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This tutorial involves the use of a multilayer AutoEncoder (AE) for feature extraction and pattern recognition by analyzing Molecular Dynamic Simulations, step by step, using the BioExcel Building Blocks library (biobb)

Design of linear and cyclic peptide binders from protein sequence information.

This tutorial aims to illustrate the process of protein-protein docking, step by step, using HADDOCK3 and the BioExcel Building Blocks (biobb)

A Python library for building, converting, and simulating Boolean and semi-quantitative Boolean network models. Wraps pyboolnet for prime-implicant-based Boolean algebra and adds network I/O (SBML-qual, GraphML, bnet, SIF, Cytoscape), ODE-based continuous/semi-quantitative relaxations of Boolean models, and simulation-result plotting.

SQUARNA is a tool for RNA secondary structure prediction. It can take a single RNA sequence or an alignment of sequences as input. SQUARNA handles pseudoknots and can predict alternative structures. SQUARNA allows structural restraints and chemical probing data as additional input and is available at https://github.com/febos/SQUARNA and https://larnal.imol.institute/.

Bias factorized, base-resolution deep learning models of chromatin accessibility (chromBPNet).

The application of 2nd and 3rd generation High Throughput Sequencing (HTS) technologies has deeply reshaped experimental method to investigate microbial communities and obtain a taxonomic and functional profile of the invetigated community. Shotgun Metagenomics allow to quickly obtain a representation of microorganisms genomes characterizing a particular environment. In order to obtain a fast e reliable taxonomic classification of microorganisms genomes we present kMetaShot, an alignment-free taxonomic classifier based on k-mer/minimizer counting.

This tutorial aims to illustrate the process of analyzing a membrane molecular dynamics (MD) simulation, step by step, using the BioExcel Building Blocks (biobb)

SCENIC+ is a python package to build gene regulatory networks (GRNs) using combined or separate single-cell gene expression (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) data.

In silico derivatization for GC. The GC-derivatization tool converts carbonyl groups to C═N-OCH3 (MeOX) and transforms acidic protons into -Si(CH3)3 (TMS). Key functionalities include checking for specific groups, removing derivatization groups, and adding derivatization groups to molecules.