nf-core/atacseq

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Introduction

This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.

Pipeline overview

The pipeline is built using Nextflow. See main README.md for a condensed overview of the steps in the pipeline, and the bioinformatics tools used at each step.

See Illumina website for more information regarding the ATAC-seq protocol, and for an extensive list of publications.

The directories listed below will be created in the output directory after the pipeline has finished. All paths are relative to the top-level results directory.

Library-level analysis

The initial QC and alignments are performed at the library-level e.g. if the same library has been sequenced more than once to increase sequencing depth. This has the advantage of being able to assess each library individually, and the ability to process multiple libraries from the same sample in parallel.

Raw read QC

FastQC gives general quality metrics about your reads. It provides information about the quality score distribution across your reads, the per base sequence content (%A/C/G/T). You get information about adapter contamination and other overrepresented sequences. For further reading and documentation see the FastQC help pages.

Adapter trimming

Trim Galore! is a wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files. By default, Trim Galore! will automatically detect and trim the appropriate adapter sequence. For most ATAC-seq datasets this will be the Nextera adapter sequence ‘CTGTCTCTTATA’. See usage.md for more details about the trimming options.

Alignment

The pipeline has been written in a way where all the files generated downstream of the alignment are placed in the same directory as specified by --aligner e.g. if --aligner bwa is specified then all the downstream results will be placed in the bwa/ directory. This helps with organising the directory structure and more importantly, allows the end-user to get the results from multiple aligners by simply re-running the pipeline with a different --aligner option along the -resume parameter. It also means that results won’t be overwritten when resuming the pipeline and can be used for benchmarking between alignment algorithms if required. Thus, <ALIGNER> in the directory structure below corresponds to the aligner set when running the pipeline.

Adapter-trimmed reads are mapped to the reference assembly using the aligner set by the --aligner parameter. Available aligners are BWA (default), Bowtie 2, Chromap and STAR. A genome index is required to run any of this aligners so if this is not provided explicitly using the corresponding parameter (e.g. --bwa_index), then it will be created automatically from the genome fasta input. The index creation process can take a while for larger genomes so it is possible to use the --save_reference parameter to save the indices for future pipeline runs, reducing processing times.

NB: Currently, paired-end files produced by Chromap are excluded from downstream analysis due to this issue. Single-end files are processed normally.

Merged library-level analysis

The library-level alignments associated with the same sample are merged and subsequently used for the downstream analyses.

Unmapped reads

The --save_unaligned parameter enables to obtain FastQ files containing unmapped reads (only available for STAR and Bowtie2).

STAR logs

Merged library-level analysis

The library-level alignments associated with the same sample are merged and subsequently used for the downstream analyses.

Alignment merging, duplicate marking, filtering and QC

Picard MergeSamFiles and MarkDuplicates are used in combination to merge the alignments, and for the marking of duplicates, respectively. If you only have one library for any given replicate then the merging step is not carried out because the library-level and merged library-level BAM files will be exactly the same.

Read duplicate marking is carried out using the Picard MarkDuplicates command. Duplicate reads are generally removed from the aligned reads to mitigate for fragments in the library that may have been sequenced more than once due to PCR biases. There is an option to keep duplicate reads with the --keep_dups parameter but its generally recommended to remove them to avoid the wrong interpretation of the results. A similar option has been provided to keep reads that are multi-mapped - --keep_multi_map. Other steps have been incorporated into the pipeline to filter the resulting alignments - see main README.md for a more comprehensive listing, and the tools used at each step. A selection of alignment-based QC metrics generated by Picard CollectMultipleMetrics and MarkDuplicates will be included in the MultiQC report.

Certain cell types and tissues yield an enormous fraction (typically 20–80%) of unusable sequences of mitochondrial origin. This is a known problem that is specific to ATAC-seq library preps - see Montefiori et al. 2017. There is an option to keep these reads using the --keep_mito parameter but its generally recommended to remove these in order to get a more reliable assessment of the duplication rate from the rest of the genome, and to avoid any biases in the downstream analyses.

The Preseq package is aimed at predicting and estimating the complexity of a genomic sequencing library, equivalent to predicting and estimating the number of redundant reads from a given sequencing depth and how many will be expected from additional sequencing using an initial sequencing experiment. The estimates can then be used to examine the utility of further sequencing, optimize the sequencing depth, or to screen multiple libraries to avoid low complexity samples. The dashed line shows a perfectly complex library where total reads = unique reads. Note that these are predictive numbers only, not absolute. The MultiQC plot can sometimes give extreme sequencing depth on the X axis - click and drag from the left side of the plot to zoom in on more realistic numbers.

Normalised bigWig files

The bigWig format is in an indexed binary format useful for displaying dense, continuous data in Genome Browsers such as the UCSC and IGV. This mitigates the need to load the much larger BAM files for data visualisation purposes which will be slower and result in memory issues. The coverage values represented in the bigWig file can also be normalised in order to be able to compare the coverage across multiple samples - this is not possible with BAM files. The bigWig format is also supported by various bioinformatics software for downstream processing such as meta-profile plotting.

Coverage QC

deepTools plotFingerprint is a useful QC for ATAC-seq data in order to see the relative enrichment of the samples in the experiment on a genome-wide basis (see plotFingerprint docs).

The results from deepTools plotProfile gives you a quick visualisation for the genome-wide enrichment of your samples at the TSS, and across the gene body. During the downstream analysis, you may want to refine the features/genes used to generate these plots in order to see a more specific condition-related effect.

