FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
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{"title"=>"FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units", "type"=>"journal", "authors"=>[{"first_name"=>"Gang", "last_name"=>"Peng", "scopus_author_id"=>"56466121600"}, {"first_name"=>"Yu", "last_name"=>"Fan", "scopus_author_id"=>"55615292000"}, {"first_name"=>"Wenyi", "last_name"=>"Wang", "scopus_author_id"=>"36464687200"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"600310960", "sgr"=>"84908343475", "issn"=>"15537358", "arxiv"=>"dx.doi.org/10.1021/nl3012853 | Nano Lett. 2012, 12, 3602−3608", "pmid"=>"25357123", "scopus"=>"2-s2.0-84908343475", "doi"=>"10.1371/journal.pcbi.1003880", "isbn"=>"9781467312288"}, "id"=>"eb360a22-9b63-353b-a1df-b4809185d142", "abstract"=>"Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequencing data are available. Here, we present a program, FamSeq, which reduces both false positive and false negative rates by incorporating the pedigree information from the Mendelian genetic model into variant calling. To accommodate variations in data complexity, FamSeq consists of four distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network algorithm, the Elston-Stewart algorithm and the Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA graphics processing unit. In order to compare the difference in the four methods, we applied FamSeq to pedigree sequencing data with family sizes that varied from 7 to 12. When there is no inbreeding loop in the pedigree, the Elston-Stewart algorithm gives analytical results in a short time. If there are inbreeding loops in the pedigree, we recommend the Bayesian network method, which provides exact answers. To improve the computing speed of the Bayesian network method, we parallelized the computation on a graphics processing unit. This allowed the Bayesian network method to process the whole genome sequencing data of a family of 12 individuals within two days, which was a 10-fold time reduction compared to the time required for this computation on a central processing unit.", "link"=>"http://www.mendeley.com/research/famseq-variant-calling-program-familybased-sequencing-data-using-graphics-processing-units", "reader_count"=>35, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Professor"=>2, "Other"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Professor"=>2, "Other"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>18, "Medicine and Dentistry"=>3, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>18}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}}, "reader_count_by_country"=>{"Sweden"=>1, "China"=>1, "Finland"=>1, "Italy"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1772031"], "description"=>"<div><p>Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequencing data are available. Here, we present a program, FamSeq, which reduces both false positive and false negative rates by incorporating the pedigree information from the Mendelian genetic model into variant calling. To accommodate variations in data complexity, FamSeq consists of four distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network algorithm, the Elston-Stewart algorithm and the Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA graphics processing unit. In order to compare the difference in the four methods, we applied FamSeq to pedigree sequencing data with family sizes that varied from 7 to 12. When there is no inbreeding loop in the pedigree, the Elston-Stewart algorithm gives analytical results in a short time. If there are inbreeding loops in the pedigree, we recommend the Bayesian network method, which provides exact answers. To improve the computing speed of the Bayesian network method, we parallelized the computation on a graphics processing unit. This allowed the Bayesian network method to process the whole genome sequencing data of a family of 12 individuals within two days, which was a 10-fold time reduction compared to the time required for this computation on a central processing unit.</p></div>", "links"=>[], "tags"=>["Markov chain Monte Carlo algorithm", "pedigree sequencing data", "FamSeq", "graphics processing unit", "pedigree information", "Graphics Processing Units", "variant", "Bayesian network method", "NVIDIA graphics processing unit", "genome sequencing data", "loop", "graphics processing unit version", "Bayesian network algorithm"], "article_id"=>1223078, "categories"=>["Biological Sciences"], "users"=>["Gang Peng", "Yu Fan", "Wenyi Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003880", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_FamSeq_A_Variant_Calling_Program_for_Family_Based_Sequencing_Data_Using_Graphics_Processing_Units_/1223078", "title"=>"FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 02:48:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772029"], "description"=>"<p>The program can be divided into two parts: a serial part and a parallel part. The serial part is processed in a CPU and the parallel part is processed in a GPU. The program: 1. Prepare the data for parallel computing in a CPU; 2. Copy the data from CPU memory to GPU memory; 3. Parallelize the 3<sup>n</sup> jobs computing in the GPU, where n is the pedigree size; 4. Copy the results from GPU memory to CPU memory; and 5. Summarize the results in the CPU.