22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article
      ,
      PeerJ
      PeerJ Inc.
      Optimal codons, Codon bias, Transgene expression optimization, Preferred codons

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references18

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          A survey of best practices for RNA-seq data analysis

          RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0881-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Streaming fragment assignment for real-time analysis of sequencing experiments

            We present eXpress, a software package for highly efficient probabilistic assignment of ambiguously mapping sequenced fragments. eXpress uses a streaming algorithm with linear run time and constant memory use. It can determine abundances of sequenced molecules in real time, and can be applied to ChIP-seq, metagenomics and other large-scale sequencing data. We demonstrate its use on RNA-seq data, showing greater efficiency than other quantification methods.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The selection-mutation-drift theory of synonymous codon usage.

              M Bulmer (1991)
              It is argued that the bias in synonymous codon usage observed in unicellular organisms is due to a balance between the forces of selection and mutation in a finite population, with greater bias in highly expressed genes reflecting stronger selection for efficiency of translation. A population genetic model is developed taking into account population size and selective differences between synonymous codons. A biochemical model is then developed to predict the magnitude of selective differences between synonymous codons in unicellular organisms in which growth rate (or possibly growth yield) can be equated with fitness. Selection can arise from differences in either the speed or the accuracy of translation. A model for the effect of speed of translation on fitness is considered in detail, a similar model for accuracy more briefly. The model is successful in predicting a difference in the degree of bias at the beginning than in the rest of the gene under some circumstances, as observed in Escherichia coli, but grossly overestimates the amount of bias expected. Possible reasons for this discrepancy are discussed.
                Bookmark

                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                4 July 2018
                2018
                : 6
                : e5099
                Affiliations
                [-1] Dipartimento di Agraria, Università degli studi di Sassari , Sassari, Italy
                Article
                5099
                10.7717/peerj.5099
                6035725
                d42660ee-01a5-4a79-934a-045175b0f8db
                ©2018 Camiolo and Porceddu

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 6 March 2018
                : 5 June 2018
                Funding
                The authors received no funding for this work.
                Categories
                Bioinformatics
                Biotechnology
                Computational Biology
                Genomics

                optimal codons,codon bias,transgene expression optimization,preferred codons

                Comments

                Comment on this article