Genomics Proteomics Bioinformatics

A prestigious journal for omics and bioinformatics areas. 20 years of excellence since 2003.

[Tao Liu (τν) @fooliu]

Also, MACS3 paper is out, updating our 18yrs old reference paper after years of maintenance and development. Many thanks to the community for the continuous contributions and to CZI @cziscience EOSS program for the support. https://t.co/Z6KYxF2mh2 2/2

[Tao Liu (τν) @fooliu]

MACS 3.1.0 is released with a jaw-dropping 40-60x improvement in speed, thanks to @jmschreiber91's auto-optimize work! AI achieved in one weekend what I've been hoping for years! Now peak calling that once took minutes can be completed in seconds! https://t.co/xv3YWIjUCd 1/2

[Genomics Proteomics Bioinformatics @GPBees]

5/5: SPAE provides a computational framework for investigating cell cycle dynamics, cellular states, and transcriptional heterogeneity in scRNA-seq data. Code available for non-commercial use: https://t.co/by1aFPJ4sh
#scRNAseq #CellCycle #ComputationalBiology

[Genomics Proteomics Bioinformatics @GPBees]

4/5: Beyond cell cycle characterization, SPAE enables accurate prediction of cancer cell cycle transitions and effective removal of cell cycle effects from gene expression data. This helps disentangle cell cycle variation from other biological signals.

[Genomics Proteomics Bioinformatics @GPBees]

3/5: Compared with existing methods, SPAE demonstrates substantially improved accuracy and robustness in characterizing cell cycle dynamics. It provides a powerful approach for reconstructing cell cycle progression and estimating cell cycle pseudotime.

[Genomics Proteomics Bioinformatics @GPBees]

1/5: New in GPB!
Can we better decode cell cycle dynamics from single-cell RNA-seq data?
Yi et al. introduce SPAE, an autoencoder-based framework for accurately characterizing cell cycle progression and cellular states.
Read more: https://t.co/dunKZQGnXg

[Genomics Proteomics Bioinformatics @GPBees]

2/5: SPAE (Sinusoidal and Piecewise AutoEncoder) integrates an autoencoder with piecewise linear modeling to capture subtle cell cycle dynamics and distinguish cell states in scRNA-seq data. A new computational approach to studying cellular heterogeneity!

[Genomics Proteomics Bioinformatics @GPBees]

5/5: Incorporating cis-molQTL annotations into genomic prediction models improves prediction accuracy for most traits, with a mean gain of 0.05 over conventional GBLUP. These findings provide a framework for informed genomic prediction and precision cattle breeding.

[Genomics Proteomics Bioinformatics @GPBees]

1/5: New in GPB!
Qiu et al. reveal how integrating four layers of cis-molQTLs improves heritability explanation and genomic prediction for economically important traits.

Read more: https://t.co/DZWduBeb0J

#Genomics #Cattle

[Genomics Proteomics Bioinformatics @GPBees]

4/5: Using 1,788 Huaxi cattle, the researchers show that multi-layer cis-molQTL SNPs collectively explain 61.9% of total SNP-based heritability across 19 complex traits. This highlights the contribution of regulatory variation to complex trait architecture.

[Genomics Proteomics Bioinformatics @GPBees]

5/5: Beyond immune cells
Attenuated CXCL12-CXCR4 and ICAM1-SPN signaling, together with a marrow-specific tumor metaprogram involving IGFBP3/NAMPT, points to disruption of hematopoietic niche support.
#CancerResearch #TumorMicroenvironment

[Genomics Proteomics Bioinformatics @GPBees]

4/5: An immunosuppressive ecosystem
Metastatic marrow exhibits progressive T/B-cell depletion and enrichment of exhausted or stressed T cells at tumor sites.

Altered LGALS9-HAVCR2 and CLEC2-KLRB1 interactions may contribute to local immune dysfunction.

[Genomics Proteomics Bioinformatics @GPBees]

3/5: Hematopoiesis reprogrammed
Bone metastasis shifts myelopoiesis toward immunosuppressive monocyte/macrophage states, with lineage bias emerging at the GMP stage.

Meanwhile, erythropoiesis and B lymphopoiesis are impaired by myeloid-like reprogramming of precursors.

[Genomics Proteomics Bioinformatics @GPBees]

2/5: Mapping the metastatic marrow
Integrating tumor, involved, and distal bone marrow from liver, prostate, and kidney cancers, alongside benign controls, researchers identified 7 major cell lineages and 66 subpopulations, revealing spatially distinct cellular remodeling.

[Genomics Proteomics Bioinformatics @GPBees]

1/5: New in GPB!
How does bone metastasis reshape hematopoiesis and immunity?

A cross-cancer single-cell atlas of 126,986 cells reveals profound remodeling of the metastatic bone marrow ecosystem.

Read the study: https://t.co/SFsft6kgCM

#BoneMetastasis #SingleCell

[Genomics Proteomics Bioinformatics @GPBees]

8/8: Additionally, HPV16 integration in host cells was associated with increased fatty acid metabolism. These findings suggest that combining anti-angiogenic drugs and fatty acid metabolism inhibitors has the potential to improve cervical cancer treatment.

[Genomics Proteomics Bioinformatics @GPBees]

1/8: New in GPB! Single-cell and spatial transcriptomics reveal that hypoxia is associated with reduced HPV16 activity, enhanced MHC-I expression, and tumor microenvironment remodeling in cervical cancer.

🔗 https://t.co/q6vtJA9gez

#CervicalCancer #HPV

[Genomics Proteomics Bioinformatics @GPBees]

7/8: A hypoxic environment was correlated with the downregulation of E5 activity and elevated MHC-I expression, which may contribute to stronger interactions between hypoxic cancer cells and cytotoxic CD8⁺ T cells.