rudson-oliveira

Calculates molecular descriptors and fingerprints for machine learning applications using RDKit, enhancing drug-likeness analysis.

Bio Orchestrator intelligently routes bioinformatics requests to specialized skills, ensuring efficient analysis and reproducibility.

Facilitates handling of paired-end FASTQ files using Biopython for efficient sequencing data management.

Facilitates bisulfite sequencing read alignment using Bismark, producing BAM files with methylation information for genomic analysis.

Detects differentially methylated regions in genomic data using R packages like methylKit, bsseq, and DMRcate for biological analysis.

Facilitates DNA methylation analysis using methylKit in R, enabling statistical comparisons and data preparation for DMR detection.

Processes 16S rRNA or ITS amplicon sequencing data using DADA2 for ASV inference, including quality filtering and error learning.

Facilitates differential abundance testing for microbiome data using advanced statistical methods to identify significant taxa differences.

Analyzes microbiome diversity using alpha and beta diversity metrics with R packages phyloseq and vegan for community composition comparisons.

Facilitates taxonomic classification of ASVs using reference databases and classifiers for microbiome research.

Facilitates unsupervised integration of multi-omics data using MOFA2 to uncover biological variation across modalities.

Performs Gene Ontology over-representation analysis to identify enriched biological functions in gene lists from differential expression studies.

Analyzes gene lists for pathway enrichment using ReactomePA, enabling over-representation analysis and GSEA with visualizations.

Enables geometric analysis of protein structures using Biopython, facilitating measurements of distances, angles, and dihedrals.

Facilitates peptide identification from MS/MS data using advanced matching techniques and FDR estimation for accurate results.

Facilitates protein grouping and inference from peptide identifications, resolving ambiguities using probabilistic methods.

Evaluates proteomics data quality through metrics like missing values, replicate correlation, and batch effects before analysis.

Enumerates chemical libraries using RDKit to generate virtual compound libraries from building blocks through reaction SMARTS transformations.

Facilitates the removal of sequencing adapters from FASTQ files using Cutadapt and Trimmomatic, ensuring clean data for analysis.

Detects sample contamination in sequencing data using FastQ Screen, identifying cross-species and microbial contamination.