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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Project description:BackgroundAdapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing.ResultsTo improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing.ConclusionsSkewer achieved as yet unmatched accuracies for adapter trimming with low time bound.

Dataset Information

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

Publications

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

Similar Datasets

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