# Announcing the 26Q3 Release **URL:** \ **Category:** Announcements\ **Created:** [October 5, 2026, 1:06pm UTC](https://forum.depmap.org/t/announcing-the-26q3-release/4692 "2026-10-05T13:06:00Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![Samantha\_Stokes](https://yyz1.discourse-cdn.com/flex035/user_avatar/forum.depmap.org/samantha_stokes/32/676_2.png) [@Samantha\_Stokes](https://forum.depmap.org/u/Samantha_Stokes)\ **Post date:** [October 5, 2026, 1:06pm UTC](https://forum.depmap.org/t/announcing-the-26q3-release/4692/1 "2026-10-05T13:06:00Z")
[![DepMap_Logosize](https://canada1.discourse-cdn.com/flex035/uploads/depmap/original/2X/d/da61a70f58a0474a923df5b01e120a8fd070a798.jpeg)](https://canada1.discourse-cdn.com/flex035/uploads/depmap/original/2X/d/da61a70f58a0474a923df5b01e120a8fd070a798.jpeg "DepMap\_Logosize") # 26Q3 DepMap Release Notes ### NEW CRISPR AND OMICS DATA The DepMap team is excited to announce the addition of new genome-wide CRISPR screening **and Omics data** , including fully i **ntegrating 150 next-generation models** data from the [Neiswiender, Maffa, Brenan et al](https://www.nature.com/articles/s41586-026-10843-7). publication. The data can now be explored in the depmap portal tools including data explorer and Celligner. ![image](https://canada1.discourse-cdn.com/flex035/uploads/depmap/original/2X/9/9581d6f49ccdd88a9c26c9cbb8d2313a8e59ee58.png) ### OTHER NEW DATASETS #### HCRSeq This collection contains DNA repair profiling of 19 cell lines using the HCR-seq technology, which employs a reporter system to profile the capacity of single cells to repair a library of diverse DNA lesions while simultaneously capturing their transcriptomes using 10x single-cell RNA-seq. Data were processed using the Cellranger pipeline, and Demuxlet was used for SNP-based demultiplexing to recover cell line identity. Cellranger outputs were post-processed using our custom HCR-seq package to derive DNA repair metrics, available on GitHub ([GitHub - getzlab/HCRseq: Code for pre-processing and analyzing data generated with HCR-seq · GitHub](https://github.com/getzlab/HCRseq)).as well as the results of the manuscript ([GitHub - getzlab/HCRseq\_manuscript\_2026 · GitHub](https://github.com/getzlab/HCRseq_manuscript_2026)). For a full description of the methodology, see Chen et al., Nature Communications, 2026. ### CRISPR PIPELINE UPDATES - **Positive ChrY gene effect centered towards 0** - **Improvements in library effect correction** - **Removal of TKOv3 and Brunello only genes** ### OMICS PIPELINE UPDATES - **Relative CN** - **Mutation dataset changes** - **GIAB/hg38 BAM patches** - **Gene list updated to v5** ### MODEL ANNOTATIONS New subtype calls have been added to our **InferredMolecularSubtype.csv** matrix (also viewable in Context Explorer and other tools as an Annotation): - SMARCB1 LOF - STAG2 LOF - Fusions: - EWSR1-WT1 - EWSR1-ATF1 - EWSR1-CREM - TCF3-HLF - SS18-SSX (SS18::SSX1, SS18::SSX2, SS18::SSX2B, or SS18::SSX4) - Breast cancer subtypes: - ERBB2 Amplification (ERBB2 Amp) (defined as ERBB2 CN \> 4, WGS + WES merged) - Breast ER+ (defined as ESR1 rna \> 2.5 & FOXA1 rna \> 6, lineages other than Breast NA) - Breast PR+ (defined as PGR rna \> 1.5, lineages other than Breast NA) - PAM50 classifications: - PAM50 Basal - PAM50 Her2 - PAM50 LumA - PAM50 LumB - PAM50 Normal - Breast PAM50 Unclear (defined as having the top 2 PAM50 subtype scores are within 0.1 of each other) - PAM50 subtype scores can be found in OmicsGlobalSignatures.csv in the following columns: - Breast\_PAM50\_Basal\_score - Breast\_PAM50\_Her2\_score - Breast\_PAM50\_LumA\_score - Breast\_PAM50\_LumB\_score - Breast\_PAM50\_Normal\_score - IDH Mutant (IDH mut) (including IDH1: p.R132H, p.R132C, p.R132L, p.R132S, p.R132G, p.R132V, p.R132Q, p.Y139D, p.R100A, p.R100Q, p.G97D and IDH2: p.R140Q, p.R172K, p.R172S, p.R172T, p.R172W, p.R140W, p.R172G, pR172M) - Amplifications: - MYCN (defined by relative CN \> 10, WGS + WES merged) ### OTHER PORTAL UPDATES - **Generating small multiples by faceting plots** - Users now have the ability to facet plots by any group of features, generating a set of small-multiples. Faceting is enabled for of both sets of models and sets of features (for example: genes, transcripts) - To generate this type of plot, enter Data Explorer and set sliders to ‘multiple’ and ‘facet’. From there, select your context to facet by. **![image](https://canada1.discourse-cdn.com/flex035/uploads/depmap/original/2X/1/1d2ce3cf81fc23850fb9688aa81830eacaa0a488.png)** - Due to plotting constraints, we set a limit of small multiples per datatype. For example, faceting by models allows you to plot 3 and genes allows for 8 small multiple plots. - After generating your plot, further refinement is possible by clicking on “Choose…” in the legend and filtering by specific characteristics of the feature you are faceting by. - **Context manager update** ---
**Author:** ![Samantha\_Stokes](https://yyz1.discourse-cdn.com/flex035/user_avatar/forum.depmap.org/samantha_stokes/32/676_2.png) [@Samantha\_Stokes](https://forum.depmap.org/u/Samantha_Stokes)\ **Post date:** [October 5, 2026, 10:22pm UTC](https://forum.depmap.org/t/announcing-the-26q3-release/4692/2 "2026-10-05T22:22:20Z")