafile
-cnv.meta.info = data.frame(
+cnv_meta_info <- data.frame(
   SampleName = c("CNV_example"),
   Type = c("tumor"),
   Group = c("tumor")
 )
+
 # track file
-track.file = system.file("extdata", "DNA-seq", "CNV_example.txt", package = "ggcoverage")
+track_file <- system.file("extdata",
+                          "DNA-seq", "CNV_example.txt", package = "ggcoverage")
+
 # load txt file
-track.df = LoadTrackFile(track.file = track.file, format = "txt", region = "chr4:61750000-62,700,000",
-                         meta.info = cnv.meta.info)
+track_df <- LoadTrackFile(
+  track.file = track_file,
+  format = "txt",
+  region = "chr4:61750000-62,700,000",
+  meta.info = cnv_meta_info
+)
+
 # check data
-head(track.df)
+head(track_df)
 ```
 
 ##### Basic coverage
+
 ```{r basic_coverage_dna, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df,color = "grey", mark.region = NULL,
-                            range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  color = "grey",
+  mark.region = NULL,
+  range.position = "out"
+)
+basic_coverage
 ```
 
 ##### Add GC annotations
+
 Add **GC**, **ideogram** and **gene** annotaions.
 
 ```{r gc_coverage, warning=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 # load genome data
 library("BSgenome.Hsapiens.UCSC.hg19")
+
 # create plot
-basic.coverage +
-  geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
-  geom_gene(gtf.gr=gtf.gr) +
+basic_coverage +
+  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
+  geom_gene(gtf.gr = gtf_gr) +
   geom_ideogram(genome = "hg19")
 ```
 
 #### Example 2
+
 ##### Load the data
+
 The DNA-seq data used here are from [Genome-wide copy number analysis of single cells](https://www.nature.com/articles/nprot.2012.039), and the accession number is [SRR054616](https://trace.ncbi.nlm.nih.gov/Traces/index.html?run=SRR054616).
 
 ```{r cnv_load_track_file}
 # track file
-track.file <- system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
+track_file <-
+  system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
+
 # load track
-track.df = LoadTrackFile(track.file = track.file, format = "bw", region = "4:1-160000000")
+track_df <- LoadTrackFile(track.file = track_file,
+                          format = "bw",
+                          region = "4:1-160000000")
+
 # add chr prefix
-track.df$seqnames = paste0("chr", track.df$seqnames)
+track_df$seqnames <- paste0("chr", track_df$seqnames)
+
 # check data
-head(track.df)
+head(track_df)
 ```
 
 ##### Basic coverage
+
 ```{r cnv_basic_coverage_dna}
-basic.coverage = ggcoverage(data = track.df, color = "grey",
-                            mark.region = NULL, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  color = "grey",
+  mark.region = NULL,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 ##### Load CNV file
+
 ```{r cnv_load_cnv}
 # prepare files
-cnv.file <- system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt", package = "ggcoverage")
+cnv_file <-
+  system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt",
+              package = "ggcoverage")
+
 # read CNV
-cnv.df = read.table(file = cnv.file, sep = "\t", header = TRUE)
+cnv_df <- read.table(file = cnv_file, sep = "\t", header = TRUE)
+
 # check data
-head(cnv.df)
+head(cnv_df)
 ```
 
 ##### Add annotations
+
 Add **GC**, **ideogram** and **CNV** annotations.
 
 ```{r cnv_gc_coverage}
-# load genome data
-library("BSgenome.Hsapiens.UCSC.hg19")
 # create plot
-basic.coverage +
-  geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
-  geom_cnv(cnv.df = cnv.df, bin.col = 3, cn.col = 4) +
-  geom_ideogram(genome = "hg19",plot.space = 0, highlight.centromere = TRUE)
+basic_coverage +
+  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
+  geom_cnv(cnv.df = cnv_df,
+           bin.col = 3,
+           cn.col = 4) +
+  geom_ideogram(
+    genome = "hg19",
+    plot.space = 0,
+    highlight.centromere = TRUE
+  )
 ```
 
 
 ### Single-nucleotide level
+
 #### Load the data
+
 ```{r load_single_nuc}
 # prepare sample metadata
-sample.meta <- data.frame(
+sample_meta <- data.frame(
   SampleName = c("tumorA.chr4.selected"),
   Type = c("tumorA"),
   Group = c("tumorA")
 )
+
 # load bam file
-bam.file = system.file("extdata", "DNA-seq", "tumorA.chr4.selected.bam", package = "ggcoverage")
-track.df <- LoadTrackFile(
-  track.file = bam.file,
-  meta.info = sample.meta,
-  single.nuc=TRUE, single.nuc.region="chr4:62474235-62474295"
+bam_file <- system.file("extdata",
+                        "DNA-seq", "tumorA.chr4.selected.bam",
+                        package = "ggcoverage")
+
+track_df <- LoadTrackFile(
+  track.file = bam_file,
+  meta.info = sample_meta,
+  single.nuc = TRUE,
+  single.nuc.region = "chr4:62474235-62474295"
 )
-head(track.df)
+
+head(track_df)
 ```
 
 #### Default color scheme
+
 For base and amino acid annotation, we have following default color schemes, you can change with `nuc.color` and `aa.color` parameters.
 
 Default color scheme for base annotation is `Clustal-style`, more popular color schemes is available [here](https://www.biostars.org/p/171056/).
+
 ```{r base_color_scheme, warning=FALSE, fig.height = 2, fig.width = 6, fig.align = "center"}
 # color scheme
-nuc.color = c("A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d")
-opar <- graphics::par() 
+nuc_color <- c(
+  "A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d"
+)
+opar <- graphics::par()
+
 # create plot
 graphics::par(mar = c(1, 5, 1, 1))
 graphics::image(
-  1:length(nuc.color), 1, as.matrix(1:length(nuc.color)),
-  col = nuc.color,
-  xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
+  seq_along(nuc_color),
+  1,
+  as.matrix(seq_along(nuc_color)),
+  col = nuc_color,
+  xlab = "",
+  ylab = "",
+  xaxt = "n",
+  yaxt = "n",
+  bty = "n"
 )
-graphics::text(1:length(nuc.color), 1, names(nuc.color))
+graphics::text(seq_along(nuc_color), 1, names(nuc_color))
 graphics::mtext(
-  text = "Base", adj = 1, las = 1,
+  text = "Base",
+  adj = 1,
+  las = 1,
   side = 2
 )
 
@@ -326,24 +454,36 @@ graphics::par(opar)
 ```
 
 Default color scheme for amino acid annotation is from [Residual colours: a proposal for aminochromography](https://academic.oup.com/peds/article/10/7/743/1593029?login=false):
+
 ```{r aa_color_scheme, warning=FALSE, fig.height = 9, fig.width = 10, fig.align = "center"}
-aa.color = c(
-  "D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000", "P" = "#F28500",
-  "C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00", "I" = "#C0FF00", "L" = "#89318C",
-  "M" = "#00FF00", "F" = "#50C878", "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3",
-  "R" = "#0000FF", "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
-  "*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF"
+aa_color <- c(
+  "D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000",
+  "P" = "#F28500", "C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00",
+  "I" = "#C0FF00", "L" = "#89318C", "M" = "#00FF00", "F" = "#50C878",
+  "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3", "R" = "#0000FF",
+  "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
+  "*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF",
+  " " = "#FFFFFF"
 )
 
 graphics::par(mar = c(1, 5, 1, 1))
 graphics::image(
-  1:5, 1:5, matrix(1:length(aa.color),nrow=5),
-  col = rev(aa.color),
-  xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
+  1:5,
+  1:5,
+  matrix(seq_along(aa_color), nrow = 5),
+  col = rev(aa_color),
+  xlab = "",
+  ylab = "",
+  xaxt = "n",
+  yaxt = "n",
+  bty = "n"
 )
-graphics::text(expand.grid(1:5,1:5), names(rev(aa.color)))
+
+graphics::text(expand.grid(1:5, 1:5), names(rev(aa_color)))
 graphics::mtext(
-  text = "Amino acids", adj = 1, las = 1,
+  text = "Amino acids",
+  adj = 1,
+  las = 1,
   side = 2
 )
 
@@ -354,211 +494,319 @@ graphics::par(opar)
 #### Add base and amino acid annotation
 
 **Use twill to mark position with SNV**:
+
+```{r, echo = FALSE}
+# wait some time to avoid 'Too Many Requests' error
+Sys.sleep(60)
+```
+
+
 ```{r base_aa_coverage, warning=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
-library(ggpattern)
 # create plot with twill mark
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "twill") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
 **Use star to mark position with SNV**:
+
+```{r, echo = FALSE}
+# wait some time to avoid 'Too Many Requests' error
+Sys.sleep(60)
+```
+
 ```{r base_aa_coverage_star, warning=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 # create plot with star mark
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "star") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
 **Highlight position with SNV**:
+
+```{r, echo = FALSE}
+# wait some time to avoid 'Too Many Requests' error
+Sys.sleep(60)
+```
+
 ```{r base_aa_coverage_highlight, warning=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
-# highlight
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+# highlight one base
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "highlight") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
 ## ChIP-seq data
+
 The ChIP-seq data used here are from [DiffBind](https://bioconductor.org/packages/release/bioc/html/DiffBind.html), I select four sample to use as example: Chr18_MCF7_input, Chr18_MCF7_ER_1, Chr18_MCF7_ER_3, Chr18_MCF7_ER_2, and all bam files are converted to bigwig file with [deeptools](https://deeptools.readthedocs.io/en/develop/).
 
 Create metadata:
+
 ```{r load_metadata_chip}
 # load metadata
-sample.meta = data.frame(SampleName=c('Chr18_MCF7_ER_1','Chr18_MCF7_ER_2','Chr18_MCF7_ER_3','Chr18_MCF7_input'),
-                         Type = c("MCF7_ER_1","MCF7_ER_2","MCF7_ER_3","MCF7_input"),
-                         Group = c("IP", "IP", "IP", "Input"))
-sample.meta
+sample_meta <- data.frame(
+  SampleName = c(
+    "Chr18_MCF7_ER_1",
+    "Chr18_MCF7_ER_2",
+    "Chr18_MCF7_ER_3",
+    "Chr18_MCF7_input"
+  ),
+  Type = c("MCF7_ER_1", "MCF7_ER_2", "MCF7_ER_3", "MCF7_input"),
+  Group = c("IP", "IP", "IP", "Input")
+)
+
+sample_meta
 ```
 
 Load track files:
+
 ```{r load_track_chip}
 # track folder
-track.folder = system.file("extdata", "ChIP-seq", package = "ggcoverage")
+track_folder <- system.file("extdata", "ChIP-seq", package = "ggcoverage")
+
 # load bigwig file
-track.df = LoadTrackFile(track.folder = track.folder, format = "bw", region = "chr18:76822285-76900000",
-                         meta.info = sample.meta)
+track_df <- LoadTrackFile(
+  track.folder = track_folder,
+  format = "bw",
+  region = "chr18:76822285-76900000",
+  meta.info = sample_meta
+)
+
 # check data
-head(track.df)
+head(track_df)
 ```
 
 Prepare mark region:
+
 ```{r prepare_mark_chip}
 # create mark region
-mark.region=data.frame(start=c(76822533),
-                       end=c(76823743),
-                       label=c("Promoter"))
+mark_region <- data.frame(
+  start = c(76822533),
+  end = c(76823743),
+  label = c("Promoter")
+)
+
 # check data
-mark.region
+mark_region
 ```
 
 ### Basic coverage
+
 ```{r basic_coverage_chip, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df,
-                            mark.region=mark.region, show.mark.label = FALSE)
-basic.coverage
+basic_coverage <- ggcoverage(data = track_df,
+                             mark.region = mark_region,
+                             show.mark.label = FALSE)
+basic_coverage
 ```
 
 ### Add annotations
+
 Add **gene**, **ideogram** and **peak** annotations. To create peak annotation, we first **get consensus peaks** with [MSPC](https://github.com/Genometric/MSPC).
 
