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      CNIT: a fast and accurate web tool for identifying protein-coding and long non-coding transcripts based on intrinsic sequence composition

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          Abstract

          As more and more high-throughput data has been produced by next-generation sequencing, it is still a challenge to classify RNA transcripts into protein-coding or non-coding, especially for poorly annotated species. We upgraded our original coding potential calculator, CNCI (Coding-Non-Coding Index), to CNIT (Coding-Non-Coding Identifying Tool), which provides faster and more accurate evaluation of the coding ability of RNA transcripts. CNIT runs ∼200 times faster than CNCI and exhibits more accuracy compared with CNCI (0.98 versus 0.94 for human, 0.95 versus 0.93 for mouse, 0.93 versus 0.92 for zebrafish, 0.93 versus 0.92 for fruit fly, 0.92 versus 0.88 for worm, and 0.98 versus 0.85 for Arabidopsis transcripts). Moreover, the AUC values of 11 animal species and 27 plant species showed that CNIT was capable of obtaining relatively accurate identification results for almost all eukaryotic transcripts. In addition, a mobile-friendly web server is now freely available at http://cnit.noncode.org/CNIT.

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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.
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            Ribosome profiling reveals pervasive translation outside of annotated protein-coding genes.

            Nicholas Ingolia, Gloria Brar, Noam Stern-Ginossar … (2014)
            Ribosome profiling suggests that ribosomes occupy many regions of the transcriptome thought to be noncoding, including 5' UTRs and long noncoding RNAs (lncRNAs). Apparent ribosome footprints outside of protein-coding regions raise the possibility of artifacts unrelated to translation, particularly when they occupy multiple, overlapping open reading frames (ORFs). Here, we show hallmarks of translation in these footprints: copurification with the large ribosomal subunit, response to drugs targeting elongation, trinucleotide periodicity, and initiation at early AUGs. We develop a metric for distinguishing between 80S footprints and nonribosomal sources using footprint size distributions, which validates the vast majority of footprints outside of coding regions. We present evidence for polypeptide production beyond annotated genes, including the induction of immune responses following human cytomegalovirus (HCMV) infection. Translation is pervasive on cytosolic transcripts outside of conserved reading frames, and direct detection of this expanded universe of translated products enables efforts at understanding how cells manage and exploit its consequences. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
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              SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping

              Yang Ping Wu, Feilong Zhang, Kuo Yang … (2018)
              Abstract Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by sym ptom map ping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.
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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): 02 July 2019
                Publication date (Electronic): 31 May 2019
                Publication date PMC-release: 31 May 2019
                Volume: 47
                Issue: W1
                Pages: W516-W522
                Affiliations
                [1 ]Beijing University of Chinese Medicine, Chaoyang District, Beijing 100029, China
                [2 ]Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou 515041, China
                [3 ]Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
                [4 ]University of Chinese Academy of Sciences, Beijing 100049, China
                [5 ]Department of Blood Transfusion, Peking University People's Hospital, Beijing 100000, China
                [6 ]The College of Life Sciences, Northwest University, Xi’an 710069, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 10 6260 0822; Fax: +86 10 6260 1356; Email: biozy@ 123456ict.ac.cn
                Correspondence may also be addressed to Liang Sun. Tel: +86 10 6260 0822; Fax: +86 10 6260 1356; Email: sunliang@ 123456ict.ac.cn
                Correspondence may also be addressed to Li-Yan Xu. Tel: +86 754 88900460; Fax: +86 754 88900847; Email: lyxu@ 123456stu.edu.cn

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Author information
                http://orcid.org/0000-0002-1618-4292
                Article
                Publisher ID: gkz400
                DOI: 10.1093/nar/gkz400
                PMC ID: 6602462
                PubMed ID: 31147700
                SO-VID: 503cbe6b-eff8-4152-9c46-c1df6cffb28d
                Copyright © © The Author(s) 2019. 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 Non-Commercial 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 : 02 May 2019
                Date revision received : 25 April 2019
                Date received : 14 February 2019
                Page count
                Pages: 7
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 91740113
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 31701141
                Award ID: 31701149
                Award ID: 31501066
                Funded by: Institute of Computing Technology, CAS
                Award ID: 20186060
                Categories
                Subject: Web Server Issue

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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