Evidence map›Paper›PMID 29036558›Full record

ArticleBioinformatics (Oxford, England)2017

MethRaFo: MeDIP-seq methylation estimate using a Random Forest Regressor.

Jun Ding, Ziv Bar-Joseph

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Jun DingComputational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Ziv Bar-JosephComputational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Funding

Reconstructing regulatory networks from time series single cell dataR01GM122096 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI BAR-JOSEPH, ZIV · 2017 to 2020
$1.4M
NIGMS NIH HHS R01 GM122096
6 · The paper itself

Abstract

motivationProfiling of genome wide DNA methylation is now routinely performed when studying development, cancer and several other biological processes. Although Whole genome Bisulfite Sequencing provides high-quality methylation measurements at the resolution of nucleotides, it is relatively costly and so several studies have used alternative methods for such profiling. One of the most widely used low cost alternatives is MeDIP-Seq. However, MeDIP-Seq is biased for CpG enriched regions and thus its results need to be corrected in order to determine accurate methylation levels.

resultsHere we present a method for correcting MeDIP-Seq results based on Random Forest regression. Applying the method to real data from several different tissues (brain, cortex, penis) we show that it achieves almost 4 fold decrease in run time while increasing accuracy by as much as 20% over prior methods developed for this task. AVAILABILITY AND IMPLEMENTATION: MethRaFo is freely available as a python package (with a R wrapper) at https://github.com/phoenixding/methrafo. CONTACT: zivbj@cs.cmu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

DNA MethylationSoftwareHumansSequence Analysis, DNA

Identifiers

PMID29036558
PMCPMC5860172

What OpenQuestion holds

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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.