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© 2020 by the Krishnan Lab

WEBSERVERS / CODE / SOFTWARE

We work very hard to make our research fully reproducible and extendable by other researchers by providing well-documented data and code for each project. We also strive to make our computational tools widely usable to biologists and biomedical scientists via reusable software and interactive webservers.

The Expresto repository contains data and code to generate/reproduce the results in our work on imputing the expression of unmeasured genes in gene-expression profiles. This work introduces a new method called SampleLASSO that uses a sparse regression-based approach that is accurately imputes unmeasured genes in samples from any platform in a way that captures context-specific biologically relevant information to guide imputation. The code includes a function that allows users to use SampleLASSO to fill in the unmeasured genes in their dataset of interest and get a report on which samples in the training data were the most helpful for imputation.

Publication

  1. Mancuso CA*, Canfield JL*, Singla D, Krishnan A (2020) A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes. bioRxiv doi.org/10.1101/2020.03.30.016675.

The GenePlexus repository contains data and code to generate/reproduce the results in our work on systematically benchmarking supervised-learning for network-based gene classification across diverse prediction tasks (functions, diseases, and traits) and molecular networks using meaningful validation schemes and evaluation metrics. We have designed the code to enable easy addition of new methods, which can then be benchmarked along with the other methods using the same evaluation environment.

Publication

  1. Liu R*, Mancuso CA*, Yannakopoulos A, Johnson KA, Krishnan A. (2020) Supervised-learning is an accurate method for network-based gene classification. Bioinformatics
    doi.org/10.1093/bioinformatics/btaa150.

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The ASD webserver contains a genome-wide ranking of human candidate genes associated with Autism Spectrum Disorder (ASD), predicted based on known ASD-related genes and their functional relationships in a human brain-specific gene interaction network (from GIANT; below). Using the ASD webserver, researchers can interactively access all autism gene predictions in the context of their relationships in the human brain-specific gene network, along with the results from subsequent analyses, including spatiotemporal brain signatures, functional modules and prioritized copy-number variants (CNVs).

Publication

  1. Krishnan A*, Zhang R*, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG. (2016) Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nature Neuroscience 19:1454-1462.

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The GIANT webserver contains data-driven human genome-scale functional interaction networks between ~26,000 genes in more than 280 tissues and cell-types. Using GIANT, researchers can (i) look-up the tissue-specific interactions of one or more genes, (ii) compare a gene's functional interaction in different tissues by selecting the relevant tissues in the dropdown menu, and (iii) reprioritize functional associations from a genome-wide association study (GWAS) using tissue-specific networks using an approach named NetWAS and identify additional candidate disease-associated genes.

Publications

  1. Wong AK, Krishnan A, Troyanskaya OG. (2018) GIANT 2.0: genome-scale integrated analysis of gene networks in tissues. Nucleic Acids Research 46:W65–W70.

  2. Greene CS*, Krishnan A*, Wong AK*, Ricciotti E, Zelaya R, Himmelstein D, Chasman D, Fitzgerald G, Dolinski K, Grosser T, Troyanskaya OG. (2015) Understanding multi-cellular function and disease with human tissue-specific gene interaction networks. Nature Genetics 47:569-576.