Dataset Information

Corseq: fast and efficient identification of favoured codons from next generation sequencing reads.

ABSTRACT:

Background

Optimization of transgene expression can be achieved by designing coding sequences with the synonymous codon usage of genes which are highly expressed in the host organism. The identification of the so-called "favoured codons" generally requires the access to either the genome or the coding sequences and the availability of expression data.

Results

Here we describe corseq, a fast and reliable software for detecting the favoured codons directly from RNAseq data without prior knowledge of genomic sequence or gene annotation. The presented tool allows the inference of codons that are preferentially used in highly expressed genes while estimating the transcripts abundance by a new kmer based approach. corseq is implemented in Python and runs under any operating system. The software requires the Biopython 1.65 library (or later versions) and is available under the 'GNU General Public License version 3' at the project webpage https://sourceforge.net/projects/corseq/files.

Conclusion

corseq represents a faster and easy-to-use alternative for the detection of favoured codons in non model organisms.

SUBMITTER: Camiolo S

PROVIDER: S-EPMC6035725 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

corseq: fast and efficient identification of favoured codons from next generation sequencing reads.

Camiolo Salvatore S Porceddu Andrea A

PeerJ 20180704

<h4>Background</h4>Optimization of transgene expression can be achieved by designing coding sequences with the synonymous codon usage of genes which are highly expressed in the host organism. The identification of the so-called "favoured codons" generally requires the access to either the genome or the coding sequences and the availability of expression data.<h4>Results</h4>Here we describe corseq, a fast and reliable software for detecting the favoured codons directly from RNAseq data without p ...[more]

PMID: 30013827

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