+```{r, echo = FALSE}
+# wait some time to avoid 'Too Many Requests' error
+Sys.sleep(60)
+```
+
 ```{r peak_coverage, warning=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 # get consensus peak file
-peak.file = system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
+peak_file <- system.file("extdata",
+                         "ChIP-seq",
+                         "consensus.peak",
+                         package = "ggcoverage")
 
-basic.coverage +
-  geom_gene(gtf.gr=gtf.gr) +
-  geom_peak(bed.file = peak.file) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr) +
+  geom_peak(bed.file = peak_file) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
 ## Hi-C data
+
 The Hi-C data are from [pyGenomeTracks: reproducible plots for multivariate genomic datasets](https://academic.oup.com/bioinformatics/article/37/3/422/5879987?login=false).
 
 The Hi-C matrix visualization is implemented by [HiCBricks](https://github.com/koustav-pal/HiCBricks).
 
 ### Load track data
+
 ```{r hic_track}
-library(ggcoverage)
-library(GenomicRanges)
 # prepare track dataframe
-track.file = system.file("extdata", "HiC", "H3K36me3.bw", package = "ggcoverage")
-track.df = LoadTrackFile(track.file = track.file, format = "bw", 
-                         region = "chr2L:8050000-8300000", extend = 0)
-track.df$score = ifelse(track.df$score <0, 0, track.df$score)
+track_file <-
+  system.file("extdata", "HiC", "H3K36me3.bw", package = "ggcoverage")
+
+track_df <- LoadTrackFile(
+  track.file = track_file,
+  format = "bw",
+  region = "chr2L:8050000-8300000",
+  extend = 0
+)
+
+track_df$score <- ifelse(track_df$score < 0, 0, track_df$score)
+
 # check the data
-head(track.df)
+head(track_df)
 ```
 
 ### Load Hi-C data
+
 Matrix:
 
 ```{r hic_load_hic_matrix}
 ## matrix
-hic.mat.file = system.file("extdata", "HiC", "HiC_mat.txt", package = "ggcoverage")
-hic.mat = read.table(file = hic.mat.file, sep = "\t")
-hic.mat = as.matrix(hic.mat)
+hic_mat_file <- system.file("extdata",
+                            "HiC", "HiC_mat.txt", package = "ggcoverage")
+hic_mat <- read.table(file = hic_mat_file, sep = "\t")
+hic_mat <- as.matrix(hic_mat)
 ```
 
 Bin table:
 
 ```{r hic_load_hic_bin}
 ## bin
-hic.bin.file = system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
-hic.bin = read.table(file = hic.bin.file, sep = "\t")
-colnames(hic.bin) = c("chr", "start", "end")
-hic.bin.gr = GenomicRanges::makeGRangesFromDataFrame(df = hic.bin)
+hic_bin_file <-
+  system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
+hic_bin <- read.table(file = hic_bin_file, sep = "\t")
+colnames(hic_bin) <- c("chr", "start", "end")
+hic_bin_gr <- GenomicRanges::makeGRangesFromDataFrame(df = hic_bin)
+
 ## transfrom func
-FailSafe_log10 <- function(x){
+failsafe_log10 <- function(x) {
   x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0
-  return(log10(x+1))
+  return(log10(x + 1))
 }
 ```
 
 Data transfromation method:
 
-```{r hic_load_hic_transformation}
-## transfrom func
-FailSafe_log10 <- function(x){
-  x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0
-  return(log10(x+1))
-}
-```
-
 ### Load link
+
 ```{r hic_load_link}
 # prepare arcs
-link.file = system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")
+link_file <-
+  system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")
 ```
 
 ### Basic coverage
+
 ```{r basic_coverage_hic, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-basic.coverage = ggcoverage(data = track.df, color = "grey",
-                            mark.region = NULL, range.position = "out")
-basic.coverage
+basic_coverage <-
+  ggcoverage(
+    data = track_df,
+    color = "grey",
+    mark.region = NULL,
+    range.position = "out"
+  )
+
+basic_coverage
 ```
 
 ### Add annotations
+
 Add **link**, **contact map**annotations:
 
 ```{r hic_coverage, warning=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
-basic.coverage +
-  geom_tad(matrix = hic.mat, granges = hic.bin.gr, value.cut = 0.99,
-           color.palette = "viridis", transform.fun = FailSafe_log10,
-           top = FALSE, show.rect = TRUE) +
-  geom_link(link.file = link.file, file.type = "bedpe", show.rect = TRUE)
+basic_coverage +
+  geom_tad(
+    matrix = hic_mat,
+    granges = hic_bin_gr,
+    value.cut = 0.99,
+    color.palette = "viridis",
+    transform.fun = failsafe_log10,
+    top = FALSE,
+    show.rect = TRUE
+  ) +
+  geom_link(link.file = link_file,
+            file.type = "bedpe",
+            show.rect = TRUE)
 ```
 
 ## Mass spectrometry protein coverage
+
 [Mass spectrometry (MS) is an important method for the accurate mass determination and characterization of proteins, and a variety of methods and instrumentations have been developed for its many uses](https://en.wikipedia.org/wiki/Protein_mass_spectrometry). After MS, we can check the coverage of protein to check the quality of the data and find the reason why the segment did not appear and improve the experiment.
 
 ### Load coverage
+
 The exported coverage from [Proteome Discoverer](https://www.thermofisher.cn/cn/zh/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/multi-omics-data-analysis/proteome-discoverer-software.html?adobe_mc=MCMID%7C90228073352279367993013412919222863692%7CMCAID%3D3208C32C269355DE-4000028116B65FEB%7CMCORGID%3D5B135A0C5370E6B40A490D44%40AdobeOrg%7CTS=1614293705):
+
 ```{r ms_coverage_data}
 library(openxlsx)
 # prepare coverage dataframe
-coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-coverage.df <- openxlsx::read.xlsx(coverage.file)
+coverage_file <-
+  system.file("extdata",
+              "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
+coverage_df <- openxlsx::read.xlsx(coverage_file, sheet = "Sheet1")
 # check the data
-head(coverage.df)
+head(coverage_df)
 ```
 
 The input protein fasta:
+
 ```{r ms_coverage_fasta}
-library(Biostrings)
-fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+fasta_file <-
+  system.file("extdata",
+              "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+
 # prepare track dataframe
-protein.set <- Biostrings::readAAStringSet(fasta.file)
+protein_set <- Biostrings::readAAStringSet(fasta_file)
+
 # check the data
-protein.set
+protein_set
 ```
 
 ### Protein coverage
-```{r basic_coverage_protein, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-protein.coverage = ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, 
-                             protein.id = "sp|P02769|ALBU_BOVIN", range.position = "out")
-protein.coverage
+
+```{r basic_coverage_protein, warning=FALSE, fig.height = 6, fig.width = 10, fig.align = "center"}
+protein_coverage <- ggprotein(
+  coverage.file = coverage_file,
+  fasta.file = fasta_file,
+  protein.id = "sp|P02769|ALBU_BOVIN",
+  range.position = "out"
+)
+
+protein_coverage
 ```
 
 ### Add annotation
+
 We can obtain features of the protein from [UniProt](https://www.uniprot.org/). For example, the above protein coverage plot shows that there is empty region in 1-24, and this empty region in [UniProt](https://www.uniprot.org/uniprotkb/P02769/entry) is annotated as Signal peptide and Propeptide peptide. When the protein is mature and released extracellular, these peptides will be cleaved. This is the reason why there is empty region in 1-24.
 
-```{r basic_coverage_protein_feature, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+```{r basic_coverage_protein_feature, warning=FALSE, fig.height = 6, fig.width = 10, fig.align = "center"}
 # protein feature obtained from UniProt
-protein.feature.df = data.frame(ProteinID = "sp|P02769|ALBU_BOVIN", start = c(1, 19, 25), 
-                                end = c(18, 24, 607), 
-                                Type = c("Signal", "Propeptide", "Chain"))
+protein_feature_df <- data.frame(
+  ProteinID = "sp|P02769|ALBU_BOVIN",
+  start = c(1, 19, 25),
+  end = c(18, 24, 607),
+  Type = c("Signal", "Propeptide", "Chain")
+)
+
 # add annotation
-protein.coverage + 
-  geom_feature(feature.df = protein.feature.df, feature.color = c("#4d81be","#173b5e","#6a521d"))
+protein_coverage +
+  geom_feature(feature.df = protein_feature_df,
+               feature.color = c("#4d81be", "#173b5e", "#6a521d"))
 ```
 
 ## Code of Conduct
diff --git a/README.md b/README.md
index d6cf7cd..c38abed 100644
--- a/README.md
+++ b/README.md
@@ -6,37 +6,40 @@
 <img src = "man/figures/ggcoverage.png" align = "right" width = "200"/>
 
 [![CRAN](https://www.r-pkg.org/badges/version/ggcoverage?color=orange)](https://cran.r-project.org/package=ggcoverage)
+[![R-CMD-check](https://github.com/showteeth/ggcoverage/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/showteeth/ggcoverage/actions/workflows/R-CMD-check.yaml)
+![GitHub
+issues](https://img.shields.io/github/issues/showteeth/ggcoverage)
+![GitHub last
+commit](https://img.shields.io/github/last-commit/showteeth/ggcoverage)
 ![License](https://img.shields.io/badge/license-MIT-green)
-[![CODE\_SIZE](https://img.shields.io/github/languages/code-size/showteeth/ggcoverage.svg)](https://github.com/showteeth/ggcoverage)
+[![CODE_SIZE](https://img.shields.io/github/languages/code-size/showteeth/ggcoverage.svg)](https://github.com/showteeth/ggcoverage)
 
 ## Introduction
 
 The goal of `ggcoverage` is simplify the process of visualizing omics
 coverage. It contains three main parts:
 
--   **Load the data**: `ggcoverage` can load BAM, BigWig (.bw),
-    BedGraph, txt/xlsx files from various omics data, including WGS,
-    RNA-seq, ChIP-seq, ATAC-seq, proteomics, et al.
--   **Create omics coverage plot**
--   **Add annotations**: `ggcoverage` supports six different
-    annotations:
-    -   **base and amino acid annotation**: Visualize genome coverage at
-        single-nucleotide level with bases and amino acids.
-    -   **GC annotation**: Visualize genome coverage with GC content
-    -   **CNV annotation**: Visualize genome coverage with copy number
-        variation (CNV)
-    -   **gene annotation**: Visualize genome coverage across genes
-    -   **transcription annotation**: Visualize genome coverage across
-        different transcripts
-    -   **ideogram annotation**: Visualize the region showing on whole
-        chromosome
-    -   **peak annotation**: Visualize genome coverage and peak
-        identified
-    -   **contact map annotation**: Visualize genome coverage with Hi-C
-        contact map
-    -   **link annotation**: Visualize genome coverage with contacts
-    -   **peotein feature annotation**: Visualize protein coverage with
-        features
+- **Load the data**: `ggcoverage` can load BAM, BigWig (.bw), BedGraph,
+  txt/xlsx files from various omics data, including WGS, RNA-seq,
+  ChIP-seq, ATAC-seq, proteomics, et al.
+- **Create omics coverage plot**
+- **Add annotations**: `ggcoverage` supports six different annotations:
+  - **base and amino acid annotation**: Visualize genome coverage at
+    single-nucleotide level with bases and amino acids.
+  - **GC annotation**: Visualize genome coverage with GC content
+  - **CNV annotation**: Visualize genome coverage with copy number
+    variation (CNV)
+  - **gene annotation**: Visualize genome coverage across genes
+  - **transcription annotation**: Visualize genome coverage across
+    different transcripts
+  - **ideogram annotation**: Visualize the region showing on whole
+    chromosome
+  - **peak annotation**: Visualize genome coverage and peak identified
+  - **contact map annotation**: Visualize genome coverage with Hi-C
+    contact map
+  - **link annotation**: Visualize genome coverage with contacts
+  - **peotein feature annotation**: Visualize protein coverage with
+    features
 
 `ggcoverage` utilizes `ggplot2` plotting system, so its usage is
 **ggplot2-style**!
@@ -45,14 +48,14 @@ coverage. It contains three main parts:
 
 `ggcoverage` is an R package distributed as part of the
 [CRAN](https://cran.r-project.org/). To install the package, start R and
-enter:
+enter one of the following commands:
 
 ``` r
-# install via CRAN
+# install via CRAN (not yet available)
 install.packages("ggcoverage")
 
-# install via Github
-# install.package("remotes")   #In case you have not installed it.
+# OR install via Github
+install.package("remotes")
 remotes::install_github("showteeth/ggcoverage")
 ```
 
@@ -60,8 +63,8 @@ In general, it is **recommended** to install from [Github
 repository](https://github.com/showteeth/ggcoverage) (update more
 timely).
 
-Once `ggcoverage` is installed, it can be loaded by the following
-command.
+Once `ggcoverage` is installed, it can be loaded (together with other
+libraries) like this:
 
 ``` r
 library("rtracklayer")
@@ -74,8 +77,8 @@ library("ggpattern")
 `ggcoverage` provides two
 [vignettes](https://showteeth.github.io/ggcoverage/):
 
--   **detailed manual**: step-by-step usage
--   **customize the plot**: customize the plot and add additional layer
+- **detailed manual**: step-by-step usage
+- **customize the plot**: customize the plot and add additional layer
 
 ## RNA-seq data
 
@@ -84,18 +87,19 @@ library("ggpattern")
 The RNA-seq data used here are from [Transcription profiling by high
 throughput sequencing of HNRNPC knockdown and control HeLa
 cells](https://bioconductor.org/packages/release/data/experiment/html/RNAseqData.HNRNPC.bam.chr14.html),
-we select four sample to use as example: ERR127307\_chr14,
-ERR127306\_chr14, ERR127303\_chr14, ERR127302\_chr14, and all bam files
-are converted to bigwig file with
+we select four sample to use as example: ERR127307_chr14,
+ERR127306_chr14, ERR127303_chr14, ERR127302_chr14, and all bam files are
+converted to bigwig file with
 [deeptools](https://deeptools.readthedocs.io/en/develop/).
 
