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Treehouse compendium of polyA selected RNA-Seq gene expression data from 12,747 tumors

ABSTRACT: We uniformly analyze sequence data to generate a resource for comparative gene expression studies. Specifically, we obtained access to primary RNA sequence data from repositories and clinical partners, consistently processed the data, harmonized metadata, and released the expression values and metadata without access restrictions

ORGANISM(S): Homo sapiens

PROVIDER: GSE268099 | GEO | 2024/05/28

REPOSITORIES: GEO

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Treehouse compendium of polyA selected RNA-Seq gene expression data from 33 PDX

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The landscape of gene expression in Arabidopsis

Project description:Transcriptome datasets from thousands of samples of the model plant Arabidopsis thaliana have been collectively generated by multiple individual labs. Although integration and meta-analysis of these samples has become routine in the plant research community, it is often hampered by the lack of metadata or differences in annotation styles by different labs. In this study, we carefully selected and integrated 6,057 Arabidopsis microarray expression samples from 304 experiments deposited to NCBI GEO. Metadata such as tissue type, growth condition, and developmental stage were manually curated for each sample. We then studied global expression landscape of the integrated dataset and found that samples of the same tissue tend to be more similar to each other than to samples of other tissues, even in different growth conditions or developmental stages. Root has the most distinct transcriptome compared to aerial tissues, but the transcriptome of cultured root is more similar to those of aerial tissues as the former samples lost their cellular identity. Using a simple computational classification method, we showed that the tissue type of a sample can be successfully predicted based on its expression profile, opening the door for automatic metadata extraction and facilitating re-use of plant transcriptome data. As a proof of principle we applied our automated annotation pipeline to 708 RNA-seq samples from public repositories and verified accuracy of our predictions with samples’ metadata provided by authors.

2015-08-01 | GSE69995 | GEO

ASTRAL compendium enhancements.

Project description:The ASTRAL compendium provides several databases and tools to aid in the analysis of protein structures, particularly through the use of their sequences. It is partially derived from the SCOP database of protein domains, and it includes sequences for each domain as well as other resources useful for studying these sequences and domain structures. Several major improvements have been made to the ASTRAL compendium since its initial release 2 years ago. The number of protein domain sequences included has doubled from 15 190 to 30 867, and additional databases have been added. The Rapid Access Format (RAF) database contains manually curated mappings linking the biological amino acid sequences described in the SEQRES records of PDB entries to the amino acid sequences structurally observed (provided in the ATOM records) in a format designed for rapid access by automated tools. This information is used to derive sequences for protein domains in the SCOP database. In cases where a SCOP domain spans several protein chains, all of which can be traced back to a single genetic source, a 'genetic domain' sequence is created by concatenating the sequences of each chain in the order found in the original gene sequence. Both the original-style library of SCOP sequences and a new library including genetic domain sequences are available. Selected representative subsets of each of these libraries, based on multiple criteria and degrees of similarity, are also included. ASTRAL may be accessed at http://astral.stanford.edu/.

| S-EPMC99063 | biostudies-literature

Homo sapiens

Project description:WHI Harmonized and Imputed GWAS Data

| PRJNA246548 | ENA

shRNA Sensor Analysis of DSIR Selected shRNAs

Project description:Purpose: Generate a large, high quality database of paired shRNA efficacy/sequence datapoints.

2014-11-24 | GSE62183 | GEO

MRTX1133 Mechanism of Action Study with Single Cell Proteomics

Project description:Bruker .d files in support of a multiomics analysis of the KRASG12D inhibitor MRTX1133. Files are broken into multiple repositories because uploading 3,000 single cell proteomes plus the bulk proteomics and the metabolomics and the metabolism stuff is really painful. This is the multiplexed (SCOPE2 on a TIMSTOF Flex) .d files for approximately 2,000 single human cells. Metadata will be provided in Orsburn, 2023

2023-01-22 | MSV000091112 | MassIVE

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