Biology subjects

Pavlichin, D. S.

Publications and source records attributed to Pavlichin, D. S..

3 recordsLinked to original sources

bioRxiv2022

The Human Pangenome's sequence conservation reveals a landscape of polymorphic structural variation

The Human Pangenome is a new reference build that addresses many of the limitations of the current reference. The first release is based on 94 high-quality haploid assemblies from individuals with diverse backgrounds. To facilitate the Pangenomes adoption in the wider research community, we conducted a multiple genome comparative analysis against the current GRCh38 reference. We applied a k-mer indexing strategy to identify highly conserved sequences that are omnipresent across all Pangenome assemblies, the reference and CHM13, a reference assembly with telomere-to-telomere chromosomes. Pan-conserved tag segments provided an informative set of universally conserved sequences. Examining the intervals between pairs of these segments defined highly conserved segments of the genome versus ones that have structurally related polymorphisms. We identified a Pangenome landscape of 60,764 polymorphic intervals with unique and geo-ethnic features. Overall, this study of the Pangenome revealed the conserved versus divergent features including the landscape of polymorphic structural variants.

genomics↗

bioRxiv2021

KmerKeys: a web resource for searching indexed genome assemblies and variants

K-mers are short DNA sequences that are used for genome sequence analysis. Applications that use k-mers include genome assembly and alignment. Despite these current applications, the wider bioinformatic use of k-mers in has challenges related to the massive scale of genomic sequence data. A single human genome assembly has billions of these short sequences. The sheer amount of computation for effective use of k-mer information is enormous, particularly when involving multiple genome assemblies. To address these issues, we developed a new k-mer indexing data structure based on a hash table tuned for the lookup of k-mer keys. This web application, referred to as KmerKeys (https://kmerkeys.dgi-stanford.org/), provides performant, rapid query speeds for cloud computation on genome assemblies. We enable fuzzy as well as exact k-mer-based searches of assemblies. To enable robust and speedy performance, the website implements cache-friendly hash tables, memory mapping and massive parallel processing. Our method employs a scalable and efficient data structure that can be used to jointly index and search a large collection of human genome assembly information. One can include variant databases and their associated metadata such as the gnomAD population variant catalog. This feature enables the incorporation of future genomic information into sequencing analysis.

bioinformatics↗

bioRxiv2020

Unique K-mer sequences for validating cancer-related substitution, insertion and deletion mutations

The cancer genome sequencing has led to important discoveries such as identifying cancer gene. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome sequencing data.

bioinformatics↗