LinkedOmics – Analyzing Multi-omics data within and across 32 Cancer Types

LinkedOmics

:: DESCRIPTION

LinkedOmics is publicly available portal that includes multi-omics data from all 32 TCGA Cancer types and 10 Clinical Proteomics Tumor Analysis Consortium (CPTAC) cancer cohorts.

::DEVELOPER

the Zhang Lab

:: SCREENSHOTS

n/a

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Vasaikar SV, Straub P, Wang J, Zhang B.
LinkedOmics: analyzing multi-omics data within and across 32 cancer types.
Nucleic Acids Res. 2018 Jan 4;46(D1):D956-D963. doi: 10.1093/nar/gkx1090. PMID: 29136207; PMCID: PMC5753188.

Taiji 1.3.0 – Multi-omics Bioinformatics Pipeline

Taiji 1.3.0

:: DESCRIPTION

The Taiji software is an integrative multi-omics data analysis framework. It can be used as a standalone pipeline to analyze ATAC-seq, RNA-seq, single cell ATAC-seq or Drop-seq data. However, the uniqueness and the power of Taiji really lie in its ability to integrate diverse datasets and use these information in a clever way to construct regulatory network and identify candidate driver genes.

::DEVELOPER

Wei Wang’s group

:: SCREENSHOTS

N/A

::REQUIREMENTS

Linux / MacOsX

:: DOWNLOAD

Taiji

:: MORE INFORMATION

Citation

Zhang K, Wang M, Zhao Y, Wang W.
Taiji: System-level identification of key transcription factors reveals transcriptional waves in mouse embryonic development.
Sci Adv. 2019 Mar 27;5(3):eaav3262. doi: 10.1126/sciadv.aav3262. PMID: 30944857; PMCID: PMC6436936.

LRAcluster 1.0 – Low Rank Approximation based Multi-omics Data Clustering

LRAcluster 1.0

:: DESCRIPTION

LRAcluster is a new method to discover molecular subtypes by detecting the low-dimensional intrinsic space of high-dimensional cancer multi-omics data.

::DEVELOPER

Bioinformatics & Intelligent Information Processing Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
R

:: DOWNLOAD

LRAcluster

:: MORE INFORMATION

Citation

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.
Wu D, Wang D, Zhang MQ, Gu J.
BMC Genomics. 2015 Dec 1;16(1):1022. doi: 10.1186/s12864-015-2223-8.