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BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity

Brandi L Cantarel, Daniel Weaver, Nathan McNeill, Jianhua Zhang, Aaron J Mackey, Justin Reese
2014 BMC Bioinformatics  
The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input.  ...  Conclusions: BAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs.  ...  Genome Project (CBP) and its funders for kindly sharing the exome sequence data from a CBP tumor-normal sample now found in COSMIC.  ... 
doi:10.1186/1471-2105-15-104 pmid:24725768 pmcid:PMC3999887 fatcat:4tc5e6ifnrcbdmiz5x5ig5zij4

NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer

Irantzu Anzar, Angelina Sverchkova, Richard Stratford, Trevor Clancy
2019 BMC Medical Genomics  
The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy.  ...  Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity  ...  Availability of data and materials The datasets analyzed during the current study are available in the GIAB and 1000 Genomes repositories:  ... 
doi:10.1186/s12920-019-0508-5 pmid:31096972 pmcid:PMC6524241 fatcat:3qloefvx45cqbmc2vmh66h3dgy

Whole Exome Sequencing Data Analysis Algorithms in Cancer Diagnostics [chapter]

Áron Bartha, Balázs Gyorffy
2020 Prime Archives in Cancer Research  
Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment.  ...  Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized.  ...  BAYSIC: A Bayesian method for combining sets of genome variants with improved specificity and sensitivity. BMC Bioinform. 2014; 15: 104. 49.  ... 
doi:10.37247/pacr.1.2020.5 fatcat:fb3xwm2drbhqhinmehksj3dgai

A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data

Chang Xu
2018 Computational and Structural Biotechnology Journal  
This review aims to enumerate these unique features of the state-of-the-art variant callers, in the hope to provide a practical guide for selecting the appropriate pipeline for specific applications.  ...  A collection of variant calling pipelines have been developed with different underlying models, filters, input data requirements, and targeted applications.  ...  Most variant callers apply a set of filters to identify these artifacts and hence improve the specificity.  ... 
doi:10.1016/j.csbj.2018.01.003 pmid:29552334 pmcid:PMC5852328 fatcat:zfe7b6ja2ne3pa3hlxhr3qms2q

BATCAVE: Calling somatic mutations with a tumor- and site-specific prior [article]

Brian K Mannakee, Ryan N Gutenkunst
2019 bioRxiv   pre-print
We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for  ...  We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection.  ...  and score potential variants and set a threshold score designed to balance sensitivity and specificity.  ... 
doi:10.1101/798348 fatcat:qk3wbjkdebfkzkbxjce26gsrwy

BATCAVE: calling somatic mutations with a tumor- and site-specific prior

Brian K Mannakee, Ryan N Gutenkunst
2020 NAR Genomics and Bioinformatics  
We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for  ...  We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection.  ...  This material is based upon High Performance Computing (HPC) resources supported by the University of Arizona TRIF, UITS, and RDI and maintained by the UA Research Technologies department.  ... 
doi:10.1093/nargab/lqaa004 pmid:32051931 pmcid:PMC7003682 fatcat:liw3c7mp4jhhtabwunp2u7pws4

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Aquillah M. Kanzi, James Emmanuel San, Benjamin Chimukangara, Eduan Wilkinson, Maryam Fish, Veron Ramsuran, Tulio de Oliveira
2020 Frontiers in Genetics  
Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.  ...  In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying  ...  It works based on a novel artifact-and polymorphism score. BAYSIC (BAYeSian Integrated Caller) is a variant caller that summarizes SNP variant calls produced by different programs.  ... 
doi:10.3389/fgene.2020.544162 pmid:33193618 pmcid:PMC7649788 fatcat:dkcsrkvggbbkvhg32l2a4ejlky

Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

Nagasundaram Nagarajan, Edward K. Y. Yapp, Nguyen Quoc Khanh Le, Balu Kamaraj, Abeer Mohammed Al-Subaie, Hui-Yuan Yeh
2019 BioMed Research International  
The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary  ...  variants, and structural biology in the cancer precision drug discovery.  ...  Acknowledgments e authors take this opportunity to thank the Nanyang Technological University for providing the facilities and for encouragement to carry out this work.  ... 
doi:10.1155/2019/8427042 pmid:31886259 pmcid:PMC6925679 fatcat:k2fm2n7pfnf63emhkap7arlg4m

Parallel genomic evolution of parasite tolerance in wild honey bee populations [article]

Katarzyna Bozek, Juliana Rangel, Jatin Arora, Mandy Tin, Emily Crotteau, Gerald Loper, Jennifer Fewell, Alexander Mikheyev
2018 biorxiv/medrxiv   pre-print
In this study we take advantage of unique decade-long data sets of two wild honey bee (Apis mellifera) populations in the United States to reconstruct the evolution of tolerance to a novel parasite, the  ...  Here we sequenced and phased genomes of 465 bees sampled from both populations annually over the decade that spanned Varroa's arrival.  ...  We are grateful to Martin Helmkampf for assistance in shipping bees, and to Miquel Grau-Lopez for help with the analysis.  ... 
doi:10.1101/498436 fatcat:4yppcootonfrplcr4d22cqn7ia

Computational analysis of genetic variation [article]

Matthew Arnell Field, University, The Australian National, University, The Australian National
2016
To effectively utilise this rich data set for maximum research benefit, as a discipline we require robust, flexible, and reproducible analysis pipelines capable of accurately detecting and prioritising  ...  to effectively manage and process large data sets with an end goal of utilising computational analysis of sequence data to further understand the relationship between genetic variation and human disease  ...  We also would like to thank Queensland Institute of Medical Research for access to the melanoma cell line generated as part of the larger Australian Melanoma Genome Project.  ... 
doi:10.25911/5d778acb1cc8f fatcat:ed7d6ogysfb6lay4dj52nkrchu