 Load metadata:
 
 ``` r
 # load metadata
-meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
-sample.meta = read.csv(meta.file)
-sample.meta
+meta_file <-
+  system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
+sample_meta <- read.csv(meta_file)
+sample_meta
 #>        SampleName    Type Group
 #> 1 ERR127302_chr14 KO_rep1    KO
 #> 2 ERR127303_chr14 KO_rep2    KO
@@ -107,31 +111,37 @@ Load track files:
 
 ``` r
 # track folder
-track.folder = system.file("extdata", "RNA-seq", package = "ggcoverage")
+track_folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")
 # load bigwig file
-track.df = LoadTrackFile(track.folder = track.folder, format = "bw",
-                         region = "chr14:21,677,306-21,737,601", extend = 2000,
-                         meta.info = sample.meta)
+track_df <- LoadTrackFile(
+  track.folder = track_folder,
+  format = "bw",
+  region = "chr14:21,677,306-21,737,601",
+  extend = 2000,
+  meta.info = sample_meta
+)
 # check data
-head(track.df)
-#>   seqnames    start      end score    Type Group
-#> 1    chr14 21675306 21675950     0 KO_rep1    KO
-#> 2    chr14 21675951 21676000     1 KO_rep1    KO
-#> 3    chr14 21676001 21676100     2 KO_rep1    KO
-#> 4    chr14 21676101 21676150     1 KO_rep1    KO
-#> 5    chr14 21676151 21677100     0 KO_rep1    KO
-#> 6    chr14 21677101 21677200     2 KO_rep1    KO
+head(track_df)
+#>   seqnames    start      end width strand score    Type Group
+#> 1    chr14 21675306 21675950   645      *     0 KO_rep1    KO
+#> 2    chr14 21675951 21676000    50      *     1 KO_rep1    KO
+#> 3    chr14 21676001 21676100   100      *     2 KO_rep1    KO
+#> 4    chr14 21676101 21676150    50      *     1 KO_rep1    KO
+#> 5    chr14 21676151 21677100   950      *     0 KO_rep1    KO
+#> 6    chr14 21677101 21677200   100      *     2 KO_rep1    KO
 ```
 
 Prepare mark region:
 
 ``` r
 # create mark region
-mark.region=data.frame(start=c(21678900,21732001,21737590),
-                       end=c(21679900,21732400,21737650),
-                       label=c("M1", "M2", "M3"))
+mark_region <- data.frame(
+  start = c(21678900, 21732001, 21737590),
+  end = c(21679900, 21732400, 21737650),
+  label = c("M1", "M2", "M3")
+)
 # check data
-mark.region
+mark_region
 #>      start      end label
 #> 1 21678900 21679900    M1
 #> 2 21732001 21732400    M2
@@ -140,23 +150,24 @@ mark.region
 
 ### Load GTF
 
-To add **gene annotation**, the gtf file should contain **gene\_type**
-and **gene\_name** attributes in **column 9**; to add **transcript
-annotation**, the gtf file should contain **transcript\_name** attribute
+To add **gene annotation**, the gtf file should contain **gene_type**
+and **gene_name** attributes in **column 9**; to add **transcript
+annotation**, the gtf file should contain **transcript_name** attribute
 in **column 9**.
 
 ``` r
-gtf.file = system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
-gtf.gr = rtracklayer::import.gff(con = gtf.file, format = 'gtf')
+gtf_file <-
+  system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
+gtf_gr <- rtracklayer::import.gff(con = gtf_file, format = "gtf")
 ```
 
 ### Basic coverage
 
 The basic coverage plot has **two types**:
 
--   **facet**: Create subplot for every track (specified by
-    `facet.key`). This is default.
--   **joint**: Visualize all tracks in a single plot.
+- **facet**: Create subplot for every track (specified by `facet.key`).
+  This is default.
+- **joint**: Visualize all tracks in a single plot.
 
 #### joint view
 
@@ -164,10 +175,16 @@ Create line plot for **every sample** (`facet.key = "Type"`) and color
 by **every sample** (`group.key = "Type"`):
 
 ``` r
-basic.coverage = ggcoverage(data = track.df,
-                            plot.type = "joint", facet.key = "Type", group.key = "Type",
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "joint",
+  facet.key = "Type",
+  group.key = "Type",
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_joint-1.png" width="100%" style="display: block; margin: auto;" />
@@ -176,21 +193,32 @@ Create **group average line plot** (sample is indicated by
 `facet.key = "Type"`, group is indicated by `group.key = "Group"`):
 
 ``` r
-basic.coverage = ggcoverage(data = track.df,
-                            plot.type = "joint", facet.key = "Type", group.key = "Group", 
-                            joint.avg = TRUE,
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "joint",
+  facet.key = "Type",
+  group.key = "Group",
+  joint.avg = TRUE,
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_joint_avg-1.png" width="100%" style="display: block; margin: auto;" />
 
-#### facet view
+#### Facet view
 
 ``` r
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage-1.png" width="100%" style="display: block; margin: auto;" />
@@ -201,9 +229,14 @@ basic.coverage
 `range.position`**:
 
 ``` r
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "in")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "in"
+)
+
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_2-1.png" width="100%" style="display: block; margin: auto;" />
@@ -211,10 +244,15 @@ basic.coverage
 **Shared/Free Y-axis scale with `facet.y.scale`**:
 
 ``` r
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "in", 
-                            facet.y.scale = "fixed")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "in",
+  facet.y.scale = "fixed"
+)
+
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_3-1.png" width="100%" style="display: block; margin: auto;" />
@@ -222,8 +260,8 @@ basic.coverage
 ### Add gene annotation
 
 ``` r
-basic.coverage + 
-  geom_gene(gtf.gr=gtf.gr)
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr)
 ```
 
 <img src="man/figures/README-gene_coverage-1.png" width="100%" style="display: block; margin: auto;" />
@@ -233,8 +271,8 @@ basic.coverage +
 **In “loose” stype (default style; each transcript occupies one line)**:
 
 ``` r
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5)
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5)
 ```
 
 <img src="man/figures/README-transcript_coverage-1.png" width="100%" style="display: block; margin: auto;" />
@@ -242,8 +280,10 @@ basic.coverage +
 **In “tight” style (place non-overlap transcripts in one line)**:
 
 ``` r
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr, overlap.style = "tight", label.vjust = 1.5)
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr,
+                  overlap.style = "tight",
+                  label.vjust = 1.5)
 ```
 
 <img src="man/figures/README-transcript_coverage_tight-1.png" width="100%" style="display: block; margin: auto;" />
@@ -251,9 +291,9 @@ basic.coverage +
 ### Add ideogram
 
 ``` r
-basic.coverage +
-  geom_gene(gtf.gr=gtf.gr) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 #> Loading ideogram...
 #> Loading ranges...
 #> Scale for x is already present.
@@ -263,9 +303,9 @@ basic.coverage +
 <img src="man/figures/README-ideogram_coverage_1-1.png" width="100%" style="display: block; margin: auto;" />
 
 ``` r
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 #> Loading ideogram...
 #> Loading ranges...
 #> Scale for x is already present.
@@ -289,18 +329,26 @@ we select tumor sample, and get bin counts with
 
 ``` r
 # prepare metafile
-cnv.meta.info = data.frame(
+cnv_meta_info <- data.frame(
   SampleName = c("CNV_example"),
   Type = c("tumor"),
   Group = c("tumor")
 )
+
 # track file
-track.file = system.file("extdata", "DNA-seq", "CNV_example.txt", package = "ggcoverage")
+track_file <- system.file("extdata",
+                          "DNA-seq", "CNV_example.txt", package = "ggcoverage")
+
 # load txt file
-track.df = LoadTrackFile(track.file = track.file, format = "txt", region = "chr4:61750000-62,700,000",
-                         meta.info = cnv.meta.info)
+track_df <- LoadTrackFile(
+  track.file = track_file,
+  format = "txt",
+  region = "chr4:61750000-62,700,000",
+  meta.info = cnv_meta_info
+)
+
 # check data
-head(track.df)
+head(track_df)
 #>   seqnames    start      end score  Type Group
 #> 1     chr4 61748000 61748000    25 tumor tumor
 #> 2     chr4 61748001 61749000    24 tumor tumor
@@ -313,9 +361,13 @@ head(track.df)
 ##### Basic coverage
 
 ``` r
-basic.coverage = ggcoverage(data = track.df,color = "grey", mark.region = NULL,
-                            range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  color = "grey",
+  mark.region = NULL,
+  range.position = "out"
+)
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_dna-1.png" width="100%" style="display: block; margin: auto;" />
@@ -335,10 +387,11 @@ library("BSgenome.Hsapiens.UCSC.hg19")
 #> The following object is masked from 'package:base':
 #> 
 #>     strsplit
+
 # create plot
-basic.coverage +
-  geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
-  geom_gene(gtf.gr=gtf.gr) +
+basic_coverage +
+  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
+  geom_gene(gtf.gr = gtf_gr) +
   geom_ideogram(genome = "hg19")
 #> Loading ideogram...
 #> Loading ranges...
@@ -359,29 +412,40 @@ accession number is
 
 ``` r
 # track file
-track.file <- system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
+track_file <-
+  system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
+
 # load track
-track.df = LoadTrackFile(track.file = track.file, format = "bw", region = "4:1-160000000")
-#> Sample without metadata!
+track_df <- LoadTrackFile(track.file = track_file,
+                          format = "bw",
+                          region = "4:1-160000000")
+#> No metadata provided, returning coverage as is.
+
 # add chr prefix
-track.df$seqnames = paste0("chr", track.df$seqnames)
+track_df$seqnames <- paste0("chr", track_df$seqnames)
+
 # check data
-head(track.df)
-#>   seqnames  start    end score         Type        Group
-#> 1     chr4      1  50000   197 SRR054616.bw SRR054616.bw
-#> 2     chr4  50001 100000   598 SRR054616.bw SRR054616.bw
-#> 3     chr4 100001 150000   287 SRR054616.bw SRR054616.bw
-#> 4     chr4 150001 200000   179 SRR054616.bw SRR054616.bw
-#> 5     chr4 200001 250000   282 SRR054616.bw SRR054616.bw
-#> 6     chr4 250001 300000   212 SRR054616.bw SRR054616.bw
+head(track_df)
+#>   seqnames  start    end width strand score         Type        Group
+#> 1     chr4      1  50000 50000      *   197 SRR054616.bw SRR054616.bw
+#> 2     chr4  50001 100000 50000      *   598 SRR054616.bw SRR054616.bw
+#> 3     chr4 100001 150000 50000      *   287 SRR054616.bw SRR054616.bw
+#> 4     chr4 150001 200000 50000      *   179 SRR054616.bw SRR054616.bw
+#> 5     chr4 200001 250000 50000      *   282 SRR054616.bw SRR054616.bw
+#> 6     chr4 250001 300000 50000      *   212 SRR054616.bw SRR054616.bw
 ```
 
 ##### Basic coverage
 
 ``` r
-basic.coverage = ggcoverage(data = track.df, color = "grey",
-                            mark.region = NULL, range.position = "out")
-basic.coverage
+basic_coverage <- ggcoverage(
+  data = track_df,
+  color = "grey",
+  mark.region = NULL,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 <img src="man/figures/README-cnv_basic_coverage_dna-1.png" width="100%" />
@@ -390,11 +454,15 @@ basic.coverage
 
 ``` r
 # prepare files
-cnv.file <- system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt", package = "ggcoverage")
+cnv_file <-
+  system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt",
+              package = "ggcoverage")
+
 # read CNV
-cnv.df = read.table(file = cnv.file, sep = "\t", header = TRUE)
+cnv_df <- read.table(file = cnv_file, sep = "\t", header = TRUE)
+
 # check data
-head(cnv.df)
+head(cnv_df)
 #>   chrom chrompos  cn.ratio copy.number
 #> 1  chr4        1 11.518554           5
 #> 2  chr4    90501  5.648878           5
@@ -409,13 +477,17 @@ head(cnv.df)
 Add **GC**, **ideogram** and **CNV** annotations.
 
