Integrating Subcellular Location for Improving Machine Learning Models of Remote Homology Detection in Eukaryotic Organisms

Journal Article · Fri Feb 23 04:00:00 EST 2007 · Computational Biology and Chemistry, 31(2):138-142

DOI:https://doi.org/10.1016/j.compbiolchem.2007.02.012· OSTI ID:903458

Shah, Anuj R; Oehmen, Chris S; Harper, Jill K; Webb-Robertson, Bobbie-Jo M

Motivation: At the center of bioinformatics, genomics, and pro-teomics is the need for highly accurate genome annotations. Producing high-quality reliable annotations depends on identifying sequences which are related evolutionarily (homologs) on which to infer function. Homology detection is one of the oldest tasks in bioinformatics, however most approaches still fail when presented with sequences that have low residue similarity despite a distant evolutionary relationship (remote homology). Recently, discriminative approaches, such as support vector machines (SVMs) have demonstrated a vast improvement in sensitivity for remote homology detection. These methods however have only focused on one aspect of the sequence at a time, e.g., sequence similarity or motif based scores. However, supplementary information, such as the sub-cellular location of a protein within the cell would give further clues as to possible homologous pairs, additionally eliminating false relationships due to simple functional roles that cannot exist due to location. We have developed a method, SVM-SimLoc that integrates sub-cellular location with sequence similarity information into a pro-tein family classifier and compared it to one of the most accurate sequence based SVM approaches, SVM-Pairwise. Results: The SCOP 1.53 benchmark data set was utilized to assess the performance of SVM-SimLoc. As cellular location prediction is dependent upon the type of sequence, eukaryotic or prokaryotic, the analysis is restricted to the 2630 eukaryotic sequences in the benchmark dataset, evaluating a total of 27 protein families. We demonstrate that the integration of sequence similarity and sub-cellular location yields notably more accurate results than using sequence similarity independently at a significance level of 0.006.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 903458
• Report Number(s): PNNL-SA-48402
• Journal Information: Computational Biology and Chemistry, 31(2):138-142, Journal Name: Computational Biology and Chemistry, 31(2):138-142 Journal Issue: 2 Vol. 31
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
BENCHMARKS
DETECTION
FORECASTING
FUNCTIONALS
LEARNING
PERFORMANCE
PROTEINS
RESIDUES
SENSITIVITY
VECTORS