Evidence map›Paper›PMID 24476358›Full record

ArticleBMC genomics2014

SHEAR: sample heterogeneity estimation and assembly by reference.

Sean R Landman, Tae Hyun Hwang, Kevin A T Silverstein, Yingming Li, Scott M Dehm, Michael Steinbach, Vipin Kumar

Abstract read
In one paragraph

Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Cestodes in the genomic era.Parasitology research · 2022
    Review
  2. Article
  3. Article
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

7 authors.

Sean R Landman
Tae Hyun HwangQuantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA. taehyun.hwang@utsouthwestern.edu.
Kevin A T Silverstein
Yingming Li
Scott M Dehm
Michael Steinbach
Vipin Kumar

Funding

AR Gene Rearrangements and AR Signaling in Prostate CancerR01CA174777 · NCI · UNIVERSITY OF MINNESOTA · PI Scott M. Dehm · 2013 to 2026
$4.9M
Integrative Training Program in NeuroimagingT32EB008389 · NIBIB · UNIVERSITY OF MINNESOTA · PI CHEN, WEI · 2008 to 2019
$1.4M
NCI NIH HHS R01 CA174777NIBIB NIH HHS T32 EB008389
6 · The paper itself

Abstract

backgroundPersonal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis.

resultsBy making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications.

conclusionSHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant.

Indexed as

SoftwareAlgorithmsGenomicsHigh-Throughput Nucleotide SequencingHumansInternetSequence Analysis, RNAUser-Computer Interface

Identifiers

PMID24476358
PMCPMC4007568

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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.