ATAC-seq analysis via pipeline

# ATAC-seq analysis via pipeline

Nathan Sheffield

<span style="font-size:0.6em"><a href="http://www.databio.org/slides">www.databio.org/slides</a></span>

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---

### Outline

<div class="previewblock" style="width:30%">Pipelines</div>
<div class="previewblock" style="width:40%">ATAC-seq pipeline (PEPATAC)</div>
<div class="previewblock" style="width:30%">ATAC-seq QC</div>

<div class="questionblock" style="background:#222; color:#eee; font-size: 0.6em; margin-top: 35px">&#9665; Questions &#9655;</div>
---

### Analysis spectrum
![Pipeline Spectrum](/_modules/pipelines/pipeline_spectrum.svg)

---

### Advantages: interactive analysis vs pipelines

<div class="col2">
<u>Interactive</u><br>
- More universal learning curve<br>
- Direct control<br>
- Quicker to get started<br>
- Easier for simple analysis<br>
</div>
<div class="col2">
<u>Pipeline</u><br>
- Easier for high volume<br>
- More robust error handling<br>
- Automatic logging<br>
- Restartable<br>
- Built-in monitoring<br>
- More repeatable<br>
- More reproducible<br>
</div>

---

### What should I use?
![Pipeline Spectrum](/_modules/pipelines/pipeline_spectrum.svg)

The combination that best fits your project requirements.

---

### ATAC-seq pipelines

<div class="well">
There is growing need for integrated pipelines to process ATAC-seq data. Several have been developed but have different focus for downstream analysis by stitching together previously discussed tools. (<a href="10.1186/s13059-020-1929-3">Yan et al. 2020</a>)
</div>

- [ENCODE ATAC-seq pipeline](https://github.com/ENCODE-DCC/atac-seq-pipeline)
- [PEPATAC](http://pepatac.databio.org)
- [esATAC](https://www.bioconductor.org/packages/release/bioc/html/esATAC.html)
- [More pipelines](https://github.com/databio/awesome-atac-analysis)
---

## <img src="/_modules/pepatac/logo_pepatac.svg" width="175" style="padding-top:25px; padding-bottom:25px; vertical-align: middle;"> PEPATAC

An optimized ATAC-seq pipeline
with serial alignments

<div class="small">
<a href="http://pepatac.databio.org">http://pepatac.databio.org</a>
</div>

<span class="small bullet"><img src="/_modules/pepatac/icons/paper.svg" height="25" class="bullet">Smith et al (2021, In press). <i>NAR Genomics and Bioinformatics</i>.</span>

---

## PEPATAC workflow

---

## PEPATAC strengths

<div style="display: flex; justify-content: space-between;">
<div style="width: 45%;">
<span style="color:goldenrod">Modular system</span>
<br><br>
<span>Prealignments</span>
</div>
<div style="width: 45%;">
<span>Flexibility and portability</span>
<br><br>
<span>Outputs</span>
</div>
</div>

---

## Command-line interface with only 3 required arguments

```
$ /pipelines/pepatac.py -h
```

```
usage: pepatac.py [-h] [-R] [-N] [-D] [-F] [-C CONFIG_FILE]
                  [-O PARENT_OUTPUT_FOLDER] [-M MEMORY_LIMIT]
                  [-P NUMBER_OF_CORES] -S SAMPLE_NAME -I INPUT_FILES
                  [INPUT_FILES ...] [-I2 [INPUT_FILES2 [INPUT_FILES2 ...]]] -G
                  GENOME_ASSEMBLY [-Q SINGLE_OR_PAIRED] [-gs GENOME_SIZE]
                  [--frip-ref-peaks FRIP_REF_PEAKS] [--TSS-name TSS_NAME]
                  [--anno-name ANNO_NAME] [--keep]
                  [--peak-caller {fseq,macs2}]
                  [--trimmer {trimmomatic,skewer}]
                  [--prealignments PREALIGNMENTS [PREALIGNMENTS ...]] [-V]

PEPATAC version 0.7.3

optional arguments:
  -h, --help            show this help message and exit
  -R, --recover         Overwrite locks to recover from previous failed run
  -N, --new-start       Overwrite all results to start a fresh run
  -D, --dirty           Don't auto-delete intermediate files
  -F, --force-follow    Always run 'follow' commands
  -C CONFIG_FILE, --config CONFIG_FILE
                        Pipeline configuration file (YAML). Relative paths are
                        with respect to the pipeline script.
  -O PARENT_OUTPUT_FOLDER, --output-parent PARENT_OUTPUT_FOLDER
                        Parent output directory of project
  -M MEMORY_LIMIT, --mem MEMORY_LIMIT
                        Memory limit (in Mb) for processes accepting such
  -P NUMBER_OF_CORES, --cores NUMBER_OF_CORES
                        Number of cores for parallelized processes
  -I2 [INPUT_FILES2 [INPUT_FILES2 ...]], --input2 [INPUT_FILES2 [INPUT_FILES2 ...]]
                        Secondary input files, such as read2
  -Q SINGLE_OR_PAIRED, --single-or-paired SINGLE_OR_PAIRED
                        Single- or paired-end sequencing protocol
  -gs GENOME_SIZE, --genome-size GENOME_SIZE
                        genome size for MACS2
  --frip-ref-peaks FRIP_REF_PEAKS
                        Reference peak set for calculating FRiP
  --TSS-name TSS_NAME   Name of TSS annotation file
  --anno-name ANNO_NAME
                        Name of reference bed file for calculating FRiF
  --keep                Keep prealignment BAM files
  --peak-caller {fseq,macs2}
                        Name of peak caller
  --trimmer {trimmomatic,pyadapt,skewer}
                        Name of read trimming program
  --prealignments PREALIGNMENTS [PREALIGNMENTS ...]
                        Space-delimited list of reference genomes to align to
                        before primary alignment.
  -V, --version         show program's version number and exit

required named arguments:
  -S SAMPLE_NAME, --sample-name SAMPLE_NAME
                        Name for sample to run
  -I INPUT_FILES [INPUT_FILES ...], --input INPUT_FILES [INPUT_FILES ...]
                        One or more primary input files
  -G GENOME_ASSEMBLY, --genome GENOME_ASSEMBLY
                        Identifier for genome assembly
```

