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HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs
Partitioning the human immunoglobulin variable region into variable (V), diversity (D), and joining (J) segments is a common sequence analysis step. We introduce a novel approximate dynamic programming method that uses conserved immunoglobulin gene motifs to improve performance of aligning V-segment...
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Published in: | BMC bioinformatics 2015-05, Vol.16 (1), p.170-170, Article 170 |
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creator | Russ, Daniel E Ho, Kwan-Yuet Longo, Nancy S |
description | Partitioning the human immunoglobulin variable region into variable (V), diversity (D), and joining (J) segments is a common sequence analysis step. We introduce a novel approximate dynamic programming method that uses conserved immunoglobulin gene motifs to improve performance of aligning V-segments of rearranged immunoglobulin (Ig) genes. Our new algorithm enhances the former JOINSOLVER algorithm by processing sequences with insertions and/or deletions (indels) and improves the efficiency for large datasets provided by high throughput sequencing.
In our simulations, which include rearrangements with indels, the V-matching success rate improved from 61% for partial alignments of sequences with indels in the original algorithm to over 99% in the approximate algorithm. An improvement in the alignment of human VDJ rearrangements over the initial JOINSOLVER algorithm was also seen when compared to the Stanford.S22 human Ig dataset with an online VDJ partitioning software evaluation tool.
HTJoinSolver can rapidly identify V- and J-segments with indels to high accuracy for mutated sequences when the mutation probability is around 30% and 20% respectively. The D-segment is much harder to fit even at 20% mutation probability. For all segments, the probability of correctly matching V, D, and J increases with our alignment score. |
doi_str_mv | 10.1186/s12859-015-0589-x |
format | article |
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In our simulations, which include rearrangements with indels, the V-matching success rate improved from 61% for partial alignments of sequences with indels in the original algorithm to over 99% in the approximate algorithm. An improvement in the alignment of human VDJ rearrangements over the initial JOINSOLVER algorithm was also seen when compared to the Stanford.S22 human Ig dataset with an online VDJ partitioning software evaluation tool.
HTJoinSolver can rapidly identify V- and J-segments with indels to high accuracy for mutated sequences when the mutation probability is around 30% and 20% respectively. The D-segment is much harder to fit even at 20% mutation probability. For all segments, the probability of correctly matching V, D, and J increases with our alignment score.</description><identifier>ISSN: 1471-2105</identifier><identifier>EISSN: 1471-2105</identifier><identifier>DOI: 10.1186/s12859-015-0589-x</identifier><identifier>PMID: 26001675</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Algorithms ; Base Sequence ; Computational Biology - methods ; Conserved Sequence ; Gene Rearrangement ; Humans ; Immunoglobulin Joining Region - genetics ; Immunoglobulin Variable Region - genetics ; Immunoglobulins ; Methodology ; Molecular Sequence Data ; Mutation - genetics ; Physiological aspects ; Software</subject><ispartof>BMC bioinformatics, 2015-05, Vol.16 (1), p.170-170, Article 170</ispartof><rights>COPYRIGHT 2015 BioMed Central Ltd.</rights><rights>Russ et al.; licensee BioMed Central. 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c570t-9323a18ab73974706af715bc399f95659eb618d868dfdf141a9b2703a44a4c603</citedby><cites>FETCH-LOGICAL-c570t-9323a18ab73974706af715bc399f95659eb618d868dfdf141a9b2703a44a4c603</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492005/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492005/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,37013,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26001675$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Russ, Daniel E</creatorcontrib><creatorcontrib>Ho, Kwan-Yuet</creatorcontrib><creatorcontrib>Longo, Nancy S</creatorcontrib><title>HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs</title><title>BMC bioinformatics</title><addtitle>BMC Bioinformatics</addtitle><description>Partitioning the human immunoglobulin variable region into variable (V), diversity (D), and joining (J) segments is a common sequence analysis step. We introduce a novel approximate dynamic programming method that uses conserved immunoglobulin gene motifs to improve performance of aligning V-segments of rearranged immunoglobulin (Ig) genes. Our new algorithm enhances the former JOINSOLVER algorithm by processing sequences with insertions and/or deletions (indels) and improves the efficiency for large datasets provided by high throughput sequencing.
In our simulations, which include rearrangements with indels, the V-matching success rate improved from 61% for partial alignments of sequences with indels in the original algorithm to over 99% in the approximate algorithm. An improvement in the alignment of human VDJ rearrangements over the initial JOINSOLVER algorithm was also seen when compared to the Stanford.S22 human Ig dataset with an online VDJ partitioning software evaluation tool.