Call peaks

MACS2 is one of the most popular peak-calling algorithms for ChIP-seq data. For ATAC-seq data we are also looking for genome-wide regions of enrichment but in this case without comparison to a standard control sample (e.g. input DNA). By default, the peaks are called with the MACS2 --broad parameter. If, however, you would like to call narrow peaks then please provide the --narrow_peak parameter when running the pipeline. See MACS2 outputs for a description of the output files generated by MACS2.

HOMER annotatePeaks.pl is used to annotate the peaks relative to known genomic features. HOMER is able to use the --gtf annotation file which is provided to the pipeline. Please note that some of the output columns will be blank because the annotation is not provided using HOMER’s in-built database format. However, the more important fields required for downstream analysis will be populated i.e. Annotation, Distance to TSS and Nearest Promoter ID.

Various QC plots per sample including number of peaks, fold-change distribution, FRiP score and peak-to-gene feature annotation are also generated by the pipeline. Where possible these have been integrated into the MultiQC report.

Create and quantify consensus set of peaks

In order to perform the differential accessibility analysis we need to be able to carry out the read quantification for the same intervals across all of the samples in the experiment. To this end, the individual peak-sets called per sample have to be merged together in order to create a consensus set of peaks.

Using the consensus peaks it is possible to assess the degree of overlap between the peaks from a set of samples e.g. Which consensus peaks contain peaks that are common/unique to a given set of samples?. This may be useful for downstream filtering of peaks based on whether they are called in multiple replicates/conditions. Please note that it is possible for a consensus peak to contain multiple peaks from the same sample. Unfortunately, this is sample-dependent but the files generated by the pipeline do have columns that report such instances and allow you to factor them into any further analysis.

By default, the peak-sets are not filtered, therefore, the consensus peaks will be generated across the union set of peaks from all samples. However, you can increment the --min_reps_consensus parameter appropriately if you are confident you have good reproducibility amongst your replicates to create a “reproducible” set of consensus of peaks. In future iterations of the pipeline more formal analyses such as IDR may be implemented to obtain reproducible and high confidence peak-sets with which to perform this sort of analysis.

The featureCounts tool is used to count the number of reads relative to the consensus peak-set across all of the samples. This essentially generates a file containing a matrix where the rows represent the consensus intervals, the columns represent all of the samples in the experiment, and the values represent the raw read counts.

Read counting and differential accessibility analysis

DESeq2 is more commonly used to perform differential expression analysis for RNA-seq datasets. However, it can also be used for ATAC-seq differential accessibility analysis, in which case you can imagine that instead of counts per gene for RNA-seq data we now have counts per accessible region.

This pipeline uses a standardised DESeq2 analysis script to get an idea of the reproducibility within the experiment, and to assess the overall differential accessibility. Please note that this will not suit every experimental design, and if there are other problems with the experiment then it may not work as well as expected.

The script included in the pipeline uses DESeq2 to normalise read counts across all of the provided samples in order to create a PCA plot and a clustered heatmap showing pairwise Euclidean distances between the samples in the experiment. These help to show the similarity between groups of samples and can reveal batch effects and other potential issues with the experiment.

By default, the pipeline uses the vst transformation which is more suited to larger experiments. You can set the parameter --deseq2_vst false if you wish to use the DESeq2 native rlog option. See DESeq2 docs for a more detailed explanation.

ataqv

ataqv is a toolkit for measuring and comparing ATAC-seq results. It was written to help understand how well ATAC-seq assays have worked, and to make it easier to spot differences that might be caused by library prep or sequencing. Please see ataqv homepage for documentation and an example report.

Merged replicate-level analysis

The alignments associated with all of the replicates from the same experimental condition can also be merged. This can be useful to increase the coverage for peak-calling and for other analyses that require high sequencing depth such as motif footprinting. The analysis steps and directory structure for <ALIGNER>/merged_library/ and <ALIGNER>/merged_replicate/ are almost identical.

File names in the resulting directory (i.e. <ALIGNER>/merged_replicate/) will have the ‘.mRp.’ suffix.

You can skip this portion of the analysis by specifying the --skip_merge_replicates parameter.

NB: Merged library-level alignments will be used for read counting relative to the consensus merged replicate-level peakset. This is the only way in which differential analysis can be performed at the merged replicate-level.

Aggregate analysis

Present QC for the raw read, alignment, peak and differential accessibility results

MultiQC is a visualisation tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available within the report data directory.

Results generated by MultiQC collate pipeline QC from FastQC, TrimGalore, samtools flagstat, samtools idxstats, samtools stats, picard CollectMultipleMetrics, picard MarkDuplicates, Preseq, deepTools plotProfile, deepTools plotFingerprint and featureCounts. The default multiqc config file also contains the provision for loading custom-content to report peak counts, FRiP scores, peak-to-gene annnotation proportions, sample-similarity heatmaps and PCA plots.

The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Create IGV session file

An IGV session file will be created at the end of the pipeline containing the normalised bigWig tracks, per-sample peaks, consensus peaks and differential sites. This avoids having to load all of the data individually into IGV for visualisation.

The genome fasta file required for the IGV session will be the same as the one that was provided to the pipeline. This will be copied into genome/ to overcome any loading issues. If you prefer to use another path or an in-built genome provided by IGV just change the genome entry in the second-line of the session file.

The file paths in the IGV session file will only work if the results are kept in the same place on your storage. If the results are moved or for example, if you prefer to load the data over the web then just replace the file paths with others that are more appropriate.

Once installed, open IGV, go to File > Open Session and select the igv_session.xml file for loading.

NB: If you are not using an in-built genome provided by IGV you will need to load the annotation yourself e.g. in .gtf and/or .bed format.

Other results

Reference genome files

Reference genome-specific files can be useful to keep for the downstream processing of the results.

Pipeline information

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.