Exploiting genomic synteny in Felidae: cross-species genome alignments and SNV discovery can aid conservation management

Gene pseudogenization in fertility-associated genes in cheetah (Acinonyx jubatus), a species with long-term low effective population size

We are witnessing an ongoing global biodiversity crisis, and an increasing number of mammalian populations are at risk of decline. Species that have survived severe historic bottlenecks, such as the cheetah (Acinonyx jubatus) exhibit symptoms of inbreeding depression including reproductive and developmental defects. Although it has long been suggested that such defects stem from an accumulation of weakly deleterious mutations, the implications of such mutations leading to pseudogenization has not been assessed. Here, we use comparative analysis of eight felid genomes to better understand the impacts of deleterious mutations in the cheetah. We find novel pseudogenization events specific to the cheetah. Through careful curation, we identify 65 genes with previously unreported premature termination codons (PTCs) that likely affect gene function. With the addition of population data (n = 6), we find 22 of these PTCs in at least one resequenced individual, four of which (DEFB116, ARL13A, CFAP119, and NT5DC4) are also found in a more recent reference genome. Mutations within three of these genes are linked with sterility, including azoospermia, which is common in cheetahs. Our results highlight the power of comparative genomic approaches for the discovery of novel causative variants in declining species.

A review of standardized high-throughput cardiovascular phenotyping with a link to metabolism in mice

Cardiovascular diseases cause a high mortality rate worldwide and represent a major burden for health care systems. Experimental rodent models play a central role in cardiovascular disease research by effectively simulating human cardiovascular diseases. Using mice, the International Mouse Phenotyping Consortium (IMPC) aims to target each protein-coding gene and phenotype multiple organ systems in single-gene knockout models by a global network of mouse clinics. In this review, we summarize the current advances of the IMPC in cardiac research and describe in detail the diagnostic requirements of high-throughput electrocardiography and transthoracic echocardiography capable of detecting cardiac arrhythmias and cardiomyopathies in mice. Beyond that, we are linking metabolism to the heart and describing phenotypes that emerge in a set of known genes, when knocked out in mice, such as the leptin receptor (Lepr), leptin (Lep), and Bardet-Biedl syndrome 5 (Bbs5). Furthermore, we are presenting not yet associated loss-of-function genes affecting both, metabolism and the cardiovascular system, such as the RING finger protein 10 (Rfn10), F-box protein 38 (Fbxo38), and Dipeptidyl peptidase 8 (Dpp8). These extensive high-throughput data from IMPC mice provide a promising opportunity to explore genetics causing metabolic heart disease with an important translational approach.

Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity

Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms. Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated. Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different. Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.