BOOTSIE – ESTIMATION OF COEFFICIENT OF VARIATION OF AFLP DATA BY BOOTSTRAP ANALYSIS

Bootsie is an English-native replacement for ASG Coelho\u27s “DBOOT” utility for estimating coefficient of variation of a population of AFLP marker data using bootstrapping. Bootsie improves on DBOOT by supporting batch processing, time-to-completion estimation, builtin graphs, and a suite of export tools for creating data files for other population genetics software

WGSOryza_CIAT_LSU_USDA_NCGR_Q40_annotated_Chr10_1.vcf

SNPs and small indels identified for the 104 varieties analyzed in this study. All genotypes have an NGSEP genotyping quality score larger or equal than 40. Genomic coordinates are relative to IRGSP1.0. This file contains chromosome 10 from basepairs 12,000,000 to 23,207,28

WGSOryza_CIAT_LSU_USDA_NCGR_Q40_annotated_Chr4_3.vcf

SNPs and small indels identified for the 104 varieties analyzed in this study. All genotypes have an NGSEP genotyping quality score larger or equal than 40. Genomic coordinates are relative to IRGSP1.0. This file contains chromosome 4 from basepairs 30,000,000 to 35,502,69

Structural variants WGSOryza_CIAT_LSU_USDA_NCGR

Structural variation identified for the 104 varieties analyzed in this study in GFF format (one file per sample). Genomic coordinates are relative to IRGSP1.0

WGSOryza_CIAT_LSU_USDA_NCGR_Q40_annotated_Chr11_1.vcf

SNPs and small indels identified for the 104 varieties analyzed in this study. All genotypes have an NGSEP genotyping quality score larger or equal than 40. Genomic coordinates are relative to IRGSP1.0. This file contains chromosome 11 from basepairs 15,000,000 to 29,021,10

WGSOryza_CIAT_LSU_USDA_NCGR_Q40_annotated_Chr10_0.vcf

SNPs and small indels identified for the 104 varieties analyzed in this study. All genotypes have an NGSEP genotyping quality score larger or equal than 40. Genomic coordinates are relative to IRGSP1.0. This file contains chromosome 10 from basepairs 1 to 12,000,00

WGSOryza_CIAT_LSU_USDA_NCGR_Q40_annotated_Chr3_0.vcf

SNPs and small indels identified for the 104 varieties analyzed in this study. All genotypes have an NGSEP genotyping quality score larger or equal than 40. Genomic coordinates are relative to IRGSP1.0. This file contains chromosome 3 from basepairs 1 to 1500000

WGSOryza_CIAT_LSU_USDA_NCGR_Q40_annotated_Chr7_0.vcf

SNPs and small indels identified for the 104 varieties analyzed in this study. All genotypes have an NGSEP genotyping quality score larger or equal than 40. Genomic coordinates are relative to IRGSP1.0. This file contains chromosome 7 from basepairs 1 to 15,000,00

Functional Genomic Insights into Regulatory Mechanisms of High-Altitude Adaptation

Recent studies of indigenous human populations at high altitude have provided proof-of-principle that genome scans of DNA polymorphism can be used to identify candidate loci for hypoxia adaptation. When integrated with experimental analyses of physiological phenotypes, genome-wide surveys of DNA polymorphism and tissue-specific transcriptional profiles can provide insights into actual mechanisms of adaptation. It has been suggested that adaptive phenotypic evolution is largely mediated by cis-regulatory changes in genes that are located at integrative control points in regulatory networks. This hypothesis can be tested by conducting transcriptomic analyses of hypoxic signaling pathways in conjunction with experimental measures of vascular oxygen supply and metabolic pathway flux. Such studies may reveal whether the architecture of gene regulatory networks can be used to predict which loci (and which types of loci) are likely to be “hot spots” for adaptive physiological evolution. Functional genomic studies of deer mice (Peromyscus maniculatus) demonstrate how the integrated analysis of variation in tissue-specific transcriptomes, whole-animal physiological performance, and various subordinate traits can yield insights into the mechanistic underpinnings of high-altitude adaptation