Smrnaseq

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Introduction

nf-core/smrnaseq is a bioinformatics best-practice analysis pipeline for Small RNA-Seq.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Online videos

A short talk about the history, current status and functionality on offer in this pipeline was given by Lorena Pantano (@lpantano) on 9th November 2021 as part of the nf-core/bytesize series.

You can find numerous talks on the nf-core events page from various topics including writing pipelines/modules in Nextflow DSL2, using nf-core tooling, running nf-core pipelines as well as more generic content like contributing to Github. Please check them out!

Pipeline summary

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1. Quality check and triming
   1. Raw read QC (FastQC)
   2. UMI extraction and miRNA adapter trimming (UMI-tools) (Optional)
   3. 3' adapter trimming (fastp)
   4. Read quality and length filter (fastp)
   5. Trim read QC (FastQC)
2. UMI deduplication (Optional)
   1. Deduplication on fastq-level (UMICollapse)
   2. Barcode and miRNA adapter extraction (UMI-tools)
   3. Read length filter (fastp)
3. miRNA QC (miRTrace)
4. Contamination filtering (Bowtie2) (Optional)
   1. rRNA filtration
   2. tRNA filtration
   3. cDNA filtration
   4. ncRNA filtration
   5. piRNA filtration
   6. Others filtration
5. UMI barcode deduplication (UMI-tools)
6. miRNA quantification
   • EdgeR
     1. Reads alignment against miRBase mature miRNA (Bowtie1)
     2. Post-alignment processing of alignment against Mature miRNA (SAMtools)
     3. Unmapped reads (from reads vs mature miRNA) alignment against miRBase hairpin
     4. Post-alignment processing of alignment against Hairpin (SAMtools)
     5. Analysis on miRBase, or MirGeneDB hairpin counts (edgeR)
        • TMM normalization and a table of top expression hairpin
        • MDS plot clustering samples
        • Heatmap of sample similarities
   • Mirtop quantification
     1. Read collapsing (seqcluster)
     2. miRNA and isomiR annotation (mirtop)
7. Genome Quantification (Optional)
   1. Reads alignment against host reference genome (Bowtie1)
   2. Post-alignment processing of alignment against host reference genome (SAMtools)
8. Novel miRNAs and known miRNAs discovery (MiRDeep2) (Optional)
   1. Mapping against reference genome with the mapper module
   2. Known and novel miRNA discovery with the mirdeep2 module
9. Present QC for raw read, alignment, and expression results (MultiQC)

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

You can test the pipeline as follows:

nextflow run nf-core/smrnaseq \
   -profile test,docker \
  --outdir <OUTDIR>

In order to use the pipeline with your own data, first prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,fastq_1
Clone1_N1,s3://ngi-igenomes/test-data/smrnaseq/C1-N1-R1_S4_L001_R1_001.fastq.gz
Clone1_N3,s3://ngi-igenomes/test-data/smrnaseq/C1-N3-R1_S6_L001_R1_001.fastq.gz
Clone9_N1,s3://ngi-igenomes/test-data/smrnaseq/C9-N1-R1_S7_L001_R1_001.fastq.gz
Clone9_N2,s3://ngi-igenomes/test-data/smrnaseq/C9-N2-R1_S8_L001_R1_001.fastq.gz
Clone9_N3,s3://ngi-igenomes/test-data/smrnaseq/C9-N3-R1_S9_L001_R1_001.fastq.gz
Control_N1,s3://ngi-igenomes/test-data/smrnaseq/Ctl-N1-R1_S1_L001_R1_001.fastq.gz
Control_N2,s3://ngi-igenomes/test-data/smrnaseq/Ctl-N2-R1_S2_L001_R1_001.fastq.gz
Control_N3,s3://ngi-igenomes/test-data/smrnaseq/Ctl-N3-R1_S3_L001_R1_001.fastq.gz

Each row represents a fastq file (single-end).

Now, you can run the pipeline using:

nextflow run nf-core/smrnaseq \
   -profile <docker/singularity/.../institute>,<protocol> \
  --input samplesheet.csv \
  --genome 'GRCh37' \
  --mirtrace_species 'hsa' \
  --outdir <OUTDIR>

[!IMPORTANT] Remember to add a protocol as an additional profile (such as illumina, nexttflex, qiaseq or cats) when running with your own data. If no protocol is indicated via -profile, the pipeline will likely fail. Alternatively, if needed to run a custom protocol, parameters must be set manually, and auto-detect feature is available. See usage documentation for more details about these profiles.

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functiona