Computational localization of transcription factor binding sites using extreme learning machines

Trends in extreme learning machines: a review

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

Integrating diverse datasets improves developmental enhancer prediction

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.

The human genome contains an immense amount of non-protein-coding DNA with unknown function. Some of this DNA regulates when, where, and at what levels genes are active during development. Enhancers, one type of regulatory element, are short stretches of DNA that can act as “switches” to turn a gene on or off at specific times in specific cells or tissues. Understanding where in the genome enhancers are located can provide insight into the genetic basis of development and disease. Enhancers are hard to identify, but clues about their locations are found in different types of data including DNA sequence, evolutionary history, and where proteins bind to DNA. Here, we introduce a new tool, called EnhancerFinder, which combines these data to predict the location and activity of enhancers during embryonic development. We trained EnhancerFinder on a large set of functionally validated human enhancers, and it proved to be very accurate. We used EnhancerFinder to predict tens of thousands of enhancers in the human genome and validated several of the predictions near three important developmental genes in mouse or zebrafish. EnhancerFinder's predictions will be useful in understanding functional regions hidden in the vast amounts of human non-coding DNA.