scholarly journals Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data

2020 ◽  
Vol 39 (12) ◽  
pp. 1746-1765
Author(s):  
Rosanna Cassidy ◽  
Theodore Kypraios ◽  
Philip D. O'Neill
Keyword(s):  
Bayesian Inference ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Model Assessment ◽  
Nosocomial Pathogens ◽  
Genome Sequence Data

TIGER: inferring DNA replication timing from whole-genome sequence data

2021 ◽  
Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci
Keyword(s):  
Dna Replication ◽  
Genome Sequence ◽  
Genomic Dna ◽  
Sequence Data ◽  
Replication Timing ◽  
Whole Genome Sequence ◽  
Supplementary Information ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Dna Replication Timing

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online

scholarly journals Whole genome sequence data of Mycobacterium tuberculosis XDR strain, isolated from patient in Kazakhstan

Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106416
Author(s):  
Asset Daniyarov ◽  
Askhat Molkenov ◽  
Saule Rakhimova ◽  
Ainur Akhmetova ◽  
Zhannur Nurkina ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data

scholarly journals Elucidating the genetic basis of an oligogenic birth defect using whole genome sequence data in a non-model organism, Bubalus bubalis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Lynsey K. Whitacre ◽  
Jesse L. Hoff ◽  
Robert D. Schnabel ◽  
Sara Albarella ◽  
Francesca Ciotola ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Birth Defect ◽  
Genetic Basis ◽  
Sequence Data ◽  
Model Organism ◽  
Bubalus Bubalis ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data

46 Footprints of Selection in Angus and Hanwoo Beef Cattle Using Imputed Whole Genome Sequence Data

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 25-25
Author(s):  
Muhammad Yasir Nawaz ◽  
Rodrigo Pelicioni Savegnago ◽  
Cedric Gondro
Keyword(s):  
Beef Cattle ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Fixation Index ◽  
Whole Genome ◽  
Extended Haplotype Homozygosity ◽  
Extended Haplotype ◽  
Genome Sequence Data ◽  
Genomic Regions

Abstract In this study, we detected genome wide footprints of selection in Hanwoo and Angus beef cattle using different allele frequency and haplotype-based methods based on imputed whole genome sequence data. Our dataset included 13,202 Angus and 10,437 Hanwoo animals with 10,057,633 and 13,241,550 imputed SNPs, respectively. A subset of data with 6,873,624 common SNPs between the two populations was used to estimate signatures of selection parameters, both within (runs of homozygosity and extended haplotype homozygosity) and between (allele fixation index, extended haplotype homozygosity) the breeds in order to infer evidence of selection. We observed that correlations between various measures of selection ranged between 0.01 to 0.42. Assuming these parameters were complementary to each other, we combined them into a composite selection signal to identify regions under selection in both beef breeds. The composite signal was based on the average of fractional ranks of individual selection measures for every SNP. We identified some selection signatures that were common between the breeds while others were independent. We also observed that more genomic regions were selected in Angus as compared to Hanwoo. Candidate genes within significant genomic regions may help explain mechanisms of adaptation, domestication history and loci for important traits in Angus and Hanwoo cattle. In the future, we will use the top SNPs under selection for genomic prediction of carcass traits in both breeds.

scholarly journals ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Shuto Hayashi ◽  
Rui Yamaguchi ◽  
Shinichi Mizuno ◽  
Mitsuhiro Komura ◽  
Satoru Miyano ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Bayesian Method ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Hla Genes
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