Evidence map›Paper›PMID 26286554›Full record

ArticleGenome research2015

Read clouds uncover variation in complex regions of the human genome.

Alex Bishara, Yuling Liu, Ziming Weng, Dorna Kashef-Haghighi, Daniel E Newburger, Robert West, Arend Sidow, Serafim Batzoglou

Abstract read
In one paragraph

Article in Genome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

  1. Article
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  5. Co-evolution ofNAR cancer · 2023
    Article
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  8. Article
  9. Article
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  11. Fourth Report on Chicken Genes and Chromosomes 2022.Cytogenetic and genome research · 2022
    Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
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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.

Alex BisharaDepartment of Computer Science, Stanford University, Stanford, California 94305, USA;
Yuling LiuDepartment of Computer Science, Stanford University, Stanford, California 94305, USA; Department of Chemistry, Stanford University, Stanford, California 94305, USA;
Ziming WengDepartment of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA;
Dorna Kashef-HaghighiDepartment of Computer Science, Stanford University, Stanford, California 94305, USA;
Daniel E NewburgerBiomedical Informatics Training Program, Stanford, California 94305, USA;
Robert WestDepartment of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA;
Arend SidowDepartment of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA.
Serafim BatzoglouDepartment of Computer Science, Stanford University, Stanford, California 94305, USA;

Funding

INSTITUTIONAL TRAINING GRANT IN GENOME SCIENCET32HG000044 · NHGRI · STANFORD UNIVERSITY · PI MICHAEL P. SNYDER · 1995 to 2026
$32.2M
POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Genomic Evolution of Breast CancerR01CA183904 · NCI · STANFORD UNIVERSITY · PI SIDOW, AREND, WEST, ROBERT B · 2015 to 2018
$2.6M
NCI NIH HHS R01 CA183904NCI NIH HHS R01CA183904NHGRI NIH HHS T32 HG000044NHGRI NIH HHS T32HG000044NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

Although an increasing amount of human genetic variation is being identified and recorded, determining variants within repeated sequences of the human genome remains a challenge. Most population and genome-wide association studies have therefore been unable to consider variation in these regions. Core to the problem is the lack of a sequencing technology that produces reads with sufficient length and accuracy to enable unique mapping. Here, we present a novel methodology of using read clouds, obtained by accurate short-read sequencing of DNA derived from long fragment libraries, to confidently align short reads within repeat regions and enable accurate variant discovery. Our novel algorithm, Random Field Aligner (RFA), captures the relationships among the short reads governed by the long read process via a Markov Random Field. We utilized a modified version of the Illumina TruSeq synthetic long-read protocol, which yielded shallow-sequenced read clouds. We test RFA through extensive simulations and apply it to discover variants on the NA12878 human sample, for which shallow TruSeq read cloud sequencing data are available, and on an invasive breast carcinoma genome that we sequenced using the same method. We demonstrate that RFA facilitates accurate recovery of variation in 155 Mb of the human genome, including 94% of 67 Mb of segmental duplication sequence and 96% of 11 Mb of transcribed sequence, that are currently hidden from short-read technologies.

Indexed as

Genetic VariationGenome, HumanAlgorithmsCarcinoma, DuctalCarcinoma, Ductal, BreastDNA FragmentationHumansSequence AlignmentSequence Analysis, DNA

Identifiers

PMID26286554
PMCPMC4579342

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.