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      FASTA Herder: a web application to trim protein sequence sets

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          Abstract

          The ever increasing number of sequences in protein databases usually turns out large numbers of homologs in sequence similarity searches. While information from homology can be very useful for functional prediction based on amino acid conservation, many of these homologs usually have high levels of identity among themselves, which hinders multiple sequence alignment computation and, especially, visualization. More generally, high redundancy reduces the usability of a protein set in machine learning applications and biases statistical analyses. We developed an algorithm to identify redundant sequence homologs that can be culled producing a streamlined FASTA file. As a difference from other automatic approaches that only aggregate sequences with high identity, our method clusters near-full length homologs allowing for lower sequence identity thresholds. Our method was fully tested and implemented in a web application called FASTA Herder, publicly available at http://fh.ogic.ca/.

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          Most cited references 16

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          Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

           SF Altschul,  TL Madden,  AA Schaffer … (1997)
          Article 0 comments Cited 1551 times – based on 0 reviews     Review now
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            UniRef: comprehensive and non-redundant UniProt reference clusters.

             Baris Suzek,  Hongzhan Huang,  Hongyu Wu … (2007)
            Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. Supplementary data are available at Bioinformatics online.
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              Database of homology-derived protein structures and the structural meaning of sequence alignment.

               C. Sander,  Raphael Schneider (1990)
              The database of known protein three-dimensional structures can be significantly increased by the use of sequence homology, based on the following observations. (1) The database of known sequences, currently at more than 12,000 proteins, is two orders of magnitude larger than the database of known structures. (2) The currently most powerful method of predicting protein structures is model building by homology. (3) Structural homology can be inferred from the level of sequence similarity. (4) The threshold of sequence similarity sufficient for structural homology depends strongly on the length of the alignment. Here, we first quantify the relation between sequence similarity, structure similarity, and alignment length by an exhaustive survey of alignments between proteins of known structure and report a homology threshold curve as a function of alignment length. We then produce a database of homology-derived secondary structure of proteins (HSSP) by aligning to each protein of known structure all sequences deemed homologous on the basis of the threshold curve. For each known protein structure, the derived database contains the aligned sequences, secondary structure, sequence variability, and sequence profile. Tertiary structures of the aligned sequences are implied, but not modeled explicitly. The database effectively increases the number of known protein structures by a factor of five to more than 1800. The results may be useful in assessing the structural significance of matches in sequence database searches, in deriving preferences and patterns for structure prediction, in elucidating the structural role of conserved residues, and in modeling three-dimensional detail by homology.
              Article 0 comments Cited 219 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Caroline Louis-Jeune: (View ORCID Profile)
                Carol Perez-Iratxeta: (View ORCID Profile)
                Journal
                Journal ID (publisher-id): SOR-LIFE
                Title: ScienceOpen Research
                Publisher: ScienceOpen
                ISSN (Electronic): 2199-1006
                Publication date: 23 July 2015
                Publication date: 15 September 2015
                Volume: 0 (ID: 5df5dc75-0b14-497d-804d-0075d0201d15 )
                Issue: 0
                Pages: 1-4
                Affiliations
                [1 ]Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, ON K1H 8L6, Canada
                [2 ]Max Delbrük Center for Molecular Medicine, Robert-Rössle-Str. 10, 13125 Berlin, Germany
                [3 ]Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
                Author notes
                [* ]Corresponding author's e-mail address: cpereziratxeta@ 123456gmail.com
                Article
                Publisher ID: 2927:XE
                DOI: 10.14293/S2199-1006.1.SOR-LIFE.A67837.v2
                Copyright statement: © 2015 Louis-Jeune et al.
                License:

                This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

                Page count
                Figures: 0, Tables: 3, References: 13, Pages: 4
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                Subject: Original article

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