Evidence map›Paper›PMID 21505028›Full record

ArticleBioinformatics (Oxford, England)2011

Efficient algorithms for tandem copy number variation reconstruction in repeat-rich regions.

Dan He, Farhad Hormozdiari, Nicholas Furlotte, Eleazar Eskin

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

13 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. CNVeM: copy number variation detection using uncertainty of read mapping.Journal of computational biology : a journal of computational molecular cell biology · 2013
    Article
  12. Article
  13. Review
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

4 authors.

Dan HeDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.
Farhad Hormozdiari
Nicholas Furlotte
Eleazar Eskin

Funding

Substance Abuse & Behavioral Disinhibition: Integrating Genes & EnvironmentU01DA024417 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2007 to 2010
$10.8M
Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
Discovering the Genetic Basis of HypertensionK25HL080079 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ESKIN, ELEAZAR · 2006 to 2010
$685k
NHGRI NIH HHS T32 HG002536NHGRI NIH HHS T32-HG002536NHLBI NIH HHS K25 HL080079NHLBI NIH HHS K25-HL080079NIDA NIH HHS U01-DA024417NIEHS NIH HHS N01-ES-45530
6 · The paper itself

Abstract

motivationStructural variations and in particular copy number variations (CNVs) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads.

resultsIn this study, we propose an efficient algorithm to handle these multi-mapping reads such that the CNVs can be reconstructed with high accuracy even for repeat-rich regions. To our knowledge, this is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Our experiments show that our method is not only computationally efficient but also accurate.

Indexed as

AlgorithmsDNA Copy Number VariationsRepetitive Sequences, Nucleic AcidAnimalsChromosome MappingDNAGenomicsMiceDNA

Identifiers

PMID21505028
PMCPMC3102223

What OpenQuestion holds

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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.