biotender-max

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

Calculates molecular fingerprints and physicochemical descriptors for similarity search, QSAR, and machine learning applications.

Facilitates reading, writing, and converting various molecular file formats using RDKit and Open Babel for chemical data processing.

Standardizes molecular structures for ML training and data deduplication, ensuring consistency across datasets and preventing data leakage.

Facilitates detection of microsatellite instability from sequencing data, aiding in ICI eligibility stratification for cancer treatments.

Integrates single-cell RNA-seq and ATAC-seq data to build enhancer-driven gene regulatory networks, identifying key regulatory elements.

Enables the creation of multi-panel publication figures in R and Python, adhering to journal specifications and conventions.

Applies multiple testing correction methods for genomics data, enhancing the accuracy of differential expression results.

Implements multiplicity control for clinical trials using advanced statistical methods to ensure regulatory compliance and accurate results.

Analyzes DNA methylation from Oxford Nanopore sequencing data using signal-level analysis for accurate detection of modifications.

Facilitates the search and classification of non-coding RNA families using advanced covariance models against the Rfam database.

Facilitates the creation of scalable bioinformatics pipelines using Nextflow, supporting Docker and cloud execution for efficient data analysis.

Creates classification models for omics data using RandomForest, XGBoost, and logistic regression, ensuring proper evaluation metrics.

Visualizes mutation patterns in somatic variant cohorts using OncoPrint and co-mutation matrices for enhanced genomic analysis.

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

Facilitates pairwise sequence alignment using Biopython, enabling optimal comparisons of DNA, RNA, or protein sequences.

Facilitates ChIP-seq peak calling using MACS3, MACS2, HOMER, or SPP, optimizing for narrow and broad genomic regions.

Facilitates peptide identification from MS/MS data using advanced algorithms for database searching and FDR estimation.

Analyzes single-cell pooled CRISPR screens using various methodologies for effective experimental design and data interpretation.

Facilitates pharmacogenomic-guided prescribing by querying drug-gene interactions and applying activity-score translations.