Pool-hmm: a Python program for estimating the allele frequency spectrum and detecting selective sweeps from next generation sequencing of pooled samples
Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool‐Seq) is becoming a popular strategy for genome‐wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool‐seq is also...
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| Vydáno v: | Molecular ecology resources Ročník 13; číslo 2; s. 337 - 340 |
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| Jazyk: | angličtina |
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Oxford
Blackwell Publishing Ltd
01.03.2013
Blackwell Wiley Subscription Services, Inc Wiley/Blackwell |
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| Abstract | Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool‐Seq) is becoming a popular strategy for genome‐wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool‐seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool‐Seq data. We introduce Pool‐hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool‐Seq sample. Pool‐hmm includes several options that allow a flexible analysis of Pool‐Seq data, and can be run in parallel on several processors. Source code and documentation for Pool‐hmm is freely available at https://qgsp.jouy.inra.fr/. |
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| AbstractList | Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for genome-wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool-seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool-Seq data. We introduce Pool-hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool-Seq sample. Pool-hmm includes several options that allow a flexible analysis of Pool-Seq data, and can be run in parallel on several processors. Source code and documentation for Pool-hmm is freely available at https://qgsp.jouy.inra.fr/. Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool‐Seq) is becoming a popular strategy for genome‐wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool‐seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool‐Seq data. We introduce Pool‐hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool‐Seq sample. Pool‐hmm includes several options that allow a flexible analysis of Pool‐Seq data, and can be run in parallel on several processors. Source code and documentation for Pool‐hmm is freely available at https://qgsp.jouy.inra.fr/ . Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for genome-wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool-seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool-Seq data. We introduce Pool-hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool-Seq sample. Pool-hmm includes several options that allow a flexible analysis of Pool-Seq data, and can be run in parallel on several processors. Source code and documentation for Pool-hmm is freely available at https://qgsp.jouy.inra.fr/.Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for genome-wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool-seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool-Seq data. We introduce Pool-hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool-Seq sample. Pool-hmm includes several options that allow a flexible analysis of Pool-Seq data, and can be run in parallel on several processors. Source code and documentation for Pool-hmm is freely available at https://qgsp.jouy.inra.fr/. Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for genome-wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool-seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool-Seq data. We introduce Pool-hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool-Seq sample. Pool-hmm includes several options that allow a flexible analysis of Pool-Seq data, and can be run in parallel on several processors. Source code and documentation for Pool-hmm is freely available at https://qgsp.jouy.inra.fr/. [PUBLICATION ABSTRACT] |
| Author | Robelin, David Boitard, Simon Futschik, Andreas Schlötterer, Christian Kofler, Robert Françoise, Pierre |
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| Cites_doi | 10.1534/genetics.110.114397 10.1101/gr.4252305 10.1093/molbev/mss090 10.1093/nar/gkm805 10.1093/bioinformatics/btp352 10.1093/genetics/160.2.765 10.1101/gr.115998.110 10.1109/5.18626 10.1093/bioinformatics/bts419 10.1534/genetics.108.100032 10.1534/genetics.106.069450 |
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| Keywords | allele frequency spectrum Program next generation sequencing pooled DNA Markov model Ophidia Spectrum hidden Markov models Vertebrata Gene frequency DNA Reptilia selective sweeps Sequencing hidden markov model pooled dna selective sweep newt generation sequencing |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor CC BY 4.0 2013 Blackwell Publishing Ltd. Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
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| References | Nielsen R, Williamson L, Kim Y, Hubisz M, Clark A, Bustamante C (2005) Genomic scans for selective sweeps using SNP data. Genome Research, 15, 1566-1575. Rabiner L (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEE, 77, 257-287. Boitard S, Schlötterer C, Futschik A (2009) Detecting selective sweeps: a new approach based on hidden Markov models. Genetics, 181, 1567-1578. Courcelle E, Beausse Y, Letort S et al. (2008) Narcisse: a mirror view of conserved syntenies. Nucleic Acids Research, 36, D485-D490. Jensen J, Thornton K, Bustamante C, Aquadro C (2007) On the utility of linkage disequilibrium as a statistic for identifying targets of positive selection in nonequilibrium populations. Genetics, 176, 2371-2379. Alachiotis N, Stamatakis A, Pavlidis P (2012) Omegaplus: a scalable tool for rapid detection of selective sweeps in whole-genome datasets. Bioinformatics, 28, 2274-2275. Li H, Handsaker B, Wysoker A et al. (2009) The sequence alignment/map (sam) format and samtools. Bioinformatics, 25, 2078-2079. Boitard S, Schlötterer C, Nolte V, Pandey R, Futschik A (2012) Detecting selective sweeps from pooled next generation sequencing samples. Molecular Biology and Evolution, 29, 2177-2186. Kim Y, Stephan W (2002) Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics, 160, 765-777. Futschik A, Schlötterer , C (2010) Massively parrallel sequencing of pooled DNA samples - the next generation of molecular markers. Genetics, 186, 207-218. Luo L, Boerwinkle E, Xiong M (2011) Association studies for next-generation sequencing. Genome Research, 21, 1099-1108. 2008; 36 2009; 25 2010; 186 2011; 21 2012; 29 2002; 160 1989; 77 2012; 28 2005; 15 2009; 181 2007; 176 e_1_2_10_12_1 e_1_2_10_9_1 e_1_2_10_10_1 e_1_2_10_11_1 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_3_1 e_1_2_10_6_1 e_1_2_10_5_1 e_1_2_10_8_1 e_1_2_10_7_1 |
