classifyIS-nf

Introduction

classifyIS-nf is a bioinformatics analysis pipeline for quantification of NS-seq data in mapped initiation sites (see iniseq-nf) and their classification on the basis of this quantification.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.

Pipeline summary

The pipeline performs the following steps:

1. Merging of called IS sites with bedtools
2. Quantification of SNS-seq reads for either condition in merged IS with deeptools
3. Perform classification of IS based on the given cutoff values
4. Plot the results

Quick Start

i. Install nextflow

ii. Install BEDTools and deepTools and the pandas, numpy, scipy and matplotlib Python packages

iii. Clone repository with

nextflow pull pavrilab/classifyIS-nf

iv. Start running your own analysis!

nextflow run pavrilab/classifyIS-nf --sitesA A_IS.bed --bamA conditionA.bam --labelA WT --sitesB B_IS.bed  --bamB conditionB.bam --labelB KD

Main arguments

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. For example -profile cbe invokes the execution of processes using the slurm workload manager. If no profile is given the pipeline will be executed locally.

--sitesA/--sitesB

Initiaton sites for conditions A and B (e.g. mapped with iniseq-nf)

--bamA/--bamB

BAM files containing aligned reads from which the initiation sites were mapped

Generic arguments

--labelA/--labelB

Optional label for conditions A and B. Use this to customize labels of the conditions in the result files (defaults to A and B respectively)

--FC

log2(RPM) cutoff to use for assigning upregulation or downregulation (i.e. differential regulation) for a given peak (Default: 0.585 = log2(1.5))

--t

log2(RPM) cutoff to use for specifying putative peak call threshold (i.e. putatively dormant or absent peaks; Default: 2)

--axMin

Lower bound for axis values of x- and y-axis (Default: 0)

--axMax

Upper bound for axis values of x- and y-axis (Default: 8)

--filePrefix

Prefix for the result files name

--outputDir

Folder to which results will be written (is created if not existing)

Results

The pipeline generates five result files:

1. The *.master.tsv file is a tab-separated file with the basic layout of a BEDfile containing the genomic coordinates of the peaks their names and quantification results for either condition and the assigned class
2. The *.count.tsv file contains the results as generated by deepTool multiBamSummary
3. The two PDF files *.density.pdf and *.class.pdf are the visualization of the quantification results. The density plot show the distribution of peaks in the quantification space and the class plot visualizes the class assignment results

Credits

The pipeline was developed by Daniel Malzl for use at the IMP, Vienna.

Many thanks to others who have helped out along the way too, including (but not limited to): @t-neumann, @pditommaso.

Citations

Pipeline tools

• Nextflow

  Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

• BEDTools

  Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010 Mar 15;26(6):841-2. doi: 10.1093/bioinformatics/btq033. Epub 2010 Jan 28. PubMed PMID: 20110278. PubMed Central PMCID: PMC2832824.

• deepTools

  Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016 Jul 8;44(W1):W160-5. doi: 10.1093/nar/gkw257. Epub 2016 Apr 13. PubMed PMID: 27079975; PubMed Central PMCID: PMC4987876.

Python Packages

• pandas

  Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51-56 (2010)

• numpy

  Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22-30 (2011). doi: 10.1109/MCSE.2011.37

• scipy

  Virtanen, P., Gommers, R., Oliphant, T.E. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17, 261–272 (2020). doi: 10.1038/s41592-019-0686-2

• matplotlib

  John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007). doi: 10.1109/MCSE.2007.55