Dataset Information

PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways.

ABSTRACT: PANTHER is a freely available, comprehensive software system for relating protein sequence evolution to the evolution of specific protein functions and biological roles. Since 2005, there have been three main improvements to PANTHER. First, the sequences used to create evolutionary trees are carefully selected to provide coverage of phylogenetic as well as functional information. Second, PANTHER is now a member of the InterPro Consortium, and the PANTHER hidden markov Models (HMMs) are distributed as part of InterProScan. Third, we have dramatically expanded the number of pathways associated with subfamilies in PANTHER. Pathways provide a detailed, structured representation of protein function in the context of biological reaction networks. PANTHER pathways were generated using the emerging Systems Biology Markup Language (SBML) standard using pathway network editing software called CellDesigner. The pathway collection currently contains approximately 1500 reactions in 130 pathways, curated by expert biologists with authorship attribution. The curation environment is designed to be easy to use, and the number of pathways is growing steadily. Because the reaction participants are linked to subfamilies and corresponding HMMs, reactions can be inferred across numerous different organisms. The HMMs can be downloaded by FTP, and tools for analyzing data in the context of pathways and function ontologies are available at http://www.pantherdb.org.

SUBMITTER: Mi H

PROVIDER: S-EPMC1716723 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Proper expression of the genes plays a vital role in the function of an organism. Recent advancements in DNA microarray technology allow for monitoring the expression level of thousands of genes. One of the important tasks in this context is to understand the underlying mechanisms of gene regulation. Recently, researchers have focused on identifying local DNA elements, or motifs to infer the relation between the expression and the nucleotide sequence of the gene. This study proposes a novel data adaptive representation approach for supervised learning to predict the response associated with the biological sequences. Biological sequences such as DNA and protein are a class of categorical sequences. In machine learning, categorical sequences are generally mapped to a lower dimensional representation for learning tasks to avoid problems with high dimensionality. The proposed method, namely SW-RF (sliding window-random forest), is a feature-based approach requiring two main steps to learn a representation for categorical sequences. In the first step, each sequence is represented by overlapping subsequences of constant length. Then a tree-based learner on this representation is trained to obtain a bag-of-words like representation which is the frequency of subsequences on the terminal nodes of the tree for each sequence. After representation learning, any classifier can be trained on the learned representation. A lasso logistic regression is trained on the learned representation to facilitate the identification of important patterns for the classification task. Our experiments show that proposed approach provides significantly better results in terms of accuracy on both synthetic data and DNA promoter sequence data. Moreover, a common problem for microarray datasets, namely missing values, is handled efficiently by the tree learners in SW-RF. Although the focus of this paper is on biological sequences, SW-RF is flexible in handling any categorical sequence data from different applications.

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PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways.

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