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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.
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Improving variant calling using population data and deep learning
Andrew Carroll
Nae-Chyun Chen
Sidharth Goel
BMC Bioinformatics (2023)
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.
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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.
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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.
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Improving variant calling using population data and deep learning
Nae-Chyun Chen
Sidharth Goel
Andrew Carroll
BMC Bioinformatics (2023)
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.
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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.
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precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions.
Andrew Carroll
Gunjan Baid
Howard Yang
Maria Nattestad
Sidharth Goel
Cell Genomics (2022)
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.
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DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer
Aaron Wenger
Andrew Walker Carroll
Armin Töpfer
Ashish Teku Vaswani
Daniel Cook
Felipe Llinares
Gunjan Baid
Howard Cheng-Hao Yang
Jean-Philippe Vert
Kishwar Shafin
Maria Nattestad
Waleed Ammar
William J. Rowell
Nature Biotechnology (2022)
Preview abstract
Genomic analysis requires accurate sequencing in sufficient coverage and over difficult genome regions. Through repeated sampling of a circular template, Pacific Biosciences developed long (10-25kb) reads with high overall accuracy, but lower homopolymer accuracy. Here, we introduce DeepConsensus, a transformer-based approach which leverages a unique alignment loss to correct sequencing errors. DeepConsensus reduces errors in PacBio HiFi reads by 42%, compared to the current approach. We show this increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27%, and at Q40 by 90%. With two SMRT cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9Mb to 17.2Mb), increase gene completeness (94% to 97%), reduce false gene duplication rate (1.1% to 0.5%), and improve assembly base accuracy (QV43 to QV45), and also reduce variant calling errors by 24%.
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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.
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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
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