TU Darmstadt / ULB / TUbiblio

Visual Analysis of Patterns in Multiple Amino Acid Mutation Graphs

Lenz, Olav and Keul, Frank and Bremm, Sebastian and Hamacher, Kay and Landesberger, Tatiana von (2014):
Visual Analysis of Patterns in Multiple Amino Acid Mutation Graphs.
IEEE Computer Society, Los Alamitos, Calif., In: IEEE Conference on Visual Analytics Science and Technology. Proceedings, DOI: 10.1109/VAST.2014.7042485, [Conference or Workshop Item]

Abstract

Proteins are essential parts in all living organisms. They consist of sequences of amino acids. An interaction with reactive agent can stimulate a mutation at a specific position in the sequence. This mutation may set off a chain reaction, which effects other amino acids in the protein. Chain reactions need to be analyzed, as they may invoke unwanted side effects in drug treatment. A mutation chain is represented by a directed acyclic graph, where amino acids are connected by their mutation dependencies. As each amino acid may mutate individually, many mutation graphs exist. To determine important impacts of mutations, experts need to analyze and compare common patterns in these mutations graphs. Experts, however, lack suitable tools for this purpose. We present a new system for the search and the exploration of frequent patterns (i.e., motifs) in mutation graphs. We present a fast pattern search algorithm specifically developed for finding biologically relevant patterns in many mutation graphs (i.e., many labeled acyclic directed graphs). Our visualization system allows an interactive exploration and comparison of the found patterns. It enables locating the found patterns in the mutation graphs and in the 3D protein structures. In this way, potentially interesting patterns can be discovered. These patterns serve as starting point for a further biological analysis. In cooperation with biologists, we use our approach for analyzing a real world data set based on multiple HIV protease sequences.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Lenz, Olav and Keul, Frank and Bremm, Sebastian and Hamacher, Kay and Landesberger, Tatiana von
Title: Visual Analysis of Patterns in Multiple Amino Acid Mutation Graphs
Language: English
Abstract:

Proteins are essential parts in all living organisms. They consist of sequences of amino acids. An interaction with reactive agent can stimulate a mutation at a specific position in the sequence. This mutation may set off a chain reaction, which effects other amino acids in the protein. Chain reactions need to be analyzed, as they may invoke unwanted side effects in drug treatment. A mutation chain is represented by a directed acyclic graph, where amino acids are connected by their mutation dependencies. As each amino acid may mutate individually, many mutation graphs exist. To determine important impacts of mutations, experts need to analyze and compare common patterns in these mutations graphs. Experts, however, lack suitable tools for this purpose. We present a new system for the search and the exploration of frequent patterns (i.e., motifs) in mutation graphs. We present a fast pattern search algorithm specifically developed for finding biologically relevant patterns in many mutation graphs (i.e., many labeled acyclic directed graphs). Our visualization system allows an interactive exploration and comparison of the found patterns. It enables locating the found patterns in the mutation graphs and in the 3D protein structures. In this way, potentially interesting patterns can be discovered. These patterns serve as starting point for a further biological analysis. In cooperation with biologists, we use our approach for analyzing a real world data set based on multiple HIV protease sequences.

Publisher: IEEE Computer Society, Los Alamitos, Calif.
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Graph visualization, Mutations, Exploratory visualization
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE Conference on Visual Analytics Science and Technology. Proceedings
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1109/VAST.2014.7042485
Export:

Optionen (nur für Redakteure)

View Item View Item