scholarly journals Whole-Genome Sequence of the Anaerobic Isosaccharinic Acid Degrading Isolate, Macellibacteroides fermentans Strain HH-ZS

2017 ◽  
Vol 9 (8) ◽  
pp. 2140-2144 ◽  
Author(s):  
Simon P. Rout ◽  
Zohier B. Salah ◽  
Christopher J. Charles ◽  
Paul N. Humphreys
Keyword(s):  
Genome Sequence ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Isosaccharinic Acid

30-OR: Whole Genome Sequence Analysis of Type 2 Diabetes Risk in 44,713 Humans of Diverse Ancestry in the TOPMed Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 30-OR
Author(s):  
HEATHER M. HIGHLAND ◽  
JENNIFER WESSEL ◽  
ALISA MANNING ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Sequence Analysis ◽  
Genome Sequence ◽  
Diabetes Risk ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Analysis

Whole-Genome Sequence Characterization of Primary Auxin-Responsive Aux/IAA Gene Family in Sorghum (Sorghum bicolor L.)

2010 ◽  
Vol 36 (4) ◽  
pp. 688-694
Author(s):  
Yi-Jun WANG ◽  
Yan-Ping LÜ ◽  
Qin XIE ◽  
De-Xiang DENG ◽  
Yun-Long BIAN
Keyword(s):  
Sorghum Bicolor ◽  
Gene Family ◽  
Genome Sequence ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Sequence Characterization ◽  
I Gene

Whole-Genome Sequence Isolation, Chromosome Location, and Characteriza-tion of Primary Auxin-ResponsiveAux/IAAGene Family inAegilops tauschii

2014 ◽  
Vol 40 (12) ◽  
pp. 2059
Author(s):  
Lin-Yi QIAO ◽  
Xin LI ◽  
Zhi-Jian CHANG ◽  
Xiao-Jun ZHANG ◽  
Hai-Xian ZHAN ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Chromosome Location

Genetic Algorithm for Whole Genome Sequence Percentage Similarity

2020 ◽  
Author(s):  
K Srija ◽  
P.P.R. Prithvi ◽  
Alka Grover ◽  
Shivani Chandra
Keyword(s):  
Genetic Algorithm ◽  
Genome Sequence ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Percentage Similarity

scholarly journals Whole genome sequence of Corynebacterium kroppenstedtii isolated from a case of recurrent granulomatous mastitis

IDCases ◽  
2020 ◽  
pp. e01034
Author(s):  
Charlie Tan ◽  
Fang-I Lu ◽  
Patryk Aftanas ◽  
Kara Tsang ◽  
Samira Mubareka ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Granulomatous Mastitis

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 On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Theo Meuwissen ◽  
Irene van den Berg ◽  
Mike Goddard
Keyword(s):  
Variable Selection ◽  
Genome Sequence ◽  
Genomic Prediction ◽  
Milk Fat ◽  
Genotype Imputation ◽  
Whole Genome Sequence ◽  
Genomic Relationship Matrix ◽  
Polygenic Effect ◽  
Relationship Matrix ◽  
Whole Genome

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

scholarly journals Whole genome sequence data of Bacillus australimaris strain B28A, isolated from Marine Water in India

Data in Brief ◽  
2021 ◽  
pp. 107240
Author(s):  
Wael Ali Mohammed Hadi ◽  
Boby T Edwin ◽  
A Jayakumaran Nair
Keyword(s):  
Genome Sequence ◽  
Sequence Data ◽  
Marine Water ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data
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