 ``` r
-# load genome data
-library("BSgenome.Hsapiens.UCSC.hg19")
 # create plot
-basic.coverage +
-  geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
-  geom_cnv(cnv.df = cnv.df, bin.col = 3, cn.col = 4) +
-  geom_ideogram(genome = "hg19",plot.space = 0, highlight.centromere = TRUE)
+basic_coverage +
+  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
+  geom_cnv(cnv.df = cnv_df,
+           bin.col = 3,
+           cn.col = 4) +
+  geom_ideogram(
+    genome = "hg19",
+    plot.space = 0,
+    highlight.centromere = TRUE
+  )
 #> Loading ideogram...
 #> Loading ranges...
 #> Scale for x is already present.
@@ -430,26 +502,35 @@ basic.coverage +
 
 ``` r
 # prepare sample metadata
-sample.meta <- data.frame(
+sample_meta <- data.frame(
   SampleName = c("tumorA.chr4.selected"),
   Type = c("tumorA"),
   Group = c("tumorA")
 )
+
 # load bam file
-bam.file = system.file("extdata", "DNA-seq", "tumorA.chr4.selected.bam", package = "ggcoverage")
-track.df <- LoadTrackFile(
-  track.file = bam.file,
-  meta.info = sample.meta,
-  single.nuc=TRUE, single.nuc.region="chr4:62474235-62474295"
+bam_file <- system.file("extdata",
+                        "DNA-seq", "tumorA.chr4.selected.bam",
+                        package = "ggcoverage")
+
+track_df <- LoadTrackFile(
+  track.file = bam_file,
+  meta.info = sample_meta,
+  single.nuc = TRUE,
+  single.nuc.region = "chr4:62474235-62474295"
 )
-head(track.df)
-#>   seqnames    start      end score   Type  Group
-#> 1     chr4 62474235 62474236     5 tumorA tumorA
-#> 2     chr4 62474236 62474237     5 tumorA tumorA
-#> 3     chr4 62474237 62474238     5 tumorA tumorA
-#> 4     chr4 62474238 62474239     6 tumorA tumorA
-#> 5     chr4 62474239 62474240     6 tumorA tumorA
-#> 6     chr4 62474240 62474241     6 tumorA tumorA
+#> No 'region' specified; extracting coverage for an example range
+#> (<=100,000 bases, first annotated sequence)
+#> Coverage extracted from sequence/chromosome: chr10
+
+head(track_df)
+#>   seqnames    start      end width strand score   Type  Group
+#> 1     chr4 62474235 62474236     1      *     5 tumorA tumorA
+#> 2     chr4 62474236 62474237     1      *     5 tumorA tumorA
+#> 3     chr4 62474237 62474238     1      *     5 tumorA tumorA
+#> 4     chr4 62474238 62474239     1      *     6 tumorA tumorA
+#> 5     chr4 62474239 62474240     1      *     6 tumorA tumorA
+#> 6     chr4 62474240 62474241     1      *     6 tumorA tumorA
 ```
 
 #### Default color scheme
@@ -463,18 +544,29 @@ popular color schemes is available
 
 ``` r
 # color scheme
-nuc.color = c("A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d")
-opar <- graphics::par() 
+nuc_color <- c(
+  "A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d"
+)
+opar <- graphics::par()
+
 # create plot
 graphics::par(mar = c(1, 5, 1, 1))
 graphics::image(
-  1:length(nuc.color), 1, as.matrix(1:length(nuc.color)),
-  col = nuc.color,
-  xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
+  seq_along(nuc_color),
+  1,
+  as.matrix(seq_along(nuc_color)),
+  col = nuc_color,
+  xlab = "",
+  ylab = "",
+  xaxt = "n",
+  yaxt = "n",
+  bty = "n"
 )
-graphics::text(1:length(nuc.color), 1, names(nuc.color))
+graphics::text(seq_along(nuc_color), 1, names(nuc_color))
 graphics::mtext(
-  text = "Base", adj = 1, las = 1,
+  text = "Base",
+  adj = 1,
+  las = 1,
   side = 2
 )
 ```
@@ -482,6 +574,7 @@ graphics::mtext(
 <img src="man/figures/README-base_color_scheme-1.png" width="100%" style="display: block; margin: auto;" />
 
 ``` r
+
 # reset par default
 graphics::par(opar)
 ```
@@ -491,23 +584,34 @@ colours: a proposal for
 aminochromography](https://academic.oup.com/peds/article/10/7/743/1593029?login=false):
 
 ``` r
-aa.color = c(
-  "D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000", "P" = "#F28500",
-  "C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00", "I" = "#C0FF00", "L" = "#89318C",
-  "M" = "#00FF00", "F" = "#50C878", "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3",
-  "R" = "#0000FF", "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
-  "*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF"
+aa_color <- c(
+  "D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000",
+  "P" = "#F28500", "C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00",
+  "I" = "#C0FF00", "L" = "#89318C", "M" = "#00FF00", "F" = "#50C878",
+  "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3", "R" = "#0000FF",
+  "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
+  "*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF",
+  " " = "#FFFFFF"
 )
 
 graphics::par(mar = c(1, 5, 1, 1))
 graphics::image(
-  1:5, 1:5, matrix(1:length(aa.color),nrow=5),
-  col = rev(aa.color),
-  xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
+  1:5,
+  1:5,
+  matrix(seq_along(aa_color), nrow = 5),
+  col = rev(aa_color),
+  xlab = "",
+  ylab = "",
+  xaxt = "n",
+  yaxt = "n",
+  bty = "n"
 )
-graphics::text(expand.grid(1:5,1:5), names(rev(aa.color)))
+
+graphics::text(expand.grid(1:5, 1:5), names(rev(aa_color)))
 graphics::mtext(
-  text = "Amino acids", adj = 1, las = 1,
+  text = "Amino acids",
+  adj = 1,
+  las = 1,
   side = 2
 )
 ```
@@ -515,6 +619,7 @@ graphics::mtext(
 <img src="man/figures/README-aa_color_scheme-1.png" width="100%" style="display: block; margin: auto;" />
 
 ``` r
+
 # reset par default
 graphics::par(opar)
 ```
@@ -524,14 +629,18 @@ graphics::par(opar)
 **Use twill to mark position with SNV**:
 
 ``` r
-library(ggpattern)
 # create plot with twill mark
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "twill") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 #> Loading ideogram...
 #> Loading ranges...
 #> Scale for x is already present.
@@ -544,12 +653,17 @@ ggcoverage(data = track.df, color = "grey", range.position = "out",
 
 ``` r
 # create plot with star mark
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "star") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 #> Loading ideogram...
 #> Loading ranges...
 #> Scale for x is already present.
@@ -561,13 +675,18 @@ ggcoverage(data = track.df, color = "grey", range.position = "out",
 **Highlight position with SNV**:
 
 ``` r
-# highlight
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+# highlight one base
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "highlight") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 #> Loading ideogram...
 #> Loading ranges...
 #> Scale for x is already present.
@@ -580,19 +699,27 @@ ggcoverage(data = track.df, color = "grey", range.position = "out",
 
 The ChIP-seq data used here are from
 [DiffBind](https://bioconductor.org/packages/release/bioc/html/DiffBind.html),
-I select four sample to use as example: Chr18\_MCF7\_input,
-Chr18\_MCF7\_ER\_1, Chr18\_MCF7\_ER\_3, Chr18\_MCF7\_ER\_2, and all bam
-files are converted to bigwig file with
+I select four sample to use as example: Chr18_MCF7_input,
+Chr18_MCF7_ER_1, Chr18_MCF7_ER_3, Chr18_MCF7_ER_2, and all bam files are
+converted to bigwig file with
 [deeptools](https://deeptools.readthedocs.io/en/develop/).
 
 Create metadata:
 
 ``` r
 # load metadata
-sample.meta = data.frame(SampleName=c('Chr18_MCF7_ER_1','Chr18_MCF7_ER_2','Chr18_MCF7_ER_3','Chr18_MCF7_input'),
-                         Type = c("MCF7_ER_1","MCF7_ER_2","MCF7_ER_3","MCF7_input"),
-                         Group = c("IP", "IP", "IP", "Input"))
-sample.meta
+sample_meta <- data.frame(
+  SampleName = c(
+    "Chr18_MCF7_ER_1",
+    "Chr18_MCF7_ER_2",
+    "Chr18_MCF7_ER_3",
+    "Chr18_MCF7_input"
+  ),
+  Type = c("MCF7_ER_1", "MCF7_ER_2", "MCF7_ER_3", "MCF7_input"),
+  Group = c("IP", "IP", "IP", "Input")
+)
+
+sample_meta
 #>         SampleName       Type Group
 #> 1  Chr18_MCF7_ER_1  MCF7_ER_1    IP
 #> 2  Chr18_MCF7_ER_2  MCF7_ER_2    IP
@@ -604,30 +731,39 @@ Load track files:
 
 ``` r
 # track folder
-track.folder = system.file("extdata", "ChIP-seq", package = "ggcoverage")
+track_folder <- system.file("extdata", "ChIP-seq", package = "ggcoverage")
+
 # load bigwig file
-track.df = LoadTrackFile(track.folder = track.folder, format = "bw", region = "chr18:76822285-76900000",
-                         meta.info = sample.meta)
+track_df <- LoadTrackFile(
+  track.folder = track_folder,
+  format = "bw",
+  region = "chr18:76822285-76900000",
+  meta.info = sample_meta
+)
+
 # check data
-head(track.df)
-#>   seqnames    start      end      score      Type Group
-#> 1    chr18 76820285 76820400 219.658005 MCF7_ER_1    IP
-#> 2    chr18 76820401 76820700   0.000000 MCF7_ER_1    IP
-#> 3    chr18 76820701 76821000 439.316010 MCF7_ER_1    IP
-#> 4    chr18 76821001 76821300 219.658005 MCF7_ER_1    IP
-#> 5    chr18 76821301 76821600   0.000000 MCF7_ER_1    IP
-#> 6    chr18 76821601 76821900 219.658005 MCF7_ER_1    IP
+head(track_df)
+#>   seqnames    start      end width strand      score      Type Group
+#> 1    chr18 76820285 76820400   116      * 219.658005 MCF7_ER_1    IP
+#> 2    chr18 76820401 76820700   300      *   0.000000 MCF7_ER_1    IP
+#> 3    chr18 76820701 76821000   300      * 439.316010 MCF7_ER_1    IP
+#> 4    chr18 76821001 76821300   300      * 219.658005 MCF7_ER_1    IP
+#> 5    chr18 76821301 76821600   300      *   0.000000 MCF7_ER_1    IP
+#> 6    chr18 76821601 76821900   300      * 219.658005 MCF7_ER_1    IP
 ```
 
 Prepare mark region:
 
 ``` r
 # create mark region
-mark.region=data.frame(start=c(76822533),
-                       end=c(76823743),
-                       label=c("Promoter"))
+mark_region <- data.frame(
+  start = c(76822533),
+  end = c(76823743),
+  label = c("Promoter")
+)
+
 # check data
-mark.region
+mark_region
 #>      start      end    label
 #> 1 76822533 76823743 Promoter
 ```
@@ -635,9 +771,10 @@ mark.region
 ### Basic coverage
 
 ``` r
-basic.coverage = ggcoverage(data = track.df,
-                            mark.region=mark.region, show.mark.label = FALSE)
-basic.coverage
+basic_coverage <- ggcoverage(data = track_df,
+                             mark.region = mark_region,
+                             show.mark.label = FALSE)
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_chip-1.png" width="100%" style="display: block; margin: auto;" />
@@ -650,12 +787,15 @@ annotation, we first **get consensus peaks** with
 
 ``` r
 # get consensus peak file
-peak.file = system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
-
-basic.coverage +
-  geom_gene(gtf.gr=gtf.gr) +
-  geom_peak(bed.file = peak.file) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+peak_file <- system.file("extdata",
+                         "ChIP-seq",
+                         "consensus.peak",
+                         package = "ggcoverage")
+
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr) +
+  geom_peak(bed.file = peak_file) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 #> Loading ideogram...
 #> Loading ranges...
 #> Scale for x is already present.
@@ -676,23 +816,29 @@ The Hi-C matrix visualization is implemented by
 ### Load track data
 
 ``` r
-library(ggcoverage)
-library(GenomicRanges)
 # prepare track dataframe
-track.file = system.file("extdata", "HiC", "H3K36me3.bw", package = "ggcoverage")
-track.df = LoadTrackFile(track.file = track.file, format = "bw", 
-                         region = "chr2L:8050000-8300000", extend = 0)
-#> Sample without metadata!
-track.df$score = ifelse(track.df$score <0, 0, track.df$score)
+track_file <-
+  system.file("extdata", "HiC", "H3K36me3.bw", package = "ggcoverage")
+
+track_df <- LoadTrackFile(
+  track.file = track_file,
+  format = "bw",
+  region = "chr2L:8050000-8300000",
+  extend = 0
+)
+#> No metadata provided, returning coverage as is.
+
+track_df$score <- ifelse(track_df$score < 0, 0, track_df$score)
+
 # check the data
-head(track.df)
-#>   seqnames   start     end      score        Type       Group
-#> 1    chr2L 8050000 8050009 1.66490245 H3K36me3.bw H3K36me3.bw
-#> 2    chr2L 8050015 8050049 1.59976900 H3K36me3.bw H3K36me3.bw
-#> 3    chr2L 8050057 8050091 1.60730922 H3K36me3.bw H3K36me3.bw
-#> 4    chr2L 8050097 8050131 1.65555012 H3K36me3.bw H3K36me3.bw
-#> 5    chr2L 8050137 8050171 1.71025538 H3K36me3.bw H3K36me3.bw
-#> 6    chr2L 8050176 8050210 1.75198197 H3K36me3.bw H3K36me3.bw
+head(track_df)
+#>   seqnames   start     end width strand      score        Type       Group
+#> 1    chr2L 8050000 8050009    10      * 1.66490245 H3K36me3.bw H3K36me3.bw
+#> 2    chr2L 8050015 8050049    35      * 1.59976900 H3K36me3.bw H3K36me3.bw
+#> 3    chr2L 8050057 8050091    35      * 1.60730922 H3K36me3.bw H3K36me3.bw
+#> 4    chr2L 8050097 8050131    35      * 1.65555012 H3K36me3.bw H3K36me3.bw
+#> 5    chr2L 8050137 8050171    35      * 1.71025538 H3K36me3.bw H3K36me3.bw
+#> 6    chr2L 8050176 8050210    35      * 1.75198197 H3K36me3.bw H3K36me3.bw
 ```
 
