Biology subjects

Dill, D. L.

Publications and source records attributed to Dill, D. L..

3 recordsLinked to original sources

bioRxiv2020

High Throughput Computational Mouse Genetic Analysis

BackgroundGenetic factors affecting multiple biomedical traits in mice have been identified when GWAS data that measured responses in panels of inbred mouse strains was analyzed using haplotype-based computational genetic mapping (HBCGM). Although this method was previously used to analyze one dataset at a time; but now, a vast amount of mouse phenotypic data is now publicly available, which could lead to many more genetic discoveries. ResultsHBCGM and a whole genome SNP map covering 53 inbred strains was used to analyze 8462 publicly available datasets of biomedical responses (1.52M individual datapoints) measured in panels of inbred mouse strains. As proof of concept, causative genetic factors affecting susceptibility for eye, metabolic and infectious diseases were identified when structured automated methods were used to analyze the output. One analysis identified a novel genetic effector mechanism; allelic differences within the mitochondrial targeting sequence affected the subcellular localization of a protein. We also found allelic differences within the mitochondrial targeting sequences of many murine and human proteins, and these could affect a wide range of biomedical phenotypes. ImplicationsThese initial results indicate that genetic factors affecting biomedical responses could be identified through analysis of very large datasets, and they provide an early indication of how this type of augmented intelligence can facilitate genetic discovery.

bioinformatics↗

bioRxiv2019

Aquila_stLFR: assembly based variant calling package for stLFR and hybrid assembly for linked-reads

MotivationIdentifying structural variants (SVs) is of critical importance in health and disease, however, detecting them remains a scientific and computing challenge. Several linked-read sequencing technologies, including 10X linked-read, TELL-Seq, and single tube long fragment read (stLFR), have been recently developed as cost-effective approaches to reconstruct multi-megabase haplotypes (phase blocks) from sequence data of a single sample. These technologies provide an optimal sequencing platform to characterize SVs, though few computational algorithms can utilize them. Thus, we developed Aquila_stLFR, an approach that resolves SVs through haplotype-based assembly of stLFR linked-reads. ResultsAquila_stLFR first partitions LFRs into two haplotype-specific blocks, by taking advantage of the potential phasing ability of the linked-read itself. Each haplotype is then assembled independently, to achieve a complete diploid assembly to finally reconstruct the genome-wide SVs. We benchmarked Aquila_stLFR on a well-studied sample, NA24385, and showed Aquila_stLFR can detect medium to large size (50bp - 10kb) deletions with a high sensitivity and insertions with a high specificity. AvailabilitySource code and documentation are available on https://github.com/maiziex/Aquila_stLFR. Contactmaizie.zhou@vanderbilt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Aquila: diploid personal genome assembly and comprehensive variant detection based on linked reads

Variant discovery in personal, whole genome sequence data is critical for uncovering the genetic contributions to health and disease. We introduce a new approach, Aquila, that uses linked-read data for generating a high quality diploid genome assembly, from which it then comprehensively detects and phases personal genetic variation. Assemblies cover >95% of the human reference genome, with over 98% in a diploid state. Thus, the assemblies support detection and accurate genotyping of the most prevalent types of human genetic variation, including single nucleotide polymorphisms (SNPs), small insertions and deletions (small indels), and structural variants (SVs), in all but the most difficult regions. All heterozygous variants are phased in blocks that can approach arm-level length. The final output of Aquila is a diploid and phased personal genome sequence, and a phased VCF file that also contains homozygous and a few unphased heterozygous variants. Aquila represents a cost-effective evolution of whole-genome reconstruction that can be applied to cohorts for variation discovery or association studies, or to single individuals with rare phenotypes that could be caused by SVs or compound heterozygosity.

genomics↗