Today, massive amounts of data are generated by Next-Generation Sequencing (NGS) technologies. In recent years, many algorithms, statistical methods, and software tools have been developed to perform the individual analysis steps of various NGS applications. However, streamlined analysis remains a significant barrier to effectively utilizing the technology. We have developed a Python package (pySeqRNA) that allows fast, efficient, manageable, and reproducible RNA-Seq analysis. It effectively uses current software and tools with newly written Python scripts without confining users to a collection of pre-defined methods and environments by combining many command-line tools and custom Python scripts.

Input

PySeqRNA requires a input file containing information of samples and input read files. Input template and example files here:

Download Example Template
# Project title/Information lines should start with #
SampleName Replication Identifier File1 File2
AddFull Sample Name Here Add Replication Here Add sample Identifier Here Add Sample File Name Here Add Reverese File here if Paired END
Example input file:
Download Example Input
#Arabidopsis transcriptome study under high light stress
SampleName Replication Identifier File1 File2
GL0.5h1 GL0.5h1 GL0.5 SRR6767632_001.fastq.gz SRR6767632_002.fastq.gz
GLO.5h2 GLO.5h2 GL0.5 SRR6767633_001.fastq.gz SRR6767633_002.fastq.gz
GL6h1 GL6h1 GL6 SRR6767634_001.fastq.gz SRR6767634_002.fastq.gz
GL6h2 GL6h2 GL6 SRR6767635_001.fastq.gz SRR6767635_002.fastq.gz
GL12h1 GL12h1 GL12 SRR6767636_001.fastq.gz SRR6767636_002.fastq.gz
GL12h2 GL12h2 GL12 SRR6767637_001.fastq.gz SRR6767637_002.fastq.gz
GL24h1 GL24h1 GL24 SRR6767639_001.fastq.gz SRR6767639_002.fastq.gz
GL24h2 GL24h2 GL24 SRR6767640_001.fastq.gz SRR6767640_002.fastq.gz
GL48h1 GL48h1 GL48 SRR6767642_001.fastq.gz SRR6767642_002.fastq.gz
GL48h2 GL48h2 GL48 SRR6767643_001.fastq.gz SRR6767643_002.fastq.gz
GL72h1 GL72h1 GL72 SRR6767644_001.fastq.gz SRR6767644_002.fastq.gz
GL72h2 GL72h2 GL72 SRR6767645_001.fastq.gz SRR6767645_002.fastq.gz

Analysis approach

The pySeqRNA perform RNA-Seq analysis in two steps:

  1. Uniquely mapped reads
  2. Multimapped reads

Development Environment and Prerequisite

This source code was developed in Linux, and has been tested on Linux and OS X. The main prerequisite is Python > 3.7. Following are the external dependencies:

Installation

The installation of pySeqRNA can be done in two ways:

  1. Create a dedicated miniconda3 environment

    Download pySeqRNA 0.2 from:

    https://bioinfo.usu.edu/pyseqrna/download/pySeqRNA-0.2.tar.gz

    Download the Miniconda installer:

    https://docs.conda.io/en/latest/miniconda.html#linux-installers

    Extract the downloaded file:

    tar -xvzf pySeqRNA-0.2.tar.gz

    cd pySeqRNA-0.2

    Create and activate a conda environment

    conda env create -f environment.yml

    conda activate pyseqrna-0.2

    pip3 install .

  2. Intall using system Python3 and installed external tools

    Download and install prerequisite from above and install

    Download pySeqRNA 0.2 from:

    https://bioinfo.usu.edu/pyseqrna/download/pySeqRNA-0.2.tar.gz

    Extract the downloaded file:

    tar -xvzf pySeqRNA-0.2.tar.gz

    cd pySeqRNA-0.2

    pip3 install .

Run pyseqrna

pyseqrna input_file, samples_path, reference_genome, feature_file [options]

Use Custom config file

Users can provide custom files for each tools in a directory

Download config file examples

Output

The pySeqRNA results are stored in given output directory or by default pySeqRNA_results. The output directory contains 6 directories:

                1_Quality
                ├── fastqc_results
                ├── trim_fastqc_results
                └── trim_galore_results
                2_Alignment
                ├── star_index
                └── star_results
                3_Quantification
                4_Differential_Expression
                └── diff_genes
                5_Visualization
                6_Functional_Annotation
                ├── Gene_Ontology
                │   ├── GO_Files
                │   ├── GO_Files_MMG
                │   ├── GO_Plots
                │   └── GO_Plots_MMG
                └── KEGG_Pathway
                    ├── KEGG_Files
                    ├── KEGG_Files_MMG
                    ├── KEGG_Plots
                    └── KEGG_Plots_MMG

Please Cite

Duhan N and Kaundal R. pySeqRNA: an automated Python package for RNA sequencing data analysis [version 1; not peer reviewed]. F1000Research 2020, 9(ISCB Comm J):1128 (poster) (https://doi.org/10.7490/f1000research.1118314.1)

Queries and Contact

Written by Naveen Duhan (naveen.duhan@usu.edu),

Kaundal Bioinformatics Lab, Utah State University,

Released under the terms of GNU General Public Licence v3

In case of technical problems (bugs etc.) please contact Naveen Duhan (naveen.duhan@usu.edu)

For any Questions on the scientific aspects of the pySeqRNA-0.2 method please contact:

Rakesh Kaundal, (rkaundal@usu.edu)

Naveen Duhan, (naveen.duhan@usu.edu)