Department of Translational Genomics

The scientific community has accumulated enormous amounts of genomic data stored in specialized public repositories. Genomic data is easily accessible and available from public genomic repositories allowing the biomedical community to effectively share the omics datasets. However, improperly annotated or incomplete metadata accompanying the raw omics data can negatively impact the utility of shared for secondary analysis. In this study, we perform a comprehensive analysis under 137 studies over 18,559 samples across six therapeutics fields to assess the completeness of metadata accompanying omics studies in both publication and its related online repositories across and make observations about how the process of data sharing could be made reliable. This analysis involved an initial literature survey in finding studies based on the seven therapeutic fields, that are Alzheimers disease, acute myeloid leukemia, cystic fibrosis, cardiovascular diseases, inflammatory bowel disease, sepsi...

Advances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in thei...

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct ...

Although precise identification of the human leukocyte antigen (HLA) allele is crucial for various clinical and research applications, HLA typing remains challenging due to high polymorphism of the HLA loci. However, with Next-Generation Sequencing (NGS) data becoming widely accessible, many computational tools have been developed to predict HLA types from RNA sequencing (RNA-seq) data. However, there is a lack of comprehensive and systematic benchmarking of RNA-seq HLA callers using large-scale and realist gold standards. In order to address this limitation, we rigorously compared the performance of 12 HLA callers over 50,000 HLA tasks including searching 30 pairwise combinations of HLA callers and reference in over 1,500 samples. In each case, we produced evaluation metrics of accuracy that is the percentage of correctly predicted alleles (two and four-digit resolution) based on six gold standard datasets spanning 650 RNA-seq samples. To determine the influence of the relationship...

The unprecedented level of genome sequencing during the SARS-CoV-2 pandemic brought about the challenge of processing this genomic data. However, the state-of-the-art phylogenetic methods were mostly designed for analyzing data that are significantly sparser and require extensive subsampling of strains. We present (ε, τ) -MSN, a novel tool that reconstructs a viral genetic relatedness network based on genetic distances, that can process hundreds of thousands of sequences in under several hours. We applied (ε, τ) -MSN to the global COVID-19 outbreak data and were able to build a genetic network on more than 100,000 SARS-CoV-2 sequences. We show that (ε, τ) -MSN can accurately detect transmission events and build a genetic network with significantly higher assortativity with respect to continent and country attributes of SARS-CoV-2 samples. The source code for this software suite is available at https://github.com/Sergey-Knyazev/eMST. © 2021, Springer Nature Switzerland AG.