Biology subjects

Duong, D.

Publications and source records attributed to Duong, D..

3 recordsLinked to original sources

bioRxiv2018

A comprehensive analysis of the usability and archival stability of omics computational tools and resources

Developing new software tools for analysis of large-scale biological data is a key component of advancing modern biomedical research. Scientific reproduction of published findings requires running computational tools on data generated by such studies, yet little attention is presently allocated to the installability and archival stability of computational software tools. Scientific journals require data and code sharing, but none currently require authors to guarantee the continuing functionality of newly published tools. We have estimated the archival stability of computational biology software tools by performing an empirical analysis of the internet presence for 36,702 omics software resources published from 2005 to 2017. We found that almost 28% of all resources are currently not accessible through URLs published in the paper they first appeared in. Among the 98 software tools selected for our installability test, 51% were deemed \"easy to install,\" and 28% of the tools failed to be installed at all due to problems in the implementation. Moreover, for papers introducing new software, we found that the number of citations significantly increased when authors provided an easy installation process. We propose for incorporation into journal policy several practical solutions for increasing the widespread installability and archival stability of published bioinformatics software.

bioinformatics↗

bioRxiv2017

A novel Word2vec based tool to estimate semantic similarity of genes by using Gene Ontology terms

The Gene Ontology (GO) database contains GO terms that describe biological functions of genes. Previous methods for comparing GO terms have relied on the fact that GO terms are organized into a tree structure. In this paradigm, the locations of two GO terms in the tree dictate their similarity score. In this paper, we introduce two new solutions for this problem, by focusing instead on the definitions of the GO terms. We apply neural network based techniques from the natural language processing (NLP) domain. The first method does not rely on the GO tree, whereas the second indirectly depends on the GO tree. In our first approach, we compare two GO definitions by treating them as two unordered sets of words. The word similarity is estimated by a word embedding model that maps words into an N-dimensional space. In our second approach, we account for the word-ordering within a sentence. We use a sentence encoder to embed GO definitions into vectors and estimate how likely one definition entails another. We validate our methods in two ways. In the first experiment, we test the models ability to differentiate a true protein-protein network from a randomly generated network. In the second experiment, we test the model in identifying orthologs from randomly-matched genes in human, mouse, and fly. In both experiments, a hybrid of NLP and GO-tree based method achieves the best classification accuracy.\n\nAvailabilitygithub.com/datduong/NLPMethods2CompareGOterms

bioinformatics↗

bioRxiv2017

Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLsand increase the number of eGenes

During the last decade, with the advent of inexpensive microarray and RNA-seq technologies, there have been many expression quantitative trait loci (eQTL) studies for identifying genetic variants called eQTLs that regulate gene expression. Discovering eQTLs has been increasingly important as they may elucidate the functional consequence of non-coding variants identified from genome-wide association studies. Recently, several eQTL studies such as the Genotype-Tissue Expression (GTEx) consortium have made a great effort to obtain gene expression from multiple tissues. One advantage of these multi-tissue eQTL datasets is that they may allow one to identify more eQTLs by combining information across multiple tissues. Although a few methods have been proposed for multi-tissue eQTL studies, they are often computationally intensive and may not achieve optimal power because they do not consider a biological insight that a genetic variant regulates gene expression similarly in related tissues. In this paper, we propose an efficient meta-analysis approach for identifying eQTLs from large multi-tissue eQTL datasets. We name our method RECOV because it uses a random effects (RE) meta-analysis with an explicit covariance (COV) term to model the correlation of effect that eQTLs have across tissues. Our approach is faster than the previous approaches and properly controls the false-positive rate. We apply our approach to the real multi-tissue eQTL dataset from GTEx that contains 44 tissues, and show that our approach detects more eQTLs and eGenes than previous approaches.

bioinformatics↗