BGC Atlas: a web resource for exploring the global chemical diversity encoded in bacterial genomes.

Secondary metabolites are compounds not essential for an organism's development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis.

Lossless Approximate Pattern Matching: Automated Design of Efficient Search Schemes.

This study introduces a pioneering approach to automate the creation of search schemes for lossless approximate pattern matching. Search schemes are combinatorial structures that define a series of searches over a partitioned pattern. Each search specifies the processing order of these parts and the cumulative lower and upper bounds on the number of errors in each part of the pattern.

Decoding the diagnostic and therapeutic potential of microbiota using pan-body pan-disease microbiomics.

The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.

A MYCN-driven de-differentiation profile identifies a subgroup of aggressive retinoblastoma.

Retinoblastoma are childhood eye tumors arising from retinal precursor cells. Two distinct retinoblastoma subtypes with different clinical behavior have been described based on gene expression and methylation profiling. Using consensus clustering of DNA methylation analysis from 61 retinoblastomas, we identify a MYCN-driven cluster of subtype 2 retinoblastomas characterized by DNA hypomethylation and high expression of genes involved in protein synthesis.

Three-Year Mortality of Older Hospitalized Patients with Osteosarcopenia: Data from the OsteoSys Study.

Osteosarcopenia, the concurrent presence of sarcopenia and osteopenia/osteoporosis, poses a significant health risk to older adults, yet its impact on clinical outcomes is not fully understood. The aim of this prospective, longitudinal multicentre study was to examine the impact of osteosarcopenia on 3-year mortality and unplanned hospitalizations among 572 older hospitalized patients (mean age 75.1 ± 10.

Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics.

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing.

EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.

Motivation: Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality.

Results: We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths.

MERIDA: a novel Boolean logic-based integer linear program for personalized cancer therapy.

Motivation: A major goal of personalized medicine in oncology is the optimization of treatment strategies given measurements of the genetic and molecular profiles of cancer cells. To further our knowledge on drug sensitivity, machine learning techniques are commonly applied to cancer cell line panels.

Results: We present a novel integer linear programming formulation, called MEthod for Rule Identification with multi-omics DAta (MERIDA), for predicting the drug sensitivity of cancer cells.

BALL-SNP: combining genetic and structural information to identify candidate non-synonymous single nucleotide polymorphisms.

Background: High-throughput genetic testing is increasingly applied in clinics. Next-Generation Sequencing (NGS) data analysis however still remains a great challenge. The interpretation of pathogenicity of single variants or combinations of variants is crucial to provide accurate diagnostic information or guide therapies.

BALL--biochemical algorithms library 1.3.

Background: The Biochemical Algorithms Library (BALL) is a comprehensive rapid application development framework for structural bioinformatics. It provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers.

Nanoparticle design characterized by in silico preparation parameter prediction using ensemble models.

Nanoparticles (NPs) are now widely applied in new drug delivery and targeting strategies. A predictive tool for the carrier design would allow for reducing the number of experiments to determine the optimal formulation. Here we investigated the performance of two different statistical approaches to predicting NP properties.

Electric field effects on membranes: gramicidin A as a test ground.

Electric fields due to transmembrane potential differences or ionic gradients across the membrane are presumably crucial for many reactions across membranes or close to membranes like signal transduction, control of ion channels or the generation of neural impulses. Molecular dynamics simulations have been used to study the influence of external electric fields on a mixed gramicidin/phospholipid bilayer system. At high field strengths, formation of membrane electropores occurred both close and distal to the gramicidin.

A fully computational model for predicting percutaneous drug absorption.

The prediction of transdermal absorption for arbitrary penetrant structures has several important applications in the pharmaceutical industry. We propose a new data-driven, predictive model for skin permeability coefficients k(p) based on an ensemble model using k-nearest-neighbor models and ridge regression. The model was trained and validated with a newly assembled data set containing experimental data and structures for 110 compounds.

Computational modeling of the sugar-lectin interaction.

In the last few years numerous experimental studies have shed light onto the details of the lectin-carbohydrate interaction. X-ray crystallography and NMR spectroscopy have been used to elucidate the structures of lectins, sugars, and their complexes. In addition, an increasing number of experimental methods has been employed to determine the thermodynamic and kinetic parameters of the binding process.

Prediction of MHC class I binding peptides, using SVMHC.

Background: T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines.