Brian L Sayre

Sequencing and automated whole-genome optical mapping of the genome of a domestic goat ( Capra hircus )

We report the ∼2.66-Gb genome sequence of a female Yunnan black goat. The sequence was obtained by combining short-read sequencing data and optical mapping data from a high-throughput whole-genome mapping instrument. The whole-genome mapping data facilitated the assembly of super-scaffolds >5× longer by the N50 metric than scaffolds augmented by fosmid end sequencing (scaffold N50 = 3.06 Mb, super-scaffold N50 = 16.3 Mb). Super-scaffolds are anchored on chromosomes based on conserved synteny with cattle, and the assembly is well supported by two radiation hybrid maps of chromosome 1. We annotate 22,175 protein-coding genes, most of which were recovered in the RNA-seq data of ten tissues. Comparative transcriptomic analysis of the primary and secondary follicles of a cashmere goat reveal 51 genes that are differentially expressed between the two types of hair follicles. This study, whose results will facilitate goat genomics, shows that whole-genome mapping technology can be used for the de novo assembly of large genomes.

Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome

The decrease in sequencing cost and increased sophistication of assembly algorithms for short-read platforms has resulted in a sharp increase in the number of species with genome assemblies. However, these assemblies are highly fragmented, with many gaps, ambiguities, and errors, impeding downstream applications. We demonstrate current state of the art for de novo assembly using the domestic goat (Capra hircus) based on long reads for contig formation, short reads for consensus validation, and scaffolding by optical and chromatin interaction mapping. These combined technologies produced what is, to our knowledge, the most continuous de novo mammalian assembly to date, with chromosome-length scaffolds and only 649 gaps. Our assembly represents a ∼400-fold improvement in continuity due to properly assembled gaps, compared to the previously published C. hircus assembly, and better resolves repetitive structures longer than 1 kb, representing the largest repeat family and immune gene complex yet produced for an individual of a ruminant species.

Design and Characterization of a 52K SNP Chip for Goats

The success of Genome Wide Association Studies in the discovery of sequence variation linked to complex traits in humans has increased interest in high throughput SNP genotyping assays in livestock species. Primary goals are QTL detection and genomic selection. The purpose here was design of a 50–60,000 SNP chip for goats. The success of a moderate density SNP assay depends on reliable bioinformatic SNP detection procedures, the technological success rate of the SNP design, even spacing of SNPs on the genome and selection of Minor Allele Frequencies (MAF) suitable to use in diverse breeds. Through the federation of three SNP discovery projects consolidated as the International Goat Genome Consortium, we have identified approximately twelve million high quality SNP variants in the goat genome stored in a database together with their biological and technical characteristics. These SNPs were identified within and between six breeds (meat, milk and mixed): Alpine, Boer, Creole, Katjang, Saanen and Savanna, comprising a total of 97 animals. Whole genome and Reduced Representation Library sequences were aligned on >10 kb scaffolds of the de novo goat genome assembly. The 60,000 selected SNPs, evenly spaced on the goat genome, were submitted for oligo manufacturing (Illumina, Inc) and published in dbSNP along with flanking sequences and map position on goat assemblies (i.e. scaffolds and pseudo-chromosomes), sheep genome V2 and cattle UMD3.1 assembly. Ten breeds were then used to validate the SNP content and 52,295 loci could be successfully genotyped and used to generate a final cluster file. The combined strategy of using mainly whole genome Next Generation Sequencing and mapping on a contig genome assembly, complemented with Illumina design tools proved to be efficient in producing this GoatSNP50 chip. Advances in use of molecular markers are expected to accelerate goat genomic studies in coming years.

Multiple genomic signatures of selection in goats and sheep indigenous to a hot arid environment.

