Evidence map›Paper›PMID 26601959›Full record

ArticleThe Journal of biological chemistry2016

From Single Variants to Protein Cascades: MULTISCALE MODELING OF SINGLE NUCLEOTIDE VARIANT SETS IN GENETIC DISORDERS.

Sabine C Mueller, Björn Sommer, Christina Backes, Jan Haas, Benjamin Meder, Eckart Meese, Andreas Keller

Abstract read
In one paragraph

Article in The Journal of biological chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Sabine C MuellerFrom the Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany,; Department of Human Genetics, Saarland University, 66421 Homburg, Germany,. Electronic address: sabine.mueller@uniklinikum-saarland.de.
Björn Sommerthe Bio-/Medical Informatics Department, Faculty of Technology, Bielefeld University, 33501 Bielefeld, Germany,; Clayton School of Information Technology, Faculty of Information Technology, Monash University, Melbourne 3800, Australia.
Christina BackesFrom the Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
Jan Haasthe Department of Internal Medicine III, Heidelberg University, 69120 Heidelberg, Germany, and; the DZHK (German Centre for Cardiovascular Research), 69120 Heidelberg, Germany.
Benjamin Mederthe Department of Internal Medicine III, Heidelberg University, 69120 Heidelberg, Germany, and; the DZHK (German Centre for Cardiovascular Research), 69120 Heidelberg, Germany.
Eckart MeeseDepartment of Human Genetics, Saarland University, 66421 Homburg, Germany.
Andreas KellerFrom the Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the role of genetics in disease has become a central part of medical research. Non-synonymous single nucleotide variants (nsSNVs) in coding regions of human genes frequently lead to pathological phenotypes. Beyond single variations, the individual combination of nsSNVs may add to pathogenic processes. We developed a multiscale pipeline to systematically analyze the existence of quantitative effects of multiple nsSNVs and gene combinations in single individuals on pathogenicity. Based on this pipeline, we detected in a data set of 842 nsSNVs discovered in 76 genes related to cardiomyopathies, associated nsSNV combinations in seven genes present in at least 70% of all 639 patient samples, but not in a control cohort of healthy humans. Structural analyses of these revealed primarily an influence on the protein stability. For amino acid substitutions located at the protein surface, we generally observed a proximity to putative binding pockets. To computationally analyze cumulative effects and their impact, pathogenicity methods are currently being developed. Our approach supports this process, as shown on the example of a cardiac phenotype but can be likewise applied to other diseases such as cancer.

Indexed as

Polymorphism, Single NucleotideAmino Acid SubstitutionCardiomyopathiesCohort StudiesComputational BiologyGene Regulatory NetworksGenetic Diseases, InbornHumansProteinsProteinsbioinformaticscardiomyopathycomputational biologygenetic polymorphismmolecular genetics

Identifiers

PMID26601959
PMCPMC4722441

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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.