---

## Portable Encapsulated Projects (PEP) provide interoperability

---

## Portable Encapsulated Projects (PEP) provide interoperability

---

## PEP specification for sample metadata

1. Configuration file: `config.yaml`

```yaml
pep_version: 2.0.0
sample_table: "path/to/sample_table.csv"
```

2. Tabular sample annotation table: `sample_table.csv`:

```csv
"sample_name", "protocol", "file"
"frog_1", "ATAC-seq", "frog1.fq.gz"
"frog_2", "ATAC-seq", "frog2.fq.gz"
"frog_3", "ATAC-seq", "frog3.fq.gz"
"frog_4", "ATAC-seq", "frog4.fq.gz"
```

<a href="http://pep.databio.org">pep.databio.org</a>

---

## MapReduce or Scatter/Gather

1. Map/Scatter PEPATAC across individual samples

```bash
looper run config.yaml
```

2. Gather results and do cross-sample analysis

```bash
looper runp config.yaml
```

---

## PEPATAC strengths

<div style="display: flex; justify-content: space-between;">
<div style="width: 45%;">
<span>Modular system</span>
<br><br>
<span style="color:goldenrod">Prealignments</span>
</div>
<div style="width: 45%;">
<span>Flexibility and portability</span>
<br><br>
<span>Outputs</span>
</div>
</div>

<div class="fragment">
Nuclear-mitochondrial DNA (NuMts) confuse aligners
</div>

---

## Nuclear-mitochondrial DNA (NuMts)

---

## NuMts alignment problems

---

## NuMts with blacklist approach

---

## Problems with region masking

- Inaccurate alignment statistics
- Requires pre-defined NuMt locations
- Wastes compute power

---

## Serial prealignments

---

## Serial prealignments process

---

## Advantages of serial alignments

- Accuracy (better rates plus no blacklist needed).
- Speed.
- Modular reference assemblies.

---

## Prealignment mapping rates

---

## Prealignment speed improvements

---

## PEPATAC strengths

<div style="display: flex; justify-content: space-between;">
<div style="width: 45%;">
<span>Modular system</span>
<br><br>
<span>Prealignments</span>
</div>
<div style="width: 45%;">
<span style="color:goldenrod">Flexibility and portability</span>
<br><br>
<span>Outputs</span>
</div>
</div>

---

## Flexibility and Portability

- trimmer options: `skewer` and `trimmomatic`
- peak caller options: `macs2` and `fseq`
- aligner options: `bowtie2` and `bwa`

```bash
./pepatac.py --trimmer trimmomatic --peak-caller fseq
```

