wf-transcriptomes-v202/docs/quickstart.md

Quickstart

The workflow uses nextflow to manage compute and software resources, as such nextflow will need to be installed before attempting to run the workflow.

The workflow can currently be run using either Docker, Singularity or conda to provide isolation of the required software. Each method is automated out-of-the-box provided either docker, singularity or conda is installed.

It is not required to clone or download the git repository in order to run the workflow. For more information on running EPI2ME Labs workflows visit out website.

Workflow options

To obtain the workflow, having installed nextflow, users can run:

nextflow run epi2me-labs/wf-transcriptomes --help

to see the options for the workflow.

Download demonstration data

A small test dataset is provided for the purposes of testing the workflow software. It consists of reads, reference, and annotations from human chromosome 20 only. It can be downloaded using:

wget -O test_data.tar.gz https://ont-exd-int-s3-euwst1-epi2me-labs.s3.amazonaws.com/wf-isoforms/wf-isoforms_test_data.tar.gz 
tar -xzvf  test_data.tar.gz

Example execution of a workflow for reference-based transcript assembly and fusion detection

OUTPUT=~/output;
nexflow run epi2me-labs/wf-transcriptomes \
  --fastq ERR6053095_chr20.fastq \
  --ref_genome chr20/hg38_chr20.fa \
  --ref_annotation chr20/gencode.v22.annotation.chr20.gtf \
  --jaffal_refBase chr20/ \
  --jaffal_genome hg38_chr20 \
  --jaffal_annotation "genCode22" \
  --out_dir outdir -w workspace_dir

Example workflow for denovo transcript assembly

OUTPUT=~/output
nextflow run . --fastq test_data/fastq \
  --denovo \
  --ref_genome test_data/SIRV_150601a.fasta \
  --out_dir ${OUTPUT} \
  -w ${OUTPUT}/workspace \
  --sample sample_id

A full list of options can be seen in nextflow_schema.json. Below are some commonly used ones.

• Threshold for including isoforms into interactive table transcript_table_cov_thresh = 50
• Run the denovo pipeline denovo = true (default false)
• To run the workflow with direct RNA reads --direct_rna (this just skips the pychopper step).

Pychopper and minimap2 can take options via minimap2_opts and pychopper_opts, for example:

• When using the SIRV synthetic test data
  • minimap2_opts = '-uf --splice-flank=no'
• pychopper needs to know which cDNA synthesis kit used
  • SQK-PCS109: use pychopper_opts = '-k PCS109' (default)
  • SQK-PCS110: use pychopper_opts = '-k PCS110'
  • SQK-PCS11: use pychopper_opts = '-k PCS111'
• pychopper can use one of two available backends for identifying primers in the raw reads
  • nhmmscan pychopper opts = '-m phmm'
  • edlib pychopper opts = '-m edlib'

Note: edlib is set by default in the config as it's quite a lot faster. However, it may be less sensitive than nhmmscan.

Fusion detection

JAFFAL from the JAFFA package is used to identify potential fusion transcripts. To get this this working, there are a couple of things that need doing first.

Install JAFFA

to install JAFFA and it's dependencies run the folllowing:

cd wf-transcriptomes/
./subworkflows/JAFFAL/install_jaffa.sh

Prepare JAFFAL reference data

To use pre-processed reference files for the hg38 genome and GENCODE v22 annotation (as used in the JFFAAL paper), do:

mkdir jaffal_data_dir
cd jaffal_data_dir/
wf-transcriptomes/subworkflows/JAFFAL/load_jaffal_references.sh

To use alternative genome and annotation files, they should be prepared as described here

Specifying the location of the JAFFA code and reference directories

--jaffal_dir Full path to the directory made by running install_jaffa.sh as shown above. eg: /home/wf-trnascriptomes/JAFFA

--jaffal_refBase The directory containing the reference data prepared for use with JAFFAL

JAFFAL annotation and genome files

The prepared JAFFAL reference files will look something like hg38_chr20_genCode22.fa. To enable JAFFAL to find these files --jaffal_genome should be set to hg38_chr20 and --jaffal_annotation to genCode22

JAFFAL Notes: g++ must be installed. JAFFAL is not currently working on Mac M1 (osx-arm64 architecture). If there are no fusion transcripts detected, the workflow will terminate with an error at the JAFFAL stage. If this happens, skip the JAFFAL stage by omitting --jaffal_refBase

Differential Expression

Differential Expression requires at least 2 replicates of each sample to compare. You can see an example condition_sheet.tsv in test_data.

