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Super enhancers (SEs) are broad enhancer domains usually containing multiple constituent enhancers that hold elevated activities in gene regulation. Disruption in one or more constituent enhancers causes aberrant SE activities that lead to gene dysregulation in diseases. To quantify SE aberrations, differential analysis is performed to compare SE activities between cell conditions. The state-of-art strategy in estimating differential SEs relies on overall activities and neglect the changes in length and structure of SEs. Here, we propose a novel computational method to identify differential SEs by weighting the combinatorial effects of constituent-enhancer activities and locations (i.e. internal dynamics). In addition to overall activity changes, our method identified four novel classes of differential SEs with distinct enhancer structural alterations. We demonstrate that these structure alterations hold distinct regulatory impact, such as regulating different number of genes and modulating gene expression with different strengths, highlighting the differentiated regulatory roles of these unexplored SE features. When compared to the existing method, our method showed improved identification of differential SEs that were linked to better discernment of cell-type-specific SE activity and functional interpretation., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The coalescent methods for species tree reconstruction are increasingly popular because they can accommodate coalescence and multilocus data sets. Herein, we present STRAW, a web server that offers workflows for reconstruction of phylogenies of species using three species tree methods-MP-EST, STAR and NJst. The input data are a collection of rooted gene trees (for STAR and MP-EST methods) or unrooted gene trees (for NJst). The output includes the estimated species tree, modified Robinson-Foulds distances between gene trees and the estimated species tree and visualization of trees to compare gene trees with the estimated species tree. The web sever is available at http://bioinformatics.publichealth.uga.edu/SpeciesTreeAnalysis/.

N-linked glycosylation is a posttranslational modification that has significantly contributed to the rapid evolution of HIV-1. In particular, enrichment of N-linked glycosylation sites can be found within Envelope variable loops, regions that play an essential role in HIV pathogenesis and immunogenicity. The web server described here, the HIV N-linked Glycosylation Site Analyzer, was developed to facilitate study of HIV diversity by tracking gp120 N-linked glycosylation sites. This server provides an automated platform for mapping and comparing variable loop N-linked glycosylation sites across populations of HIV-1 sequences. Furthermore, this server allows for refinement of HIV-1 sequence alignment by using N-linked glycosylation sites in variable loops as alignment anchors. Availability of this web server solves one of the difficult problems in HIV gp120 alignment and analysis imposed by the extraordinary HIV-1 diversity. The HIV N-linked Glycosylation Site Analyzer web server is available at http://hivtools.publichealth.uga.edu/N-Glyco/.