WARP Doc Site Updates (#1371)

* Update Multiome, ATAC, and Optimus readmes

website/docs/Pipelines/ATAC/README.md

@@ -8,13 +8,13 @@ slug: /Pipelines/ATAC/README
 
 | Pipeline Version | Date Updated | Documentation Author | Questions or Feedback |
 | :----: | :---: | :----: | :--------------: |
-| [2.0.0](https://github.com/broadinstitute/warp/releases) | May, 2024 | Kaylee Mathews | Please [file an issue in WARP](https://github.com/broadinstitute/warp/issues). |
+| [2.3.0](https://github.com/broadinstitute/warp/releases) | September, 2024 | Kaylee Mathews | Please [file an issue in WARP](https://github.com/broadinstitute/warp/issues). |
 
 
 ## Introduction to the ATAC workflow
 ATAC is an open-source, cloud-optimized pipeline developed in collaboration with members of the [BRAIN Initiative](https://braininitiative.nih.gov/) (BICCN and [BICAN](https://brainblog.nih.gov/brain-blog/brain-issues-suite-funding-opportunities-advance-brain-cell-atlases-through-centers) Sequencing Working Group) and [SCORCH](https://nida.nih.gov/about-nida/organization/divisions/division-neuroscience-behavior-dnb/basic-research-hiv-substance-use-disorder/scorch-program) (see [Acknowledgements](#acknowledgements) below). It supports the processing of 10x single-nucleus data generated with 10x Multiome [ATAC-seq (Assay for Transposase-Accessible Chromatin)](https://www.10xgenomics.com/products/single-cell-multiome-atac-plus-gene-expression), a technique used in molecular biology to assess genome-wide chromatin accessibility. 
 
-This workflow is the ATAC component of the [Mutiome wrapper workflow](../Multiome_Pipeline/README). It corrects cell barcodes (CBs), aligns reads to the genome, and produces a fragment file as well as per barcode metrics. 
+This workflow is the ATAC component of the [Mutiome wrapper workflow](../Multiome_Pipeline/README). It corrects cell barcodes (CBs), aligns reads to the genome, and produces a fragment file as well as [per barcode metrics](../ATAC/count-matrix-overview.md) and [library-level metrics](../ATAC/library-metrics.md).
 
 
 ## Quickstart table
@@ -30,6 +30,7 @@ The following table provides a quick glance at the ATAC pipeline features:
 | Fragment quantification | SnapATAC2 | [Zhang, K. et al., 2021](https://pubmed.ncbi.nlm.nih.gov/34774128/)
 | Data input file format | File format in which sequencing data is provided | [FASTQ](https://academic.oup.com/nar/article/38/6/1767/3112533) |
 | Data output file format | File formats in which ATAC output is provided | TSV, h5ad, BAM |
+| Library-level metrics | The [ATAC](../ATAC/README.md) pipeline uses [SnapATAC2](https://github.com/kaizhang/SnapATAC2) to generate library-level metrics in CSV format. | [Library-level metrics](../ATAC/library-metrics.md) |
 
 ## Set-up
 
@@ -113,3 +114,4 @@ Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konw
 ## Acknowledgements
 
 We are immensely grateful to the members of the BRAIN Initiative (BICAN Sequencing Working Group) and SCORCH for their invaluable and exceptional contributions to this pipeline. Our heartfelt appreciation goes to Alex Dobin, Aparna Bhaduri, Alec Wysoker, Anish Chakka, Brian Herb, Daofeng Li, Fenna Krienen, Guo-Long Zuo, Jeff Goldy, Kai Zhang, Khalid Shakir, Bo Li, Mariano Gabitto, Michael DeBerardine, Mengyi Song, Melissa Goldman, Nelson Johansen, James Nemesh, and Theresa Hodges for their unwavering dedication and remarkable efforts.  
+

website/docs/Pipelines/Multiome_Pipeline/README.md

@@ -7,7 +7,7 @@ slug: /Pipelines/Multiome_Pipeline/README
 
 | Pipeline Version | Date Updated | Documentation Author | Questions or Feedback |
 | :----: | :---: | :----: | :--------------: |
-| [Multiome v5.1.0](https://github.com/broadinstitute/warp/releases) | July, 2024 | Kaylee Mathews | Please [file an issue in WARP](https://github.com/broadinstitute/warp/issues). |
+| [Multiome v5.6.0](https://github.com/broadinstitute/warp/releases) | July, 2024 | Kaylee Mathews | Please [file an issue in WARP](https://github.com/broadinstitute/warp/issues). |
 
 ![Multiome_diagram](./multiome_diagram.png)
 
@@ -17,9 +17,9 @@ Multiome is an open-source, cloud-optimized pipeline developed in collaboration
 
 The workflow is a wrapper WDL script that calls two subworkflows: the [Optimus workflow](../Optimus_Pipeline/README) for single-cell GEX data and the [ATAC workflow](../ATAC/README) for single-cell ATAC data. 
 