 ### Load Hi-C data
@@ -701,49 +847,51 @@ Matrix:
 
 ``` r
 ## matrix
-hic.mat.file = system.file("extdata", "HiC", "HiC_mat.txt", package = "ggcoverage")
-hic.mat = read.table(file = hic.mat.file, sep = "\t")
-hic.mat = as.matrix(hic.mat)
+hic_mat_file <- system.file("extdata",
+                            "HiC", "HiC_mat.txt", package = "ggcoverage")
+hic_mat <- read.table(file = hic_mat_file, sep = "\t")
+hic_mat <- as.matrix(hic_mat)
 ```
 
 Bin table:
 
 ``` r
 ## bin
-hic.bin.file = system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
-hic.bin = read.table(file = hic.bin.file, sep = "\t")
-colnames(hic.bin) = c("chr", "start", "end")
-hic.bin.gr = GenomicRanges::makeGRangesFromDataFrame(df = hic.bin)
+hic_bin_file <-
+  system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
+hic_bin <- read.table(file = hic_bin_file, sep = "\t")
+colnames(hic_bin) <- c("chr", "start", "end")
+hic_bin_gr <- GenomicRanges::makeGRangesFromDataFrame(df = hic_bin)
+
 ## transfrom func
-FailSafe_log10 <- function(x){
+failsafe_log10 <- function(x) {
   x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0
-  return(log10(x+1))
+  return(log10(x + 1))
 }
 ```
 
 Data transfromation method:
 
-``` r
-## transfrom func
-FailSafe_log10 <- function(x){
-  x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0
-  return(log10(x+1))
-}
-```
-
 ### Load link
 
 ``` r
 # prepare arcs
-link.file = system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")
+link_file <-
+  system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")
 ```
 
 ### Basic coverage
 
 ``` r
-basic.coverage = ggcoverage(data = track.df, color = "grey",
-                            mark.region = NULL, range.position = "out")
-basic.coverage
+basic_coverage <-
+  ggcoverage(
+    data = track_df,
+    color = "grey",
+    mark.region = NULL,
+    range.position = "out"
+  )
+
+basic_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_hic-1.png" width="100%" style="display: block; margin: auto;" />
@@ -753,11 +901,19 @@ basic.coverage
 Add **link**, **contact map**annotations:
 
 ``` r
-basic.coverage +
-  geom_tad(matrix = hic.mat, granges = hic.bin.gr, value.cut = 0.99,
-           color.palette = "viridis", transform.fun = FailSafe_log10,
-           top = FALSE, show.rect = TRUE) +
-  geom_link(link.file = link.file, file.type = "bedpe", show.rect = TRUE)
+basic_coverage +
+  geom_tad(
+    matrix = hic_mat,
+    granges = hic_bin_gr,
+    value.cut = 0.99,
+    color.palette = "viridis",
+    transform.fun = failsafe_log10,
+    top = FALSE,
+    show.rect = TRUE
+  ) +
+  geom_link(link.file = link_file,
+            file.type = "bedpe",
+            show.rect = TRUE)
 #> Read 534 lines after Skipping 0 lines
 #> Inserting Data at location: 1
 #> Data length: 534
@@ -789,10 +945,12 @@ Discoverer](https://www.thermofisher.cn/cn/zh/home/industrial/mass-spectrometry/
 ``` r
 library(openxlsx)
 # prepare coverage dataframe
-coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-coverage.df <- openxlsx::read.xlsx(coverage.file)
+coverage_file <-
+  system.file("extdata",
+              "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
+coverage_df <- openxlsx::read.xlsx(coverage_file, sheet = "Sheet1")
 # check the data
-head(coverage.df)
+head(coverage_df)
 #>   Confidence                            Annotated.Sequence
 #> 1       High  [K].ATEEQLKTVMENFVAFVDKCCAADDKEACFAVEGPK.[L]
 #> 2       High  [K].ATEEQLKTVMENFVAFVDKCCAADDKEACFAVEGPK.[L]
@@ -847,12 +1005,15 @@ head(coverage.df)
 The input protein fasta:
 
 ``` r
-library(Biostrings)
-fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+fasta_file <-
+  system.file("extdata",
+              "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+
 # prepare track dataframe
-protein.set <- Biostrings::readAAStringSet(fasta.file)
+protein_set <- Biostrings::readAAStringSet(fasta_file)
+
 # check the data
-protein.set
+protein_set
 #> AAStringSet object of length 2:
 #>     width seq                                               names               
 #> [1]   607 MKWVTFISLLLLFSSAYSRGVFR...DDKEACFAVEGPKLVVSTQTALA sp|P02769|ALBU_BOVIN
@@ -862,9 +1023,14 @@ protein.set
 ### Protein coverage
 
 ``` r
-protein.coverage = ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, 
-                             protein.id = "sp|P02769|ALBU_BOVIN", range.position = "out")
-protein.coverage
+protein_coverage <- ggprotein(
+  coverage.file = coverage_file,
+  fasta.file = fasta_file,
+  protein.id = "sp|P02769|ALBU_BOVIN",
+  range.position = "out"
+)
+
+protein_coverage
 ```
 
 <img src="man/figures/README-basic_coverage_protein-1.png" width="100%" style="display: block; margin: auto;" />
@@ -881,12 +1047,17 @@ is the reason why there is empty region in 1-24.
 
 ``` r
 # protein feature obtained from UniProt
-protein.feature.df = data.frame(ProteinID = "sp|P02769|ALBU_BOVIN", start = c(1, 19, 25), 
-                                end = c(18, 24, 607), 
-                                Type = c("Signal", "Propeptide", "Chain"))
+protein_feature_df <- data.frame(
+  ProteinID = "sp|P02769|ALBU_BOVIN",
+  start = c(1, 19, 25),
+  end = c(18, 24, 607),
+  Type = c("Signal", "Propeptide", "Chain")
+)
+
 # add annotation
-protein.coverage + 
-  geom_feature(feature.df = protein.feature.df, feature.color = c("#4d81be","#173b5e","#6a521d"))
+protein_coverage +
+  geom_feature(feature.df = protein_feature_df,
+               feature.color = c("#4d81be", "#173b5e", "#6a521d"))
 ```
 
 <img src="man/figures/README-basic_coverage_protein_feature-1.png" width="100%" style="display: block; margin: auto;" />
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diff --git a/man/LoadTrackFile.Rd b/man/LoadTrackFile.Rd
index b1d0dff..0032b8d 100644
--- a/man/LoadTrackFile.Rd
+++ b/man/LoadTrackFile.Rd
@@ -8,7 +8,7 @@ LoadTrackFile(
   track.file,
   track.folder = NULL,
   format = c("bam", "wig", "bw", "bedgraph", "txt"),
-  region = "chr14:21,677,306-21,737,601",
+  region = NULL,
   extend = 2000,
   gtf.gr = NULL,
   gene.name = "HNRNPC",
@@ -31,8 +31,8 @@ LoadTrackFile(
 
 \item{format}{Track file format, chosen from bam, wig, bw(bigwig), bedgraph(bedGraph) and txt.}
 
-\item{region}{Region used to create coverage plot, eg: chr14:21,677,306-21,737,601 or chr14:21,677,306.
-Default: "chr14:21,677,306-21,737,601"}
+\item{region}{Region to extract coverage for, eg: chr14:21,677,306-21,737,601 or chr14:21,677,306.
+Default: NULL, coverage is extracted from the first annotated chromosome/sequence.}
 
 \item{extend}{Extend length of \code{region}. Default: 2000.}
 
@@ -58,11 +58,12 @@ Default: RPKM.}
 
 \item{single.nuc.region}{Region for \code{single.nuc}. Default: NULL}
 
-\item{bin.size}{Size of the bins, in bases. Default: 50.}
+\item{bin.size}{Size of the bins, in bases. Default: 10. Only used for BAM files, ignored for Wig, Bigwig, etc.
+Set to NULL to turn binning off.}
 
 \item{bc.extra.para}{Extra parameters for \code{bamCoverage}, eg: "--effectiveGenomeSize 2700000000 --ignoreForNormalization chrX"}
 
-\item{n.cores}{The number of cores to be used for this job. Default:1.}
+\item{n.cores}{The number of cores to be used for this job. Default: 1.}
 }
 \value{
 A dataframe.
@@ -73,13 +74,19 @@ Load Track File to Dataframe.
 \examples{
 library(ggcoverage)
 library(utils)
+
 meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
 sample.meta <- utils::read.csv(meta.file)
+
 # track folder
 track.folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")
+
 # load bigwig file
 track.df <- LoadTrackFile(
-  track.folder = track.folder, format = "bw", region = "chr14:21,677,306-21,737,601",
-  extend = 2000, meta.info = sample.meta
+  track.folder = track.folder,
+  format = "bw",
+  region = "chr14:21,677,306-21,737,601",
+  extend = 2000,
+  meta.info = sample.meta
 )
 }
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diff --git a/man/geom_feature.Rd b/man/geom_feature.Rd
index 1cdb6d7..a22b6b8 100644
--- a/man/geom_feature.Rd
+++ b/man/geom_feature.Rd
@@ -34,12 +34,27 @@ Add Feature Annotation to Coverage Plot.
 }
 \examples{
 # library(ggcoverage)
-# coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-# fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+# coverage.file <- system.file(
+#   "extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage"
+# )
+# fasta.file <- system.file(
+#   "extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage"
+# )
 # protein.id = "sp|P02769|ALBU_BOVIN"
-# protein.coverage = ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
-# feature.df = data.frame(ProteinID = protein.id, start = c(1, 19, 25), end = c(18, 24, 607),
-#                         Type = c("Signal", "Propeptide", "Chain"))
+# protein.coverage = ggprotein(
+#   coverage.file = coverage.file,
+#   fasta.file = fasta.file,
+#   protein.id = protein.id
+# )
+# feature.df = data.frame(
+#   ProteinID = protein.id,
+#   start = c(1, 19, 25),
+#   end = c(18, 24, 607),
+#   Type = c("Signal", "Propeptide", "Chain")
+# )
 # protein.coverage +
-#    geom_feature(feature.df = feature.df, feature.color = c("#4d81be","#173b5e","#6a521d"))
+#   geom_feature(
+#     feature.df = feature.df,
+#     feature.color = c("#4d81be","#173b5e","#6a521d")
+#   )
 }
diff --git a/man/geom_protein.Rd b/man/geom_protein.Rd
index 5577086..3f8e370 100644
--- a/man/geom_protein.Rd
+++ b/man/geom_protein.Rd
@@ -17,7 +17,7 @@ geom_protein(
   mark.color = "red",
   mark.alpha = 0.5,
   show.table = TRUE,
-  table.position = c("right_top", "left_top", "left_bottom", "right_bottom"),
+  table.position = c("top_right", "top_left", "bottom_right", "bottom_left"),
   table.size = 4,
   table.color = "black",
   range.size = 3,
@@ -52,7 +52,7 @@ geom_protein(
 \item{table.position}{The position of the coverage summary table, choose from right_top, left_top, left_bottom, right_bottom.
 Default: right_top.}
 
-\item{table.size}{The font size of coverage summary table. Default: 4.}
+\item{table.size}{The font size of coverage summary table. Default: 12.}
 
