SEEK – Search-Based Exploration of Expression Compendium

SEEK

:: DESCRIPTION

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.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

SEEK

:: MORE INFORMATION

Citation

Zhu Q, Wong AK, Krishnan A, Aure MR, Tadych A, Zhang R, Corney DC, Greene CS, Bongo LA, Kristensen VN, Charikar M, Li K, Troyanskaya OG.
Targeted exploration and analysis of large cross-platform human transcriptomic compendia.
Nat Methods. 2015 Mar;12(3):211-4, 3 p following 214. doi: 10.1038/nmeth.3249. Epub 2015 Jan 12. PMID: 25581801; PMCID: PMC4768301.

YETI2 – Your Evidence Tailored Integration

YETI2

:: DESCRIPTION

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.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Lee YS, Wong AK, Tadych A, Hartmann BM, Park CY, DeJesus VA, Ramos I, Zaslavsky E, Sealfon SC, Troyanskaya OG.
Interpretation of an individual functional genomics experiment guided by massive public data.
Nat Methods. 2018 Dec;15(12):1049-1052. doi: 10.1038/s41592-018-0218-5. Epub 2018 Nov 26. PMID: 30478325; PMCID: PMC6941785.

URSAHD – Unveiling RNA Sample Annotation for Human Diseases

URSAHD

:: DESCRIPTION

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.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Lee YS, Krishnan A, Oughtred R, Rust J, Chang CS, Ryu J, Kristensen VN, Dolinski K, Theesfeld CL, Troyanskaya OG.
A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.
Cell Syst. 2019 Feb 27;8(2):152-162.e6. doi: 10.1016/j.cels.2018.12.010. Epub 2019 Jan 23. PMID: 30685436; PMCID: PMC7374759.

ASD – Genome-wide predictions of Autism-associated genes

ASD

:: DESCRIPTION

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.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Krishnan A, Zhang R, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG.
Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder.
Nat Neurosci. 2016 Nov;19(11):1454-1462. doi: 10.1038/nn.4353. Epub 2016 Aug 1. PMID: 27479844; PMCID: PMC5803797.

ExPecto – Tissue-specific Gene Expression Effect Prediction for human Mutations

ExPecto

:: DESCRIPTION

ExPecto is a framework for ab initio sequence-based prediction of mutation gene expression effects and disease risks.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG.
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.
Nat Genet. 2018 Aug;50(8):1171-1179. doi: 10.1038/s41588-018-0160-6. Epub 2018 Jul 16. PMID: 30013180; PMCID: PMC6094955.

Antigen Explorer – Antigen Combinations for Precision Cancer Recognition

Antigen Explorer

:: DESCRIPTION

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.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Dannenfelser R, Allen GM, VanderSluis B, Koegel AK, Levinson S, Stark SR, Yao V, Tadych A, Troyanskaya OG, Lim WA.
Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies.
Cell Syst. 2020 Sep 23;11(3):215-228.e5. doi: 10.1016/j.cels.2020.08.002. Epub 2020 Sep 10. PMID: 32916097; PMCID: PMC7814417.

DeepArk – Deep Learning models of Regulatory Activity for Model Species

DeepArk

:: DESCRIPTION

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)

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

DeepArk: modeling cis-regulatory codes of model species with deep learning
Evan M. CoferJoão RaimundoAlicja TadychYuji YamazakiAaron K. WongChandra L. TheesfeldMichael S. LevineOlga G. Troyanskaya

Fenrir – Tissue-specific Enhancer Functional Networks for Associating Distal Regulatory Regions to disease

Fenrir

:: DESCRIPTION

FENRIR integrates tissue-specific enhancer networks with disease GWAS or genes and reprioritizes ~48,000 enhancers.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Chen X, Zhou J, Zhang R, Wong AK, Park CY, Theesfeld CL, Troyanskaya OG.
Tissue-specific enhancer functional networks for associating distal regulatory regions to disease.
Cell Syst. 2021 Mar 3:S2405-4712(21)00041-7. doi: 10.1016/j.cels.2021.02.002. Epub ahead of print. PMID: 33689683.

IntervalStats – Statistical Evaluation of ChIPseq Dataset Similarity

IntervalStats

:: DESCRIPTION

IntervalStats is an effective statistical evaluation of ChIPseq dataset similarity.

::DEVELOPER

Chikina Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler

:: DOWNLOAD

 IntervalStats

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Mar 1;28(5):607-13. doi: 10.1093/bioinformatics/bts009.
An effective statistical evaluation of ChIPseq dataset similarity.
Chikina MD, Troyanskaya OG.

Sleipnir 3.0 / COALESCE – Library for Computational Functional Genomics

Sleipnir 3.0 / COALESCE

:: DESCRIPTION

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.

::DEVELOPER

Troyanskaya Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • C Compiler

:: DOWNLOAD

 Sleipnir

:: MORE INFORMATION

Citation

Curtis Huttenhower, Mark Schroeder, Maria D. Chikina, and Olga G. Troyanskaya
The Sleipnir library for computational functional genomics”,
Bioinformatics. 2008 Jul 1;24(13):1559-61. Epub 2008 May 21.

Bioinformatics. 2009 Dec 15;25(24):3267-74. doi: 10.1093/bioinformatics/btp588.
Detailing regulatory networks through large scale data integration.
Huttenhower C, Mutungu KT, Indik N, Yang W, Schroeder M, Forman JJ, Troyanskaya OG, Coller HA.