Improving hifiasm haplotypes for autopolyploid genome assemblies using constraint programming

Over the past 30 years, software for searching tandem mass spectrometry data against a protein database has improved dramatically in speed and statistical power. However, existing tools can still struggle to analyze truly massive datasets, when either the number of spectra or the number of proteins being analyzed grows too large. Here we describe enhancements to the Tide search engine that allow i...

For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Currently, the main techniques for determining the species require hours of overnight culturing of a bacterial sample and a larger quantity of cells to function effectively. This study uses one-hour phase-contrast time-lapses of bacterial growth collected from a micr...

The analysis of cell-free DNA (cfDNA) offers a non-invasive approach for cancer detection and monitoring. Nucleosome positioning, inferred from cfDNA coverage data, serves as a proxy for gene expression, but existing peak callers fail to resolve overlapping peaks, limiting their utility in identifying precise nucleosome configurations. Here, we introduce SAUNA (Simulated Annealing for Unique Nucle...

Tumors exhibit highly heterogeneous genomic and transcriptomic landscapes. Characterization of this intratumor heterogeneity (ITH) and its implications for the cancer therapies has been one of the focal points of computational cancer biology in the past years. However, while ITH at the level of genomic changes, such as copy number aberrations, has been well characterized, the downstream impact of ...

Influenza poses a persistent global health challenge, necessitating innovative approaches to predict host-virus interactions and inform antiviral strategies. Despite advancements, machine-learning-based computational methods typically struggle with limited high-quality negative samples and inadequate modeling of complex host-virus interactions, which hinder predictive accuracy and generalization. ...

Shifting from hand-crafted to learned representations of data has revolutionized fields like natural language processing and computer vision. Despite this, current approaches to bacterial phenotype prediction from the genome rely on training machine learning models on hand-crafted features, often binary indicators or counts of the presence of different conserved genomic elements and protein domain...

Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is critical for comprehensive genomic analysis. While traditional short-read sequencing performs well for SNP and INDEL detection, it struggles to resolve SVs, especially in complex genomic regions, due to inherent read length limitations...

Advances in AI are transforming scientific discovery, yet spatial biology, a field that deciphers the molecular organization within tissues, remains constrained by labor-intensive workflows. Here, we present SpatialAgent, a fully autonomous AI agent dedicated for spatial-biology research. SpatialAgent integrates large language models with dynamic tool execution and adaptive reasoning. SpatialAgent...

Gene expression is shaped by transcription regulatory networks (TRNs), where transcription regulators interact within regulatory elements in a context-specific manner. Despite significant efforts, understanding the intricate interactions of transcription regulators across different genomic regions and cell types remains a major challenge, largely due to data sparsity. Here, we introduce ChromBERT,...