.. toctree:: :maxdepth: 2
RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. Some basic modules quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, transcript level RNA integrity etc.
pip install RSeQC
Prerequisite: gcc; python2.7; numpy; R
Install RSeQC (Example):
tar zxf RSeQC-VERSION.tar.gz cd RSeQC-VERSION #type 'python setup.py install --help' to see options python setup.py install #Note this requires root privilege or python setup.py install --root=/home/user/XXX/ #install RSeQC to user specificed location, does NOT require root privilege #This is only an example. Change path according to your system configuration export PYTHONPATH=/home/user/lib/python2.7/site-packages:$PYTHONPATH #This is only an example. Change path according to your system configuration export PATH=/home/user/bin:$PATH
Finally, type: python -c 'from qcmodule import SAM'. If no error message comes out, RSeQC modules have been installed successfully.
RSeQC accepts 4 file formats as input:
- BED file is tab separated, 12-column, plain text file to represent gene model. Here is an example.
- SAM or BAM files are used to store reads alignments. SAM file is human readable plain text file, while BAM is binary version of SAM, a compact and index-able representation of reads alignments. Here is an example.
- Chromosome size file is a two-column, plain text file. Here is an example for human hg19 assembly. Use this script to download chromosome size files of other genomes.
- Fasta file.
NOTE: If you have GFF/GTF format gene files, we found this Perl script might be useful to convert them to BED.
download this script and save as 'fetchChromSizes':
# Make sure it's executable chmod +x fetchChromSizes fetchChromSizes hg19 >hg19.chrom.sizes fetchChromSizes danRer7 >zebrafish.chrom.sizes
- Liguo Wang: wangliguo78@gmail.com
- Shengqin Wang: wzsqwang@gmail.com
- Wei Li: superliwei@gmail.com
- Wang, L., Wang, S., & Li, W. (2012). RSeQC: quality control of RNA-seq experiments. Bioinformatics (Oxford, England), 28(16), 2184–2185. http://doi.org/10.1093/bioinformatics/bts356
- Wang, L., Nie, J., Sicotte, H., Li, Y., Eckel-Passow, J. E., Dasari, S., et al. (2016). Measure transcript integrity using RNA-seq data. BMC Bioinformatics, 17(1), 1–16. http://doi.org/10.1186/s12859-016-0922-z