 \item{table.color}{The font color of coverage summary table. Default: black.}
 
@@ -70,8 +70,12 @@ Layer for Protein Coverage Plot.
 \examples{
 library(ggplot2)
 library(ggcoverage)
-coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+coverage.file <- system.file(
+  "extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage"
+)
+fasta.file <- system.file(
+  "extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage"
+)
 protein.id = "sp|P02769|ALBU_BOVIN"
 ggplot() +
   geom_protein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
diff --git a/man/ggprotein.Rd b/man/ggprotein.Rd
index ffc1b45..62022f8 100644
--- a/man/ggprotein.Rd
+++ b/man/ggprotein.Rd
@@ -17,12 +17,11 @@ ggprotein(
   mark.color = "red",
   mark.alpha = 0.5,
   show.table = TRUE,
-  table.position = c("right_top", "left_top", "left_bottom", "right_bottom"),
-  table.size = 4,
+  table.position = c("top_right", "top_left", "bottom_left", "bottom_right"),
+  table.size = 10,
   table.color = "black",
   range.size = 3,
-  range.position = c("in", "out"),
-  plot.space = 0.2
+  range.position = c("in", "out")
 )
 }
 \arguments{
@@ -69,9 +68,18 @@ A ggplot2 object.
 Create Mass Spectrometry Protein Coverage Plot.
 }
 \examples{
-# library(ggcoverage)
-# coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-# fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
-# protein.id = "sp|P02769|ALBU_BOVIN"
-# ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
+library(ggcoverage)
+coverage.file <- system.file(
+  "extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage"
+)
+fasta.file <- system.file(
+  "extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage"
+)
+protein.id = "sp|P02769|ALBU_BOVIN"
+
+ggprotein(
+  coverage.file = coverage.file,
+  fasta.file = fasta.file,
+  protein.id = protein.id
+)
 }
diff --git a/vignettes/ggcoverage.Rmd b/vignettes/ggcoverage.Rmd
index 43c0cc9..6ffa104 100644
--- a/vignettes/ggcoverage.Rmd
+++ b/vignettes/ggcoverage.Rmd
@@ -26,380 +26,453 @@ BiocStyle::markdown()
 ```
 
 ```{r setup, echo=FALSE, warning=FALSE}
-library(knitr)
-htmltools::tagList(rmarkdown::html_dependency_font_awesome())
-
-# set dpi
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  dpi=60
+  dpi = 60,
+  fig.path = "png/",
+  crop = NULL
 )
 ```
 
-# Getting started
+## Introduction
+
+The goal of `ggcoverage` is simplify the process of visualizing omics coverage. It contains three main parts:
+
+* **Load the data**: `ggcoverage` can load BAM, BigWig (.bw), BedGraph, txt/xlsx files from various omics data, including WGS, RNA-seq, ChIP-seq, ATAC-seq, proteomics, et al.
+* **Create omics coverage plot**
+* **Add annotations**: `ggcoverage` supports six different annotations:
+  * **base and amino acid annotation**: Visualize genome coverage at single-nucleotide level with bases and amino acids.
+  * **GC annotation**: Visualize genome coverage with GC content
+  * **CNV annotation**: Visualize genome coverage with copy number variation (CNV)
+  * **gene annotation**: Visualize genome coverage across genes
+  * **transcription annotation**: Visualize genome coverage across different transcripts
+  * **ideogram annotation**: Visualize the region showing on whole chromosome
+  * **peak annotation**: Visualize genome coverage and peak identified
+  * **contact map annotation**: Visualize genome coverage with Hi-C contact map
+  * **link annotation**: Visualize genome coverage with contacts
+  * **protein feature annotation**: Visualize protein coverage with features
+
+`ggcoverage` utilizes `ggplot2` plotting system, so its usage is **ggplot2-style**!
+
+
+## Installation
 
 `ggcoverage` is an R package distributed as part of the [CRAN](https://cran.r-project.org/).
-To install the package, start R and enter:
+To install the package, start R and enter one of the following commands:
   
 ```{r install, eval=FALSE}
-# install via CRAN (v0.7.1) # old version, it's better to install via Github
+# install via CRAN (not yet available)
 install.packages("ggcoverage")
 
-# install via Github (v1.2.0)
-# install.package("remotes")   #In case you have not installed it.
-# BiocManager::install("areyesq89/GenomeMatrix") # In case of possible dependency error
+# OR install via Github
+install.package("remotes")
 remotes::install_github("showteeth/ggcoverage")
 ```
 
 In general, it is **recommended** to install from [Github repository](https://github.com/showteeth/ggcoverage) (update more timely).
 
-Once `ggcoverage` is installed, it can be loaded by the following command.
+Once `ggcoverage` is installed, it can be loaded (together with other libraries) like this:
 
 ```{r library, message=FALSE, warning=FALSE}
 library("rtracklayer")
-library("graphics")
 library("ggcoverage")
 library("ggpattern")
 ```
 
-# Introduction
-<img src = "../man/figures/ggcoverage.png" align = "right" width = "200"/>
+## Manual
 
-  The goal of `ggcoverage` is simplify the process of visualizing omics coverage. It contains three main parts:
+`ggcoverage` provides two [vignettes](https://showteeth.github.io/ggcoverage/): 
+
+* **detailed manual**: step-by-step usage
+* **customize the plot**: customize the plot and add additional layer
 
-* **Load the data**: `ggcoverage` can load `BAM`, `BigWig (.bw)`, `BedGraph`, `txt/xlsx` files from various omics data, including WGS, RNA-seq, ChIP-seq, ATAC-seq, proteomics, et al.
-* **Create omics coverage plot**
-* **Add annotations**: `ggcoverage` supports six different annotations:
-  * **base and amino acid annotation**: Visualize genome coverage at single-nucleotide level with bases and amino acids.
-  * **GC annotation**: Visualize genome coverage with GC content
-  * **CNV annotation**: Visualize genome coverage with copy number variation (CNV)
-  * **gene annotation**: Visualize genome coverage across genes
-  * **transcription annotation**: Visualize genome coverage across different transcripts
-  * **ideogram annotation**: Visualize the region showing on whole chromosome
-  * **peak annotation**: Visualize genome coverage and peak identified
-  * **contact map annotation**: Visualize genome coverage with Hi-C contact map
-  * **link annotation**: Visualize genome coverage with contacts
-  * **peotein feature annotation**: Visualize protein coverage with features
 
--------------
+## RNA-seq data
+
+### Load the data
 
-# RNA-seq data
-## Load the data
 The RNA-seq data used here are from [Transcription profiling by high throughput sequencing of HNRNPC knockdown and control HeLa cells](https://bioconductor.org/packages/release/data/experiment/html/RNAseqData.HNRNPC.bam.chr14.html), we select four sample to use as example: ERR127307_chr14, ERR127306_chr14, ERR127303_chr14, ERR127302_chr14, and all bam files are converted to bigwig file with [deeptools](https://deeptools.readthedocs.io/en/develop/).
 
 Load metadata:
+
 ```{r load_metadata}
 # load metadata
-meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
-sample.meta = read.csv(meta.file)
-sample.meta
+meta_file <-
+  system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
+sample_meta <- read.csv(meta_file)
+sample_meta
 ```
 
 Load track files:
+
 ```{r load_track}
 # track folder
-track.folder = system.file("extdata", "RNA-seq", package = "ggcoverage")
+track_folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")
 # load bigwig file
-track.df = LoadTrackFile(track.folder = track.folder, format = "bw",
-                         region = "chr14:21,677,306-21,737,601", extend = 2000,
-                         meta.info = sample.meta)
+track_df <- LoadTrackFile(
+  track.folder = track_folder,
+  format = "bw",
+  region = "chr14:21,677,306-21,737,601",
+  extend = 2000,
+  meta.info = sample_meta
+)
 # check data
-head(track.df)
+head(track_df)
 ```
 
 Prepare mark region:
+
 ```{r prepare_mark}
 # create mark region
-mark.region=data.frame(start=c(21678900,21732001,21737590),
-                       end=c(21679900,21732400,21737650),
-                       label=c("M1", "M2", "M3"))
+mark_region <- data.frame(
+  start = c(21678900, 21732001, 21737590),
+  end = c(21679900, 21732400, 21737650),
+  label = c("M1", "M2", "M3")
+)
 # check data
-mark.region
+mark_region
 ```
 
--------------
+### Load GTF
 
-## Load GTF
 To add **gene annotation**, the gtf file should contain **gene_type** and **gene_name** attributes in **column 9**; to add **transcript annotation**, the gtf file should contain **transcript_name** attribute in **column 9**.
+
 ```{r load_gtf}
-gtf.file = system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
-gtf.gr = rtracklayer::import.gff(con = gtf.file, format = 'gtf')
+gtf_file <-
+  system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
+gtf_gr <- rtracklayer::import.gff(con = gtf_file, format = "gtf")
 ```
 
--------------
+### Basic coverage
 
-## Basic coverage
 The basic coverage plot has **two types**: 
 
 * **facet**: Create subplot for every track (specified by `facet.key`). This is default.
 * **joint**: Visualize all tracks in a single plot.
 
 #### joint view
+
 Create line plot for **every sample** (`facet.key = "Type"`) and color by **every sample** (`group.key = "Type"`):
-```{r basic_coverage_joint, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df,
-                            plot.type = "joint", facet.key = "Type", group.key = "Type",
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
-```
 
-```{r basic_coverage_joint_plot, echo=FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage_joint-1.png")
+```{r basic_coverage_joint, warning=FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "joint",
+  facet.key = "Type",
+  group.key = "Type",
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 Create **group average line plot** (sample is indicated by `facet.key = "Type"`, group is indicated by `group.key = "Group"`):
-```{r basic_coverage_joint_avg, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df,
-                            plot.type = "joint", facet.key = "Type", group.key = "Group", 
-                            joint.avg = TRUE,
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
-```
 
-```{r basic_coverage_joint_avg_plot, echo=FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage_joint_avg-1.png")
-```
+```{r basic_coverage_joint_avg, warning=FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "joint",
+  facet.key = "Type",
+  group.key = "Group",
+  joint.avg = TRUE,
+  mark.region = mark_region,
+  range.position = "out"
+)
 
-#### facet view
-```{r basic_coverage, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "out")
-basic.coverage
+basic_coverage
 ```
 
-```{r basic_coverage_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage-1.png")
+#### Facet view
+
+```{r basic_coverage, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
 #### Custom Y-axis style
+
 **Change the Y-axis scale label in/out of plot region with `range.position`**:
-```{r basic_coverage_2, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "in")
-basic.coverage
-```
 
-```{r basic_coverage_2_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage_2-1.png")
+```{r basic_coverage_2, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "in"
+)
+
+basic_coverage
 ```
 
 **Shared/Free Y-axis scale with `facet.y.scale`**:
-```{r basic_coverage_3, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df, plot.type = "facet",
-                            mark.region = mark.region, range.position = "in", 
-                            facet.y.scale = "fixed")
-basic.coverage
-```
 
-```{r basic_coverage_3_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage_3-1.png")
-```
+```{r basic_coverage_3, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+basic_coverage <- ggcoverage(
+  data = track_df,
+  plot.type = "facet",
+  mark.region = mark_region,
+  range.position = "in",
+  facet.y.scale = "fixed"
+)
 
--------------
+basic_coverage
+```
 
-## Add gene annotation
+### Add gene annotation
 
-```{r gene_coverage, eval=FALSE}
-basic.coverage + 
-  geom_gene(gtf.gr=gtf.gr)
+```{r gene_coverage, warning=FALSE, fig.height = 8, fig.width = 12, fig.align = "center"}
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr)
 ```
 
-```{r gene_coverage_plot, echo=FALSE, fig.height = 8, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-gene_coverage-1.png")
-```
 
--------------
+### Add transcript annotation
 
-## Add transcript annotation
 **In "loose" stype (default style; each transcript occupies one line)**:
-```{r transcript_coverage, eval=FALSE}
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5)
-```
 
-```{r transcript_coverage_plot, echo=FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-transcript_coverage-1.png")
+```{r transcript_coverage, warning=FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5)
 ```
 
 **In "tight" style (place non-overlap transcripts in one line)**:
-```{r transcript_coverage_tight, eval=FALSE}
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr, overlap.style = "tight", label.vjust = 1.5)
-```
 
-```{r transcript_coverage_tight_plot, echo=FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-transcript_coverage_tight-1.png")
+```{r transcript_coverage_tight, warning=FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr,
+                  overlap.style = "tight",
+                  label.vjust = 1.5)
 ```
 
