multiome

mRNArchitect: sequence design of mRNA medicines

mRNArchitect is a software toolkit for designing and optimising mRNA sequences to improve translation, stability, and immunogenicity, validated across multiple cell types and tissues.

RNA 3' end tailing safeguards cells against products of pervasive transcription termination

The paper identifies RNA 3' end tailing mechanisms that protect cells from unadenylated RNA by modifying and removing it through different pathways.

Pan-Cancer Genetic Profiles of Mitotic DNA Integrity Checkpoint Protein Kinases

The paper provides pan-cancer profiles of 16 mitotic DNA integrity checkpoint protein kinase genes across multiple omic layers to identify potential biomarkers and therapeutic targets.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

The paper introduces a deep learning model to augment cancer cell line multi-omic data, increasing the number of profiles and uncovering drug resistance mechanisms.

ACE: a versatile contrastive learning framework for single-cell mosaic integration

ACE provides a contrastive learning framework for mosaic integration of single-cell multi-omics data, improving representation of cellular heterogeneities in datasets with incomplete modalities.

Born Connected: The Early Emergence of Adult-Like Multi-Scale Brain Networks in Infancy

This study identifies adult-like multi-scale brain networks in infants using rsfMRI, showing early emergence of functional connectivity patterns.

ProFit-1D for quantifying J-difference edited data at 3T

ProFit-1D was adapted to fit J-difference edited spectra at 3T and showed higher accuracy and precision than existing software for estimating metabolite concentrations.

Integrated Analyses of Multi-omic Data Derived from Paired Primary Lung Cancer and Brain Metastasis Reveals the Metabolic Vulnerability as a Novel Therapeutic Target

This study integrates multi-omic data from lung cancer and brain metastases to identify mitochondrial metabolism as a therapeutic target in brain metastases.

Rigosertib reverses hypertrophic cardiomyopathy in RAF1-associated Noonan syndrome

This paper shows rigosertib reverses hypertrophic cardiomyopathy in RAF1-associated Noonan syndrome using Drosophila, iPSC, and mouse models.

Interpreting variants associated with congenital heart disease with a regulatory map of the human fetal heart

This study creates a gene regulation map of the human fetal heart to interpret genetic variants associated with congenital heart disease and cardiac traits, identifying valvular interstitial cells as key regulators.

Beyond Integration: SuperGLUE Facilitates Explainable Training Framework for Multi-modal Data Analysis

This paper introduces a scalable probabilistic deep learning framework for multi-modal data integration, demonstrating improved preservation of local and global structures in complex biological systems.

Nanoparticles co-delivering siRNA and mRNA for simultaneous restoration and silencing of gene/protein expression in vitro and invivo

This study develops nanoparticles that co-deliver siRNA and mRNA to simultaneously silence and restore gene/protein expression in vitro and in vivo.

CPSM: R-package of an Automated Machine Learning Pipeline for Predicting the Survival Probability of Single Cancer Patient

The paper introduces CPSM, an R package that provides an automated machine learning pipeline for predicting individual cancer patient survival probabilities using multi-omics data.

Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics

This study evaluates force field limitations in constant pH molecular dynamics simulations of a mini-protein, showing significant pKa errors due to force field and water model choices.

Ex Vivo Delivery of mRNA to Immune Cells Via a Non-Endosomal Route Obviates the Need for Nucleoside Modification

This study shows that unmodified mRNA can be effectively delivered to immune cells ex vivo via a non-endosomal route without needing nucleoside modifications.

Simulation-Based Inference at the Theoretical Limit: Fast, Accurate Microstructural MRI with Minimal diffusion MRI Data

This paper introduces a simulation-based inference method for diffusion MRI that reduces scan time and improves accuracy with minimal data.

Different alterations in mRNP composition trigger an increase in harmful R-loops

The paper identifies RNA-binding proteins and mRNP assembly perturbations that lead to R-loop accumulation and DNA damage, linking these processes to cancer and neurodegenerative diseases.

Spatial Transcriptomics and Single-Nucleus Multi-omics Analysis Revealing the Impact of High Maternal Folic Acid Supplementation on Offspring Brain Development

Critical role of Spatio-Temporally Regulated Maternal RNAs in Zebrafish Embryogenesis

The paper identifies five maternal mRNAs critical for zebrafish embryogenesis and reveals Cth1's role as a spatio-temporal RNA decay factor.

Unified Analysis of Multi-order Tensors for Integrative Molecular Profiling

The paper introduces MUSIC, a Bayesian tensor decomposition framework for integrating multi-modal molecular data, showing it reveals interpretable patterns across patients, cell types, and modalities.

Machine learning engineered PoLixNano nanoparticles overcome delivery barriers for nebulized mRNA therapeutics

The paper develops PoLixNano nanoparticles using machine learning to enhance mRNA delivery through nebulization, achieving high transfection efficiency and therapeutic efficacy in animal models.

Temporal recording of mammalian development and precancer

The paper introduces NSC-seq, a CRISPR-based molecular clock to track temporal and clonal events in development and precancer, revealing polyancestral initiation in colonic precancers.

Hijacking and recording intracellular RNAs in human cells using eukaryotic reprogrammed tracrRNAs

The paper introduces CHEETAH, a system using reprogrammed tracrRNAs to capture and record intracellular RNAs in human cells, demonstrating its ability to self-edit, selectively record viral RNA fragments, and enable multiplexed, time-resolved recording.

Structural compromise in spiking cortex and connected networks

The paper identifies structural and functional alterations in cortical regions associated with epileptic spikes and their connected networks using multimodal MRI and EEG.

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

This paper develops BioNet, a biologically-informed deep learning model, to predict intratumoral heterogeneity in post-treatment glioblastoma using MRI and biopsy data, showing improved performance over existing methods.

Critical issues found in "Dissecting cell identity via network inference and in silico gene perturbation"

This paper identifies critical methodological flaws in the CellOracle approach for gene regulatory network inference and perturbation analysis.

A novel classification framework for genome-wide association study of whole brain MRI images using deep learning

This study uses deep learning to identify genetic variants associated with brain MRI images by classifying MRI data based on SNP genotypes.

Bridging biomolecular modalities for knowledge transfer in bio-language models

This paper adapts DNA and protein bio-language models for mRNA-focused tasks through various finetuning techniques, showing that medium-scale models perform best for cross-modal knowledge transfer.

Targetable Treatment Resistance in Thyroid Cancer with Clonal Hematopoiesis

This study identifies TET2-mutant clonal hematopoiesis in thyroid cancer microenvironments and shows that TGFβ signaling from these macrophages mediates resistance to BRAF/MEK inhibition, with TGFβ inhibition restoring sensitivity.

Interpretable multi-omics integration with UMAP embeddings and density-based clustering

GAUDI integrates multi-omics data using UMAP embeddings and density-based clustering to uncover non-linear relationships and identify interpretable latent factors across datasets.