---

## Flexibility and Portability

Parameterization via config file `pepatac.yaml`:

```yaml
# basic tools
tools:  # absolute paths to required tools
  java: java
  python: python
  samtools: samtools
  bedtools: bedtools
  bowtie2: bowtie2
  fastqc: fastqc
  macs2: macs2
  picard: ${PICARD}
  skewer: skewer
  perl: perl
  # ucsc tools
  bedGraphToBigWig: bedGraphToBigWig
  wigToBigWig: wigToBigWig
  bigWigCat: bigWigCat
  bedSort: bedSort
  bedToBigBed: bedToBigBed
  # optional tools
  fseq: fseq
  trimmo: ${TRIMMOMATIC}
  Rscript: Rscript

# user configure
resources:
  genomes: ${GENOMES}
  adapters: null  # Set to null to use default adapters

parameters:  # parameters passed to bioinformatic tools
  samtools:
    q: 10
  macs2:
    f: BED
    q: 0.01
    shift: 0
  fseq:
    of: npf    # narrowPeak as output format
    l: 600     # feature length
    t: 4.0     # "threshold" (standard deviations)
    s: 1       # wiggle track step
```

---

## Flexibility and Portability

Running options:
- natively
- conda
- containers using `docker` or `singularity`.
- use bulker to manage containers for your (http://bulker.io)

```bash
git clone github.com/databio/pepatac
docker pull databio/pepatac
docker run --rm -it databio/pepatac pipelines/pepatac.py
```

---

## PEPATAC strengths

<div style="display: flex; justify-content: space-between;">
<div style="width: 45%;">
<span>Modular system</span>
<br><br>
<span>Prealignments</span>
</div>
<div style="width: 45%;">
<span>Flexibility and portability</span>
<br><br>
<span style="color:goldenrod">Outputs</span>
</div>
</div>

---

## Output

---

## Summary report

<a href="http://pepatac.databio.org/en/latest/files/examples/gold/gold_summary.html">http://pepatac.databio.org/en/latest/files/examples/gold/gold_summary.html</a>

---

## PEPATAC in practice

- **O'Connor et al. (2021).** *bioRxiv*. DOI: [10.1101/2021.07.15.452570](http://dx.doi.org/10.1101/2021.07.15.452570)
- **Ram-Mohan et al. (2021).** *Life Science Alliance*. DOI: [10.26508/lsa.202000976](http://dx.doi.org/10.26508/lsa.202000976)
- **Robertson et al. (2021).** *Nature Genetics*. DOI: [10.1038/s41588-021-00880-5](http://dx.doi.org/10.1038/s41588-021-00880-5)
- **Cheung et al. (2021).** DOI: [10.1038/s41590-021-00928-y](http://dx.doi.org/10.1038/s41590-021-00928-y)
- **Hasegawa et al. (2021).** *bioRxiv*. DOI: [10.1101/2021.04.28.441728](http://dx.doi.org/10.1101/2021.04.28.441728)
- **Weber et al. (2021).** *Science*. DOI: [10.1126/science.aba1786](http://dx.doi.org/10.1126/science.aba1786)
- **Tovar et al. (2021).** *bioRxiv*. DOI: [10.1101/2021.01.29.428733](http://dx.doi.org/10.1101/2021.01.29.428733)
- **Granja et al. (2021).** *Nature Genetics*. DOI: [10.1038/s41588-021-00790-6](http://dx.doi.org/10.1038/s41588-021-00790-6)
- **Fan et al. (2020).** *Cell Reports*. DOI: [10.1016/j.celrep.2020.108473](http://dx.doi.org/10.1016/j.celrep.2020.108473)
- **Smith and Sheffield (2020).** *Current Protocols in Human Genetics*. DOI: [10.1002/cphg.101](http://dx.doi.org/10.1002/cphg.101)
- **Liu (2020).** DOI: [10.18632/oncotarget.27584](http://dx.doi.org/10.18632/oncotarget.27584)
- **Zhou et al. (2020).** *bioRxiv*. DOI: [10.1101/2020.05.16.099325](http://dx.doi.org/10.1101/2020.05.16.099325)
- **Cai et al. (2020).** DOI: [10.1186/s12920-020-0695-0](http://dx.doi.org/10.1186/s12920-020-0695-0)
- **Li et al. (2020).** DOI: [10.1038/s41419-020-2303-9](http://dx.doi.org/10.1038/s41419-020-2303-9)
- **Liang et al. (2019).** DOI: [10.1002/1873-3468.13549](http://dx.doi.org/10.1002/1873-3468.13549)
- **Corces et al. (2018).** *Science*. DOI: [10.1126/science.aav1898](http://dx.doi.org/10.1126/science.aav1898)

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---

# Thank You

<span class="small bullet"><img src="/images/external/github_bug_black.svg" height="20" class="bullet"><a href="http://github.com/nsheff">nsheff</a></span> &middot;
<span class="small bullet"><img src="/images/icons/web.svg" height="25" class="bullet"><a href="http://databio.org">databio.org</a></span> &middot;
<span class="small bullet"><img src="/images/icons/letter.svg" height="25" class="bullet"><a href="mailto:nsheffield@virginia.edu">nsheffield@virginia.edu</a></span>

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