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7 repository-uri

Awesome GitHub RepositoriesGenomic Sequence Interpreters

Deep learning models for processing and interpreting genetic data to identify variants.

Distinct from Sequence Analysis: Distinct from general sequence analysis: focuses on genomic-specific deep learning interpretation.

Explore 7 awesome GitHub repositories matching data & databases · Genomic Sequence Interpreters. Refine with filters or upvote what's useful.

Awesome Genomic Sequence Interpreters GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • google-research/google-researchAvatar google-research

    google-research/google-research

    38,139Vezi pe GitHub↗

    This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

    Applies deep learning to process and interpret genetic data for variant identification.

    Jupyter Notebookaimachine-learningresearch
    Vezi pe GitHub↗38,139
  • k-dense-ai/claude-scientific-skillsAvatar K-Dense-AI

    K-Dense-AI/claude-scientific-skills

    8,907Vezi pe GitHub↗

    This project is a scientific agent framework and workflow orchestrator designed to extend large language models with specialized tools for genomic, chemical, and biological research. It provides a system for planning research hypotheses and executing automated workflows by integrating scientific databases with dynamic code execution. The framework includes a cheminformatics modeling suite for predicting molecular bioactivity and performing virtual screening, alongside a bioinformatics analysis toolkit for processing genomic sequences and single-cell data. It also features an academic document

    Analyzes DNA and protein sequences to annotate genetic variants and identify pathogenicity.

    Pythonai-scientistbioinformaticschemoinformatics
    Vezi pe GitHub↗8,907
  • deepchem/deepchemAvatar deepchem

    deepchem/deepchem

    6,545Vezi pe GitHub↗

    DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data, serving as a comprehensive toolkit for drug discovery and materials science. At its core, it provides a featurizer-pipeline abstraction that converts raw molecular data into numerical representations, including graph-based molecular structures, SMILES tokenization vocabularies, and disk-sharded dataset persistence for handling large-scale data that exceeds RAM capacity. The framework distinguishes itself through integrated molecular docking workflows that automate pocket detecti

    Featurizes genomic and proteomic sequences from alignment files for downstream machine learning models.

    Pythonbiologydeep-learningdrug-discovery
    Vezi pe GitHub↗6,545
  • biopython/biopythonAvatar biopython

    biopython/biopython

    5,078Vezi pe GitHub↗

    Biopython este o bibliotecă de bioinformatică pentru Python care oferă instrumente pentru a analiza, manipula și interpreta secvențe biologice, structuri moleculare și arbori filogenetici. Servește ca un parser de secvențe biologice pentru date genomice și proteomice în mai multe formate de fișiere standard din industrie și acționează ca o interfață pentru interogarea datelor biologice și a citărilor din depozitele NCBI Entrez. Proiectul se distinge prin seturi de instrumente specializate pentru analiza structurii proteinelor și construcția arborilor filogenetici. Include un analizor de structură proteică pentru procesarea fișierelor PDB și mmCIF pentru a calcula geometria moleculară, precum și un set de instrumente pentru arbori filogenetici pentru analizarea relațiilor evolutive dintre specii. Biblioteca acoperă o gamă largă de capabilități de bioinformatică, inclusiv analiza secvențelor genomice pentru transcriere și traducere, gestionarea alinierilor de secvențe și calcule de genetică a populațiilor. Oferă, de asemenea, instrumente de analiză structurală pentru manipularea coordonatelor atomice 3D, precum și utilitare pentru vizualizarea caracteristicilor genomice și modelarea datelor biogeografice. Sistemul se integrează cu binare externe de bioinformatică prin „wrapping” și suportă stocarea persistentă a înregistrărilor biologice prin stocare de secvențe bazată pe SQL.

    Isolates non-coding DNA sequences located between genes from a larger genomic sequence.

    Pythonbioinformaticsbiopythondna
    Vezi pe GitHub↗5,078
  • jwohlwend/boltzAvatar jwohlwend

    jwohlwend/boltz

    4,038Vezi pe GitHub↗

    Boltz is a deep learning molecular modeler and biomolecular structure prediction system. It uses neural network architectures to simulate the physical folding and docking of biomolecules, specifically predicting the three-dimensional shapes of protein and ligand complexes. The project functions as a protein-ligand complex predictor and binding affinity predictor, estimating the strength and probability of molecular interactions between ligands and targets. These capabilities are applied to computer aided drug design, including ligand binding affinity prediction and protein-ligand interaction

    Transforms raw protein sequences into high-dimensional feature vectors using evolutionary information from related sequences.

    Python
    Vezi pe GitHub↗4,038
  • arcinstitute/evo2Avatar ArcInstitute

    ArcInstitute/evo2

    3,951Vezi pe GitHub↗

    evo2 is a genomic large language model and foundation model designed to predict, generate, and analyze genetic information across different species. It functions as a nucleotide sequence modeler and a DNA sequence generator, using transformer-based sequence modeling to process genomic data. The system provides capabilities for synthetic DNA generation, creating new genetic sequences based on biological prompts or species-specific tags. It also performs nucleotide likelihood prediction to score genomic variants and analyze biological properties within DNA sequences. The model supports genomic

    Predicts nucleotide likelihoods across a sequence to score genomic variants or analyze biological properties.

    Jupyter Notebook
    Vezi pe GitHub↗3,951
  • space-wizards/space-station-14Avatar space-wizards

    space-wizards/space-station-14

    3,523Vezi pe GitHub↗

    Space Station 14 is a C# multiplayer game and roleplay simulation framework. It is built upon an Entity-Component-System (ECS) game engine that separates logic into systems and data into components to manage complex entity interactions. The project functions as a grid-based physics simulator with a YAML data-driven prototype system for defining game objects. The project features a specialized 2D sprite rendering engine that maps server-side appearance data to client-side shaders. It implements a networking model with client-side prediction and dirty-flagged state synchronization to reduce inp

    Allows editing base pairs within a plant's genetic sequence via a hex-style interface.

    C#c-sharpgamehacktoberfest
    Vezi pe GitHub↗3,523
  1. Home
  2. Data & Databases
  3. Data Analysis & Visualization
  4. Analytical Platforms and Engines
  5. Sequence Analysis
  6. Genomic Sequence Interpreters

Explorează sub-etichetele

  • Genome Editing InterfacesUser interfaces and logic for directly modifying base pairs within a genetic sequence. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on the editing/modification of the sequence via an interface rather than interpretation/analysis.
  • Genomic TokenizationProcesses raw nucleotide sequences into discrete tokens for structured genomic analysis. **Distinct from Genomic Sequence Interpreters:** Focuses on the encoding/tokenization process for genomic data, whereas Genomic Sequence Interpreters focus on the analysis of the resulting data.
  • Non-Coding Region ExtractionIdentifying and isolating intergenic or non-coding DNA sequences within a genome. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on the specific task of isolating intergenic regions rather than general variant interpretation.
  • Sequence FeaturizationProcessing and featurizing genomic and proteomic sequences from alignment files for downstream modeling. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on featurizing sequences into numerical representations, not deep learning interpretation.
  • Therapeutic Variant MatchingMatching genetic variants to targeted therapies using pharmacogenomics and cancer genomics data. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on the clinical application of matching variants to specific therapies.