-The GEX component corrects cell barcodes (CBs) and Unique Molecular Identifiers (UMIs), aligns reads to the genome, calculates per-barcode and per-gene quality metrics, and produces a raw cell-by-gene count matrix. 
+The GEX component corrects cell barcodes (CBs) and Unique Molecular Identifiers (UMIs), aligns reads to the genome, calculates per-barcode and per-gene quality metrics, and produces a raw cell-by-gene count matrix. It also produces [library-level metrics](../Optimus_Pipeline/Library-metrics.md) calculated from STARsolo aligner metrics. 
 
-The ATAC component corrects CBs, aligns reads to the genome, calculates per-barcode quality metrics, and produces a fragment file.
+The ATAC component corrects CBs, aligns reads to the genome, calculates [per-barcode quality metrics](../ATAC/count-matrix-overview.md), [library-level metrics](../ATAC/library-metrics.md) and produces a fragment file.
 
 The wrapper WDL is available in the WARP repository (see the [code here](https://github.com/broadinstitute/warp/blob/develop/pipelines/skylab/multiome/Multiome.wdl)).
 
@@ -37,6 +37,7 @@ The following table provides a quick glance at the Multiome pipeline features:
 | Transcript and fragment quantification | STARsolo (GEX), SnapATAC2 (ATAC) | [Kaminow et al. 2021](https://www.biorxiv.org/content/10.1101/2021.05.05.442755v1), [SnapATAC2](https://kzhang.org/SnapATAC2/) |
 | Data input file format | File format in which sequencing data is provided | [FASTQ](https://academic.oup.com/nar/article/38/6/1767/3112533) |
 | Data output file format | File formats in which Multiome output is provided | [BAM](http://samtools.github.io/hts-specs/) and [h5ad](https://anndata.readthedocs.io/en/latest/) |
+| Library-level metrics | Library-level metrics produced by the Optimus and ATAC workflows | [Optimus ibrary-level metrics](../Optimus_Pipeline/Library-metrics.md) and [ATAC library-level metrics](../ATAC/library-metrics.md)| 
 
 ## Set-up
 
@@ -155,8 +156,9 @@ This pipeline is supported by the [BRAIN Initiative](https://braininitiative.nih
 If your organization also uses this pipeline, we would like to list you! Please reach out to us by [filing an issue in WARP](https://github.com/broadinstitute/warp/issues).
 
 ## Acknowledgements
+
 We are immensely grateful to the members of the BRAIN Initiative (BICAN Sequencing Working Group) and SCORCH for their invaluable and exceptional contributions to this pipeline. Our heartfelt appreciation goes to Alex Dobin, Aparna Bhaduri, Alec Wysoker, Anish Chakka, Brian Herb, Daofeng Li, Fenna Krienen, Guo-Long Zuo, Jeff Goldy, Kai Zhang, Khalid Shakir, Bo Li, Mariano Gabitto, Michael DeBerardine, Mengyi Song, Melissa Goldman, Nelson Johansen, James Nemesh, and Theresa Hodges for their unwavering dedication and remarkable efforts. 
 
 ## Feedback
 
-Please help us make our tools better by [filing an issue in WARP](https://github.com/broadinstitute/warp/issues); we welcome pipeline-related suggestions or questions.
+Please help us make our tools better by [filing an issue in WARP](https://github.com/broadinstitute/warp/issues); we welcome pipeline-related suggestions or questions.

website/docs/Pipelines/Optimus_Pipeline/README.md

@@ -7,7 +7,7 @@ slug: /Pipelines/Optimus_Pipeline/README
 
 | Pipeline Version | Date Updated | Documentation Author | Questions or Feedback |
 | :----: | :---: | :----: | :--------------: |
-| [optimus_v7.2.0](https://github.com/broadinstitute/warp/releases?q=optimus&expanded=true) | July, 2024 | Elizabeth Kiernan | Please [file an issue in WARP](https://github.com/broadinstitute/warp/issues) |
+| [optimus_v7.6.0](https://github.com/broadinstitute/warp/releases?q=optimus&expanded=true) | September, 2024 | Elizabeth Kiernan | Please [file an issue in WARP](https://github.com/broadinstitute/warp/issues) |
 