| References_xml | – reference: Courcelle E, Beausse Y, Letort S et al. (2008) Narcisse: a mirror view of conserved syntenies. Nucleic Acids Research, 36, D485-D490. – reference: Boitard S, Schlötterer C, Futschik A (2009) Detecting selective sweeps: a new approach based on hidden Markov models. Genetics, 181, 1567-1578. – reference: Kim Y, Stephan W (2002) Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics, 160, 765-777. – reference: Nielsen R, Williamson L, Kim Y, Hubisz M, Clark A, Bustamante C (2005) Genomic scans for selective sweeps using SNP data. Genome Research, 15, 1566-1575. – reference: Futschik A, Schlötterer , C (2010) Massively parrallel sequencing of pooled DNA samples - the next generation of molecular markers. Genetics, 186, 207-218. – reference: Li H, Handsaker B, Wysoker A et al. (2009) The sequence alignment/map (sam) format and samtools. Bioinformatics, 25, 2078-2079. – reference: Rabiner L (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEE, 77, 257-287. – reference: Jensen J, Thornton K, Bustamante C, Aquadro C (2007) On the utility of linkage disequilibrium as a statistic for identifying targets of positive selection in nonequilibrium populations. Genetics, 176, 2371-2379. – reference: Luo L, Boerwinkle E, Xiong M (2011) Association studies for next-generation sequencing. Genome Research, 21, 1099-1108. – reference: Boitard S, Schlötterer C, Nolte V, Pandey R, Futschik A (2012) Detecting selective sweeps from pooled next generation sequencing samples. Molecular Biology and Evolution, 29, 2177-2186. – reference: Alachiotis N, Stamatakis A, Pavlidis P (2012) Omegaplus: a scalable tool for rapid detection of selective sweeps in whole-genome datasets. Bioinformatics, 28, 2274-2275. – volume: 25 start-page: 2078 year: 2009 end-page: 2079 article-title: The sequence alignment/map (sam) format and samtools publication-title: Bioinformatics – volume: 21 start-page: 1099 year: 2011 end-page: 1108 article-title: Association studies for next‐generation sequencing publication-title: Genome Research – volume: 15 start-page: 1566 year: 2005 end-page: 1575 article-title: Genomic scans for selective sweeps using SNP data publication-title: Genome Research – volume: 36 start-page: D485 year: 2008 end-page: D490 article-title: Narcisse: a mirror view of conserved syntenies publication-title: Nucleic Acids Research – volume: 160 start-page: 765 year: 2002 end-page: 777 article-title: Detecting a local signature of genetic hitchhiking along a recombining chromosome publication-title: Genetics – volume: 77 start-page: 257 year: 1989 end-page: 287 article-title: A tutorial on hidden markov models and selected applications in speech recognition publication-title: Proceedings of the IEE – volume: 181 start-page: 1567 year: 2009 end-page: 1578 article-title: Detecting selective sweeps: a new approach based on hidden Markov models publication-title: Genetics – volume: 28 start-page: 2274 year: 2012 end-page: 2275 article-title: Omegaplus: a scalable tool for rapid detection of selective sweeps in whole‐genome datasets publication-title: Bioinformatics – volume: 29 start-page: 2177 year: 2012 end-page: 2186 article-title: Detecting selective sweeps from pooled next generation sequencing samples publication-title: Molecular Biology and Evolution – volume: 186 start-page: 207 year: 2010 end-page: 218 article-title: Massively parrallel sequencing of pooled DNA samples – the next generation of molecular markers publication-title: Genetics – volume: 176 start-page: 2371 year: 2007 end-page: 2379 article-title: On the utility of linkage disequilibrium as a statistic for identifying targets of positive selection in nonequilibrium populations publication-title: Genetics – ident: e_1_2_10_6_1 doi: 10.1534/genetics.110.114397 – ident: e_1_2_10_11_1 doi: 10.1101/gr.4252305 – ident: e_1_2_10_4_1 doi: 10.1093/molbev/mss090 – ident: e_1_2_10_5_1 doi: 10.1093/nar/gkm805 – ident: e_1_2_10_9_1 doi: 10.1093/bioinformatics/btp352 – ident: e_1_2_10_8_1 doi: 10.1093/genetics/160.2.765 – ident: e_1_2_10_10_1 doi: 10.1101/gr.115998.110 – ident: e_1_2_10_12_1 doi: 10.1109/5.18626 – ident: e_1_2_10_2_1 doi: 10.1093/bioinformatics/bts419 – ident: e_1_2_10_3_1 doi: 10.1534/genetics.108.100032 – ident: e_1_2_10_7_1 doi: 10.1534/genetics.106.069450 |
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| Snippet | Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool‐Seq) is becoming a popular strategy for genome‐wide estimation of... Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for genome-wide estimation of... |
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| SubjectTerms | allele frequency spectrum Amphibia. Reptilia Animal and plant ecology Animal, plant and microbial ecology Animals Biological and medical sciences Computer programs computer software cost effectiveness design detection estimation Fundamental and applied biological sciences. Psychology Gene Frequency General aspects Genetics Genetics of eukaryotes. Biological and molecular evolution genomics Genomics - instrumentation hidden Markov models High-Throughput Nucleotide Sequencing information Internet Life Sciences Models, Genetic next generation sequencing Polymorphism, Single Nucleotide pooled DNA population Population genetics Population genetics, reproduction patterns Quail - genetics Resource sampling selective sweeps Sequence Analysis, DNA - instrumentation Software Synecology Vertebrates: general zoology, morphology, phylogeny, systematics, cytogenetics, geographical distribution |
| Title | Pool-hmm: a Python program for estimating the allele frequency spectrum and detecting selective sweeps from next generation sequencing of pooled samples |
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