iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach

A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover because they are scattered among the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here we present the results of chromatin immunoprecipitation with the enhancer-associated protein p300 followed by massively parallel sequencing, and map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases demonstrated reproducible enhancer activity in the tissues that were predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities, and suggest that such data sets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.

Antimicrobial peptides (AMPs), also called host defense peptides, are an evolutionarily conserved component of the innate immune response and are found among all classes of life. According to their special functions, AMPs are generally classified into ten categories: Antibacterial Peptides, Anticancer/tumor Peptides, Antifungal Peptides, Anti-HIV Peptides, Antiviral Peptides, Antiparasital Peptides, Anti-protist Peptides, AMPs with Chemotactic Activity, Insecticidal Peptides, and Spermicidal Peptides. Given a query peptide, how can we identify whether it is an AMP or non-AMP? If it is, can we identify which functional type or types it belong to? Particularly, how can we deal with the multi-type problem since an AMP may belong to two or more functional types? To address these problems, which are obviously very important to both basic research and drug development, a multi-label classifier was developed based on the pseudo amino acid composition (PseAAC) and fuzzy K-nearest neighbor (FKNN) algorithm, where the components of PseAAC were featured by incorporating five physicochemical properties. The novel classifier is called iAMP-2L, where "2L" means that it is a 2-level predictor. The 1st-level is to answer the 1st question above, while the 2nd-level is to answer the 2nd and 3rd questions that are beyond the reach of any existing methods in this area. For the conveniences of users, a user-friendly web-server for iAMP-2L was established at http://www.jci-bioinfo.cn/iAMP-2L. Copyright © 2013 Elsevier Inc. All rights reserved.