Interrogating conserved elements of diseases using Boolean combinations of orthologous phenotypes

John O Woods , Matthew Z Tien , Edward M Marcotte
doi: http://dx.doi.org/10.1101/017947

Conserved genetic programs often predate the homologous structures and phenotypes to which they give rise; eyes, for example, have evolved several dozen times, but their development seems to involve a common set of conserved genes. Recently, the concept of orthologous phenotypes (or phenologs) offered a quantitative way to describe this property. Phenologs are phenotypes or diseases from separate species who share an unexpectedly large set of their associated gene orthologs. It has been shown that the phenotype pairs which make up a phenolog are mutually predictive in terms of the genes involved. Recently, we demonstrated the ranking of gene–phenotype association predictions using multiple phenologs from an array of species. In this work, we demonstrate a computational method which provides a more targeted view of the conserved pathways which give rise to diseases. Our approach involves the generation of synthetic pseudo-phenotypes made up of Boolean combinations (union, intersection, and difference) of the gene sets for phenotypes from our database. We search for diseases that overlap significantly with these Boolean phenotypes, and find a number of highly predictive combinations. While set unions produce less specific predictions (as expected), intersection and difference-based combinations appear to offer insights into extremely specific aspects of target diseases. For example, breast cancer is predicted by zebrafish methylmercury response minus metal ion response, with predictions MT-COI, JUN, SOD2, GADD45B, and BAX all involved in the pro-apoptotic response to reactive oxygen species, thought to be a key player in cancer. We also demonstrate predictions from Arabidopsis Boolean phenotypes for increased brown adipose tissue in mouse (salt stress response’s intersection with sucrose stimulus response); and for human myopathy (red light response minus water deprivation response). We demonstrate the ranking of predictions for human holoprosencephaly from the set intersections between each pair of a variety of closely-related zebrafish phenotypes. Our results suggest that Boolean phenolog combinations may provide a more informed insight into the conserved pathways underlying diseases than either regular phenologs or the naïve Bayes approach.

Bayesian Modeling of Epigenetic Variation in Multiple Human Cell Types

Yu Zhang , Feng Yue , Ross C. Hardison
doi: http://dx.doi.org/10.1101/018028

With high-throughput sequencing data generated for multiple epigenetic features in many cell types, a chief challenge is to explain the dynamics in multiple epigenomes that lead to differential regulation and phenotypes. We introduce a Bayesian framework for jointly annotating multiple epigenomes and detecting differential regulation among multiple cell types. Our method, IDEAS (integrative and discriminative epigenome annotation system), achieves superior power by modeling both position and cell type specific epigenetic activities. Using ENCODE data sets in 6 cell types, we identified epigenetic variation strongly associated with differential gene expression. The detected regions are significantly enriched in disease genetic variants with much stronger enrichment scores than achievable by existing methods, and the enriched phenotypes are highly relevant to the corresponding cell types. IDEAS is a powerful tool for integrative epigenome annotation and detection of variation, which could be of important utility in elucidating the interplay between genetics, gene regulation and diseases.

Ultra-large alignments using Phylogeny-aware Profiles

Nam-phuong Nguyen, Siavash Mirarab, Keerthana Kumar, Tandy Warnow
(Submitted on 5 Apr 2015)

Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments (MSAs) and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, an MSA method that uses a new machine learning technique – the Ensemble of Hidden Markov Models – that we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at this https URL