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      KEGG Mapper for inferring cellular functions from protein sequences

      Author(s): 1 , 2
      Publication date Created:
      Publication date (Electronic):
      Journal: Protein Science
      Publisher: Wiley

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          Abstract

          KEGG is a reference knowledge base for biological interpretation of large-scale molecular datasets, such as genome and metagenome sequences. It accumulates experimental knowledge about high-level functions of the cell and the organism represented in terms of KEGG molecular networks, including KEGG pathway maps, BRITE hierarchies, and KEGG modules. By the process called KEGG mapping, a set of protein coding genes in the genome, for example, can be converted to KEGG molecular networks enabling interpretation of cellular functions and other high-level features. Here we report a new version of KEGG Mapper, a suite of KEGG mapping tools available at the KEGG website (https://www.kegg.jp/ or https://www.genome.jp/kegg/), together with the KOALA family tools for automatic assignment of KO (KEGG Orthology) identifiers used in the mapping.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms.

            Terry W. Pearson (1991)
            The sensitivity and selectivity of the FASTA and the Smith-Waterman protein sequence comparison algorithms were evaluated using the superfamily classification provided in the National Biomedical Research Foundation/Protein Identification Resource (PIR) protein sequence database. Sequences from each of the 34 superfamilies in the PIR database with 20 or more members were compared against the protein sequence database. The similarity scores of the related and unrelated sequences were determined using either the FASTA program or the Smith-Waterman local similarity algorithm. These two sets of similarity scores were used to evaluate the ability of the two comparison algorithms to identify distantly related protein sequences. The FASTA program using the ktup = 2 sensitivity setting performed as well as the Smith-Waterman algorithm for 19 of the 34 superfamilies. Increasing the sensitivity by setting ktup = 1 allowed FASTA to perform as well as Smith-Waterman on an additional 7 superfamilies. The rigorous Smith-Waterman method performed better than FASTA with ktup = 1 on 8 superfamilies, including the globins, immunoglobulin variable regions, calmodulins, and plastocyanins. Several strategies for improving the sensitivity of FASTA were examined. The greatest improvement in sensitivity was achieved by optimizing a band around the best initial region found for every library sequence. For every superfamily except the globins and immunoglobulin variable regions, this strategy was as sensitive as a full Smith-Waterman. For some sequences, additional sensitivity was achieved by including conserved but nonidentical residues in the lookup table used to identify the initial region.
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              GHOSTX: An Improved Sequence Homology Search Algorithm Using a Query Suffix Array and a Database Suffix Array

              Shuji Suzuki, Masanori Kakuta, Takashi Ishida … (2014)
              DNA sequences are translated into protein coding sequences and then further assigned to protein families in metagenomic analyses, because of the need for sensitivity. However, huge amounts of sequence data create the problem that even general homology search analyses using BLASTX become difficult in terms of computational cost. We designed a new homology search algorithm that finds seed sequences based on the suffix arrays of a query and a database, and have implemented it as GHOSTX. GHOSTX achieved approximately 131–165 times acceleration over a BLASTX search at similar levels of sensitivity. GHOSTX is distributed under the BSD 2-clause license and is available for download at http://www.bi.cs.titech.ac.jp/ghostx/. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We offer this tool as a potential solution to this problem.
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                Author and article information

                Journal
                Title: Protein Science
                Abbreviated Title: Protein Science
                Publisher: Wiley
                ISSN (Print): 0961-8368
                ISSN (Electronic): 1469-896X
                Publication date Created: November 05 2019
                Publication date Created: January 2020
                Publication date (Electronic): August 29 2019
                Publication date (Print): January 2020
                Volume: 29
                Issue: 1
                Pages: 28-35
                Affiliations
                [1 ]Institute for Chemical ResearchKyoto University Uji Kyoto Japan
                [2 ]Social ICT Solutions DepartmentFujitsu Kyushu Systems Ltd. Hakata‐ku Fukuoka Japan
                Article
                DOI: 10.1002/pro.3711
                PMC ID: 6933857
                PubMed ID: 31423653
                SO-VID: 765f94ed-f35f-48d0-89f5-47fd358c994b
                Copyright © © 2020
                License:

                http://onlinelibrary.wiley.com/termsAndConditions#am

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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