@gpbees.bsky.social🧵 New in #GPB | Could the “hidden” proteome reveal new targets for liver cancer immunotherapy?
A new study integrates transcriptomics, proteomics & immunological validation to uncover non-canonical antigens in hepatocellular carcinoma (HCC).
@gpbees.bsky.social5/ 💻 MACS3 is open source and distributed through standard software channels, with continuous testing across operating systems, Python versions, and CPU architectures.
The new #GPB article provides an updated reference for its architecture, capabilities, and recommended use.
@gpbees.bsky.social🧵 New in #GPB | Meet MACS3 — the next generation of the widely used Model-based Analysis for ChIP-Seq (MACS) framework for peak calling in regulatory genomics.
From bulk ChIP-seq to single-cell ATAC-seq, MACS3 brings peak calling into modern genomics workflows.
@gpbees.bsky.social🧵 How can multi-omics data be translated into digital white blood cell counts?
A new study in #GPB introduces MOFUN-CCC, an intermediate-fusion network integrating gene expression and DNA methylation data for absolute blood cell count prediction.
@gpbees.bsky.social🧵 How can we reconstruct cellular spatial organization when spatial transcriptomics lacks single-cell resolution?
A new #GPB study introduces SPCC, a deep-learning framework integrating #scRNAseq and #SpatialTranscriptomics to infer spatial cell–cell interactions. 1/6
@gpbees.bsky.social6/6 StarFunc demonstrates that deep learning and template-based approaches are complementary, not competing, strategies for protein function annotation.
Read the article in #GPB:
🔗 doi.org/10.1093/gpbj...
@gpbees.bsky.social🧵 1/6 How can protein function prediction benefit from both biological templates and deep learning?
New in #GPB: “StarFunc: Fusing Template-based and Deep Learning Approaches for Accurate Protein Function Prediction”