Homogeneity of Arabian Peninsula dromedary camel populations with signals of geographic distinction based on whole genome sequence data

Dromedary camels in the Arabian Peninsula distribute along different geographical and ecological locations, e.g. desert, mountains and coasts. Here, we are aiming to explore the whole genome sequence data of ten dromedary populations from the Arabian Peninsula to assess their genetic structure, admixture levels, diversity and similarity indices. Upon including reference dromedary and Bactrian camel populations from Iran and Kazakhstan, we characterise inter-species and geographic genetic distinction between the dromedary and the Bactrian camels. Individual-based alpha genetic diversity profiles are found to be generally higher in Bactrian camels than dromedary populations, with the exception of five autosomes (NC_044525.1, NC_044534.1, NC_044540.1, NC_044542.1, NC_044544.1) at diversity orders (q ≥ 2). The Arabian Peninsula camels are generally homogenous, with a small degree of genetic distinction correlating with three geographic groups: North, Central and West; Southwest; and Southeast of the Arabian Peninsula. No significant variation in diversity or similarity indices are observed among the different Arabian Peninsula dromedary populations. This study contributes to our understanding of the genetic diversity of Arabian Peninsula dromedary camels. It will help conserve the genetic stock of this species and support the design of breeding programmes for genetic improvement of favorable traits.

Phylo-taxonogenomics supports revision of taxonomic status of twenty Xanthomonas pathovars to Xanthomonas citri

Based on phylo-taxonogenomics criteria, we present amended descriptions for twenty pathovars to Xanthomonas citri. Incidentally, eighteen were first reported from India. Seven out of twenty are currently classified as X. axonopodis, twelve out of twenty as X. campestris, and one as X. cissicola. In this study, we have generated genome sequence data of four pathovars, and the genomes of the remaining sixteen were used from the published data. Comprehensive genome-based phylogenomic and taxonogenomic analyses reveal that all these pathovars belong to X. citri and need to reconcile their taxonomic status. The present proposal will aid in systematic studies of a major species and its constitutent members that infect economically important plants.

Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature)

The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.

Optimizing Sequencing Resources in Genotyped Livestock Populations Using Linear Programming

Low-cost genome-wide single-nucleotide polymorphisms (SNPs) are routinely used in animal breeding programs. Compared to SNP arrays, the use of whole-genome sequence data generated by the next-generation sequencing technologies (NGS) has great potential in livestock populations. However, sequencing a large number of animals to exploit the full potential of whole-genome sequence data is not feasible. Thus, novel strategies are required for the allocation of sequencing resources in genotyped livestock populations such that the entire population can be imputed, maximizing the efficiency of whole genome sequencing budgets. We present two applications of linear programming for the efficient allocation of sequencing resources. The first application is to identify the minimum number of animals for sequencing subject to the criterion that each haplotype in the population is contained in at least one of the animals selected for sequencing. The second application is the selection of animals whose haplotypes include the largest possible proportion of common haplotypes present in the population, assuming a limited sequencing budget. Both applications are available in an open source program LPChoose. In both applications, LPChoose has similar or better performance than some other methods suggesting that linear programming methods offer great potential for the efficient allocation of sequencing resources. The utility of these methods can be increased through the development of improved heuristics.

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

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.