Biology subjects

Medvedev, P.

Publications and source records attributed to Medvedev, P..

5 recordsLinked to original sources

bioRxiv2018

IsoCon: Deciphering highly similar multigene family transcripts from Iso-Seq data

A significant portion of genes in vertebrate genomes belongs to multigene families, with each family containing several gene copies whose presence/absence can be highly variable across individuals. For example, each Y chromosome ampliconic gene family harbors several nearly identical (up to 99.99%) gene copies. Existing de novo techniques for assaying the sequences of such highly-similar gene families fall short of reconstructing end to end transcripts with nucleotide-level precision or assigning them to their respective gene copies. We present IsoCon, a novel approach that combines experimental and computational techniques that leverage the power of long PacBio Iso-Seq reads to determine the full-length transcripts of highly similar multicopy gene families. IsoCon uses a cautiously iterative process to correct errors, followed by a statistical framework that allows it to distinguish errors from true variants with high precision. IsoCon outperforms existing methods for transcriptome analysis of Y ampliconic gene families in both simulated and real human data and is able to detect rare transcripts that differ by as little as one base pair from much more abundant transcripts. IsoCon has allowed us to detect an unprecedented number of novel isoforms, as well as to derive estimates on the number of gene copies in human Y ampliconic gene families.

genomics↗

bioRxiv2017

Accelerating SNP genotyping from whole genome sequencing data for bedside diagnostics

MotivationGenotyping a set of variants from a database is an important step for identifying known genetic traits and disease related variants within an individual. The growing size of variant databases as well as the high depth of sequencing data pose an efficiency challenge. In clinical applications, where time is crucial, alignment-based methods are often not fast enough. To fill the gap, Shajii et al. (2016) propose LAVA, an alignment-free genotyping method which is able to more quickly genotype SNPs; however, there remains large room for improvements in running time and accuracy.\n\nResultsWe present the VarGeno method for SNP genotyping from lllumina whole genome sequencing data. VarGeno builds upon LAVA by improving the speed of k-mer querying as well as the accuracy of the genotyping strategy. We evaluate VarGeno on several read datasets using different genotyping SNP lists. VarGeno performs 7-13 times faster than LAVA with similar memory usage, while improving accuracy.\n\nAvailabilityVarGeno is freely available at: https://github.com/medvedevgroup/vargeno.

bioinformatics↗

bioRxiv2017

Correcting palindromes in long reads after whole-genome amplification

Next-generation sequencing requires sufficient DNA to be available. If limited, whole-genome amplification is applied to generate additional amounts of DNA. Such amplification often results in many chimeric DNA fragments, in particular artificial palindromic sequences, which limit the usefulness of long reads from technologies such as PacBio and Oxford Nanopore. Here, we present Pacasus, a tool for correcting such errors in long reads. We demonstrate on two real-world datasets that it markedly improves subsequent read mapping and de novo assembly, yielding results similar to these that would be obtained with non-amplified DNA. With Pacasus long-read technologies become readily available for sequencing targets with very small amounts of DNA, such as single cells or even single chromosomes.

genomics↗

bioRxiv2017

RecoverY : K-mer based read classification for Y-chromosome specific sequencing and assembly

MotivationThe haploid mammalian Y chromosome is usually under-represented in genome assemblies due to high repeat content and low depth due to its haploid nature. One strategy to ameliorate the low coverage of Y sequences is to experimentally enrich Y-specific material before assembly. Since the enrichment process is imperfect, algorithms are needed to identify putative Y-specific reads prior to downstream assembly. A strategy that uses k-mer abundances to identify such reads was used to assemble the gorilla Y (Tomaszkiewicz et al 2016). However, the strategy required the manual setting of key parameters, a time-consuming process leading to sub-optimal assemblies.\n\nResultsWe develop a method, RecoverY, that selects Y-specific reads by automatically choosing the abundance level at which a k-mer is deemed to originate from the Y. This algorithm uses prior knowledge about the Y chromosome of a related species or known Y transcript sequences. We evaluate RecoverY on both simulated and real data, for human and gorilla, and investigate its robustness to important parameters. We show that RecoverY leads to a vastly superior assembly compared to alternate strategies of filtering the reads or contigs. Compared to the preliminary strategy used in Tomaszkiewicz et al (2016), we achieve a 33% improvement in assembly size and a 20% improvement in the NG50, demonstrating the power of automatic parameter selection.\n\nAvailabilityOur tool RecoverY is freely available at https://github.com/makovalab-psu/RecoverY\n\nContactkmakova@bx.psu.edu, pashadag@cse.psu.edu\n\nSupplementary informationAttached as an additional file.

bioinformatics↗

bioRxiv2016

AllSome Sequence Bloom Trees

The ubiquity of next generation sequencing has transformed the size and nature of many databases, pushing the boundaries of current indexing and searching methods. One particular example is a database of 2,652 human RNA-seq experiments uploaded to the Sequence Read Archive. Recently, Solomon and Kingsford proposed the Sequence Bloom Tree data structure and demonstrated how it can be used to accurately identify SRA samples that have a transcript of interest potentially expressed. In this paper, we propose an improvement called the AllSome Sequence Bloom Tree. Results show that our new data structure significantly improves performance, reducing the tree construction time by 52.7% and query time by 39 - 85%, with a price of up to 3x memory consumption during queries. Notably, it can query a batch of 198,074 queries in under 8 hours (compared to around two days previously) and a whole set of k-mers from a sequencing experiment (about 27 mil k-mers) in under 11 minutes.

bioinformatics↗