Goats and sheep are versatile domesticates that have been integrated into diverse environments and production systems. Natural and artificial selection have shaped the variation in the two species, but natural selection has played the major role among indigenous flocks. To investigate signals of natural selection, we analyzed genotype data generated using the caprine and ovine 50K SNP BeadChips from Barki goats and sheep that are indigenous to a hot arid environment in Egypt’s Coastal Zone of the Western Desert. We identify several candidate regions under selection that spanned 119 genes. A majority of the genes were involved in multiple signaling and signal transduction pathways in a wide variety of cellular and biochemical processes. In particular, selection signatures spanning several genes that directly or indirectly influenced traits for adaptation to hot arid environments, such as thermo-tolerance (melanogenesis) (FGF2, GNAI3, PLCB1), body size and development (BMP2, BMP4, GJA3, GJB2), energy and digestive metabolism (MYH, TRHDE, ALDH1A3), and nervous and autoimmune response (GRIA1, IL2, IL7, IL21, IL1R1) were identified. We also identified eight common candidate genes under selection in the two species and a shared selection signature that spanned a conserved syntenic segment to bovine chromosome 12 on caprine and ovine chromosomes 12 and 10, respectively, providing, most likely, the evidence for selection in a common environment in two different but closely related species. Our study highlights the importance of indigenous livestock as model organisms for investigating selection sweeps and genome-wide association mapping.

Generation and analysis of expressed sequence tags (ESTs) for marker development in yam (Dioscorea alata L.)

BackgroundAnthracnose (Colletotrichumgloeosporioides) is a major limiting factor in the production of yam (Dioscorea spp.) worldwide. Availability of high quality sequence information is necessary for designing molecular markers associated with resistance. However, very limited sequence information pertaining to yam is available at public genome databases. Therefore, this collaborative project was developed for genetic improvement and germplasm characterization of yams using molecular markers. The current investigation is focused on studying gene expression, by large scale generation of ESTs, from one susceptible (TDa 95-0310) and two resistant yam genotypes (TDa 87-01091, TDa 95-0328) challenged with the fungus. Total RNA was isolated from young leaves of resistant and susceptible genotypes and cDNA libraries were sequenced using Roche 454 technology.ResultsA total of 44,757 EST sequences were generated from the cDNA libraries of the resistant and susceptible genotypes. Greater than 56% of ESTs were annotated using MapMan Mercator tool and Blast2GO search tools. Gene annotations were used to characterize the transcriptome in yam and also perform a differential gene expression analysis between the resistant and susceptible EST datasets. Mining for SSRs in the ESTs revealed 1702 unique sequences containing SSRs and 1705 SSR markers were designed using those sequences.ConclusionWe have developed a comprehensive annotated transcriptome data set in yam to enrich the EST information in public databases. cDNA libraries were constructed from anthracnose fungus challenged leaf tissues for transcriptome characterization, and differential gene expression analysis. Thus, it helped in identifying unique transcripts in each library for disease resistance. These EST resources provide the basis for future microarray development, marker validation, genetic linkage mapping and QTL analysis in Dioscorea species.

Single-molecule sequencing and conformational capture enable de novo mammalian reference genomes

The decrease in sequencing cost and increased sophistication of assembly algorithms for short-read platforms has resulted in a sharp increase in the number of species with genome assemblies. However, these assemblies are highly fragmented, with many gaps, ambiguities, and errors, impeding downstream applications. We demonstrate current state of the art for de novo assembly using the domestic goat (Capra hircus), based on long reads for contig formation, short reads for consensus validation, and scaffolding by optical and chromatin interaction mapping. These combined technologies produced the most contiguous de novo mammalian assembly to date, with chromosome-length scaffolds and only 663 gaps. Our assembly represents a >250-fold improvement in contiguity compared to the previously published C. hircus assembly, and better resolves repetitive structures longer than 1 kb, supporting the most complete repeat family and immune gene complex representation ever produced for a ruminant species.

Correction: Design and Characterization of a 52K SNP Chip for Goats.

We have forgotten to thank our collaborators who provided the Creole samples. The Acknowledgements section should read:

Component Selection to Optimize Distance Function Learning in Complex Scientific Data Sets

Analyzing complex scientific data, e.g., graphs and images, often requires comparison of features: regions on graphs, visual aspects of images and related metadata, some features being relatively more important. The notion of similarity for comparison is typically distance between data objects which could be expressed as distance between features. We refer to distance based on each feature as a component. Weights of components representing relative importance of features could be learned using distance function learning algorithms. However, it is seldom known which components optimize learning, given criteria such as accuracy, efficiency and simplicity. This is the problem we address. We propose and theoretically compare four component selection approaches: Maximal Path Traversal, Minimal Path Traversal, Maximal Path Traversal with Pruning and Minimal Path Traversal with Pruning. Experimental evaluation is conducted using real data from Materials Science, Nanotechnology and Bioinformatics. A trademarked software tool is developed as a highlight of this work.