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HangOut: generating clean PSI-BLAST profiles for domains with long insertions

Summary: Profile-based similarity search is an essential step in structure-function studies of proteins. However, inclusion of non-homologous sequence segments into a profile causes its corruption and results in false positives. Profile corruption is common in multidomain proteins, and single domain...

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Detalles Bibliográficos
Autores principales: Kim, Bong-Hyun, Cong, Qian, Grishin, Nick V.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881392/
https://www.ncbi.nlm.nih.gov/pubmed/20413635
http://dx.doi.org/10.1093/bioinformatics/btq208
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author Kim, Bong-Hyun
Cong, Qian
Grishin, Nick V.
author_facet Kim, Bong-Hyun
Cong, Qian
Grishin, Nick V.
author_sort Kim, Bong-Hyun
collection PubMed
description Summary: Profile-based similarity search is an essential step in structure-function studies of proteins. However, inclusion of non-homologous sequence segments into a profile causes its corruption and results in false positives. Profile corruption is common in multidomain proteins, and single domains with long insertions are a significant source of errors. We developed a procedure (HangOut) that, for a single domain with specified insertion position, cleans erroneously extended PSI-BLAST alignments to generate better profiles. Availability: HangOut is implemented in Python 2.3 and runs on all Unix-compatible platforms. The source code is available under the GNU GPL license at http://prodata.swmed.edu/HangOut/ Contact: kim@chop.swmed.edu; grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-28813922010-06-08 HangOut: generating clean PSI-BLAST profiles for domains with long insertions Kim, Bong-Hyun Cong, Qian Grishin, Nick V. Bioinformatics Applications Note Summary: Profile-based similarity search is an essential step in structure-function studies of proteins. However, inclusion of non-homologous sequence segments into a profile causes its corruption and results in false positives. Profile corruption is common in multidomain proteins, and single domains with long insertions are a significant source of errors. We developed a procedure (HangOut) that, for a single domain with specified insertion position, cleans erroneously extended PSI-BLAST alignments to generate better profiles. Availability: HangOut is implemented in Python 2.3 and runs on all Unix-compatible platforms. The source code is available under the GNU GPL license at http://prodata.swmed.edu/HangOut/ Contact: kim@chop.swmed.edu; grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2010-06-15 2010-04-22 /pmc/articles/PMC2881392/ /pubmed/20413635 http://dx.doi.org/10.1093/bioinformatics/btq208 Text en © The Author 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Kim, Bong-Hyun
Cong, Qian
Grishin, Nick V.
HangOut: generating clean PSI-BLAST profiles for domains with long insertions
title HangOut: generating clean PSI-BLAST profiles for domains with long insertions
title_full HangOut: generating clean PSI-BLAST profiles for domains with long insertions
title_fullStr HangOut: generating clean PSI-BLAST profiles for domains with long insertions
title_full_unstemmed HangOut: generating clean PSI-BLAST profiles for domains with long insertions
title_short HangOut: generating clean PSI-BLAST profiles for domains with long insertions
title_sort hangout: generating clean psi-blast profiles for domains with long insertions
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881392/
https://www.ncbi.nlm.nih.gov/pubmed/20413635
http://dx.doi.org/10.1093/bioinformatics/btq208
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