scholarly journals A population genomics approach to uncover the CNVs, and their evolutionary significance, hidden in reduced‐representation sequencing data sets

2020 ◽  
Vol 29 (24) ◽  
pp. 4749-4753
Author(s):  
Anna Tigano
Keyword(s):  
Population Genomics ◽  
Evolutionary Significance ◽  
Data Sets ◽  
Sequencing Data ◽  
Reduced Representation ◽  
Reduced Representation Sequencing

Distribution of runs of homozygosity in Chinese and Western pig breeds evaluated by reduced-representation sequencing data

2018 ◽  
Vol 49 (6) ◽  
pp. 579-591 ◽  
Author(s):  
Zhe Zhang ◽  
Qianqian Zhang ◽  
Qian Xiao ◽  
Hao Sun ◽  
Hongding Gao ◽  
...  
Keyword(s):  
Runs Of Homozygosity ◽  
Sequencing Data ◽  
Reduced Representation ◽  
Pig Breeds ◽  
Reduced Representation Sequencing

scholarly journals GBStools: A Unified Approach for Reduced Representation Sequencing and Genotyping

2015 ◽  
Author(s):  
Thomas F Cooke ◽  
Muh-Ching Yee ◽  
Marina Muzzio ◽  
Alexandra Sockell ◽  
Ryan Bell ◽  
...  
Keyword(s):  
Restriction Site ◽  
Variant Calling ◽  
Simulated Data ◽  
Error Rates ◽  
Genomic Diversity ◽  
Model Organisms ◽  
Data Sets ◽  
Reduced Representation ◽  
Restriction Site Polymorphisms ◽  
Reduced Representation Sequencing

Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.

scholarly journals The population genomics of structural variation in a songbird genus

2019 ◽  
Author(s):  
Matthias H. Weissensteiner ◽  
Ignas Bunikis ◽  
Ana Catalán ◽  
Kees-Jan Francoijs ◽  
Ulrich Knief ◽  
...  
Keyword(s):  
Structural Variation ◽  
Population Genomics ◽  
De Novo ◽  
Natural Populations ◽  
Evolutionary Significance ◽  
Substantial Part ◽  
Sequencing Data ◽  
Premating Isolation ◽  
Population Genetic Analysis ◽  
Long Read

AbstractStructural variation (SV) accounts for a substantial part of genetic mutations segregating across eukaryotic genomes with important medical and evolutionary implications. Here, we characterized SV across evolutionary time scales in the songbird genus Corvus using de novo assembly and read mapping approaches. Combining information from short-read (N = 127) and long-read re-sequencing data (N = 31) as well as from optical maps (N = 16) revealed a total of 201,738 insertions, deletions and inversions. Population genetic analysis of SV in the Eurasian crow speciation model revealed an evolutionary young (~530,000 years) cis-acting 2.25-kb retrotransposon insertion reducing expression of the NDP gene with consequences for premating isolation. Our results attest to the wealth of SV segregating in natural populations and demonstrate its evolutionary significance.

Degenerate adaptor sequences for detecting PCR duplicates in reduced representation sequencing data improve genotype calling accuracy

2014 ◽  
Vol 15 (2) ◽  
pp. 329-336 ◽  
Author(s):  
M. M. Y. Tin ◽  
F. E. Rheindt ◽  
E. Cros ◽  
A. S. Mikheyev
Keyword(s):  
Sequencing Data ◽  
Genotype Calling ◽  
Reduced Representation ◽  
Pcr Duplicates ◽  
Reduced Representation Sequencing

scholarly journals Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 16 ◽  
Author(s):  
Maja P Greminger ◽  
Kai N Stölting ◽  
Alexander Nater ◽  
Benoit Goossens ◽  
Natasha Arora ◽  
...  
Keyword(s):  
Population Genomics ◽  
Reduced Representation ◽  
Snp Calling ◽  
Reduced Representation Sequencing

scholarly journals From reference genomes to population genomics: comparing three reference-aligned reduced-representation sequencing pipelines in two wildlife species

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Belinda Wright ◽  
Katherine A. Farquharson ◽  
Elspeth A. McLennan ◽  
Katherine Belov ◽  
Carolyn J. Hogg ◽  
...  
Keyword(s):  
Population Genomics ◽  
Reduced Representation ◽  
Wildlife Species ◽  
Reference Genomes ◽  
Reduced Representation Sequencing

scholarly journals GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data

PLoS Genetics ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. e1005631 ◽  
Author(s):  
Thomas F. Cooke ◽  
Muh-Ching Yee ◽  
Marina Muzzio ◽  
Alexandra Sockell ◽  
Ryan Bell ◽  
...  
Keyword(s):  
Statistical Method ◽  
Sequencing Data ◽  
Allelic Dropout ◽  
Reduced Representation ◽  
Reduced Representation Sequencing

Positive selection signatures in Anqing six‐end‐white pig population based on reduced‐representation genome sequencing data

2021 ◽  
Author(s):  
L. Guo ◽  
H. Sun ◽  
Q. Zhao ◽  
Z. Xu ◽  
Z. Zhang ◽  
...  
Keyword(s):  
Positive Selection ◽  
Genome Sequencing ◽  
Population Based ◽  
Sequencing Data ◽  
Selection Signatures ◽  
Reduced Representation

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

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