Biology subjects

Mao, W.

Publications and source records attributed to Mao, W..

6 recordsLinked to original sources

bioRxiv2018

RERconverge: an R package for associating evolutionary rates with convergent traits

Motivation: When different lineages of organisms independently adapt to similar environments, selection often acts repeatedly upon the same genes, leading to signatures of convergent evolutionary rate shifts at these genes. With the increasing availability of genome sequences for organisms displaying a variety of convergent traits, the ability to identify genes with such convergent rate signatures would enable new insights into the molecular basis of these traits.\n\nResults: Here we present the R package RERconverge, which tests for association between relative evolutionary rates of genes and the evolution of traits across a phylogeny. RERconverge can perform associations with binary and continuous traits, and it contains tools for visualization and enrichment analyses of association results.\n\nAvailability: RERconverge source code, documentation, and a detailed usage walk-through are freely available at https://github.com/nclark-lab/RERconverge. Datasets for mammals, Drosophila, and yeast are available at https://bit.ly/2J2QBnj.\n\nContact: mchikina@pitt.edu\n\nSupplementary information: Supplementary information, containing detailed vignettes for usage of RERconverge, are available at Bioinformatics online.

evolutionary biology↗

bioRxiv2018

The perils of interaction prediction

The availability of genome-wide maps of enhancer-promoter interactions (EPIs) has made it possible to use machine learning approaches to extract and interpret features that determine these interactions in different biological contexts. Multiple methods have claimed to accomplish the task of predicting enhancer-promoter interactions based on corresponding genomic features, but this problem is actually still far from being solved. In our analysis, we show that individual enhancer and promoter regions have widely different marginal interaction probabilities, e.g. propensities, which can lead to overfitting and memorization when random cross-validation is employed. Further even when a proper cross-validation scheme is adopted, a simple propensity-based model can still achieve a competitive performance without capturing any information about the EPI mechanism.

genomics↗

bioRxiv2018

DataRemix: a universal data transformation for optimal inference from gene expression datasets

MotivationRNAseq technology provides unprecedented power in the assesment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study. ResultsWe describe a generalization of SVD-based reconstruction for which the common techniques of whitening, rank-k approximation, and removing the top k principle components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweight the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson Sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally we reanalyze the Depression Gene Networks (DGN) dataset, and we highlight new trans-eQTL networks which were not reported in the initial study. AvailabilityDataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix). Contactmchikina@pitt.edu

genomics↗

bioRxiv2017

Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks

BackgroundGene regulatory sequences play critical roles in ensuring tightly controlled RNA expression patterns that are essential in a large variety of biological processes. Specifically, enhancer sequences drive expression of their target genes, and the availability of genome-wide maps of enhancer-promoter interactions has opened up the possibility to use machine learning approaches to extract and interpret features that define these interactions in different biological contexts.\n\nMethodsInspired by machine translation models we develop an attention-based neural network model, EPIANN, to predict enhancer-promoter interactions based on DNA sequences. Codes and data are available at https://github.com/wgmao/EPIANN.\n\nResultsOur approach accurately predicts enhancer-promoter interactions across six cell lines. In addition, our method generates pairwise attention scores at the sequence level, which specify how short regions in the enhancer and promoter pair-up to drive the interaction prediction. This allows us to identify over-represented transcription factors (TF) binding sites and TF-pair interactions in the context of enhancer function.

bioinformatics↗

bioRxiv2017

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in {beta} strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in {beta}-{beta} interactions. This information may benefit the tertiary structure prediction of mainly {beta} proteins. In this work, we introduce a novel ridge-detection-based {beta}-{beta} contact predictor, RDb2C, to identify residue pairing in {beta} strands from any predicted residue contact map. The algorithm adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb2C remarkably outperforms all state-of-the-art methods on two conventional test sets of {beta} proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~62% and ~76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb2C achieves impressively higher performance, with F1-scores reaching ~76% and ~86% at the residue level and strand level, respectively. According to our tests on 61 mainly {beta} proteins, improvement in the {beta}-{beta} contact prediction can further ameliorate the structural prediction.\n\nAvailability: All source data and codes are available at http://166.111.152.91/Downloads.html or at the GitHub address of https://github.com/wzmao/RDb2C.\n\nAuthor summaryDue to the topological complexity, mainly {beta} proteins are challenging targets in protein structure prediction. Knowledge of the pairing between {beta} strands, especially the residue pairing pattern, can greatly facilitate the tertiary structure prediction of mainly {beta} proteins. In this work, we developed a novel algorithm to identify the residue pairing in {beta} strands from a predicted residue contact map. This method adopts the ridge detection technique to capture the characteristic pattern of {beta}-{beta} interactions from the map and then utilizes a multi-stage random forest framework to predict {beta}-{beta} contacts at the residue level. According to our tests, our method could effectively improve the prediction of {beta}-{beta} contacts even from a highly noisy contact map. Moreover, the refined {beta}-{beta} contact information could effectively improve the structural modeling of mainly {beta} proteins.

bioinformatics↗

bioRxiv2017

Pathway-Level Information ExtractoR (PLIER): a generative model for gene expression data

A major challenge in gene expression analysis is to accurately infer relevant biological insight, such as regulation of cell type proportion or pathways, from global gene expression studies. We present a general solution for this problem that outperforms available cell proportion inference algorithms, and is more widely useful to automatically identify specific pathways that regulate gene expression. Our method improves replicability and biological insight when applied to trans-eQTL identification.

genomics↗