Add support for both geniml and gtars

docs/scientific-packages.md

@@ -7,6 +7,8 @@
 - **BioServices** - Programmatic access to 40+ biological web services (KEGG, UniProt, ChEBI, ChEMBL)
 - **Cellxgene Census** - Query and analyze large-scale single-cell RNA-seq data
 - **gget** - Efficient genomic database queries (Ensembl, UniProt, NCBI, PDB, COSMIC)
+- **geniml** - Genomic interval machine learning toolkit providing unsupervised methods for building ML models on BED files. Key capabilities include Region2Vec (word2vec-style embeddings of genomic regions and region sets using tokenization and neural language modeling), BEDspace (joint embeddings of regions and metadata labels using StarSpace for cross-modal queries), scEmbed (Region2Vec applied to single-cell ATAC-seq data generating cell-level embeddings for clustering and annotation with scanpy integration), consensus peak building (four statistical methods CC/CCF/ML/HMM for creating reference universes from BED collections), and comprehensive utilities (BBClient for BED caching, BEDshift for genomic randomization preserving context, evaluation metrics for embedding quality, Text2BedNN for neural search backends). Part of BEDbase ecosystem. Supports Python API and CLI workflows, pre-trained models on Hugging Face, and integration with gtars for tokenization. Use cases: region similarity searches, dimension reduction of chromatin accessibility data, scATAC-seq clustering and cell-type annotation, metadata-aware genomic queries, universe construction for standardized references, and any ML task requiring genomic region feature vectors
+- **gtars** - High-performance Rust toolkit for genomic interval analysis providing specialized tools for overlap detection using IGD (Integrated Genome Database) indexing, coverage track generation (uniwig module for WIG/BigWig formats), genomic tokenization for machine learning applications (TreeTokenizer for deep learning models), reference sequence management (refget protocol compliance), fragment processing for single-cell genomics (barcode-based splitting and cluster analysis), and fragment scoring against reference datasets. Offers Python bindings with NumPy integration, command-line tools (gtars-cli), and Rust library. Key modules include: tokenizers (convert genomic regions to ML tokens), overlaprs (efficient overlap computation), uniwig (ATAC-seq/ChIP-seq/RNA-seq coverage profiles), refget (GA4GH-compliant sequence digests), bbcache (BEDbase.org integration), scoring (fragment enrichment metrics), and fragsplit (single-cell fragment manipulation). Supports parallel processing, memory-mapped files, streaming for large datasets, and serves as foundation for geniml genomic ML package. Ideal for genomic ML preprocessing, regulatory element analysis, variant annotation, chromatin accessibility profiling, and computational genomics workflows
 - **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
 - **PyDESeq2** - Differential gene expression analysis for bulk RNA-seq data
 - **Scanpy** - Single-cell RNA-seq analysis with clustering, marker genes, and UMAP/t-SNE visualization