Evidence map›Paper›PMID 32938926›Full record

ArticleNature communications2020

Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets.

Emily Berger, Deniz Yorukoglu, Lillian Zhang, Sarah K Nyquist, Alex K Shalek, Manolis Kellis, Ibrahim Numanagić, Bonnie Berger

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  12. flopp: Extremely Fast Long-Read Polyploid Haplotype Phasing by Uniform Tree Partitioning.Journal of computational biology : a journal of computational molecular cell biology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Emily Berger *Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Deniz Yorukoglu *Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Lillian Zhang *Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0001-8823-5338
Sarah K NyquistComputer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Alex K ShalekComputer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0001-5670-8778
Manolis KellisComputer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0001-7113-9630
Ibrahim NumanagićComputer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. inumanag@uvic.ca.ORCID http://orcid.org/0000-0002-2970-7937
Bonnie BergerComputer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. bab@mit.edu.ORCID http://orcid.org/0000-0002-2724-7228

Funding

Graduate Training in Computational and Systems BiologyT32GM087237 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BURGE, CHRISTOPHER B · 2009 to 2023
$4.6M
Compressive genomics for large omics data sets: Algorithms applications & toolsR01GM108348 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BERGER, BONNIE · 2013 to 2019
$2.1M
NIGMS NIH HHS R01 GM108348NIGMS NIH HHS T32 GM087237
6 · The paper itself

Abstract

Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. We demonstrate HapTree-X's feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10×  faster than other tools. The advantage of HapTree-X's ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases.

Indexed as

Allelic ImbalanceHaplotypesSequence Analysis, RNAAlgorithmsDatabases, GeneticDiploidyHumansK562 CellsModels, GeneticModels, StatisticalPolymorphism, Single NucleotidePolyploidyRNA-Seq

Identifiers

PMID32938926
PMCPMC7494856

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Registered trials

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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.