GPTomics

Integrates multiple omics data using MOFA2 to uncover latent biological factors driving variation across modalities.

Facilitates mapping single-cell data to reference atlases using advanced transfer learning techniques for accurate cell type annotation.

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

Designs PCR primers for target sequences using primer3-py, optimizing for product size and melting temperature.

Generates reproducible bioinformatics analysis reports using R Markdown, integrating code, results, and visualizations in various formats.

Selects restriction enzymes based on specific criteria using Biopython, aiding in cloning and analysis tasks.

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

Enables parsing and writing of protein structure files using Biopython, facilitating data manipulation in bioinformatics.

Facilitates pairwise sequence alignment of DNA, RNA, or protein sequences using Biopython for optimal matching and similarity scoring.

Creates publication-ready multi-panel figures by combining multiple plots with shared legends and labels using R and Python libraries.

Facilitates the creation of 3D pharmacophore models for drug discovery, enhancing virtual screening and scaffold-hopping capabilities.

Facilitates genomic interval operations like intersecting, subtracting, and merging using bedtools and pybedtools for efficient data analysis.

Balances and transforms Hi-C contact matrices using cooler and cooltools, applying iterative correction and generating observed/expected matrices.

Facilitates cell segmentation from multiplexed tissue images using deep learning and classical methods for single-cell data extraction.

Facilitates the preprocessing of imaging mass cytometry data, including hot pixel removal and normalization for accurate analysis.

Facilitates interactive annotation of cell types in IMC data using napari, enhancing training and validation of classifiers.

Facilitates cell type assignment from IMC data using clustering, manual gating, and automated classification methods.

Facilitates spatial analysis of cell interactions in imaging mass cytometry data, enabling insights into cellular neighborhoods and interactions.

Scores and prioritizes neoantigens for immunogenicity, aiding in vaccine design through multi-factor models and MHC binding analysis.

Predicts peptide-MHC binding affinities using neural networks for vaccine design and neoantigen identification.