Evidence map›Paper›PMID 37745389›Full record

ArticlebioRxiv : the preprint server for biology2023

Local read haplotagging enables accurate long-read small variant calling.

Alexey Kolesnikov, Daniel Cook, Maria Nattestad, Brandy McNulty, John Gorzynski, Sneha Goenka, Euan A Ashley, Miten Jain, Karen H Miga, Benedict Paten and 3 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Alexey KolesnikovGoogle Inc, 1600 Amphitheatre Pkwy, Mountain View, CA.
Daniel CookGoogle Inc, 1600 Amphitheatre Pkwy, Mountain View, CA.
Maria NattestadGoogle Inc, 1600 Amphitheatre Pkwy, Mountain View, CA.
Brandy McNultyUC Santa Cruz Genomics Institute, University of California, Santa Cruz, California, USA.
John GorzynskiStanford University, Stanford, CA, USA.
Sneha GoenkaStanford University, Stanford, CA, USA.
Euan A AshleyStanford University, Stanford, CA, USA.
Miten JainNortheastern university, Boston, MA, USA.
Karen H MigaUC Santa Cruz Genomics Institute, University of California, Santa Cruz, California, USA.
Benedict PatenUC Santa Cruz Genomics Institute, University of California, Santa Cruz, California, USA.
Pi-Chuan ChangGoogle Inc, 1600 Amphitheatre Pkwy, Mountain View, CA.
Andrew CarrollGoogle Inc, 1600 Amphitheatre Pkwy, Mountain View, CA.
Kishwar ShafinGoogle Inc, 1600 Amphitheatre Pkwy, Mountain View, CA.

Funding

The AnVIL Data Ecosystem DACReS SupplementU24HG010262 · NHGRI · BROAD INSTITUTE, INC. · PI Robert J Carroll, Jonathan Lawson · 2018 to 2026
$40.5M
The Integration of Trans-omics for Precision Medicine (TOPMED) and Other Heart, Lung, Blood and Sleep (HLBS) Data Sets with the Data CommonsOT3HL142481 · NHLBI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI GROSSMAN, ROBERT L., PATEN, BENEDICT · 2017 to 2020
$24.9M
ELSI Administrative Supplement - Center for Human Reference Genome DiversityU01HG010971 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI EICHLER, EVAN, JARVIS, ERICH D · 2019 to 2023
$18.4M
Dockstore: The Community Platform for Reproducible Biomedical Workflows and ApplicationsU24HG011853 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI Jonathan Lawson, Benedict Paten · 2021 to 2026
$5.7M
Telomere-to-telomere assemblies of human genomesR01HG011274 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI Karen Hayden Miga · 2020 to 2026
$4.5M
Computational tools for uniform processing and integration of human reference atlas data [2 of 5]OT2OD033761 · OD · CARNEGIE-MELLON UNIVERSITY · PI PATEN, BENEDICT, RUFFALO, MATTHEW · 2022 to 2025
$4.2M
The construction and utility of reference pan-genome graphsU01HG010961 · NHGRI · DANA-FARBER CANCER INST · PI LI, HENG, PATEN, BENEDICT · 2020 to 2023
$3.2M
Enabling Comparative PangenomicsR01HG010485 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H, MARTIN, FERGAL JAMES · 2020 to 2023
$2.6M
NHGRI NIH HHS R01 HG010485NHGRI NIH HHS R01 HG011274NHGRI NIH HHS U01 HG010961NHGRI NIH HHS U01 HG010971NHGRI NIH HHS U24 HG010262NHGRI NIH HHS U24 HG011853NHLBI NIH HHS OT3 HL142481NIH HHS OT2 OD033761
6 · The paper itself

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 platforms 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 system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.

Indexed as

DeepVariantLong ReadsNanoporePacBio HiFiPacBio RevioVariant Calling

Identifiers

PMID37745389
PMCPMC10515762

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.