--------------
+### Add ideogram
 
-## Add ideogram
-```{r ideogram_coverage_1, eval=FALSE}
-basic.coverage +
-  geom_gene(gtf.gr=gtf.gr) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+```{r ideogram_coverage_1, eval = FALSE}
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
-```{r ideogram_coverage_1_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+```{r ideogram_coverage_1_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-ideogram_coverage_1-1.png")
 ```
 
-```{r ideogram_coverage_2, eval=FALSE}
-basic.coverage +
-  geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+```{r ideogram_coverage_2, eval = FALSE}
+basic_coverage +
+  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
-```{r ideogram_coverage_2_plot, echo=FALSE, fig.height = 14, fig.width = 12, fig.align = "center"}
+```{r ideogram_coverage_2_plot, echo = FALSE, fig.height = 14, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-ideogram_coverage_2-1.png")
 ```
 
--------------
+## DNA-seq data
+
+### CNV
+
+#### Example 1
+
+##### Load the data
 
-# DNA-seq data
-## CNV
-### Example 1
-#### Load the data
 The DNA-seq data used here are from [Copy number work flow](http://bioconductor.org/help/course-materials/2014/SeattleOct2014/B02.2.3_CopyNumber.html), we select tumor sample, and get bin counts with `cn.mops::getReadCountsFromBAM` with `WL` 1000.
 
 ```{r load_bin_counts}
 # prepare metafile
-cnv.meta.info = data.frame(
+cnv_meta_info <- data.frame(
   SampleName = c("CNV_example"),
   Type = c("tumor"),
   Group = c("tumor")
 )
+
 # track file
-track.file = system.file("extdata", "DNA-seq", "CNV_example.txt", package = "ggcoverage")
+track_file <- system.file("extdata",
+                          "DNA-seq", "CNV_example.txt", package = "ggcoverage")
+
 # load txt file
-track.df = LoadTrackFile(track.file = track.file, format = "txt", region = "chr4:61750000-62,700,000",
-                         meta.info = cnv.meta.info)
+track_df <- LoadTrackFile(
+  track.file = track_file,
+  format = "txt",
+  region = "chr4:61750000-62,700,000",
+  meta.info = cnv_meta_info
+)
+
 # check data
-head(track.df)
+head(track_df)
 ```
 
-#### Basic coverage
-```{r basic_coverage_dna, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df,color = "grey", mark.region = NULL,
-                            range.position = "out")
-basic.coverage
-```
+##### Basic coverage
 
-```{r basic_coverage_dna_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage_dna-1.png")
+```{r basic_coverage_dna, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+basic_coverage <- ggcoverage(
+  data = track_df,
+  color = "grey",
+  mark.region = NULL,
+  range.position = "out"
+)
+
+basic_coverage
 ```
 
-#### Add annotations
-Add **GC**, **ideogram** and **gene** annotations.
+##### Add GC annotations
+
+Add **GC**, **ideogram** and **gene** annotaions.
 
-```{r gc_coverage, eval=FALSE}
+```{r gc_coverage, eval = FALSE}
 # load genome data
 library("BSgenome.Hsapiens.UCSC.hg19")
+
 # create plot
-basic.coverage +
-  geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
-  geom_gene(gtf.gr=gtf.gr) +
+basic_coverage +
+  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
+  geom_gene(gtf.gr = gtf_gr) +
   geom_ideogram(genome = "hg19")
 ```
 
-```{r gc_coverage_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+
+```{r gc_coverage_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-gc_coverage-1.png")
 ```
 
+#### Example 2
+
+##### Load the data
 
-### Example 2
-#### Load the data
 The DNA-seq data used here are from [Genome-wide copy number analysis of single cells](https://www.nature.com/articles/nprot.2012.039), and the accession number is [SRR054616](https://trace.ncbi.nlm.nih.gov/Traces/index.html?run=SRR054616).
 
 ```{r cnv_load_track_file}
 # track file
-track.file <- system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
+track_file <-
+  system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
+
 # load track
-track.df = LoadTrackFile(track.file = track.file, format = "bw", region = "4:1-160000000")
+track_df <- LoadTrackFile(track.file = track_file,
+                          format = "bw",
+                          region = "4:1-160000000")
+
 # add chr prefix
-track.df$seqnames = paste0("chr", track.df$seqnames)
+track_df$seqnames <- paste0("chr", track_df$seqnames)
+
 # check data
-head(track.df)
+head(track_df)
 ```
 
-#### Basic coverage
-```{r cnv_basic_coverage_dna, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df, color = "grey",
-                            mark.region = NULL, range.position = "out")
-basic.coverage
-```
+##### Basic coverage
+
+```{r cnv_basic_coverage_dna}
+basic_coverage <- ggcoverage(
+  data = track_df,
+  color = "grey",
+  mark.region = NULL,
+  range.position = "out"
+)
 
-```{r cnv_basic_coverage_dna_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-cnv_basic_coverage_dna-1.png")
+basic_coverage
 ```
 
-#### Load CNV file
+##### Load CNV file
+
 ```{r cnv_load_cnv}
 # prepare files
-cnv.file <- system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt", package = "ggcoverage")
+cnv_file <-
+  system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt",
+              package = "ggcoverage")
+
 # read CNV
-cnv.df = read.table(file = cnv.file, sep = "\t", header = TRUE)
+cnv_df <- read.table(file = cnv_file, sep = "\t", header = TRUE)
+
 # check data
-head(cnv.df)
+head(cnv_df)
 ```
 
-#### Add annotations
+##### Add annotations
+
 Add **GC**, **ideogram** and **CNV** annotations.
 
 ```{r cnv_gc_coverage, eval=FALSE}
-# load genome data
-library("BSgenome.Hsapiens.UCSC.hg19")
 # create plot
-basic.coverage +
-  geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
-  geom_cnv(cnv.df = cnv.df, bin.col = 3, cn.col = 4) +
-  geom_ideogram(genome = "hg19",plot.space = 0, highlight.centromere = TRUE)
-```
-
-```{r cnv_gc_coverage_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+basic_coverage +
+  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
+  geom_cnv(cnv.df = cnv_df,
+           bin.col = 3,
+           cn.col = 4) +
+  geom_ideogram(
+    genome = "hg19",
+    plot.space = 0,
+    highlight.centromere = TRUE
+  )
+```
+
+```{r cnv_gc_coverage_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-cnv_gc_coverage-1.png")
 ```
 
----------------------
 
-## Single-nucleotide level
-### Load the data
+### Single-nucleotide level
+
+#### Load the data
+
 ```{r load_single_nuc}
 # prepare sample metadata
-sample.meta <- data.frame(
+sample_meta <- data.frame(
   SampleName = c("tumorA.chr4.selected"),
   Type = c("tumorA"),
   Group = c("tumorA")
 )
+
 # load bam file
-bam.file = system.file("extdata", "DNA-seq", "tumorA.chr4.selected.bam", package = "ggcoverage")
-track.df <- LoadTrackFile(
-  track.file = bam.file,
-  meta.info = sample.meta,
-  single.nuc=TRUE, single.nuc.region="chr4:62474235-62474295"
+bam_file <- system.file("extdata",
+                        "DNA-seq", "tumorA.chr4.selected.bam",
+                        package = "ggcoverage")
+
+track_df <- LoadTrackFile(
+  track.file = bam_file,
+  meta.info = sample_meta,
+  single.nuc = TRUE,
+  single.nuc.region = "chr4:62474235-62474295"
 )
-head(track.df)
+
+head(track_df)
 ```
 
-### Default color scheme
+#### Default color scheme
+
 For base and amino acid annotation, we have following default color schemes, you can change with `nuc.color` and `aa.color` parameters.
 
 Default color scheme for base annotation is `Clustal-style`, more popular color schemes is available [here](https://www.biostars.org/p/171056/).
+
 ```{r base_color_scheme, warning=FALSE, fig.height = 2, fig.width = 6, fig.align = "center"}
 # color scheme
-nuc.color = c("A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d")
-opar <- graphics::par() 
+nuc_color <- c(
+  "A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d"
+)
+opar <- graphics::par()
+
 # create plot
 graphics::par(mar = c(1, 5, 1, 1))
 graphics::image(
-  1:length(nuc.color), 1, as.matrix(1:length(nuc.color)),
-  col = nuc.color,
-  xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
+  seq_along(nuc_color),
+  1,
+  as.matrix(seq_along(nuc_color)),
+  col = nuc_color,
+  xlab = "",
+  ylab = "",
+  xaxt = "n",
+  yaxt = "n",
+  bty = "n"
 )
-graphics::text(1:length(nuc.color), 1, names(nuc.color))
+graphics::text(seq_along(nuc_color), 1, names(nuc_color))
 graphics::mtext(
-  text = "Base", adj = 1, las = 1,
+  text = "Base",
+  adj = 1,
+  las = 1,
   side = 2
 )
 
@@ -408,24 +481,36 @@ graphics::par(opar)
 ```
 
 Default color scheme for amino acid annotation is from [Residual colours: a proposal for aminochromography](https://academic.oup.com/peds/article/10/7/743/1593029?login=false):
+
 ```{r aa_color_scheme, warning=FALSE, fig.height = 9, fig.width = 10, fig.align = "center"}
-aa.color = c(
-  "D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000", "P" = "#F28500",
-  "C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00", "I" = "#C0FF00", "L" = "#89318C",
-  "M" = "#00FF00", "F" = "#50C878", "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3",
-  "R" = "#0000FF", "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
-  "*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF"
+aa_color <- c(
+  "D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000",
+  "P" = "#F28500", "C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00",
+  "I" = "#C0FF00", "L" = "#89318C", "M" = "#00FF00", "F" = "#50C878",
+  "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3", "R" = "#0000FF",
+  "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
+  "*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF",
+  " " = "#FFFFFF"
 )
 
 graphics::par(mar = c(1, 5, 1, 1))
 graphics::image(
-  1:5, 1:5, matrix(1:length(aa.color),nrow=5),
-  col = rev(aa.color),
-  xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
+  1:5,
+  1:5,
+  matrix(seq_along(aa_color), nrow = 5),
+  col = rev(aa_color),
+  xlab = "",
+  ylab = "",
+  xaxt = "n",
+  yaxt = "n",
+  bty = "n"
 )
-graphics::text(expand.grid(1:5,1:5), names(rev(aa.color)))
+
+graphics::text(expand.grid(1:5, 1:5), names(rev(aa_color)))
 graphics::mtext(
-  text = "Amino acids", adj = 1, las = 1,
+  text = "Amino acids",
+  adj = 1,
+  las = 1,
   side = 2
 )
 
@@ -433,290 +518,341 @@ graphics::mtext(
 graphics::par(opar)
 ```
 
-### Add base and amino acid annotation
+#### Add base and amino acid annotation
+
 **Use twill to mark position with SNV**:
-```{r base_aa_coverage, eval=FALSE}
-library(ggpattern)
+
+```{r base_aa_coverage, eval =FALSE}
 # create plot with twill mark
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "twill") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
-```{r base_aa_coverage_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+
+```{r base_aa_coverage_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-base_aa_coverage-1.png")
 ```
 
 **Use star to mark position with SNV**:
-```{r base_aa_coverage_star, eval=FALSE}
+
+```{r base_aa_coverage_star, eval = FALSE}
 # create plot with star mark
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "star") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
-```{r base_aa_coverage_star_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+
+```{r base_aa_coverage_star_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-base_aa_coverage_star-1.png")
 ```
 
