Dataset Information

Mutadelic: mutation analysis using description logic inferencing capabilities.

ABSTRACT:

Motivation

As next generation sequencing gains a foothold in clinical genetics, there is a need for annotation tools to characterize increasing amounts of patient variant data for identifying clinically relevant mutations. While existing informatics tools provide efficient bulk variant annotations, they often generate excess information that may limit their scalability.

Results

We propose an alternative solution based on description logic inferencing to generate workflows that produce only those annotations that will contribute to the interpretation of each variant. Workflows are dynamically generated using a novel abductive reasoning framework called a basic framework for abductive workflow generation (AbFab). Criteria for identifying disease-causing variants in Mendelian blood disorders were identified and implemented as AbFab services. A web application was built allowing users to run workflows generated from the criteria to analyze genomic variants. Significant variants are flagged and explanations provided for why they match or fail to match the criteria.

Availability and implementation

The Mutadelic web application is available for use at http://krauthammerlab.med.yale.edu/mutadelic.

Contact

michael.krauthammer@yale.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Holford ME

PROVIDER: S-EPMC6078193 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Publications

Mutadelic: mutation analysis using description logic inferencing capabilities.

Holford Matthew E ME Krauthammer Michael M

Bioinformatics (Oxford, England) 20150812 23

<h4>Motivation</h4>As next generation sequencing gains a foothold in clinical genetics, there is a need for annotation tools to characterize increasing amounts of patient variant data for identifying clinically relevant mutations. While existing informatics tools provide efficient bulk variant annotations, they often generate excess information that may limit their scalability.<h4>Results</h4>We propose an alternative solution based on description logic inferencing to generate workflows that pro ...[more]

PMID: 26272983

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Project description:BackgroundMany of the functional units in cells are multi-protein complexes such as RNA polymerase, the ribosome, and the proteasome. For such units to work together, one might expect a high level of regulation to enable co-appearance or repression of sets of complexes at the required time. However, this type of coordinated regulation between whole complexes is difficult to detect by existing methods for analyzing mRNA co-expression. We propose a new methodology that is able to detect such higher order relationships.ResultsWe detect coordinated regulation of multiple protein complexes using logic analysis of gene expression data. Specifically, we identify gene triplets composed of genes whose expression profiles are found to be related by various types of logic functions. In order to focus on complexes, we associate the members of a gene triplet with the distinct protein complexes to which they belong. In this way, we identify complexes related by specific kinds of regulatory relationships. For example, we may find that the transcription of complex C is increased only if the transcription of both complex A AND complex B is repressed. We identify hundreds of examples of coordinated regulation among complexes under various stress conditions. Many of these examples involve the ribosome. Some of our examples have been previously identified in the literature, while others are novel. One notable example is the relationship between the transcription of the ribosome, RNA polymerase and mannosyltransferase II, which is involved in N-linked glycan processing in the Golgi.ConclusionsThe analysis proposed here focuses on relationships among triplets of genes that are not evident when genes are examined in a pairwise fashion as in typical clustering methods. By grouping gene triplets, we are able to decipher coordinated regulation among sets of three complexes. Moreover, using all triplets that involve coordinated regulation with the ribosome, we derive a large network involving this essential cellular complex. In this network we find that all multi-protein complexes that belong to the same functional class are regulated in the same direction as a group (either induced or repressed).

Dataset Information

Mutadelic: mutation analysis using description logic inferencing capabilities.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Mutadelic: mutation analysis using description logic inferencing capabilities.

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