HTJoinSolver can rapidly identify V- and J-segments with indels to high accuracy for mutated sequences when the mutation probability is around 30% and 20% respectively. The D-segment is much harder to fit even at 20% mutation probability. For all segments, the probability of correctly matching V, D, and J increases with our alignment score.</description><subject>Algorithms</subject><subject>Base Sequence</subject><subject>Computational Biology - methods</subject><subject>Conserved Sequence</subject><subject>Gene Rearrangement</subject><subject>Humans</subject><subject>Immunoglobulin Joining Region - genetics</subject><subject>Immunoglobulin Variable Region - genetics</subject><subject>Immunoglobulins</subject><subject>Methodology</subject><subject>Molecular Sequence Data</subject><subject>Mutation - genetics</subject><subject>Physiological aspects</subject><subject>Software</subject><issn>1471-2105</issn><issn>1471-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNptUlFr3SAUDmNj7br9gL2MwF66h7QaNeoeBqXtdlsKhbXbq5hEM0fUTM3l3n9fs9uVXhiCnnP8vs_j4SuK9xCcQMia0whrRngFIKkAYbzavCgOIaawqiEgL5_FB8WbGH8DACkD5HVxUDc5big5LLar-2tv3J0f1yp8Llezla401s7OD6Nv59G48ufFdTnJkEwy3hk3lHNcdjlNwW-MlUmV_dZJa7oyV4YgrV3uO-9iCtI41Zft9m-qwjon1iej49vilZZjVO8ez6Pix9fL-_NVdXP77er87KbqCAWp4qhGEjLZUsQppqCRmkLSdohzzUlDuGobyHrWsF73GmIoeVtTgCTGEncNQEfFl53uNLdW9Z1yualRTCF3HrbCSyP2b5z5JQa_FhjzGgCSBY4fBYL_M6uYhDWxU-MonfJzFLBhiFDM8PLWxx10kKMSxmmfFbsFLs4IhogwiuuMOvkPKq9e5Rl6p7TJ9T3Cpz1CxiS1SYOcYxRXd9_3sXCH7YKPMSj99FMIxOIasXONyK4Ri2vEJnM-PB_RE-OfTdADgFa-9A</recordid><startdate>20150523</startdate><enddate>20150523</enddate><creator>Russ, Daniel E</creator><creator>Ho, Kwan-Yuet</creator><creator>Longo, Nancy S</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20150523</creationdate><title>HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs</title><author>Russ, Daniel E ; Ho, Kwan-Yuet ; Longo, Nancy S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c570t-9323a18ab73974706af715bc399f95659eb618d868dfdf141a9b2703a44a4c603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Base Sequence</topic><topic>Computational Biology - methods</topic><topic>Conserved Sequence</topic><topic>Gene Rearrangement</topic><topic>Humans</topic><topic>Immunoglobulin Joining Region - genetics</topic><topic>Immunoglobulin Variable Region - genetics</topic><topic>Immunoglobulins</topic><topic>Methodology</topic><topic>Molecular Sequence Data</topic><topic>Mutation - genetics</topic><topic>Physiological aspects</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Russ, Daniel E</creatorcontrib><creatorcontrib>Ho, Kwan-Yuet</creatorcontrib><creatorcontrib>Longo, Nancy S</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Russ, Daniel E</au><au>Ho, Kwan-Yuet</au><au>Longo, Nancy S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs</atitle><jtitle>BMC bioinformatics</jtitle><addtitle>BMC Bioinformatics</addtitle><date>2015-05-23</date><risdate>2015</risdate><volume>16</volume><issue>1</issue><spage>170</spage><epage>170</epage><pages>170-170</pages><artnum>170</artnum><issn>1471-2105</issn><eissn>1471-2105</eissn><abstract>Partitioning the human immunoglobulin variable region into variable (V), diversity (D), and joining (J) segments is a common sequence analysis step. We introduce a novel approximate dynamic programming method that uses conserved immunoglobulin gene motifs to improve performance of aligning V-segments of rearranged immunoglobulin (Ig) genes. Our new algorithm enhances the former JOINSOLVER algorithm by processing sequences with insertions and/or deletions (indels) and improves the efficiency for large datasets provided by high throughput sequencing.
In our simulations, which include rearrangements with indels, the V-matching success rate improved from 61% for partial alignments of sequences with indels in the original algorithm to over 99% in the approximate algorithm. An improvement in the alignment of human VDJ rearrangements over the initial JOINSOLVER algorithm was also seen when compared to the Stanford.S22 human Ig dataset with an online VDJ partitioning software evaluation tool.
HTJoinSolver can rapidly identify V- and J-segments with indels to high accuracy for mutated sequences when the mutation probability is around 30% and 20% respectively. The D-segment is much harder to fit even at 20% mutation probability. For all segments, the probability of correctly matching V, D, and J increases with our alignment score.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>26001675</pmid><doi>10.1186/s12859-015-0589-x</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Base Sequence Computational Biology - methods Conserved Sequence Gene Rearrangement Humans Immunoglobulin Joining Region - genetics Immunoglobulin Variable Region - genetics Immunoglobulins Methodology Molecular Sequence Data Mutation - genetics Physiological aspects Software |
title | HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs |
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