 
 ![Optimus_diagram](Optimus_diagram.png)
@@ -18,7 +18,7 @@ Optimus is an open-source, cloud-optimized pipeline developed by the Data Coordi
 
 It is an alignment and transcriptome quantification pipeline that corrects cell barcodes (CBs), aligns reads to the genome, corrects Unique Molecular Identifiers (UMIs), generates a count matrix in a UMI-aware manner, calculates summary metrics for genes and cells, detects empty droplets, returns read outputs in BAM format, and returns cell gene counts in numpy matrix and h5ad file formats.
 
-In addition to providing commonly used metrics such as empty drop detection and mitochondrial reads, Optimus takes special care to **keep all reads in the output BAM that may be useful to the downstream user**, such as unaligned reads or reads with uncorrectable barcodes. This design provides flexibility to the downstream user and allows for alternative filtering or leveraging the data for novel methodological development. 
+In addition to providing [cell-level](./Loom_schema.md) and [library-level](./Library-metrics.md) metrics, Optimus takes special care to **keep all reads in the output BAM that may be useful to the downstream user**, such as unaligned reads or reads with uncorrectable barcodes. This design provides flexibility to the downstream user and allows for alternative filtering or leveraging the data for novel methodological development.
 
 Optimus has been validated for analyzing both human and mouse single-cell or single-nucleus datasets. Learn more in the [validation section](#validation-against-cell-ranger).
 
@@ -38,7 +38,10 @@ The following table provides a quick glance at the Optimus pipeline features:
 | Transcriptomic reference annotation | V43 GENCODE human transcriptome and M32 mouse transcriptome | GENCODE [human GTF](https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_43/gencode.v43.annotation.gtf.gz) and [mouse GTF](https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M32/gencode.vM32.annotation.gtf.gz) |
 | Aligner and transcript quantification | STARsolo | [Dobin, et al.,2021](https://www.biorxiv.org/content/10.1101/2021.05.05.442755v1) |
 | Data input file format | File format in which sequencing data is provided | [FASTQ](https://academic.oup.com/nar/article/38/6/1767/3112533) |
-| Data output file format | File formats in which Optimus output is provided | [BAM](http://samtools.github.io/hts-specs/), Python numpy arrays (internal), h5ad |
+| Data output file format | File formats in which Optimus output is provided | 
+[BAM](http://samtools.github.io/hts-specs/), Python numpy arrays (internal), h5ad |
+| Library-level metrics | Library-level metrics produced by the Optimus workflow | [Library-level metrics](https://github.com/broadinstitute/warp/blob/develop/website/docs/Pipelines/Optimus_Pipeline/Library-metrics.md) | 
+
 
 ## Set-up
 
@@ -375,4 +378,12 @@ In the case of multi-mapped pseudogenes, Optimus and Cell Ranger will produce di
 Overall, the estimated cells produced by Optimus and Cell Ranger should only slightly vary. However, if you are using Optimus in the Multiome pipeline and trying to compare estimated cells to Cell Ranger ARC, you might find that ARC calls fewer cells. This is because ARC sets a threshold that both the ATAC and gene expression cells must pass, whereas Optimus is only setting a threshold for the gene expression side of the pipeline.
 :::
 
+:::note Question What are [library-level metrics](https://github.com/broadinstitute/warp/blob/develop/website/docs/Pipelines/Optimus_Pipeline/Library-metrics.md) in the Optimus pipeline?
+
+Library-level metrics provide a summary of the sequencing library's quality and performance across all cells, as opposed to per-cell metrics. These metrics offer insights into the overall efficiency, coverage, and quality of the sequencing data produced.
+:::
 
+:::note How are [library-level metrics](https://github.com/broadinstitute/warp/blob/develop/website/docs/Pipelines/Optimus_Pipeline/Library-metrics.md) calculated in Optimus?
+
+Library-level metrics in Optimus are calculated using a combination of STARsolo metrics and custom metrics as defined in the library metrics table linked in the actual documentation for gene expression data. These metrics assess key aspects like total reads, sequencing depth, and overall complexity of the library, offering a higher-level view of the data's quality.
+:::