OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs
By Jordan Anaya. PeerJ Computer Science, 2016
638 citations recorded by the source corpus.
OncoLnc is a tool for interactively exploring survival correlations, and for downloading clinical data coupled to expression data for mRNAs, miRNAs, or long noncoding RNAs (lncRNAs). OncoLnc contains survival data for 8,647 patients from 21 cancer studies performed by The Cancer Genome Atlas (TCGA), along with RNA-SEQ expression for mRNAs and miRNAs from TCGA, and lncRNA expression from MiTranscriptome beta. Storing this data gives users the ability to separate patients by gene expression, and then create publication-quality Kaplan-Meier plots or download the data for further analyses. OncoLnc also stores precomputed survival analyses, allowing users to quickly explore survival correlations for up to 21 cancers in a single click. This resource allows researchers studying a specific gene to quickly investigate if it may have a role in cancer, and the supporting data allows researchers studying a specific cancer to identify the mRNAs, miRNAs, and lncRNAs most correlated with survival, and researchers looking for a novel lncRNA involved with cancer lists of potential candidates. OncoLnc is available at http://www.oncolnc.org. Subjects Bioinformatics, Computational Biology, Databases
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ff46430924f3c77d3c51033cd4acf71cd0ddf839
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In Cancer Research
- Tiago C. Silva
- Gianluca Bontempi
- M. Lucchetta
- Elena Papaleo
- Antonio Colaprico
- Catharina Olsen
- Mohamed Mounir
- Xi Chen
In Molecular Biology
- Tiago C. Silva
- Gianluca Bontempi
- M. Lucchetta
- Elena Papaleo
- Antonio Colaprico
- Catharina Olsen
- Mohamed Mounir
- Xi Chen
Citations in the corpus
- New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx (2019, methodology): “Results Overview of TCGAbiolinks For the sake of clarity, we will briefly introduce the main functions of TCGAbiolinks that are extensively discussed in the original publication and a recently published workflow [19,20].”