nf-core/cageseq
CAGE-sequencing analysis pipeline with trimming, alignment and counting of CAGE tags.
22.10.6.
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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 run.
The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.
Pipeline overview
The pipeline is built using Nextflow and processes data using the following steps:
- FastQC - Raw input read QC
- Trimming - Adapter + EcoP15 + 5’G + artifact fragments removal
- Ribomosal RNA removal - (optional) rRNA filtering
- trimmed reads FASTQC - Read QC after trimming and filtering
- Alignment - Read alignment to a reference genome
- CAGE tag identifcation - CAGE tag/CAGE transcription start site (CTSS) identification
- CAGE tag clustering - CAGE tag clustering
- QC of tag clusters - read distribution of CAGE tags along the gene models
- MultiQC - Aggregate report describing results and QC from the whole pipeline
- Pipeline information - Report metrics generated during the workflow execution
1. FastQC
Output files
fastqc/*_fastqc.html: FastQC report containing quality metrics.*_fastqc.zip: Zip archive containing the FastQC report, tab-delimited data file and plot images.
FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.
2. Trimming
Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.
By default this pipeline trims the cut enzyme binding site at the 5’-end and
linkers at the 3’-end (can be disabled by setting --trim_ecop or --trim_linkers to false).
Furthermore, to combat the leading-G-bias of CAGE-seq, G’s at the 5’-end are removed (disabled by setting --trim_5g to false). Additional artifacts generated in the sequencing process can be removed via the --trim_artifacts parameter (artifact sequence are provided via --artifacts_5end [path] and --artifacts_3end [path]).
All the following trimming process are skipped if --skip_trimming is supplied and the fastq files below are only available if --save_trimmed is supplied.
Output files
cutadapt/cutadapt/logs/: Trimming reportscutadapt/reads/:*5g.trim.fastq.gz: 5’ G-bias corrected FastQ file.*adapter.trim.fastq.gz: FastQ file after removal of linkers and EcoP15 site.*artifacts.trim.fastq.gz: FastQ file without artifact sequences.
3. Ribomosal RNA removal
SortMeRNA is a program for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data.
The MultiQC report shows the overall percentage of rRNA in the sample in the general statistics section. The SortMeRNA section shows a bar plot of the filtered rRNA types.
Output files
sortmerna/sortmerna/logs/: ribosomal RNA mapping reports
4. Trimmed reads FASTQC
Output files
fastqc_post/*post_fastqc.html: FastQC report containing quality metrics.*post_fastqc.zip: Zip archive containing the FastQC report, tab-delimited data file and plot images.
5. Alignment
The reads are aligned either with STAR or with bowtie, set via --aligner.
STAR
STAR is a read aligner designed for RNA sequencing. STAR stands for Spliced Transcripts Alignment to a Reference.
The STAR section of the MultiQC report shows a stacked bar plot with alignment rates: good samples should have most reads as Uniquely mapped and few Unmapped reads.
Output files
star/star/star_log*.SJ.out.tab: File containing filtered splice junctions detected after mapping the reads.*.Log.final.out: STAR alignment report containing the mapping results summary.*.Log.outand*.Log.progress.out: STAR log files containing detailed information about the run. Typically only useful for debugging purposes.
star/unmapped/*.fastq.gz: If--save_unalignedis specified, FastQ files containing unmapped reads will be placed in this directory.
Bowtie 1
Bowtie 1 is an ultrafast, memory-efficient short read aligner.
The bowtie 1 section of the MultiQC report shows a stacked bar plot with alignment rates: good samples should have most reads as aligned and few Not aligned reads.
Output files
bowtie/bowtie/bowtie_log*.out: The bowtie alignment report, contains mapping results summary
bowtie/unmapped/*.fastq.gz: If--save_unalignedis specified, FastQ files containing unmapped reads will be placed in this directory.
SAMtools
The original BAM files generated by the selected alignment algorithm are further processed with SAMtools to sort them by coordinate, for indexing, as well as to generate read mapping statistics. $ALIGNER stands either for star or bowtie depending on the used aligner.
Output files
<ALIGNER>/samtools_stats/- SAMtools
<SAMPLE>.sorted.bam.flagstat,<SAMPLE>.sorted.bam.idxstatsand<SAMPLE>.sorted.bam.statsfiles generated from the alignment files.
- SAMtools
6. CAGE tag identification
The aligned reads are converted and grouped to BED files (and bigWig files with --bigwig) with 1bp long CAGE tags/transcription start sites (CTSS).
Output files
$ALIGNER/ctss$ALIGNER/ctss/bed/*.ctss.bed: A BED6 file with the mapped cage tags$ALIGNER/ctss/bigwig/*.ctss.bigWig: A bigWig file with the mapped cage tags
7. CAGE tag clustering
paraclu
paraclu finds clusters in data attached to sequences. It is applied on the pool of all ctss bed files to cluster and returns a BED file with the clustered tags. These clusters are then intersected with the BED files and a BED file with counts only in the tag clusters is created for each file. Additionally these counts are summarized in a tab-separated count table, where each row stands for a tag cluster and each column for a sample.
Output files
$ALIGNER/tag_clusters$ALIGNER/tag_clusters/ctss.clustered.simplified.bed: A BED6 file with the found tag clusters and their pooled expression as the score.$ALIGNER/tag_clusters/bed/*.ctss.bed_counts.bed: A BED6 file with the expression of the tag clusters in this sample.$ALIGNER/tag_clusters/count_table.tsv: Each column of the count table stands for one sample and each row for one tag cluster. The first column contains the tag cluster coordinates. The first row of this table is the header with “coordinates” and the sample names.
8. QC of tag clusters
RSeQC
RSeQC is a package of scripts designed to evaluate the quality of RNA seq data. You can find out more about the package at the RSeQC website.
This pipeline only runs the read distribution RSeQC scripts on the tag clusters. These calculate how mapped reads are distributed over genomic features. The results are summarized within the MultiQC report.
9. MultiQC
Output files
multiqc/multiqc_report.html: a standalone HTML file that can be viewed in your web browser.multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.multiqc_plots/: directory containing static images from the report in various formats.
MultiQC is a visualization 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 in the report data directory.
Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. 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.
10. Pipeline information
Output files
pipeline_info/- Reports generated by Nextflow:
execution_report.html,execution_timeline.html,execution_trace.txtandpipeline_dag.dot/pipeline_dag.svg. - Reports generated by the pipeline:
pipeline_report.html,pipeline_report.txtandsoftware_versions.yml. Thepipeline_report*files will only be present if the--email/--email_on_failparameter’s are used when running the pipeline. - Reformatted samplesheet files used as input to the pipeline:
samplesheet.valid.csv.
- Reports generated by Nextflow:
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.