「biology」の検索結果

Performs proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window searches, strand-aware promoter construction, and interval extension. Covers the closest -d/-D a/b/ref/-t/-k/-io/-iu/-id flags, the -D ref strand sign-flip, silent chromosome-end clipping in slop/flank, -t all tie double-counting, and the critical distinction between a geometry answer (nearest TSS) and a biology answer (which gene an element regulates). Use when assigning peaks or variants to genes, defining promoters from a gene model, building distance-to-TSS distributions, finding features within a window, or extending intervals - and when deciding whether nearest-gene is a fair prior (GWAS locus) or a trap (distal enhancer).

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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.

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Biological sequence analysis, alignment, and molecular biology computations

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Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.

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Comprehensive collection of 128+ ready-to-use scientific skills for Claude enabling research across biology, chemistry, medicine, genomics, and advanced analysis domains.

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Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

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Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

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Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields

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Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.

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Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).

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Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.

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Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.

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Python library for biology: sequence manipulation (DNA/RNA/protein), pairwise/multiple alignment, phylogenetic trees (NJ, UPGMA), diversity (Shannon, Faith PD, Bray-Curtis, UniFrac), ordination (PCoA, CCA, RDA), stats (PERMANOVA, ANOSIM, Mantel), file I/O (FASTA, FASTQ, Newick, BIOM). Use for microbiome, community ecology, or phylogenetics.

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Use Biotite for computational molecular biology workflows: sequence analysis, structure analysis, biological database access, file-format conversion, application wrappers, and optional molecular interfaces.

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Route Biopython computational biology workflows across sequence objects, file formats, alignments, structures, web databases, BioSQL, motifs, restriction enzymes, and specialized modules.

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This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

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Apply the bioengineering, mechanobiology, and lymphatic transport reasoning of J. Brandon Dixon, professor of mechanical and biomedical engineering at Georgia Institute of Technology. Reach for this skill whenever analyzing lymphatic biomechanics, active vessel contractility versus passive drainage, peristaltic fluid transport, microfluidic organ-on-a-chip design, preclinical lymphedema models, non-invasive functional imaging, targeted nanomedicine delivery, or automated disease staging. Use this skill to critique bioengineering assumptions, guide quantitative protocol design, evaluate biomechanical pump failure, and formulate interdisciplinary biomedical solutions.

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Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).

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Think and work like an expert Extremophile Biologist. Use when a task calls for Extremophile Biologist judgment. Reason from physicochemical limits—T, pH, salinity, pressure, and redox—as filters on membrane chemistry, osmoadaptation, chaperones, and cultivation fidelity before astrobiology or extremozyme claims.

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Think and work like an expert Food Engineer. Use when a task calls for Food Engineer judgment. Reasons from water activity, thermal microbiology, transport-coupled reaction, and rheology through heat-penetration studies, F0/D/z lethality integration, HACCP with prerequisite programs, and CFR Title 21 LACF/acidified-food rules while treating cold-point under-processing, aw and pH drift, post-process contamination, and unvalidated scale-up as first-class failure modes.

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Think and work like an expert Gene Therapy Scientist. Use when a task calls for Gene Therapy Scientist judgment. Reasons from vector biology, biodistribution, linked potency, and integration risk through ddPCR titer, empty/full analytics, ISA and off-target NGS (GUIDE-seq, CIRCLE-seq), and ICH S12 nonclinical design while treating pre-existing capsid neutralizing antibodies, RCV/RCA/RCR positivity, gonadal vector genome, and oligoclonal VCN expansion as first-class failure modes.

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