SEEK is a computational gene co-expression search engine. SEEK provides biologists with a way to navigate the massive human expression compendium that now contains thousands of expression datasets. SEEK returns a robust ranking of co-expressed genes in the biological area of interest defined by the user’s query genes. In the meantime, it also prioritizes thousands of expression datasets according to the user’s query of interest. The unique strengths of SEEK include its support for multi-gene query and cross-platform analysis, as well as its rich visualization features.
YETI2 is a computational framework which creates specialized functional interaction maps from large public datasets relevant to an individual omics experiment. Using this tailored integration, we predicted and experimentally confirmed an unexpected divergence in viral replication after seasonal or pandemic human influenza virus infection.
URSA (Unveiling RNA Sample Annotation), originally released in 2013, simultaneously estimated the probabilities that a given sample is associated with a particular tissue or cell-type. Individual cell-type models were constructed from more than ten thousand manually curated samples from GEO and then aggregated using Bayesian Correction. This method has been shown effective for both array-based and sequence-based genome-scale experiments.
ASD is a web-interface for exploring autism-associated genes.ASD (Autism spectrum disorder) is a neurodevelopmental disorder characterized by deficits in social communication and restricted, repetitive patterns of behavior. ASD has a strong genetic basis but we still lack the full complement of autism-associated genes.
Antigen Explorer is an interactive resource for browsing antigen combinations for more precise tumor recognition. Leveraging expression data from TCGA and GTEx, the discrimination potential of all possible combinations of surface antigens were scored for 33 tumor types. Users can explore the top predictions and make interactive plots to evaluate an antigen pair against normal tissue cross-reactivity.
DeepArk is a set of deep learning algorithms capable of predicting regulatory activity (e.g. transcription factor binding) from genomic sequences. DeepArk consists of four distinct neural networks for mouse (Mus musculus), fly (Drosophila melanogaster), worm (Caenorhabditis elegans), and zebrafish (Danio rerio)
Sleipnir is a C++ library enabling efficient analysis, integration, mining, and machine learning over genomic data. This includes a particular focus on microarrays, since they make up the bulk of available data for many organisms, but Sleipnir can also integrate a wide variety of other data types, from pairwise physical interactions to sequence similarity or shared transcription factor binding sites.
COALESCE (Combinatorial Algorithm for Expression and Sequence-based Cluster Extraction) can use large collections of genomic data and Bayesian integration to predict coregulated gene modules, the conditions of regulation, and the consensus binding motifs for regulation.