Differential Gene expression in the mouse mammary tumors of PyMT-Malat1 wild-type (WT), PyMT-Malat1 knockout (KO) and PyMT-Malat1 knockout with Malat1 transgene expression (TG)
Previously, lncRNA Malat1 knockout mice were generated by insertional inactivation. By crossing this line to MMTV-PyMT mammary tumor mouse model, we produced PyMT;Malat1 wild-type (WT) and PyMT;Malat1 knockout (KO). Furthermore, we generated Malat1 transgenic mice by targeting ROSA26 locus and bred them to PyMT;Malat1 knockout mice to produce Malat1-rescued PyMT;Malat1 knockout;Malat1 transgenic animals (TG). Using mammary tumors from the three groups of animals, we performed RNA-Seq analysis to...
Provenance — who produced it, who reused it
Linked to 1 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.
Deep data QC
97/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq of Mus musculus with unavailable platform metadata (HTTP error), but exceptional quality (97% Q30, 76 bp read length, 0.016% N content) with minimal adapter contamination. This dataset enables robust mouse gene expression analysis. The consistent high quality across samples suggests excellent library preparation and sequencing performance, though platform verification requires GEO contact.
The A grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0