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      CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features

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          Abstract

          With advances in next-generation sequencing technologies, numerous novel transcripts in a large number of organisms have been identified. With the goal of fast, accurate assessment of the coding ability of RNA transcripts, we upgraded the coding potential calculator CPC1 to CPC2. CPC2 runs ∼1000 times faster than CPC1 and exhibits superior accuracy compared with CPC1, especially for long non-coding transcripts. Moreover, the model of CPC2 is species-neutral, making it feasible for ever-growing non-model organism transcriptomes. A mobile-friendly web server, as well as a downloadable standalone package, is freely available at http://cpc2.cbi.pku.edu.cn.

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          LIBSVM: A library for support vector machines

          Chih-Chung Chang, Chih-Jen Lin (2011)
          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
          Article 0 comments Cited 2017 times – based on 0 reviews     Review now
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            PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions

            Michael Lin, Irwin Jungreis, Manolis Kellis (2011)
            Motivation: As high-throughput transcriptome sequencing provides evidence for novel transcripts in many species, there is a renewed need for accurate methods to classify small genomic regions as protein coding or non-coding. We present PhyloCSF, a novel comparative genomics method that analyzes a multispecies nucleotide sequence alignment to determine whether it is likely to represent a conserved protein-coding region, based on a formal statistical comparison of phylogenetic codon models. Results: We show that PhyloCSF's classification performance in 12-species Drosophila genome alignments exceeds all other methods we compared in a previous study. We anticipate that this method will be widely applicable as the transcriptomes of many additional species, tissues and subcellular compartments are sequenced, particularly in the context of ENCODE and modENCODE, and as interest grows in long non-coding RNAs, often initially recognized by their lack of protein coding potential rather than conserved RNA secondary structures. Availability and Implementation: The Objective Caml source code and executables for GNU/Linux and Mac OS X are freely available at http://compbio.mit.edu/PhyloCSF Contact: mlin@mit.edu; manoli@mit.edu
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              Non-coding RNA genes and the modern RNA world.

              S. Eddy (2001)
              Non-coding RNA (ncRNA) genes produce functional RNA molecules rather than encoding proteins. However, almost all means of gene identification assume that genes encode proteins, so even in the era of complete genome sequences, ncRNA genes have been effectively invisible. Recently, several different systematic screens have identified a surprisingly large number of new ncRNA genes. Non-coding RNAs seem to be particularly abundant in roles that require highly specific nucleic acid recognition without complex catalysis, such as in directing post-transcriptional regulation of gene expression or in guiding RNA modifications.
              Article 0 comments Cited 380 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 03 July 2017
                Publication date (Electronic): 18 May 2017
                Publication date PMC-release: 18 May 2017
                Volume: 45
                Issue: Web Server issue
                Pages: W12-W16
                Affiliations
                State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Center for Bioinformatics, Peking University, Beijing 100871, People's Republic of China
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +86 10 6275 5206; Fax: +86 10 6275 5206; Email: gaog@ 123456mail.cbi.pku.edu.cn
                [† ]These authors contributed equally to the paper as first authors.
                Author information
                http://orcid.org/0000-0002-7590-8633
                Article
                Publisher ID: gkx428
                DOI: 10.1093/nar/gkx428
                PMC ID: 5793834
                PubMed ID: 28521017
                SO-VID: 273094ff-f9c5-4566-9e5d-a036a8b9b3bb
                Copyright © © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date accepted : 03 May 2017
                Date revision received : 30 April 2017
                Date received : 01 March 2017
                Page count
                Pages: 5
                Categories
                Subject: Web Server Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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