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      iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

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          Abstract

          The σ 54 promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the σ 54 promoters. Here, a predictor called ‘ iPro54-PseKNC’ was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called ‘pseudo k-tuple nucleotide composition’, which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC. For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ 54 promoters.

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          RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more

          This article summarizes our progress with RegulonDB (http://regulondb.ccg.unam.mx/) during the past 2 years. We have kept up-to-date the knowledge from the published literature regarding transcriptional regulation in Escherichia coli K-12. We have maintained and expanded our curation efforts to improve the breadth and quality of the encoded experimental knowledge, and we have implemented criteria for the quality of our computational predictions. Regulatory phrases now provide high-level descriptions of regulatory regions. We expanded the assignment of quality to various sources of evidence, particularly for knowledge generated through high-throughput (HT) technology. Based on our analysis of most relevant methods, we defined rules for determining the quality of evidence when multiple independent sources support an entry. With this latest release of RegulonDB, we present a new highly reliable larger collection of transcription start sites, a result of our experimental HT genome-wide efforts. These improvements, together with several novel enhancements (the tracks display, uploading format and curational guidelines), address the challenges of incorporating HT-generated knowledge into RegulonDB. Information on the evolutionary conservation of regulatory elements is also available now. Altogether, RegulonDB version 8.0 is a much better home for integrating knowledge on gene regulation from the sources of information currently available.
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            Recent progress in protein subcellular location prediction.

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              iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types.

              Antimicrobial peptides (AMPs), also called host defense peptides, are an evolutionarily conserved component of the innate immune response and are found among all classes of life. According to their special functions, AMPs are generally classified into ten categories: Antibacterial Peptides, Anticancer/tumor Peptides, Antifungal Peptides, Anti-HIV Peptides, Antiviral Peptides, Antiparasital Peptides, Anti-protist Peptides, AMPs with Chemotactic Activity, Insecticidal Peptides, and Spermicidal Peptides. Given a query peptide, how can we identify whether it is an AMP or non-AMP? If it is, can we identify which functional type or types it belong to? Particularly, how can we deal with the multi-type problem since an AMP may belong to two or more functional types? To address these problems, which are obviously very important to both basic research and drug development, a multi-label classifier was developed based on the pseudo amino acid composition (PseAAC) and fuzzy K-nearest neighbor (FKNN) algorithm, where the components of PseAAC were featured by incorporating five physicochemical properties. The novel classifier is called iAMP-2L, where "2L" means that it is a 2-level predictor. The 1st-level is to answer the 1st question above, while the 2nd-level is to answer the 2nd and 3rd questions that are beyond the reach of any existing methods in this area. For the conveniences of users, a user-friendly web-server for iAMP-2L was established at http://www.jci-bioinfo.cn/iAMP-2L. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                01 December 2014
                31 October 2014
                31 October 2014
                : 42
                : 21
                : 12961-12972
                Affiliations
                [1 ]Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
                [2 ]Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China
                [3 ]Gordon Life Science Institute, Belmont, MA, USA
                [4 ]Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +86 28 8320 2351; Fax: +86 28 8320 8238; Email: hlin@ 123456gordonlifescience.org or hlin@ 123456uestc.edu.cn
                Correspondence may also be addressed to Wei Chen. Tel: +86 315 3725715; Fax: +86 315 3725715; Email: chenweiimu@ 123456gmail.com
                Correspondence may also be addressed to Kuo-Chen Chou. Tel: +1 858 380 4623; Fax: +1 858 380 4623; Email: kcchou@ 123456gordonlifescience.org
                Article
                10.1093/nar/gku1019
                4245931
                25361964
                dd80ed60-c6c8-45a6-905f-1b867277a5d1
                © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

                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
                : 08 October 2014
                : 03 October 2014
                : 26 March 2014
                Page count
                Pages: 12
                Categories
                7
                24
                Computational Biology
                Custom metadata
                01 December 2014

                Genetics
                Genetics

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