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      Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit

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          Abstract

          Summary

          PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow.

          Availability and implementation

          PRALINE 2 is implemented in Python and available as open source software on GitHub: https://github.com/ibivu/PRALINE/.

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          Most cited references12

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          Amino acid substitution matrices from protein blocks.

          S Henikoff, J. Henikoff (1992)
          Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
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            Clustal Omega for making accurate alignments of many protein sequences.

            Fabian Sievers, Desmond Higgins (2018)
            Clustal Omega is a widely used package for carrying out multiple sequence alignment. Here, we describe some recent additions to the package and benchmark some alternative ways of making alignments. These benchmarks are based on protein structure comparisons or predictions and include a recently described method based on secondary structure prediction. In general, Clustal Omega is fast enough to make very large alignments and the accuracy of protein alignments is high when compared to alternative packages. The package is freely available as executables or source code from www.clustal.org or can be run on-line from a variety of sites, especially the EBI www.ebi.ac.uk.
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              The PROSITE database

              Nicolas Hulo, Amos Bairoch, Virginie Bulliard … (2006)
              The PROSITE database consists of a large collection of biologically meaningful signatures that are described as patterns or profiles. Each signature is linked to a documentation that provides useful biological information on the protein family, domain or functional site identified by the signature. The PROSITE database is now complemented by a series of rules that can give more precise information about specific residues. During the last 2 years, the documentation and the ScanProsite web pages were redesigned to add more functionalities. The latest version of PROSITE (release 19.11 of September 27, 2005) contains 1329 patterns and 552 profile entries. Over the past 2 years more than 200 domains have been added, and now 52% of UniProtKB/Swiss-Prot entries (release 48.1 of September 27, 2005) have a cross-reference to a PROSITE entry. The database is accessible at .
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                Author and article information

                Contributors
                Arne Elofsson: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 December 2019
                Publication date (Electronic): 01 August 2019
                Publication date PMC-release: 01 August 2019
                Volume: 35
                Issue: 24
                Pages: 5315-5317
                Affiliations
                Department of Computer Science, Vrije Universiteit , 1081 HV Amsterdam, The Netherlands
                Author notes
                To whom correspondence should be addressed. E-mail: m.j.j.dijkstra@ 123456vu.nl
                Author information
                http://orcid.org/0000-0002-7971-6209
                http://orcid.org/0000-0002-2779-7174
                Article
                Publisher ID: btz572
                DOI: 10.1093/bioinformatics/btz572
                PMC ID: 6954659
                PubMed ID: 31368486
                SO-VID: 2be0f922-44f2-4ca8-82ef-13746e8ba0ed
                Copyright © © The Author(s) 2019. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 29 May 2019
                Date revision received : 29 May 2019
                Date accepted : 29 July 2019
                Page count
                Pages: 3
                Categories
                Subject: Applications Notes
                Subject: Sequence Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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