ALEXEY KOLESNIKOV

ALEXEY KOLESNIKOV

Authored Publications
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    Accurate human genome analysis with Element Avidity sequencing
    Andrew Carroll
    Bryan Lajoie
    Daniel Cook
    Kelly N. Blease
    Kishwar Shafin
    Lucas Brambrink
    Maria Nattestad
    Semyon Kruglyak
    bioRxiv(2023)
    Preview abstract We investigate the new sequencing technology Avidity from Element Biosciences. We show that Avidity whole genome sequencing matches mapping and variant calling accuracy with Illumina at high coverages (30x-50x) and is noticeably more accurate at lower coverages (20x-30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Element’s ability to generate paired end sequencing with longer insert sizes than typical short–read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving noticeably more accurate genome analyses at all coverages. View details
    A draft human pangenome reference
    Wen-Wei Liao
    Mobin Asri
    Jana Ebler
    Daniel Doerr
    Marina Haukness
    Shuangjia Lu
    Julian K. Lucas
    Jean Monlong
    Haley J. Abel
    Silvia Buonaiuto
    Xian Chang
    Haoyu Cheng
    Justin Chu
    Vincenza Colonna
    Jordan M. Eizenga
    Xiaowen Feng
    Christian Fischer
    Robert S. Fulton
    Shilpa Garg
    Cristian Groza
    Andrea Guarracino
    William T. Harvey
    Simon Heumos
    Kerstin Howe
    Miten Jain
    Tsung-Yu Lu
    Charles Markello
    Fergal J. Martin
    Matthew W. Mitchell
    Katherine M. Munson
    Moses Njagi Mwaniki
    Adam M. Novak
    Hugh E. Olsen
    Trevor Pesout
    David Porubsky
    Pjotr Prins
    Jonas A. Sibbesen
    Jouni Sirén
    Chad Tomlinson
    Flavia Villani
    Mitchell R. Vollger
    Lucinda L Antonacci-Fulton
    Gunjan Baid
    Carl A. Baker
    Anastasiya Belyaeva
    Konstantinos Billis
    Andrew Carroll
    Sarah Cody
    Daniel Cook
    Robert M. Cook-Deegan
    Omar E. Cornejo
    Mark Diekhans
    Peter Ebert
    Susan Fairley
    Olivier Fedrigo
    Adam L. Felsenfeld
    Giulio Formenti
    Adam Frankish
    Yan Gao
    Nanibaa’ A. Garrison
    Carlos Garcia Giron
    Richard E. Green
    Leanne Haggerty
    Kendra Hoekzema
    Thibaut Hourlier
    Hanlee P. Ji
    Eimear E. Kenny
    Barbara A. Koenig
    Jan O. Korbel
    Jennifer Kordosky
    Sergey Koren
    HoJoon Lee
    Alexandra P. Lewis
    Hugo Magalhães
    Santiago Marco-Sola
    Pierre Marijon
    Ann McCartney
    Jennifer McDaniel
    Jacquelyn Mountcastle
    Maria Nattestad
    Sergey Nurk
    Nathan D. Olson
    Alice B. Popejoy
    Daniela Puiu
    Mikko Rautiainen
    Allison A. Regier
    Arang Rhie
    Samuel Sacco
    Ashley D. Sanders
    Valerie A. Schneider
    Baergen I. Schultz
    Kishwar Shafin
    Michael W. Smith
    Heidi J. Sofia
    Ahmad N. Abou Tayoun
    Francoise Thibauld-Nissen
    Francesa Floriana Tricomi
    Justin Wagner
    Brian Walenz
    Jonathan M. D. Wood
    Aleksey V. Zimin
    Guillaume Borque
    Mark J. P. Chaisson
    Paul Flicek
    Adam M. Phillippy
    Justin Zook
    Evan E. Eichler
    David Haussler
    Ting Wang
    Erich D. Jarvis
    Karen H. Miga
    Glenn Hickey
    Erik Garrison
    Tobias Marschall
    Ira M. Hall
    Heng Li
    Benedict Paten
    Nature (2023)
    Preview abstract Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample. View details
    Local read haplotagging enables accurate long-read small variant calling
    Daniel Cook
    Maria Nattestad
    John E. Gorzynski
    Sneha D. Goenka
    Euan Ashley
    Miten Jain
    Karen Miga
    Benedict Paten
    Andrew Carroll
    Kishwar Shafin
    bioRxiv(2023)
    Preview abstract Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third- generation sequencing like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio revio platfrom, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for all long-read sequencing platforms. View details
    Preview abstract Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel. View details
    Preview abstract Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel. View details
    Ultra-rapid whole genome nanopore sequencing in a critical care setting
    Andrew Carroll
    Ankit Sethia
    Benedict Paten
    Christopher Wright
    Courtney J. Wusthoff
    Daniel R Garalde
    Dianna G. Fisk
    Elizabeth Spiteri
    Euan Ashley
    Fritz J. Sedlazeck
    Gunjan Baid
    Henry Chubb
    Jeffrey W Christle
    Jeffrey W. Christle
    John E. Gorzynski
    Jonathan A Bernstein
    Joseph Guillory
    Joshua W. Knowles
    Katherine Xiong
    Kishwar Shafin
    Kyla Dunn
    Marco Perez
    Maria Nattestad
    Maura RZ Ruzhnikov
    Megan E. Grove
    Mehrzad Samadi
    Michael Ma
    Miten Jain
    Scott R. Ceresnak
    Sneha D. Goenka
    Tanner D. Jensen
    Tia Moscarello
    Tong Zhu
    Trevor Pesout
    New England Journal of Medicine(2022)