</p>", "links"=>[], "tags"=>["Markov chain Monte Carlo algorithm", "pedigree sequencing data", "FamSeq", "graphics processing unit", "pedigree information", "Graphics Processing Units", "variant", "Bayesian network method", "NVIDIA graphics processing unit", "genome sequencing data", "loop", "graphics processing unit version", "Bayesian network algorithm"], "article_id"=>1223075, "categories"=>["Biological Sciences"], "users"=>["Gang Peng", "Yu Fan", "Wenyi Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003880.g003", "stats"=>{"downloads"=>3, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_GPU_parallel_computing_in_FamSeq_/1223075", "title"=>"Illustration of GPU parallel computing in FamSeq.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 02:48:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772030"], "description"=>"<p>PU: processing unit; E-S: Elston-Stewart algorithm; MCMC: Markov chain Monte Carlo algorithm; BN: Bayesian network algorithm; N: No, inbreeding loops are not considered; Y: Yes, inbreeding loops are considered.</p>a<p>We called only 100,000 variants due to excessive running time for the MCMC algorithm. The time shown here is 10× the time required to call 100,000 variants.</p>b<p>The time in parentheses is the GPU computing time.</p><p>The total time (in seconds) needed for computation using FamSeq at one million positions.</p>", "links"=>[], "tags"=>["Markov chain Monte Carlo algorithm", "pedigree sequencing data", "FamSeq", "graphics processing unit", "pedigree information", "Graphics Processing Units", "variant", "Bayesian network method", "NVIDIA graphics processing unit", "genome sequencing data", "loop", "graphics processing unit version", "Bayesian network algorithm"], "article_id"=>1223076, "categories"=>["Biological Sciences"], "users"=>["Gang Peng", "Yu Fan", "Wenyi Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003880.t001", "stats"=>{"downloads"=>4, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_total_time_in_seconds_needed_for_computation_using_FamSeq_at_one_million_positions_/1223076", "title"=>"The total time (in seconds) needed for computation using FamSeq at one million positions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 02:48:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772027"], "description"=>"<p>We use a pedigree file and a file that includes the likelihood () as the input to estimate the posterior probability () for each variant genotype. (E-S: Elston-Stewart algorithm; BN: Bayesian network method; BN-GPU: The computer needs a GPU card installed to run the GPU version of the Bayesian network method; MCMC: Markov chain Monte Carlo method; VCF: variant call format.)</p>", "links"=>[], "tags"=>["Markov chain Monte Carlo algorithm", "pedigree sequencing data", "FamSeq", "graphics processing unit", "pedigree information", "Graphics Processing Units", "variant", "Bayesian network method", "NVIDIA graphics processing unit", "genome sequencing data", "loop", "graphics processing unit version", "Bayesian network algorithm"], "article_id"=>1223073, "categories"=>["Biological Sciences"], "users"=>["Gang Peng", "Yu Fan", "Wenyi Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003880.g001", "stats"=>{"downloads"=>4, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_of_FamSeq_/1223073", "title"=>"Workflow of FamSeq.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 02:48:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772028"], "description"=>"<p>A.) Pedigree structure. B.) Pedigree structure file storing the pedigree structure shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003880#pcbi-1003880-g002\" target=\"_blank\">Fig. 2A</a>. From the left-most column to the right-most column, the data are ID, mID (mother ID), fID (father ID), gender and sample name. C.) Part of VCF file. From the VCF file, we can find that the genome of the grandfather (G-Father) was not sequenced. We add his information to the pedigree structure file to avoid ambiguity. For example, if we include only one parent of two siblings in the pedigree structure file, it will be unclear whether they are full or half siblings. The sample name in the pedigree structure file should be the same as the sample name in the VCF file. When the actual genome was not sequenced, we set the corresponding sample name as NA in the pedigree structure file.</p>", "links"=>[], "tags"=>["Markov chain Monte Carlo algorithm", "pedigree sequencing data", "FamSeq", "graphics processing unit", "pedigree information", "Graphics Processing Units", "variant", "Bayesian network method", "NVIDIA graphics processing unit", "genome sequencing data", "loop", "graphics processing unit version", "Bayesian network algorithm"], "article_id"=>1223074, "categories"=>["Biological Sciences"], "users"=>["Gang Peng", "Yu Fan", "Wenyi Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003880.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_input_files_/1223074", "title"=>"Illustration of input files.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 02:48:54"}

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