Evidence map›Paper›PMID 27177143›Full record

ArticlePLoS computational biology2016

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.

John Wiedenhoeft, Eric Brugel, Alexander Schliep

Abstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Bayesian localization of CNV candidates in WGS data within minutes.Algorithms for molecular biology : AMB · 2019
    Article
  4. 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

3 authors.

John WiedenhoeftDepartment of Computer Science, Rutgers University, Piscataway, New Jersey, United States of America.
Eric BrugelDepartment of Computer Science, Rutgers University, Piscataway, New Jersey, United States of America.
Alexander SchliepDepartment of Computer Science, Rutgers University, Piscataway, New Jersey, United States of America.

Funding

Meaningful Data Compression and Reduction of High-Throughput Sequencing DataU01CA198952 · NCI · RUTGERS, THE STATE UNIV OF N.J. · PI FARACH-COLTON, MARTIN · 2015 to 2017
$981k
NCI NIH HHS U01 CA198952
6 · The paper itself

Abstract

By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.

Indexed as

DNA Copy Number VariationsModels, GeneticBayes TheoremBreast NeoplasmsCell LineComparative Genomic HybridizationComputational BiologyComputer SimulationData CompressionFemaleGenome, HumanHumansMarkov ChainsSoftware

Identifiers

PMID27177143
PMCPMC4866742

What OpenQuestion holds

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LicenceCC BY
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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.