Example workflow for differential expression transcript assembly

Condition sheet

The condition sheet should be a .tsv with two columns.

• The sample_id column will need to match the 6 directories in the input fastq directory, if you are additionally using a sample_sheet they will need to correspond to the sample_ids in that.
• The condition column will need to contain one of two keys to indicate the two samples being compared.

In the default condition_sheet.tsv available in the test_data directory we have used the following.

eg. condition_sheet.tsv

sample_id,condition
barcode01,untreated
barcode02,untreated
barcode03,untreated
barcode04,treated
barcode05,treated
barcode06,treated

You will also need to provide a reference genome and a reference annotation file. Here is an example cmd to run the workflow. First you will need to download the data with wget. eg.

wget -O differential_expression.tar.gz https://ont-exd-int-s3-euwst1-epi2me-labs.s3.amazonaws.com/wf-isoforms/differential_expression.tar.gz && tar -xzvf differential_expression.tar.gz
OUTPUT=~/output;
nextflow run epi2me-labs/wf-transcriptomes \
  --fastq  differential_expression/differential_expression_fastq \
  --de_analysis \
  --ref_genome differential_expression/hg38_chr20.fa \
  --ref_annotation differential_expression/gencode.v22.annotation.chr20.gtf \
  --direct_rna --minimap_index_opts \-k15

You can also run the differential expression section of the workflow on its own by providing a reference transcriptome and setting the transcriptome assembly parameter to false. eg.

nextflow run epi2me-labs/wf-transcriptomes \
  --fastq  differential_expression/differential_expression_fastq \
  --de_analysis \
  --ref_genome differential_expression/hg38_chr20.fa \
  --ref_annotation differential_expression/gencode.v22.annotation.chr20.gtf \
  --direct_rna --minimap_index_opts \-k15 \
  --ref_transcriptome differential_expression/ref_transcriptome.fasta \
  --transcriptome_assembly false

Workflow outputs

• an HTML report document detailing the primary findings of the workflow.
• for each sample:
  • gffcomapre output directories
  • read_aln_stats.tsv - alignment summary statistics
  • transcriptome.fas - the assembled transcriptome
  • merged_transcritptome.fas - annotated, assembled transcriptome
  • jaffal ooutput directories

Fusion detection outputs

in ${out_dir}/jaffal_output_${sample_id} you will find:

• jaffa_results.csv - the csv results summary file
• jaffa_results.fasta - fusion transcritpt sequences

Differential Expression outputs

• de_analysis/results_dge.tsv and de_analysis/results_dge.pdf- results of edgeR differential gene expression analysis.
• de_analysis/results_dtu_gene.tsv, de_analysis/results_dtu_transcript.tsv and de_analysis/results_dtu.pdf - results of differential transcript usage by DEXSeq.
• de_analysis/results_dtu_stageR.tsv - results of the stageR analysis of the DEXSeq output.
• de_analysis/dtu_plots.pdf - DTU results plot based on the stageR results and filtered counts.

References

• Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society. Series B (Methodological) 57 (1): 289–300. http://www.jstor.org/stable/2346101.
• McCarthy, Davis J., Chen, Yunshun, Smyth, and Gordon K. 2012. “Differential Expression Analysis of Multifactor Rna-Seq Experiments with Respect to Biological Variation.” Nucleic Acids Research 40 (10): 4288–97.
• Nowicka, Malgorzata, and Mark D. Robinson. 2016. “DRIMSeq: A Dirichlet-Multinomial Framework for Multivariate Count Outcomes in Genomics [Version 2; Referees: 2 Approved].” F1000Research 5 (1356). https://doi.org/10.12688/f1000research.8900.2.
• Patro, Robert, Geet Duggal, Michael I Love, Rafael A Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” Nature Methods 14 (March). https://doi.org/10.1038/nmeth.4197.
• Robinson, Mark D, Davis J McCarthy, and Gordon K Smyth. 2010. “EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40.
• Love, Michael I., et al. Swimming Downstream: Statistical Analysis of Differential Transcript Usage Following Salmon Quantification. 7:952, F1000Research, 14 Sept. 2018. f1000research.com, https://f1000research.com/articles/7-952