Evidence map›Paper›PMID 29813016›Full record

ArticleMicrobial genomics2018

SuperDCA for genome-wide epistasis analysis.

Santeri Puranen, Maiju Pesonen, Johan Pensar, Ying Ying Xu, John A Lees, Stephen D Bentley, Nicholas J Croucher, Jukka Corander

Abstract read
In one paragraph

Article in Microbial genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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.

Santeri Puranen2​Department of Mathematics and Statistics, Helsinki Institute of Information Technology (HIIT), FI-00014 University of Helsinki, Finland.
Maiju Pesonen1​Department of Computer Science, Aalto University, FI-00076 Espoo, Finland.
Johan Pensar2​Department of Mathematics and Statistics, Helsinki Institute of Information Technology (HIIT), FI-00014 University of Helsinki, Finland.
Ying Ying Xu1​Department of Computer Science, Aalto University, FI-00076 Espoo, Finland.
John A Lees3​Pathogen Genomics, Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK.
Stephen D Bentley3​Pathogen Genomics, Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK.
Nicholas J Croucher4​Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK.
Jukka Corander5​Department of Biostatistics, University of Oslo, 0317 Oslo, Norway.

Funding

Medical Research Council MR/R015600/1Wellcome Trust
6 · The paper itself

Abstract

The potential for genome-wide modelling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has previously been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10

Indexed as

Epistasis, GeneticGenome, BacterialGenetic Association StudiesGenetic LociGenomicsHumansModels, GeneticPhylogenyPolymorphism, Single NucleotideProtein ConformationStreptococcus pneumoniaeepistasislinkage disequilibriumpopulation genomics

Identifiers

PMID29813016
PMCPMC6096938

What OpenQuestion holds

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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.