 **Highlight position with SNV**:
-```{r base_aa_coverage_highlight, eval=FALSE}
-# highlight
-ggcoverage(data = track.df, color = "grey", range.position = "out", 
-           single.nuc=T, rect.color = "white") +
-  geom_base(bam.file = bam.file,
+
+```{r base_aa_coverage_highlight, eval = FALSE}
+# highlight one base
+ggcoverage(
+  data = track_df,
+  color = "grey",
+  range.position = "out",
+  single.nuc = TRUE,
+  rect.color = "white"
+) +
+  geom_base(bam.file = bam_file,
             bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,
             mark.type = "highlight") +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
-```{r base_aa_coverage_highlight_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+
+```{r base_aa_coverage_highlight_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-base_aa_coverage_highlight-1.png")
 ```
 
----------------------
+## ChIP-seq data
 
-# ChIP-seq data
 The ChIP-seq data used here are from [DiffBind](https://bioconductor.org/packages/release/bioc/html/DiffBind.html), I select four sample to use as example: Chr18_MCF7_input, Chr18_MCF7_ER_1, Chr18_MCF7_ER_3, Chr18_MCF7_ER_2, and all bam files are converted to bigwig file with [deeptools](https://deeptools.readthedocs.io/en/develop/).
 
 Create metadata:
+
 ```{r load_metadata_chip}
 # load metadata
-sample.meta = data.frame(SampleName=c('Chr18_MCF7_ER_1','Chr18_MCF7_ER_2','Chr18_MCF7_ER_3','Chr18_MCF7_input'),
-                         Type = c("MCF7_ER_1","MCF7_ER_2","MCF7_ER_3","MCF7_input"),
-                         Group = c("IP", "IP", "IP", "Input"))
-sample.meta
+sample_meta <- data.frame(
+  SampleName = c(
+    "Chr18_MCF7_ER_1",
+    "Chr18_MCF7_ER_2",
+    "Chr18_MCF7_ER_3",
+    "Chr18_MCF7_input"
+  ),
+  Type = c("MCF7_ER_1", "MCF7_ER_2", "MCF7_ER_3", "MCF7_input"),
+  Group = c("IP", "IP", "IP", "Input")
+)
+
+sample_meta
 ```
 
 Load track files:
+
 ```{r load_track_chip}
 # track folder
-track.folder = system.file("extdata", "ChIP-seq", package = "ggcoverage")
+track_folder <- system.file("extdata", "ChIP-seq", package = "ggcoverage")
+
 # load bigwig file
-track.df = LoadTrackFile(track.folder = track.folder, format = "bw", region = "chr18:76822285-76900000",
-                         meta.info = sample.meta)
+track_df <- LoadTrackFile(
+  track.folder = track_folder,
+  format = "bw",
+  region = "chr18:76822285-76900000",
+  meta.info = sample_meta
+)
+
 # check data
-head(track.df)
+head(track_df)
 ```
 
 Prepare mark region:
+
 ```{r prepare_mark_chip}
 # create mark region
-mark.region=data.frame(start=c(76822533),
-                       end=c(76823743),
-                       label=c("Promoter"))
+mark_region <- data.frame(
+  start = c(76822533),
+  end = c(76823743),
+  label = c("Promoter")
+)
+
 # check data
-mark.region
+mark_region
 ```
 
--------------
-
-## Basic coverage
-```{r basic_coverage_chip, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df,
-                            mark.region=mark.region, show.mark.label = FALSE)
-basic.coverage
-```
+### Basic coverage
 
-```{r basic_coverage_chip_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage_chip-1.png")
+```{r basic_coverage_chip, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+basic_coverage <- ggcoverage(data = track_df,
+                             mark.region = mark_region,
+                             show.mark.label = FALSE)
+basic_coverage
 ```
 
--------------
-
-## Get consensus peaks
-Before create peak annotation, we first **get consensus peaks** from replicates with [MSPC](https://github.com/Genometric/MSPC).
-```{r consensus_peaks}
-# load peak file
-peak.file <- system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
-# get consensus peak (do nothing when there is only one file)
-# notice: this step requires MSPC, specific the installation path with mspc.path
-peak.df <- GetConsensusPeak(peak.file = peak.file)
-```
+### Add annotations
 
+Add **gene**, **ideogram** and **peak** annotations. To create peak annotation, we first **get consensus peaks** with [MSPC](https://github.com/Genometric/MSPC).
 
-## Add annotations
-Add **gene**, **ideogram** and **peak** annotations:
-```{r peak_coverage, eval=FALSE}
+```{r peak_coverage, eval = FALSE}
 # get consensus peak file
-peak.file = system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
-# create with peak file
-basic.coverage +
-  geom_gene(gtf.gr=gtf.gr) +
-  geom_peak(bed.file = peak.file) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+peak_file <- system.file("extdata",
+                         "ChIP-seq",
+                         "consensus.peak",
+                         package = "ggcoverage")
 
-# create with peak dataframe
-basic.coverage +
-  geom_gene(gtf.gr=gtf.gr) +
-  geom_peak(peak.df = peak.df) +
-  geom_ideogram(genome = "hg19",plot.space = 0)
+basic_coverage +
+  geom_gene(gtf.gr = gtf_gr) +
+  geom_peak(bed.file = peak_file) +
+  geom_ideogram(genome = "hg19", plot.space = 0)
 ```
 
-```{r peak_coverage_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+```{r peak_coverage_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-peak_coverage-1.png")
 ```
 
----------------------
+## Hi-C data
 
-# Hi-C data
 The Hi-C data are from [pyGenomeTracks: reproducible plots for multivariate genomic datasets](https://academic.oup.com/bioinformatics/article/37/3/422/5879987?login=false).
 
 The Hi-C matrix visualization is implemented by [HiCBricks](https://github.com/koustav-pal/HiCBricks).
 
-## Load track data
+### Load track data
+
 ```{r hic_track}
-library(ggcoverage)
-library(GenomicRanges)
 # prepare track dataframe
-track.file = system.file("extdata", "HiC", "H3K36me3.bw", package = "ggcoverage")
-track.df = LoadTrackFile(track.file = track.file, format = "bw", 
-                         region = "chr2L:8050000-8300000", extend = 0)
-track.df$score = ifelse(track.df$score <0, 0, track.df$score)
+track_file <-
+  system.file("extdata", "HiC", "H3K36me3.bw", package = "ggcoverage")
+
+track_df <- LoadTrackFile(
+  track.file = track_file,
+  format = "bw",
+  region = "chr2L:8050000-8300000",
+  extend = 0
+)
+
+track_df$score <- ifelse(track_df$score < 0, 0, track_df$score)
+
 # check the data
-head(track.df)
+head(track_df)
 ```
 
-## Load Hi-C data
+### Load Hi-C data
+
 Matrix:
 
 ```{r hic_load_hic_matrix}
 ## matrix
-hic.mat.file = system.file("extdata", "HiC", "HiC_mat.txt", package = "ggcoverage")
-hic.mat = read.table(file = hic.mat.file, sep = "\t")
-hic.mat = as.matrix(hic.mat)
+hic_mat_file <- system.file("extdata",
+                            "HiC", "HiC_mat.txt", package = "ggcoverage")
+hic_mat <- read.table(file = hic_mat_file, sep = "\t")
+hic_mat <- as.matrix(hic_mat)
 ```
 
 Bin table:
 
 ```{r hic_load_hic_bin}
 ## bin
-hic.bin.file = system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
-hic.bin = read.table(file = hic.bin.file, sep = "\t")
-colnames(hic.bin) = c("chr", "start", "end")
-hic.bin.gr = GenomicRanges::makeGRangesFromDataFrame(df = hic.bin)
+hic_bin_file <-
+  system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
+hic_bin <- read.table(file = hic_bin_file, sep = "\t")
+colnames(hic_bin) <- c("chr", "start", "end")
+hic_bin_gr <- GenomicRanges::makeGRangesFromDataFrame(df = hic_bin)
+
 ## transfrom func
-FailSafe_log10 <- function(x){
+failsafe_log10 <- function(x) {
   x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0
-  return(log10(x+1))
+  return(log10(x + 1))
 }
 ```
 
 Data transfromation method:
 
-```{r hic_load_hic_transformation}
-## transfrom func
-FailSafe_log10 <- function(x){
-  x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0
-  return(log10(x+1))
-}
-```
+### Load link
 
-## Load link
 ```{r hic_load_link}
 # prepare arcs
-link.file = system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")
+link_file <-
+  system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")
 ```
 
-## Basic coverage
-```{r basic_coverage_hic, eval=FALSE}
-basic.coverage = ggcoverage(data = track.df, color = "grey",
-                            mark.region = NULL, range.position = "out")
-basic.coverage
-```
+### Basic coverage
+
+```{r basic_coverage_hic, warning=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+basic_coverage <-
+  ggcoverage(
+    data = track_df,
+    color = "grey",
+    mark.region = NULL,
+    range.position = "out"
+  )
 
-```{r basic_coverage_hic_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
-knitr::include_graphics("../man/figures/README-basic_coverage_hic-1.png")
+basic_coverage
 ```
 
-## Add annotations
-Add **link**, **contact map**annotations:
+### Add annotations
 
-```{r hic_coverage, eval=FALSE}
-basic.coverage +
-  geom_tad(matrix = hic.mat, granges = hic.bin.gr, value.cut = 0.99,
-           color.palette = "viridis", transform.fun = FailSafe_log10,
-           top = FALSE, show.rect = TRUE) +
-  geom_link(link.file = link.file, file.type = "bedpe", show.rect = TRUE)
-```
+Add **link**, **contact map**annotations:
 
-```{r hic_coverage_plot, echo=FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
+```{r hic_coverage, eval = FALSE}
+basic_coverage +
+  geom_tad(
+    matrix = hic_mat,
+    granges = hic_bin_gr,
+    value.cut = 0.99,
+    color.palette = "viridis",
+    transform.fun = failsafe_log10,
+    top = FALSE,
+    show.rect = TRUE
+  ) +
+  geom_link(link.file = link_file,
+            file.type = "bedpe",
+            show.rect = TRUE)
+```
+
+```{r hic_coverage_plot, echo = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-hic_coverage-1.png")
 ```
 
----------------------
+## Mass spectrometry protein coverage
 
-# Mass spectrometry protein coverage
 [Mass spectrometry (MS) is an important method for the accurate mass determination and characterization of proteins, and a variety of methods and instrumentations have been developed for its many uses](https://en.wikipedia.org/wiki/Protein_mass_spectrometry). After MS, we can check the coverage of protein to check the quality of the data and find the reason why the segment did not appear and improve the experiment.
 
-## Load coverage
+### Load coverage
+
 The exported coverage from [Proteome Discoverer](https://www.thermofisher.cn/cn/zh/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/multi-omics-data-analysis/proteome-discoverer-software.html?adobe_mc=MCMID%7C90228073352279367993013412919222863692%7CMCAID%3D3208C32C269355DE-4000028116B65FEB%7CMCORGID%3D5B135A0C5370E6B40A490D44%40AdobeOrg%7CTS=1614293705):
+
 ```{r ms_coverage_data}
 library(openxlsx)
 # prepare coverage dataframe
-coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
-coverage.df <- openxlsx::read.xlsx(coverage.file)
+coverage_file <-
+  system.file("extdata",
+              "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
+coverage_df <- openxlsx::read.xlsx(coverage_file, sheet = "Sheet1")
 # check the data
-head(coverage.df)
+head(coverage_df)
 ```
 
 The input protein fasta:
+
 ```{r ms_coverage_fasta}
-library(Biostrings)
-fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+fasta_file <-
+  system.file("extdata",
+              "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
+
 # prepare track dataframe
-protein.set <- Biostrings::readAAStringSet(fasta.file)
+protein_set <- Biostrings::readAAStringSet(fasta_file)
+
 # check the data
-protein.set
+protein_set
 ```
 
-## Protein coverage
-```{r basic_coverage_protein, eval=FALSE}
-protein.coverage = ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, 
-                             protein.id = "sp|P02769|ALBU_BOVIN", range.position = "out")
-protein.coverage
+### Protein coverage
+
+```{r basic_coverage_protein, eval = FALSE}
+protein_coverage <- ggprotein(
+  coverage.file = coverage_file,
+  fasta.file = fasta_file,
+  protein.id = "sp|P02769|ALBU_BOVIN",
+  range.position = "out"
+)
+
+protein_coverage
 ```
 
-```{r basic_coverage_protein_plot, echo=FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}
+```{r basic_coverage_protein_plot, echo = FALSE, fig.height = 6, fig.width = 10, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-basic_coverage_protein-1.png")
 ```
 
-## Add annotation
+### Add annotation
+
 We can obtain features of the protein from [UniProt](https://www.uniprot.org/). For example, the above protein coverage plot shows that there is empty region in 1-24, and this empty region in [UniProt](https://www.uniprot.org/uniprotkb/P02769/entry) is annotated as Signal peptide and Propeptide peptide. When the protein is mature and released extracellular, these peptides will be cleaved. This is the reason why there is empty region in 1-24.
 
-```{r basic_coverage_protein_feature, eval=FALSE}
+```{r basic_coverage_protein_feature, eval = FALSE}
 # protein feature obtained from UniProt
-protein.feature.df = data.frame(ProteinID = "sp|P02769|ALBU_BOVIN", start = c(1, 19, 25), 
-                                end = c(18, 24, 607), 
-                                Type = c("Signal", "Propeptide", "Chain"))
+protein_feature_df <- data.frame(
+  ProteinID = "sp|P02769|ALBU_BOVIN",
+  start = c(1, 19, 25),
+  end = c(18, 24, 607),
+  Type = c("Signal", "Propeptide", "Chain")
+)
+
 # add annotation
-protein.coverage + 
-  geom_feature(feature.df = protein.feature.df, feature.color = c("#4d81be","#173b5e","#6a521d"))
+protein_coverage +
+  geom_feature(feature.df = protein_feature_df,
+               feature.color = c("#4d81be", "#173b5e", "#6a521d"))
 ```
 
-```{r basic_coverage_protein_feature_plot, echo=FALSE, fig.height = 8, fig.width = 12, fig.align = "center"}
+```{r basic_coverage_protein_feature_plot, echo = FALSE, fig.height = 6, fig.width = 10, fig.align = "center"}
 knitr::include_graphics("../man/figures/README-basic_coverage_protein_feature-1.png")
 ```
 
+## Code of Conduct
+
+Please note that the `ggcoverage` project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
+
 ---------------------
 
-# Session info
+## Session info
 ```{r session}
 sessionInfo()
 ```
-
-
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