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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments.

ABSTRACT: Sequence alignments may be the most fundamental computational resource for molecular biology. The best methods that identify sequence relatedness through profile-profile comparisons are much slower and more complex than sequence-sequence and sequence-profile comparisons such as, respectively, BLAST and PSI-BLAST. Families of related genes and gene products (proteins) can be represented by consensus sequences that list the nucleic/amino acid most frequent at each sequence position in that family. Here, we propose a novel approach for consensus-sequence-based comparisons. This approach improved searches and alignments as a standard add-on to PSI-BLAST without any changes of code. Improvements were particularly significant for more difficult tasks such as the identification of distant structural relations between proteins and their corresponding alignments. Despite the fact that the improvements were higher for more divergent relations, they were consistent even at high accuracy/low error rates for non-trivially related proteins. The improvements were very easy to achieve; no parameter used by PSI-BLAST was altered and no single line of code changed. Furthermore, the consensus sequence add-on required relatively little additional CPU time. We discuss how advanced users of PSI-BLAST can immediately benefit from using consensus sequences on their local computers. We have also made the method available through the Internet (http://www.rostlab.org/services/consensus/).

SUBMITTER: Przybylski D

PROVIDER: S-EPMC1874647 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments.

Przybylski Dariusz D Rost Burkhard B

Nucleic acids research 20070316 7

Sequence alignments may be the most fundamental computational resource for molecular biology. The best methods that identify sequence relatedness through profile-profile comparisons are much slower and more complex than sequence-sequence and sequence-profile comparisons such as, respectively, BLAST and PSI-BLAST. Families of related genes and gene products (proteins) can be represented by consensus sequences that list the nucleic/amino acid most frequent at each sequence position in that family. ...[more]

PMID: 17369271

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Project description:MotivationWhole-genome alignment (WGA) methods show insufficient scalability toward the generation of large-scale WGAs. Profile alignment-based approaches revolutionized the fields of multiple sequence alignment construction methods by significantly reducing computational complexity and runtime. However, WGAs need to consider genomic rearrangements between genomes, which make the profile-based extension of several whole-genomes challenging. Currently, none of the available methods offer the possibility to align or extend WGA profiles.ResultsHere, we present genome profile alignment, an approach that aligns the profiles of WGAs and that is capable of producing large-scale WGAs many times faster than conventional methods. Our concept relies on already available whole-genome aligners, which are used to compute several smaller sets of aligned genomes that are combined to a full WGA with a divide and conquer approach. To align or extend WGA profiles, we make use of the SuperGenome data structure, which features a bidirectional mapping between individual sequence and alignment coordinates. This data structure is used to efficiently transfer different coordinate systems into a common one based on the principles of profiles alignments. The approach allows the computation of a WGA where alignments are subsequently merged along a guide tree. The current implementation uses progressiveMauve and offers the possibility for parallel computation of independent genome alignments. Our results based on various bacterial datasets up to several hundred genomes show that we can reduce the runtime from months to hours with a quality that is negligibly worse than the WGA computed with the conventional progressiveMauve tool.Availability and implementationGPA is freely available at https://lambda.informatik.uni-tuebingen.de/gitlab/ahennig/GPA. GPA is implemented in Java, uses progressiveMauve and offers a parallel computation of WGAs.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments.

Publications

Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments.

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