SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements
Motivation: The inference of pre-mutation immunoglobulin (Ig) rearrangements is essential in the study of the antibody repertoires produced in response to infection, in B-cell neoplasms and in autoimmune disease. Often, there are several rearrangements that are nearly equivalent as candidates for a...
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| Veröffentlicht in: | Bioinformatics 2010-04, Vol.26 (7), p.867-872 |
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| creator | Munshaw, Supriya Kepler, Thomas B. |
| description | Motivation: The inference of pre-mutation immunoglobulin (Ig) rearrangements is essential in the study of the antibody repertoires produced in response to infection, in B-cell neoplasms and in autoimmune disease. Often, there are several rearrangements that are nearly equivalent as candidates for a given Ig gene, but have different consequences in an analysis. Our aim in this article is to develop a probabilistic model of the rearrangement process and a Bayesian method for estimating posterior probabilities for the comparison of multiple plausible rearrangements. Results: We have developed SoDA2, which is based on a Hidden Markov Model and used to compute the posterior probabilities of candidate rearrangements and to find those with the highest values among them. We validated the software on a set of simulated data, a set of clonally related sequences, and a group of randomly selected Ig heavy chains from Genbank. In most tests, SoDA2 performed better than other available software for the task. Furthermore, the output format has been redesigned, in part, to facilitate comparison of multiple solutions. Availability: SoDA2 is available online at https://hippocrates.duhs.duke.edu/soda. Simulated sequences are available upon request. Contact: kepler@duke.edu |
| doi_str_mv | 10.1093/bioinformatics/btq056 |
| format | Article |
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Often, there are several rearrangements that are nearly equivalent as candidates for a given Ig gene, but have different consequences in an analysis. Our aim in this article is to develop a probabilistic model of the rearrangement process and a Bayesian method for estimating posterior probabilities for the comparison of multiple plausible rearrangements. Results: We have developed SoDA2, which is based on a Hidden Markov Model and used to compute the posterior probabilities of candidate rearrangements and to find those with the highest values among them. We validated the software on a set of simulated data, a set of clonally related sequences, and a group of randomly selected Ig heavy chains from Genbank. In most tests, SoDA2 performed better than other available software for the task. Furthermore, the output format has been redesigned, in part, to facilitate comparison of multiple solutions. Availability: SoDA2 is available online at https://hippocrates.duhs.duke.edu/soda. 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Published by Oxford University Press. 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c576t-6ac6c6380e0a371cd0f15783c98be0d6acedf4a99b6e59ed1fc84df4a005fb4b3</citedby><cites>FETCH-LOGICAL-c576t-6ac6c6380e0a371cd0f15783c98be0d6acedf4a99b6e59ed1fc84df4a005fb4b3</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/PMC2844993/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2844993/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22576141$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20147303$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Munshaw, Supriya</creatorcontrib><creatorcontrib>Kepler, Thomas B.</creatorcontrib><title>SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Motivation: The inference of pre-mutation immunoglobulin (Ig) rearrangements is essential in the study of the antibody repertoires produced in response to infection, in B-cell neoplasms and in autoimmune disease. Often, there are several rearrangements that are nearly equivalent as candidates for a given Ig gene, but have different consequences in an analysis. Our aim in this article is to develop a probabilistic model of the rearrangement process and a Bayesian method for estimating posterior probabilities for the comparison of multiple plausible rearrangements. Results: We have developed SoDA2, which is based on a Hidden Markov Model and used to compute the posterior probabilities of candidate rearrangements and to find those with the highest values among them. We validated the software on a set of simulated data, a set of clonally related sequences, and a group of randomly selected Ig heavy chains from Genbank. In most tests, SoDA2 performed better than other available software for the task. Furthermore, the output format has been redesigned, in part, to facilitate comparison of multiple solutions. Availability: SoDA2 is available online at