Here, we developed a computational pipeline that integrates RNA-seq and small RNA-seq data, denoted RSCS, and this strategy greatly improves the resolution and accuracy of transcriptome annotation in a wide variety of mammalian samples.
To install RSCS, be sure to have the appropriate rights and run :
tar -zxvf v1.1.0.tar.gz
cd RSCS-1.1.0
make
Here is the help message:
Description: RSCS:RNA-seq and small RNA-seq combined strategy,
a newly cpmputational pipeline to predict mouse transcripts.
usage: RSCS <ARGUMENTS> [OPTIONS]
ARGUMENTS:
-r --rnaseq_dir RNA-seq dirctory,file format in this dirctory
-s --srnaseq_dir Small RNA-seq dirctory,file format in this dirctory
-e --reference The basename of the index for the reference genome. The basename is the name of any of the
index files up to but not including the final .1.ht2 / etc. hisat2 looks for the specified
index first in the current directory, then in the directory specified in the HISAT2_INDEX
environment variable
--single_or_pairedr Logical value of RNA-seq[TRUE or FALSE]
--single_or_paireds Logical value of Small RNA-seq[TRUE or FALSE]
-m --meta_data Merge bam meta-data file,which is tab separate. file format:
sample1 sample2
SRR2089677 SRR1200367
SRR1005345 SRR1234123
-o --outputdir Output dirctory
OPTIONS:
-h --help Show this message
-p --threads INT Number of input/output compression threads to use in addition to main thread. Default[1]
-k --kmer INT It searches for at most <int> distinct, primary alignments for each read. Default[5]
-v --version show version
Please see the example folder for the specific RSCS example.