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63 lines
5.5 KiB
Markdown
63 lines
5.5 KiB
Markdown
# Scientific Packages
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## Bioinformatics & Genomics
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- **AnnData** - Annotated data matrices for single-cell genomics and h5ad files
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- **Arboreto** - Gene regulatory network inference using GRNBoost2 and GENIE3
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- **BioPython** - Sequence manipulation, NCBI database access, BLAST searches, alignments, and phylogenetics
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- **BioServices** - Programmatic access to 40+ biological web services (KEGG, UniProt, ChEBI, ChEMBL)
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- **Cellxgene Census** - Query and analyze large-scale single-cell RNA-seq data
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- **gget** - Efficient genomic database queries (Ensembl, UniProt, NCBI, PDB, COSMIC)
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- **pysam** - Read, write, and manipulate genomic data files (SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences) with pileup analysis, coverage calculations, and bioinformatics workflows
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- **PyDESeq2** - Differential gene expression analysis for bulk RNA-seq data
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- **Scanpy** - Single-cell RNA-seq analysis with clustering, marker genes, and UMAP/t-SNE visualization
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## Cheminformatics & Drug Discovery
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- **Datamol** - Molecular manipulation and featurization with enhanced RDKit workflows
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- **DeepChem** - Molecular machine learning, graph neural networks, and MoleculeNet benchmarks
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- **DiffDock** - Diffusion-based molecular docking for protein-ligand binding prediction
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- **MedChem** - Medicinal chemistry analysis, ADMET prediction, and drug-likeness assessment
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- **Molfeat** - 100+ molecular featurizers including fingerprints, descriptors, and pretrained models
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- **PyTDC** - Therapeutics Data Commons for drug discovery datasets and benchmarks
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- **RDKit** - Cheminformatics toolkit for molecular I/O, descriptors, fingerprints, and SMARTS
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- **TorchDrug** - PyTorch-based machine learning platform for drug discovery with 40+ datasets, 20+ GNN models for molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis planning
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## Proteomics & Mass Spectrometry
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- **matchms** - Processing and similarity matching of mass spectrometry data with 40+ filters, spectral library matching (Cosine, Modified Cosine, Neutral Losses), metadata harmonization, molecular fingerprint comparison, and support for multiple file formats (MGF, MSP, mzML, JSON)
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- **pyOpenMS** - Comprehensive mass spectrometry data analysis for proteomics and metabolomics (LC-MS/MS processing, peptide identification, feature detection, quantification, chemical calculations, and integration with search engines like Comet, Mascot, MSGF+)
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## Machine Learning & Deep Learning
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- **PyMC** - Bayesian statistical modeling and probabilistic programming
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- **PyMOO** - Multi-objective optimization with evolutionary algorithms
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- **PyTorch Lightning** - Deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automates training workflows (40+ tasks including epoch/batch iteration, optimizer steps, gradient management, checkpointing), supports multi-GPU/TPU training with DDP/FSDP/DeepSpeed strategies, includes LightningModule for model organization, Trainer for automation, LightningDataModule for data pipelines, callbacks for extensibility, and integrations with TensorBoard, Wandb, MLflow for experiment tracking
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- **scikit-learn** - Machine learning algorithms, preprocessing, and model selection
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- **statsmodels** - Statistical modeling and econometrics (OLS, GLM, logit/probit, ARIMA, time series forecasting, hypothesis testing, diagnostics)
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- **Torch Geometric** - Graph Neural Networks for molecular and geometric data
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- **Transformers** - State-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks. Provides 1M+ pre-trained models accessible via pipelines (text-classification, NER, QA, summarization, translation, text-generation, image-classification, object-detection, ASR, VQA), comprehensive training via Trainer API with distributed training and mixed precision, flexible text generation with multiple decoding strategies (greedy, beam search, sampling), and Auto classes for automatic architecture selection (BERT, GPT, T5, ViT, BART, etc.)
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- **UMAP-learn** - Dimensionality reduction and manifold learning
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## Materials Science & Chemistry
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- **Astropy** - Astronomy and astrophysics (coordinates, cosmology, FITS files)
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- **COBRApy** - Constraint-based metabolic modeling and flux balance analysis
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- **Pymatgen** - Materials structure analysis, phase diagrams, and electronic structure
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## Data Analysis & Visualization
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- **Dask** - Parallel computing for larger-than-memory datasets with distributed DataFrames, Arrays, Bags, and Futures
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- **Matplotlib** - Publication-quality plotting and visualization
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- **Polars** - High-performance DataFrame operations with lazy evaluation
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- **Seaborn** - Statistical data visualization with dataset-oriented interface, automatic confidence intervals, publication-quality themes, colorblind-safe palettes, and comprehensive support for exploratory analysis, distribution comparisons, correlation matrices, regression plots, and multi-panel figures
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- **ReportLab** - Programmatic PDF generation for reports and documents
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## Phylogenetics & Trees
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- **ETE Toolkit** - Phylogenetic tree manipulation, visualization, and analysis
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## Genomics Tools
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- **deepTools** - NGS data analysis (ChIP-seq, RNA-seq, ATAC-seq) with BAM/bigWig files
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- **FlowIO** - Flow Cytometry Standard (FCS) file reading and manipulation
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- **scikit-bio** - Bioinformatics sequence analysis and diversity metrics
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- **Zarr** - Chunked, compressed N-dimensional array storage
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## Multi-omics & Integration
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- **BIOMNI** - Multi-omics data integration with LLM-powered analysis
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