Merge branch 'main' of https://github.com/fhdsl/Choosing_Genomics_Tools…

… into main

docs/08-annotating-genomes.md

@@ -42,7 +42,7 @@ Although we can't walk you through every organism and database set up, we will w
 
 ![](resources/images/08-annotating-genomes_files/figure-docx//1YwxXy2rnUgbx_7B7ENH9wpDX-j6JpJz6lGVzOkjo0qY_g1b625723c80_0_28.png){width=100%}
 
-In the above screenshot, [from Ensembl](https://useast.ensembl.org/info/data/ftp/index.html), it shows different organisms in the rows, but also a variety of different files across the columns. In this example, DNA reference to the DNA sequence of the organism's genome, but cDNA refers to complementary DNA -- aka DNA that has been reversed transcribed from RNA. If you are working with RNA data you may want to use the cDNA file. Whereas CDS files are referring to only coding sequences and ncRNA files are showing only non coding sequences. Gene sets are also annotated and are in their own files. Most of these files are FASTA files. For a reminder on what these different file types are [see the previous chapter](http://hutchdatascience.org/Choosing_Genomics_Tools/a-very-general-genomics-overview.html#basic-file-formats).
+In the above screenshot, [from Ensembl](https://useast.ensembl.org/info/data/ftp/index.html), it shows different organisms in the rows, but also a variety of different files across the columns. In this example, DNA reference to the DNA sequence of the organism's genome, but cDNA refers to complementary DNA -- aka DNA that has been reversed transcribed from RNA. If you are working with RNA data you may want to use the cDNA file. Whereas CDS files are referring to only coding sequences and ncRNA files are showing only non coding sequences. Most of these files are FASTA files. Gene sets are also their own annotation files called GTF or GFF files. Ensembl provides more [detailed information about what these files contain](https://useast.ensembl.org/info/website/upload/gff.html), but briefly, each row is a feature and has information describing that feature such as genomic locations, the relevant feature type (gene, coding sequence, pseudogene, etc.), and the gene ID or name. For a reminder on what these different file types are [see the previous chapter](http://hutchdatascience.org/Choosing_Genomics_Tools/a-very-general-genomics-overview.html#basic-file-formats).
 
 Depending on the tool you are using, the data file and type you need will vary. Some tools have these data built in or are compatible with other packages that have annotation. If a tool automatically includes annotation within it, you will need to ensure that any additional tools you are using are also pulling from the same genome and version. Look into a tool's documentation to find out what genome versions it is based on. If it doesn't tell you at all, you don't want to be using that tool. You cannot assume that cross genome analyses will translate.
 

docs/09a-WGS-and-WXS.md

@@ -47,7 +47,7 @@ For WXS or other targeted sequencing specifically (so not relevant to WGS data),
 
     - [Hybridization based enrichment](https://www.paragongenomics.com/target-enrichment/). This includes a variety of widely used methods that we will broadly categorize in two groups: Array-based and In-solution:
       - [Array-based capture](https://en.wikipedia.org/wiki/Exome_sequencing#:~:text=Target%2Denrichment%20strategies-,Array%2Dbased%20capture,-In%2Dsolution%20capture) uses microarrays that have probes designed to bind to known coding sequences. Fragments that do not bind to these probes are washed away, leaving the sample with known coding sequences bound and ready for PCR amplification [@Hodges2007; @Turner2009].
-      - [In-solution capture](https://en.wikipedia.org/wiki/Exome_sequencing#In-solution_capture) has become more popular in recent years because it [requires less sample DNA than array-base capture](https://sequencing.roche.com/global/en/article-listing/what-is-ngs-target-enrichment-and-why-is-it-important.html). To enrich for coding sequences, in-solution capture has a pool of custom probes that are designed to bind to the coding regions in the sample. Attached to these probes are beads which can be physically separated from DNA that is not bound to the probes (this should be the non-coding sequences) [@Mamanova2010].  
+      - [In-solution capture](https://en.wikipedia.org/wiki/Exome_sequencing#In-solution_capture) has become more popular in recent years because it [requires less sample DNA than array-base capture](https://sequencing.roche.com/us/en/products/product-category/target-enrichment.html). To enrich for coding sequences, in-solution capture has a pool of custom probes that are designed to bind to the coding regions in the sample. Attached to these probes are beads which can be physically separated from DNA that is not bound to the probes (this should be the non-coding sequences) [@Mamanova2010].  
     - [PCR/Amplicon based enrichment](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318977/) requires even less sample than the other two strategies and so is ideal for when the amount of sample is limited or the DNA has been otherwise processed harshly (e.g. with paraffin embedding). Because the other two enrichment methods are done after PCR amplification has been done to the whole genomic DNA sample, its thought that this method of selective PCR amplification for enrichment can result in more uniformly amplified DNA in the resulting sample. However this is less suitable the more gene targets you have (like if you truly need to sequence all of the exome) since amplicons need to be designed for each target. Overall it is much more affordable of a method. There are several variations of this method that are [discussed thoroughly by @Singh2022](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318977/).
 
