From b92ede79ea7ac7680104c382b5b382001c3ab25b Mon Sep 17 00:00:00 2001
From: jlmaier
TrIdent is the result of the combined effort and brain power of many -individuals. Specifically, we would like to thank Dr. Manuel Kleiner, -Dr. Ben Callahan, Dr. Breck Duerkop and Dr. Craig Gin for their -individual expertise and overall support!
The development of TrIdent was supported by a seed grant from the -North Carolina State University Data Science Academy and by the National -Institutes of Health under Award Numbers R35GM138362 and -R01Al171046.
-
-if (!require("pak", quietly = TRUE)) {
- install.packages("pak")
+if (!require("BiocManager", quietly = TRUE)) {
+ install.packages("BiocManager")
}
-pak::pak("jlmaier12/TrIdent")
+BiocManager::install("TrIdent")
library(TrIdent)
-if (!require("devtools", quietly = TRUE)) {
- install.packages("devtools")
-}
-
-devtools::install_github("jlmaier12/TrIdent")
+BiocManager::install("jlmaier12/TrIdent")
library(TrIdent)
TrIdentClassifier()
outputs a histogram displaying the
-overall abundance and quality of pattern-matches in addition to the
-composition of classifications. The displayed pattern-match scores are
-normalized by dividing each score by its associated contig length. The
-scores are normalized to visualize the overall quality of
-pattern-matching for the entire dataset. Remember, smaller pattern-match
-scores correspond to better pattern-matches.
The output of TrIdentClassifier()
is a list containing
five objects:
windowSize
used.Save the desired list-item to a new variable using its associated name.
@@ -1169,7 +1151,7 @@
specializedTransductionID(VLPpileup, TrIdentResults,
noReadCov = 500, specTransLength = 2000,
- logScale = FALSE, matchScoreFilter,
+ logScale = FALSE, verbose = TRUE, matchScoreFilter,
SaveFilesTo, specificContig
)
specTransLength
: Number of basepairs of non-zero read
coverage needed for specialized transduction to be considered. Default
-is 2000. Must be at leastlogScale
: TRUE or FALSE, display VLP-fraction read
coverage in log10 scale. Default is FALSE.suggFiltThresh=TRUE
.
verbose
: TRUE or FALSE. Print progress messages to
+console. Default is TRUE.SaveFilesTo
: Optional, Provide a path to the directory
you wish to save output to. A folder will be made within the provided
directory to store results.TrIdent is the result of the combined effort and brain power of many +individuals. Specifically, we would like to thank Dr. Manuel Kleiner, +Dr. Ben Callahan, Dr. Breck Duerkop and Dr. Craig Gin for their +individual expertise and overall support!
+The development of TrIdent was supported by a seed grant from the +North Carolina State University Data Science Academy and by the National +Institutes of Health under Award Numbers R35GM138362 and +R01Al171046.
+
sessionInfo()
#> R version 4.2.1 (2022-06-23 ucrt)
@@ -1853,24 +1853,30 @@ Session Information#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
-#> [1] kableExtra_1.4.0 ggplot2_3.5.1 patchwork_1.3.0 knitr_1.49
-#> [5] TrIdent_0.0.0.9000
+#> [1] kableExtra_1.4.0 ggplot2_3.5.1 patchwork_1.3.0 knitr_1.49
+#> [5] TrIdent_0.99.2 BiocStyle_2.26.0
#>
#> loaded via a namespace (and not attached):
-#> [1] Rcpp_1.0.13-1 bslib_0.8.0 compiler_4.2.1 pillar_1.9.0
-#> [5] jquerylib_0.1.4 tools_4.2.1 digest_0.6.37 viridisLite_0.4.2
-#> [9] jsonlite_1.8.9 evaluate_1.0.1 lifecycle_1.0.4 tibble_3.2.1
-#> [13] gtable_0.3.6 pkgconfig_2.0.3 rlang_1.1.4 cli_3.6.2
-#> [17] rstudioapi_0.17.1 roll_1.1.7 yaml_2.3.10 pkgdown_2.1.1
-#> [21] xfun_0.49 fastmap_1.2.0 xml2_1.3.6 withr_3.0.2
-#> [25] stringr_1.5.1 dplyr_1.1.4 generics_0.1.3 desc_1.4.3
-#> [29] fs_1.6.5 vctrs_0.6.5 htmlwidgets_1.6.4 sass_0.4.9
-#> [33] systemfonts_1.1.0 grid_4.2.1 tidyselect_1.2.1 svglite_2.1.3
-#> [37] glue_1.8.0 R6_2.5.1 textshaping_0.4.1 fansi_1.0.6
-#> [41] rmarkdown_2.29 purrr_1.0.2 tidyr_1.3.1 farver_2.1.2
-#> [45] magrittr_2.0.3 scales_1.3.0 htmltools_0.5.8.1 colorspace_2.1-1
-#> [49] labeling_0.4.3 ragg_1.3.3 utf8_1.2.4 stringi_1.8.4
-#> [53] RcppParallel_5.1.9 munsell_0.5.1 cachem_1.1.0
Source: DESCRIPTION
Maier J, Rabasco J, Gin C, Callahan B, Kleiner M (2024). -TrIdent: Detect Genetic Transduction Events in Metagenomes using Read Coverage Pattern-Matching. +
Maier J, Rabasco J, Gin C, Callahan B, Kleiner M (2025). +TrIdent: TrIdent - Transduction Identification. https://github.com/jlmaier12/TrIdent, https://jlmaier12.github.io/TrIdent/.
