L1000 Connectivity Map perturbational profiles from Broad Institute LINCS Center for Transcriptomics LINCS PHASE *II* (n=354,123; updated March 30, 2017)
The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to build a Connectivity Map which seeks to enable the discovery of functional connections between drugs, genes and diseases through analysis of patterns induced by common gen...
Provenance — who produced it, who reused it
Linked to 62 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- De novo generation of hit-like molecules from gene expression si... 2020 · 416 cites
- L1000CDS2: LINCS L1000 characteristic direction signatures searc... 2016 · 383 cites
- Cas9 activates the p53 pathway and selects for p53-inactivating... 2020 · 285 cites
- Multiplexed single-cell transcriptional response profiling to de... 2020 · 183 cites
- EuRBPDB: a comprehensive resource for annotation, functional and... 2019 · 132 cites
- PLATE-Seq for genome-wide regulatory network analysis of high-th... 2017 · 128 cites
- Extending the small-molecule similarity principle to all levels... 2020 · 128 cites
- A novel computational approach for drug repurposing using system... 2018 · 123 cites
- Connecting omics signatures and revealing biological mechanisms... 2022 · 115 cites
- Focal adhesion kinase-YAP signaling axis drives drug-tolerant pe... 2024 · 86 cites
- Deep learning of pharmacogenomics resources: moving towards prec... 2019 · 77 cites
51 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.