Required python version:

• python >= 3.6

Sevral external software were depended for rapvis:

• trimmomatic
• STAR
• hisat2
• stringtie
• bwa
• samtools
• featureCounts

• pandas >= 1.1.2
• numpy
• matplotlib
• seaborn
• GSEApy
• rpy2

# Clone remote repository
$ git clone https://github.com/liuwell/rapvis.git
  
# Install required python pacakge
$ cd rapvis
$ pip install -r requirements.txt
  
# Add execution path
# The path of current dir can get by shell command "pwd"
$ echo "export PATH=$PATH:current_dir/rapvis" >> ~/.bashrc
$ source ~/.bashrc

# Then you can type -h option to check whether the installation is successful,  
# If the output as follows, it means your installation is successful
$ rapvis_run.py -h

usage: rapvis_run.py [-h] -i INPUT [-o OUTPUT] [-p THREADS] [-lib path]
                     [-m {STAR,hisat2}] [-a ADAPTER] [-minlen N] [-trim5 N]
                     [--counts] [--rRNA] [-v]

A tool for RNAseq processing and visualization

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        the input data
  -o OUTPUT, --output OUTPUT
                        output directory (default: processed_data)
  -p THREADS, --threads THREADS
                        number of threads (CPUs) to use (default: 5)
  -lib path, --libraryPath path
                        choose reference species for mapping and annotaion
  -m {STAR,hisat2}, --mapper {STAR,hisat2}
                        choose the mapping program (default: STAR)
  -a ADAPTER, --adapter ADAPTER
                        choose illumina adaptor (default: universal), choices
                        {universal, nextera, pAAAAA}
  -minlen N             discard reads shorter than N (default: 35)
  -trim5 N              remove N bases from the begining of each read
                        (default:0)
  --counts              Get gene counts
  --rRNA                whether mapping to rRNA(Human)
  -v, --version         show program's version number and exit

You can download genome sequence and annotations GTF file from GENCODE. Strongly recommended for mouse and human (files marked with PRI) : https://www.gencodegenes.org/.

Other species can download from ENSEMBL, such as Zebrafish,
genome sequences: ftp://ftp.ensembl.org/pub/release-101/fasta/danio_rerio/dna/Danio_rerio.GRCz11.dna.primary_assembly.fa.gz
GTF file: ftp://ftp.ensembl.org/pub/release-101/gtf/danio_rerio/Danio_rerio.GRCz11.101.gtf.gz

rapvis support STAR and hisat2 for mapping.

$ rapvis_build.py -mapper STAR -genome GRCh38.primary_assembly.genome.fa.gz -gtf gencode.v35.primary_assembly.annotation.gtf.gz

$ rapvis_build.py -mapper hisat2 -genome GRCh38.primary_assembly.genome.fa.gz -gtf gencode.v35.primary_assembly.annotation.gtf.gz

$ rapvis_run.py -i tests/data1/ -o TestsResult -p 5 -lib STAR_index -m STAR

$ rapvis_submit.py -i tests/data1/ -o TestsResult -lib STAR_index -m STAR -p 5 -t 2

rapvis can caculated different expressed genes, based on R limma:

$ rapvis_DE.py -i input_TPM.txt -wt 0:3 -ko 3:6 -p output:

We can perform gene ontology enrichment analysis by -go aption, and the -s also needed for determining species:

$ rapvis_DE.py -i input_TPM.txt -wt 0:3 -ko 3:6 -p output -go -s Human

If the input gene matrix not be normalized, we can use -norm option to normalize, it based on limma voom:

$ rapvis_DE.py -i input_counts.txt -wt 0:3 -ko 3:6 -p output -norm

We can get the correlation coeffcient heatmap of gene expresstion between samples:

$ rapvis_corr.py -i input_gene_TPM.txt

Several files included in the output directory:

• merge_gene_TPM.txt
  the gene expression profiles for all samples, normalized by TPM
• merge_qc_percent.pdf
  a barplot of quality contrl details by trimmomatic
• merge_mapping_percent.pdf
  a barplot of the mapping details in each sample
• merge_gene_TPM_species_type.pdf
  a stat of detected gene species in each sample, group by gene type
• merge_gene_TPM_species_EI.pdf
  a stat of detected gene species in each sample, group by expression interval
• merge_gene_TPM_density.pdf
  a density plot for gene expression distribution