@Manual{, - title = {TrIdent: Detect Genetic Transduction Events in Metagenomes using Read Coverage Pattern-Matching}, + title = {TrIdent: TrIdent - Transduction Identification}, author = {Jessie Maier and Jorden Rabasco and Craig Gin and Benjamin Callahan and Manuel Kleiner}, - year = {2024}, + year = {2025}, note = {https://github.com/jlmaier12/TrIdent, https://jlmaier12.github.io/TrIdent/}, }diff --git a/favicon-96x96.png b/favicon-96x96.png index a0f8bc31aa99196591fb869a72fd65564a156820..1c949b98a192e25cb145912daf2cde60277bf34d 100644 GIT binary patch delta 2765 zcmZ`*`9Bl>AD=a4mCQ6pn)@i^%$3Mu?%PI4=Gt;LXA9X9q84+E+>&!Bl2FdvA
TrIdent automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events.
+TrIdent - Transduction Identification
+TrIdent automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events.
Transductomics, developed by Kleiner et al. (2020), is a DNA sequencing-based method for the detection and characterization of transduction events in pure cultures and complex communities. Transductomics relies on mapping sequencing reads from a viral-like particle (VLP)-fraction of a sample to contigs assembled from the metagenome (whole-community) of the same sample. Reads from bacterial DNA carried by VLPs will map back to the bacterial contigs of origin creating read coverage patterns indicative of ongoing transduction. The read coverage patterns detected represent DNA being actively carried or transduced by VLPs. The read coverage patterns do not represent complete transduction events (i.e integration of transduced DNA into new bacterial chromosomes).
To obtain the data needed for transductomics, a microbiome sample of interest is split to prepare two sub-sample types: - Whole-community: Represents the ‘whole-community’ (all bacteria, fungi, virus, etc) in the microbiome of interest - VLP-fraction: Represents only the virus and ‘viral-like particles’ associated with the microbiome of interest - The VLP-fraction must be obtained by an appropriate ultra-purification protocol for your sample type to remove bacterial cells and contaminating free bacterial DNA.
With transductomics and TrIdent, a researcher can obtain information about the phage-host pairs involved in transduction, the types of transduction occuring, and the region of the host genome that is potentially transduced, which allows exploration of transferred genes.
@@ -88,18 +86,32 @@You can install the development version of TrIdent from GitHub with:
+Install TrIdent with BiocManager:
-# install.packages("pak")
-pak::pak("jlmaier12/TrIdent")
if (!require("BiocManager", quietly = TRUE)) {
+ install.packages("BiocManager")
+}
+
+BiocManager::install("TrIdent")
+library(TrIdent)
+Install the development version of TrIdent through Github with BiocManager:
+
-library(TrIdent)
+
+## Load TrIdent
+library(TrIdent)
-## Run first
+## Load sample datasets
+data("VLPFractionSamplePileup")
+data("WholeCommunitySamplePileup")
+
+## Run TrIdent:
+## Run first:
TrIdentOutput <- TrIdentClassifier(
VLPpileup = VLPFractionSamplePileup,
WCpileup = WholeCommunitySamplePileup
@@ -111,7 +123,7 @@ Quick Start#> Almost done with pattern-matching!
#> Determining sizes (bp) of pattern matches
#> Identifying highly active/abundant or heterogenously integrated
-#> Prophage-like elements
+#> Prophage-like elements
#> Finalizing output
#> Execution time: 14.56secs
#> 1 contigs were filtered out based on low read coverage
@@ -121,25 +133,66 @@ Quick Start#> 1 1 4 3
#> 3 of the prophage-like classifications are highly active or abundant
#> 1 of the prophage-like classifications are mixed, i.e. heterogenously
-#> integrated into their bacterial host population
-
-
-
-## Run second
-TrIdentPlots <- plotTrIdentResults(
+#> integrated into their bacterial host population
+
+## Run second:
+plotTrIdentResults(
VLPpileup = VLPFractionSamplePileup,
WCpileup = WholeCommunitySamplePileup,
TrIdentResults = TrIdentOutput
)
-
-## Run third
-SpecTransduc <- specializedTransductionID(
+#> $NODE_62
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Run third:
+specializedTransductionID(
VLPpileup = VLPFractionSamplePileup,
TrIdentResults = TrIdentOutput
)
#> 2 contigs have potential specialized transduction
#> We recommend that you also view the results of this search with
-#> logScale=TRUE
+#> logScale=TRUE
+#> $summaryTable
+#> contigName specTransduc left right lengthLeft lengthRight
+#> 1 NODE_62 yes yes no 45400 <NA>
+#> 2 NODE_135 no no no <NA> <NA>
+#> 3 NODE_368 no no no <NA> <NA>
+#> 4 NODE_617 yes yes yes 33300 9800
+#>
+#> $Plots
+#> $Plots$NODE_62