https://hippocrates.duhs.duke.edu/soda. Simulated sequences are available upon request. Contact: kepler@duke.edu</description><subject>Amino Acid Sequence</subject><subject>B-Lymphocytes - immunology</subject><subject>Base Sequence</subject><subject>Biological and medical sciences</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Gene Rearrangement, B-Lymphocyte</subject><subject>General aspects</subject><subject>Genes, Immunoglobulin</subject><subject>Immunoglobulins - genetics</subject><subject>Markov Chains</subject><subject>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</subject><subject>Molecular Sequence Data</subject><subject>Original Papers</subject><subject>Sequence Alignment</subject><subject>Software</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU9v1DAQxS0EomXLRwD5gnoKtWPHsTkgVeVPKm1VobYIuFgTx94aknhrJxX99nW1y0JPnGz5_WbG8x5Cryh5S4liR60PfnQhDjB5k47a6YZU4gnap1yQoiSVeprvTNQFl4TtoRcp_SSkopzz52ivJJTXjLB99P0ifDgu32HAje86O-IziL_CLT4Lne0xrNcxgLnGeRD2WZ688yZPDCMODvthmMew6kM7937E0UKMMK7skMF0gJ456JN9uT0X6OrTx8uTpliefz49OV4WpqrFVAgwwggmiSXAamo64mhVS2aUbC3psmw7x0GpVthK2Y46I_nDS97GtbxlC_R-03c9t4PtTJ4dodfr6AeIdzqA14-V0V_rVbjVpeRcKZYbHG4bxHAz2zTpwSdj-x5GG-ak6-yo5FLI_5OMMUJFNnaBqg1pYkgpWrf7DyX6IT_9OD-9yS_Xvf53mV3Vn8Ay8GYLQDLQu-y38ekvV2ZTKaeZKzacT5P9vdNzuFrUrK508-2H_qJ483XZlFqxeza-uwc</recordid><startdate>20100401</startdate><enddate>20100401</enddate><creator>Munshaw, Supriya</creator><creator>Kepler, Thomas B.</creator><general>Oxford University Press</general><scope>BSCLL</scope><scope>IQODW</scope><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>7X8</scope><scope>7QO</scope><scope>7T5</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>5PM</scope></search><sort><creationdate>20100401</creationdate><title>SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements</title><author>Munshaw, Supriya ; Kepler, Thomas B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c576t-6ac6c6380e0a371cd0f15783c98be0d6acedf4a99b6e59ed1fc84df4a005fb4b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Amino Acid Sequence</topic><topic>B-Lymphocytes - immunology</topic><topic>Base Sequence</topic><topic>Biological and medical sciences</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Gene Rearrangement, B-Lymphocyte</topic><topic>General aspects</topic><topic>Genes, Immunoglobulin</topic><topic>Immunoglobulins - genetics</topic><topic>Markov Chains</topic><topic>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</topic><topic>Molecular Sequence Data</topic><topic>Original Papers</topic><topic>Sequence Alignment</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Munshaw, Supriya</creatorcontrib><creatorcontrib>Kepler, Thomas B.</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Munshaw, Supriya</au><au>Kepler, Thomas B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2010-04-01</date><risdate>2010</risdate><volume>26</volume><issue>7</issue><spage>867</spage><epage>872</epage><pages>867-872</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>Motivation: The inference of pre-mutation immunoglobulin (Ig) rearrangements is essential in the study of the antibody repertoires produced in response to infection, in B-cell neoplasms and in autoimmune disease. Often, there are several rearrangements that are nearly equivalent as candidates for a given Ig gene, but have different consequences in an analysis. Our aim in this article is to develop a probabilistic model of the rearrangement process and a Bayesian method for estimating posterior probabilities for the comparison of multiple plausible rearrangements. Results: We have developed SoDA2, which is based on a Hidden Markov Model and used to compute the posterior probabilities of candidate rearrangements and to find those with the highest values among them. We validated the software on a set of simulated data, a set of clonally related sequences, and a group of randomly selected Ig heavy chains from Genbank. In most tests, SoDA2 performed better than other available software for the task. Furthermore, the output format has been redesigned, in part, to facilitate comparison of multiple solutions. Availability: SoDA2 is available online at https://hippocrates.duhs.duke.edu/soda. Simulated sequences are available upon request. Contact: kepler@duke.edu</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><pmid>20147303</pmid><doi>10.1093/bioinformatics/btq056</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Amino Acid Sequence B-Lymphocytes - immunology Base Sequence Biological and medical sciences Fundamental and applied biological sciences. Psychology Gene Rearrangement, B-Lymphocyte General aspects Genes, Immunoglobulin Immunoglobulins - genetics Markov Chains Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Molecular Sequence Data Original Papers Sequence Alignment Software |
| title | SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements |
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