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Joint genotype inference with germline and somatic mutations
The joint sequencing of related genomes has become an important means to discover rare variants. Normal-tumor genome pairs are routinely sequenced together to find somatic mutations and their associations with different cancers. Parental and sibling genomes reveal de novo germline mutations and inhe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622648/ https://www.ncbi.nlm.nih.gov/pubmed/23734724 http://dx.doi.org/10.1186/1471-2105-14-S5-S3 |
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author | Bareke, Eric Saillour, Virginie Spinella, Jean-François Vidal, Ramon Healy, Jasmine Sinnett, Daniel Csűrös, Miklós |
author_facet | Bareke, Eric Saillour, Virginie Spinella, Jean-François Vidal, Ramon Healy, Jasmine Sinnett, Daniel Csűrös, Miklós |
author_sort | Bareke, Eric |
collection | PubMed |
description | The joint sequencing of related genomes has become an important means to discover rare variants. Normal-tumor genome pairs are routinely sequenced together to find somatic mutations and their associations with different cancers. Parental and sibling genomes reveal de novo germline mutations and inheritance patterns related to Mendelian diseases. Acute lymphoblastic leukemia (ALL) is the most common paediatric cancer and the leading cause of cancer-related death among children. With the aim of uncovering the full spectrum of germline and somatic genetic alterations in childhood ALL genomes, we conducted whole-exome re-sequencing on a unique cohort of over 120 exomes of childhood ALL quartets, each comprising a patient's tumor and matched-normal material, and DNA from both parents. We developed a general probabilistic model for such quartet sequencing reads mapped to the reference human genome. The model is used to infer joint genotypes at homologous loci across a normal-tumor genome pair and two parental genomes. We describe the algorithms and data structures for genotype inference, model parameter training. We implemented the methods in an open-source software package (QUADGT) that uses the standard file formats of the 1000 Genomes Project. Our method's utility is illustrated on quartets from the ALL cohort. |
format | Online Article Text |
id | pubmed-3622648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36226482013-04-15 Joint genotype inference with germline and somatic mutations Bareke, Eric Saillour, Virginie Spinella, Jean-François Vidal, Ramon Healy, Jasmine Sinnett, Daniel Csűrös, Miklós BMC Bioinformatics Proceedings The joint sequencing of related genomes has become an important means to discover rare variants. Normal-tumor genome pairs are routinely sequenced together to find somatic mutations and their associations with different cancers. Parental and sibling genomes reveal de novo germline mutations and inheritance patterns related to Mendelian diseases. Acute lymphoblastic leukemia (ALL) is the most common paediatric cancer and the leading cause of cancer-related death among children. With the aim of uncovering the full spectrum of germline and somatic genetic alterations in childhood ALL genomes, we conducted whole-exome re-sequencing on a unique cohort of over 120 exomes of childhood ALL quartets, each comprising a patient's tumor and matched-normal material, and DNA from both parents. We developed a general probabilistic model for such quartet sequencing reads mapped to the reference human genome. The model is used to infer joint genotypes at homologous loci across a normal-tumor genome pair and two parental genomes. We describe the algorithms and data structures for genotype inference, model parameter training. We implemented the methods in an open-source software package (QUADGT) that uses the standard file formats of the 1000 Genomes Project. Our method's utility is illustrated on quartets from the ALL cohort. BioMed Central 2013-04-10 /pmc/articles/PMC3622648/ /pubmed/23734724 http://dx.doi.org/10.1186/1471-2105-14-S5-S3 Text en Copyright © 2013 Bareke et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Bareke, Eric Saillour, Virginie Spinella, Jean-François Vidal, Ramon Healy, Jasmine Sinnett, Daniel Csűrös, Miklós Joint genotype inference with germline and somatic mutations |
title | Joint genotype inference with germline and somatic mutations |
title_full | Joint genotype inference with germline and somatic mutations |
title_fullStr | Joint genotype inference with germline and somatic mutations |
title_full_unstemmed | Joint genotype inference with germline and somatic mutations |
title_short | Joint genotype inference with germline and somatic mutations |
title_sort | joint genotype inference with germline and somatic mutations |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622648/ https://www.ncbi.nlm.nih.gov/pubmed/23734724 http://dx.doi.org/10.1186/1471-2105-14-S5-S3 |
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