Population-scale cellular GUIDE-seq-2 and biochemical CHANGE-seq-R profiles reveal human genetic variation frequently affects Cas9 off-target activity.

Genome editing enzymes can introduce targeted changes to the DNA in living cells , transforming biological research and enabling the first approved gene editing therapy for sickle cell disease . However, their genome-wide activity can be altered by genetic variation at on- or off-target sites , potentially impacting both their precision and therapeutic safety. Due to a lack of scalable methods to measure genome-wide editing activity in cells from large populations and diverse target libraries, the frequency and extent of these variant effects on editing remains unknown.

DNALongBench: A Benchmark Suite for Long-Range DNA Prediction Tasks.

Modeling long-range DNA dependencies is crucial for understanding genome structure and function across a wide range of biological contexts. However, effectively capturing these extensive dependencies, which may span millions of base pairs in tasks such as three-dimensional (3D) chromatin folding prediction, remains a significant challenge. Furthermore, a comprehensive benchmark suite for evaluating tasks that rely on long-range dependencies is notably absent.

Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.

Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems.

Interkingdom Hormonal Regulations between Plants and Animals Provide New Insight into Food Safety.

Food safety has become an attractive topic among consumers. Raw material production for food is also a focus of social attention. As hormones are widely used in agriculture and human disease control, consumers' concerns about the safety of hormone agents have never disappeared.

Computational methods for analysing multiscale 3D genome organization.

Recent progress in whole-genome mapping and imaging technologies has enabled the characterization of the spatial organization and folding of the genome in the nucleus. In parallel, advanced computational methods have been developed to leverage these mapping data to reveal multiscale three-dimensional (3D) genome features and to provide a more complete view of genome structure and its connections to genome functions such as transcription. Here, we discuss how recently developed computational tools, including machine-learning-based methods and integrative structure-modelling frameworks, have led to a systematic, multiscale delineation of the connections among different scales of 3D genome organization, genomic and epigenomic features, functional nuclear components and genome function.

UNADON: transformer-based model to predict genome-wide chromosome spatial position.

Motivation: The spatial positioning of chromosomes relative to functional nuclear bodies is intertwined with genome functions such as transcription. However, the sequence patterns and epigenomic features that collectively influence chromatin spatial positioning in a genome-wide manner are not well understood.

Results: Here, we develop a new transformer-based deep learning model called UNADON, which predicts the genome-wide cytological distance to a specific type of nuclear body, as measured by TSA-seq, using both sequence features and epigenomic signals.

UNADON: Transformer-based model to predict genome-wide chromosome spatial position.

The spatial positioning of chromosomes relative to functional nuclear bodies is intertwined with genome functions such as transcription. However, the sequence patterns and epigenomic features that collectively influence chromatin spatial positioning in a genome-wide manner are not well understood. Here, we develop a new transformer-based deep learning model called UNADON, which predicts the genome-wide cytological distance to a specific type of nuclear body, as measured by TSA-seq, using both sequence features and epigenomic signals.

Changes in cropland soil carbon through improved management practices in China: A meta-analysis.

Recommended management practices (RMPs, e.g., manuring, no-tillage, crop residue return) can increase soil organic carbon (SOC), reduce greenhouse gas emissions, and maintain soil health in croplands.

Effects of tillage and cropping sequences on crop production and environmental benefits in the North China Plain.

The ever-increasing trend of greenhouse gas (GHG) emissions is accelerating global warming and threatening food security. Environmental benefits and sustainable food production must be pursued locally and globally. Thus, a field experiment was conducted in 2015 to understand how to balance the trade-offs between agronomic productivity and environment quality in the North China Plain (NCP).

Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization.

In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. However, DNA sequence determinants that modulate the formation of 3D genome organization remain poorly characterized.

[Effects of PD-L1 on immunosuppression of bacterial sepsis and its relevant mechanism].

To investigate the expression of programmed death ligand-1 (PD-L1) in dendritic cells (DCS) and its related signaling pathway in lipopolysaccharide (LPS)-induced immunosuppression of bacterial sepsis. Stimulating with bacterial LPS, bone marrow-derived dendritic cells could induce T lymphocyte immunosuppression imitating bacterial sepsis model. The experiments were divided into 5 groups: control group, LPS group, 2-(4-morpholinyl)-8-phenyl-4H-1- benzopyran-4-one (LY294002)+LPS group, pyrrolidinedithiocarbamate(PDTC)+LPS group and LPS+anti-PD-L1 group with 6 multiple wells in each group.

Cancer mutational signatures representation by large-scale context embedding.

Motivation: The accumulation of somatic mutations plays critical roles in cancer development and progression. However, the global patterns of somatic mutations, especially non-coding mutations, and their roles in defining molecular subtypes of cancer have not been well characterized due to the computational challenges in analysing the complex mutational patterns.

Results: Here, we develop a new algorithm, called MutSpace, to effectively extract patient-specific mutational features using an embedding framework for larger sequence context.