    Preview abstract Background Genetic disease is a major contributor to critical care hospitalization, especially in younger patients. While early genetic diagnosis can guide clinical management, the turnaround time for whole genome based diagnostic testing has traditionally been measured in months. Recent programs in neonatal populations have reduced turnaround time into the range of days and shown that rapid genetic diagnosis enhances patient care and reduces healthcare costs. Yet, most decisions in critical care need to be made on hourly timescales. Methods We developed a whole genome sequencing approach designed to provide a genetic diagnosis within hours. Optimized highly parallel nanopore sequencing was coupled to a high-performance cloud compute system to implement near real-time basecalling and alignment followed by accelerated central and graphics processor unit variant calling. A custom scheme for variant prioritization took only minutes to rank variants most likely to be deleterious allowing efficient manual review and classification according to American College of Medical Genetics and Genomics guidelines. Results We performed whole genome sequencing on 12 patients from the critical care units of Stanford hospitals. In 10 cases, the pipeline produced diagnostic results faster than all previously published clinical genome analyses. Per patient, DNA extraction, library preparation, and nanopore sequencing across 48 flow cells generated 173–236 GigaBases of sequencing data in as little as 1:50 hours. After optimization, the average turnaround time was 7:58 hours (range 7:18–9:0 hours). A pathogenic or likely pathogenic variant was identified in five out of 12 patients (42%). After Sanger or short read sequencing confirmation in a CLIA-approved laboratory, this validated diagnosis altered clinical management in every case. Conclusions We developed an approach to make a genetic diagnosis from whole genome sequencing in hours, returning actionable, cost-saving diagnostic information on critical care timescales. View details
    Knowledge distillation for fast and accurate DNA sequence correction
    Anastasiya Belyaeva
    Joel Shor
    Daniel Cook
    Kishwar Shafin
    Daniel Liu
    Armin Töpfer
    Aaron Wenger
    William J. Rowell
    Howard Yang
    Andrew Carroll
    Maria Nattestad
    Learning Meaningful Representations of Life (LMRL) Workshop NeurIPS 2022
    Preview abstract Accurate genome sequencing can improve our understanding of biology and the genetic basis of disease. The standard approach for generating DNA sequences from PacBio instruments relies on HMM-based models. Here, we introduce Distilled DeepConsensus - a distilled transformer–encoder model for sequence correction, which improves upon the HMM-based methods with runtime constraints in mind. Distilled DeepConsensus is 1.3x faster and 1.5x smaller than its larger counterpart while improving the yield of high quality reads (Q30) over the HMM-based method by 1.69x (vs. 1.73x for larger model). With improved accuracy of genomic sequences, Distilled DeepConsensus improves downstream applications of genomic sequence analysis such as reducing variant calling errors by 39% (34% for larger model) and improving genome assembly quality by 3.8% (4.2% for larger model). We show that the representations learned by Distilled DeepConsensus are similar between faster and slower models. View details
    Technical development of rapid whole genome nanopore sequencing and variant identification pipeline
    Andrew Carroll
    Ankit Sethia
    Benedict Paten
    Christopher Wright
    Daniel R Garalde
    Dianna G. Fisk
    Elizabeth Spiteri
    Euan Ashley
    Fritz J. Sedlazeck
    Gunjan Baid
    Jean Monlong
    Jeffrey W Christle
    John E. Gorzynski
    Jonathan A Bernstein
    Joseph Guillory
    Karen P. Dalton
    Katherine Xiong
    Kishwar Shafin
    Maria Nattestad
    Maura RZ Ruzhnikov
    Megan E. Grove
    Mehrzad Samadi
    Miten Jain
    Sneha D. Goenka
    Tanner D. Jensen
    Tong Zhu
    Trevor Pesout
    Nature Biotechnology(2022)
    Preview abstract Whole genome sequencing can identify pathogenic variants for genetic disease but the time required for sequencing and analysis has been a barrier to its use in acutely ill patients. Here, we develop an approach to ultra-rapid nanopore whole genome sequencing that combines an efficient sample preparation protocol, distributed sequencing over 48 flow cells, near real-time base calling and alignment, accelerated variant calling, and fast variant filtration. We show that this framework provides accurate variant prioritization in less than half the fastest time recorded for an equivalent analysis to date. View details
    How DeepConsensus Works
    Aaron Wenger
    Anastasiya Belyaeva
    Andrew Carroll
    Armin Töpfer
    Ashish Teku Vaswani
    Daniel Cook
    Felipe Llinares
    Gunjan Baid
    Howard Yang
    Jean-Philippe Vert
    Kishwar Shafin
    Maria Nattestad
    Waleed Ammar
    William J. Rowell
    (2022)
    Preview abstract N/A These are slides for a public video about DeepConsensus View details
    Preview abstract The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with fastq files, 20 challenge participants applied their variant calling pipeline and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based methods and machine-learning methods scoring best for short-reads and long-read datasets, respectively. New methods out-performed the winners of the 2016 Truth Challenge across technologies, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants. View details