 ## DNA Sequencing Pipeline Overview

docs/11a-ATAC-Seq.md

@@ -231,7 +231,7 @@ This section has been written by AI and needs verification by experts. This is m
 ## More resources about ATAC-seq data
 
 - [ATAC-seq overview from Galaxy](https://training.galaxyproject.org/training-material/topics/epigenetics/tutorials/atac-seq/slides.html#1) - these slides explain the overarching concepts of ATAC-seq.
-- [ATAC seq guidelines from Harvard](https://informatics.fas.harvard.edu/atac-seq-guidelines.html) - this workflow runs through step by step how to analysis ATAC-seq data and what different parameters mean.
+- [ATAC seq guidelines from Harvard](https://github.com/harvardinformatics/ATAC-seq) - this workflow runs through step by step how to analysis ATAC-seq data and what different parameters mean.
 - [ATAC-seq review](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1929-3) - this paper gives a great overview of ATAC-seq data and step by step what needs to be considered.
 - [Identifying and mitigating bias in chromatin](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4473780/)
 - [CHIP Snakemake pipeline for analyzing ChIP-seq and chromatin accessibility data](https://f1000research.com/articles/10-517)

docs/11c-ChIP-Seq.md

@@ -133,7 +133,7 @@ Annotation
 - [EnrichedHeatmap](https://bioconductor.org/packages/release/bioc/html/EnrichedHeatmap.html)is an R package for making heatmaps that visualize the enrichment of genomic signals on specific target regions.
 - [SeqMonk](https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/) is a software package designed for the visualization and analysis of large-scale genomic data. It includes a heatmap function that can generate heatmaps from ChIP-seq data.
 - [ngs.plot](https://github.com/shenlab-sinai/ngsplot) is a tool that can generate different types of plots, including heatmaps, from NGS data. It includes a ChIP-seq specific mode that can be used to generate heatmaps from ChIP-seq data.
-- [ChAsE: ChAsE (ChIP-seq Analysis Engine)](http://chase.cs.univie.ac.at/overview) is a web-based platform for ChIP-seq analysis that includes a heatmap function that can generate heatmaps from ChIP-seq data.
+- [ChAsE: ChAsE (ChIP-seq Analysis Engine)](https://github.com/hyounesy/ChAsE?tab=readme-ov-file) is a web-based platform for ChIP-seq analysis that includes a heatmap function that can generate heatmaps from ChIP-seq data.
 
 These tools allow users to generate heatmaps of ChIP-seq data, which can be used to identify enriched regions of binding and to visualize patterns of binding across genomic regions.
 

docs/13-tool-glossary.md

@@ -69,7 +69,7 @@ Get started at www.cancermodels.org to browse and query models by cancer type
 
 ## CTAT
 
-The Trinity Cancer Transcriptome Analysis Toolkit (CTAT, https://github.com/NCIP/Trinity_CTAT/wiki) provides a diverse collection of tools to gain insights into the biology of cancer through the lens of the transcriptome. Using RNA-seq as input, CTAT modules enable detection of mutations, fusion transcripts, copy number aberrations, cancer-specific splicing aberrations, and oncogenic viruses including insertions into the human genome.  CTAT uses both read mapping and de novo assembly methods to analyze RNA-seq, leveraging tumor bulk and single cell transcriptomes. CTAT modules provide interactive visualizations as outputs, are easily installed for local execution or run via cloud computing (eg. Terra), have detailed user guides and tutorials, and are well-supported through user forums.
+[The Trinity Cancer Transcriptome Analysis Toolkit (CTAT)](https://github.com/NCIP/Trinity_CTAT/wiki) provides a diverse collection of tools to gain insights into the biology of cancer through the lens of the transcriptome. Using RNA-seq as input, CTAT modules enable detection of mutations, fusion transcripts, copy number aberrations, cancer-specific splicing aberrations, and oncogenic viruses including insertions into the human genome.  CTAT uses both read mapping and de novo assembly methods to analyze RNA-seq, leveraging tumor bulk and single cell transcriptomes. CTAT modules provide interactive visualizations as outputs, are easily installed for local execution or run via cloud computing (eg. Terra), have detailed user guides and tutorials, and are well-supported through user forums.
 
 ## DeepPhe
 

docs/About.md

@@ -39,26 +39,26 @@ These credits are based on our [course contributors table guidelines](https://gi
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docs/about-the-authors.html

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