BACKGROUND: Super-enhancers or stretch enhancers are clusters of active enhancers that often coordinate cell-type specific gene regulation during development and differentiation. In addition, the enrichment of disease-associated single nucleotide polymorphism in super-enhancers indicates their critical function in disease-specific gene regulation. However, little is known about the function of super-enhancers beyond gene regulation. RESULTS: In this study, through a comprehensive analysis of super-enhancers in 30 human cell/tissue types, we identified a new class of super-enhancers which are constitutively active across most cell/tissue types. These 'common' super-enhancers are associated with universally highly expressed genes in contrast to the canonical definition of super-enhancers that assert cell-type specific gene regulation. In addition, the genome sequence of these super-enhancers is highly conserved by evolution and among humans, advocating their universal function in genome regulation. Integrative analysis of 3D chromatin loops demonstrates that, in comparison to the cell-type specific super-enhancers, the cell-type common super-enhancers present a striking association with rapidly recovering loops. CONCLUSIONS: In this study, we propose that a new class of super-enhancers may play an important role in the early establishment of 3D chromatin structure.

BACKGROUND: Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable. RESULTS: Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors. CONCLUSION: Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.

MOTIVATION: Detecting homologous proteins is one of the fundamental problems in computational biology. Many tools to solve this problem have been developed, but development of a simple, effective and generally applicable method is still desirable. RESULTS: We propose a simple but effective information retrieval approach, named SIMPRO, to identify homology relationship between proteins. The key idea of our approach is that by accumulating and comparing indirect signals from conventional homology search methods, the search sensitivity can be increased. We tested the idea on the problem of detecting homology relationship between Pfam families, as well as detecting structural homologs based on SCOP, and found that our method achieved significant improvement. Our results indicate that simple manipulation of conventional homology search outputs by SIMPRO algorithm can remarkably improve homology search accuracy.

BACKGROUND: Nonnegative matrix factorization (NMF) is a feature extraction method that has the property of intuitive part-based representation of the original features. This unique ability makes NMF a potentially promising method for biological sequence analysis. Here, we apply NMF to fold recognition and remote homolog detection problems. Recent studies have shown that combining support vector machines (SVM) with profile-profile alignments improves performance of fold recognition and remote homolog detection remarkably. However, it is not clear which parts of sequences are essential for the performance improvement. RESULTS: The performance of fold recognition and remote homolog detection using NMF features is compared to that of the unmodified profile-profile alignment (PPA) features by estimating Receiver Operating Characteristic (ROC) scores. The overall performance is noticeably improved. For fold recognition at the fold level, SVM with NMF features recognize 30% of homolog proteins at > 0.99 ROC scores, while original PPA feature, HHsearch, and PSI-BLAST recognize almost none. For detecting remote homologs that are related at the superfamily level, NMF features also achieve higher performance than the original PPA features. At > 0.90 ROC50 scores, 25% of proteins with NMF features correctly detects remotely related proteins, whereas using original PPA features only 1% of proteins detect remote homologs. In addition, we investigate the effect of number of positive training examples and the number of basis vectors on performance improvement. We also analyze the ability of NMF to extract essential features by comparing NMF basis vectors with functionally important sites and structurally conserved regions of proteins. The results show that NMF basis vectors have significant overlap with functional sites from PROSITE and with structurally conserved regions from the multiple structural alignments generated by MUSTANG. The correlation between NMF basis vectors and biologically essential parts of proteins supports our conjecture that NMF basis vectors can explicitly represent important sites of proteins. CONCLUSION: The present work demonstrates that applying NMF to profile-profile alignments can reveal essential features of proteins and that these features significantly improve the performance of fold recognition and remote homolog detection.