Alexey Kolesnikov

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

DeepSomatic is a deep learning method for detecting somatic variants from short-read and long-read sequencing data, outperforming existing callers, especially for indels.

Accurate human genome analysis with Element Avidity sequencing

This study compares Element Avidity sequencing to Illumina sequencing, finding higher accuracy in mapping and variant calling, especially at lower coverages and in challenging genomic regions.

Highly accurate assembly polishing with DeepPolisher

DeepPolisher improves genome assembly accuracy by reducing errors using a transformer model and phasing method, achieving over 70% indel error reduction.

Pangenome-aware DeepVariant

This paper introduces pangenome-aware DeepVariant, a variant caller that improves accuracy by using a pangenome reference, outperforming linear-reference-based DeepVariant by up to 25.5%.

Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads

Severus detects and characterizes somatic structural variations in tumor genomes using long reads, outperforming existing methods in accuracy and revealing clinically relevant rearrangements.

A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

The paper develops a benchmark for detecting low-frequency mosaic variants in the HG002 reference material using high-coverage sequencing data and validates its utility across multiple technologies.

Local read haplotagging enables accurate long-read small variant calling

This paper introduces a local haplotype approximation method to improve variant calling accuracy across long-read sequencing platforms without increasing computational overhead.

Pangenome Graph Construction from Genome Alignment with Minigraph-Cactus

The paper introduces Minigraph-Cactus, a pangenome pipeline that constructs pangenome graphs directly from whole-genome alignments, demonstrating improved performance in mapping and variant calling across human and Drosophila data.

A Draft Human Pangenome Reference

The paper presents a draft human pangenome reference that improves variant discovery and structural variant detection compared to existing references.

Recombination between heterologous human acrocentric chromosomes

The paper shows recombination between heterologous human acrocentric chromosomes using pangenome data, revealing pseudo-homologous regions linked to Robertsonian translocations.

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The paper evaluates variant calling in difficult genomic regions using short- and long-read sequencing, showing improved performance with new methods and multi-technology approaches.

Haplotype-aware variant calling enables high accuracy in nanopore long-reads using deep neural networks

The paper introduces a haplotype-aware variant calling pipeline for nanopore and PacBio HiFi data, achieving high accuracy in variant detection and de novo assembly polishing.

DeepConsensus: Gap-Aware Sequence Transformers for Sequence Correction

DeepConsensus improves PacBio HiFi read accuracy and assembly quality using a gap-aware transformer model.

DeepTrio: Variant Calling in Families Using Deep Learning

DeepTrio improves variant calling in family trios using deep learning, achieving higher accuracy than DeepVariant, especially at lower coverages.

Improving variant calling using population data and deep learning

This study improves variant calling by integrating population data into deep learning models, enhancing precision and recall across diverse populations.

An Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development

The paper provides a dataset of 9 gold-standard samples sequenced on multiple platforms and protocols to benchmark sequencing and analysis methods.

Highly-accurate long-read sequencing improves variant detection and assembly of a human genome

This paper develops a long-read sequencing protocol to improve variant detection and genome assembly in a human genome, achieving high accuracy and contiguity.