Evidence map›Paper›PMID 28821014›Full record

ArticlePLoS computational biology2017

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.

Lingfei Wang, Tom Michoel

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 14% of its field
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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Lingfei WangDivision of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, United Kingdom.ORCID http://orcid.org/0000-0001-9175-7006
Tom MichoelDivision of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, United Kingdom.ORCID http://orcid.org/0000-0003-4749-4725
Roslin Institute · GB

Funding

Biotechnology and Biological Sciences Research Council BBS/E/D/20211551Biotechnology and Biological Sciences Research Council BBS/E/D/20211552
6 · The paper itself

Abstract

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr.

Indexed as

Chromosome MappingHigh-Throughput Nucleotide SequencingAlgorithmsDatabases, GeneticGenetic VariationModels, StatisticalTranscriptome

Identifiers

PMID28821014
PMCPMC5576763
OpenAlexW2606363045

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.