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17 Publications visible to you, out of a total of 17
CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows.

Abstract (Expand)

The KEGG pathway database is a widely accepted source for biomolecular pathway maps. In this paper we present the CyKEGGParser app ( http://apps.cytoscape.org/apps/cykeggparser) for Cytoscape 3 that … allows manipulation with KEGG pathway maps. Along with basic functionalities for pathway retrieval, visualization and export in KGML and BioPAX formats, the app provides unique features for computer-assisted adjustment of inconsistencies in KEGG pathway KGML files and generation of tissue- and protein-protein interaction specific pathways. We demonstrate that using biological context-specific KEGG pathways created with CyKEGGParser makes systems biology analysis more sensitive and appropriate compared to original pathways.

Authors: L. Nersisyan, R. Samsonyan, A. Arakelyan

Date Published: 13th Nov 2014

Publication Type: Journal

KEGGParser: parsing and editing KEGG pathway maps in Matlab.

Abstract (Expand)

SUMMARY: KEGG pathway database is a collection of manually drawn pathway maps accompanied with KGML format files intended for use in automatic analysis. KGML files, however, do not contain the required … information for complete reproduction of all the events indicated in the static image of a pathway map. Several parsers and editors of KEGG pathways exist for processing KGML files. We introduce KEGGParser-a MATLAB based tool for KEGG pathway parsing, semiautomatic fixing, editing, visualization and analysis in MATLAB environment. It also works with Scilab. AVAILABILITY AND IMPLEMENTATION: The source code is available at http://www.mathworks.com/matlabcentral/fileexchange/37561.

Authors: A. Arakelyan, L. Nersisyan

Date Published: 15th Feb 2013

Publication Type: Journal

A topological map of the genetic components of grapevine-Admixture meets SOMmelier machine learning

Abstract (Expand)

Inferring the genetic structure of populations at the subpopulation level is crucial for understanding the evolutionary forces and demographic histories that shape genetic diversity. Among the most … widely used approaches in population genetics are methods based on admixture and structure modeling—named after the respective software tools—which have become standard due to their intuitive, interpretable outputs. In this study, we address a key methodological question: how does traditional admixture-based decomposition of genetic components in multilocus population data relate to clustering approaches that leverage machine learning, specifically Self-Organizing Maps (SOMs)? We implemented this approach through our custom SOM-based tool, SOMmelier, which enables the portrayal of genetic structure by identifying modules of co-mutated SNPs and arranging them in a topology-aware genetic landscape. In this context, topology-awareness refers to the organization of genetic modules in a two-dimensional map, where their spatial proximity reflects mutual similarity. As a case study, we applied SOMmelier to investigate the population genetics of European grapevine. Based on prior literature, we considered up to six genetic components, which formed a genetic landscape that closely mirrors the geographic expanse of the classical Mediterranean world—from Western Asia through the Caucasus to Western Europe. The resulting topology reflects the dynamic spatial and temporal nature of grapevine domestication and diffusion. SOMmelier thus represents a complementary and powerful framework for genetic data analysis. By integrating the topological structure of SNP co-variation, it offers new perspectives on population structure, evolutionary history, and trait associations in grapevine—and has broader applicability to other species and systems in population genetics.

Author: Anush Baloyan, Tomas Konecny, Emma Hovhannisyan, Nate Zadirako, Maria Nikoghosyan, Hans Binder

Date Published: No date defined

Publication Type: Journal

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