CNCI

About CNCI

It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. We developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense-antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan.

Current Version

Release : CNCI version 2 Feb 28, 2014

Documentation

CNCI has update to version 2,in this version some bugs have be fixed,and more friendly for users. CNCI can run in 32-bit Linux, 64-bit Linux. Please note that: CNCI’s input file must not be empty.

Install CNCI

At the first time to running CNCI, we suggest you to install “libsvm-3.0” that stored in our package.

git clone git@github.com:www-bioinfo-org/CNCI.git
cd CNCI
unzip libsvm-3.0.zip
cd libsvm-3.0
make
cd ..

HELP for CNCI subroutines

compare.py: compare the merged/assembled transcripts with known gene annotation!

Usage: compare.py [-h] -c coding_ref -n noncoding_ref -i input_gtf -o out_dir

Parameters:

-h, –help show this help message and exit.

-c CODING_REF, –coding_ref=CODING_REF

(Required.) The path of coding reference gtf file. Two mandatory attributes (gene_id “value”; transcript_id “value”) should be provided in the file. Some files which has been prepared could be download at http://www.bioinfo.org/np/

-n NONCODING_REF, –noncoding_ref=NONCODING_REF.

(Required.) The path of lincRNA reference gtf file. Two mandatory attributes (gene_id “value”; transcript_id “value”) should be provided in the file. Some files which has been prepared could be download at http://www.bioinfo.org/np/

-i INPUT_GTF, –input_gtf=INPUT_GTF

(Required.) The path of user input assemble gtf file. This file usually be generated by cufflinks/cuffcompare/cuffmerge. Also, two mandatory attributes (gene_id “value”; transcript_id “value”) should be provided in the file.

-o OUT_DIR, –out_dir=OUT_DIR

(Required.) Output dirctory of the results.

CNCI.py: A classification tool for identify coding or non-coding transcripts (fasta files and gtf files)

Parameters:

-f or –file : input files

-o or –out : assign your output file in current directory (this parameter will produce a Temp sub-folder in current directory, and will remove it automatically at the end of programming), and the result is stored in xxx.index

-p or –parallel : assign the running CUP numbers

-m or –model : assign the classification models (“ve” for vertebrate species, “pl” for plat species)

-g or –gtf : if you input files is gtf format please use this parameter

-d or –directory : if you use the -g or –gtf this parameter must be assigned, within this parameter please assign the path of your reference genome.

filter_novel_lincRNA.py: A tool that can convert the index file which produced by python CNCI_package/CNCI.py to four gene classes (novel_lincRNA,novel_coding, ambiguous_genes and filter_out_noncoding)

Usage: filter_novel_lincRNA.py [-h] [-s 0] [-l 200] [-e 2] -i cnci_index -g unannotated_gtf -o out_dir

Parameters:

-h, –help show this help message and exit

-i INDEX, –index=INDEX

(Required.) The path of coding/noncoding index file. This file is the output file of CNCI.py.

-g GTF, –gtf=GTF

(Required.) The path of potentially_novel gtf file. This file could be generated by compare.py.

-s SCORE, –score=SCORE

(Optional.) Threoshold of CNCI score. RNAs with score less than SCORE will be classified as noncoding. The Default is 0 .

-l LENGTH, –length=LENGTH

(Optional.) Minimal length of lincRNA. lincRNA with length >= LENGTH will be kept. The Default is 200.

-e EXON_NUM, –exon_num=EXON_NUM

(Optional.) Minimal exon number of lincRNA. lincRNA with exon number >= EXON_NUM will be kept. The Default is 2.

-o OUT_DIR, –out_dir=OUT_DIR

(Requried.) Output directory of the results.

EXAMPLE

you can use CNCI subroutines like our example:

python CNCI_package/CNCI.py -f unannotation.gtf -g -o test -m ve -p 8 -d hg19.2bit

python filter_novel_lincRNA.py -i test.index -g unannotation.gtf -s 0 -l 200 -e exon_num -o out_dir 

python extract.py -i novel-noncoding.gtf,nov.gtf -n known-non-coding.gtf -c known-coding.gtf

Please note that : “libsvm-3.0 must be installed accordance with our instruction in SETUP section”

Citation

• Liang Sun, Haitao Luo, Dechao Bu, Guoguang Zhao, Kuntao Yu, Changhai Zhang, Yuanning Liu, RunSheng Chen and Yi Zhao* Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Research (2013), doi: 10.1093/nar/gkt646

Contact

• Yi Zhao : biozy@ict.ac.cn