FreedomIntelligence/OpenClaw-Medical-Skills - الصفحة ٣

bio-filter-sequences

Filter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-bead-normalization

Bead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization. Use when correcting instrument drift in CyTOF or harmonizing data across batches.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-clustering-phenotyping

Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-compensation-transformation

Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-cytometry-qc

Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-differential-analysis

Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-doublet-detection

Detect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods. Use when filtering out cell aggregates before clustering or quantitative analysis.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-fcs-handling

Read and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration. Use when loading and inspecting flow or mass cytometry data before preprocessing.

لغة النص الأصلي: الإنجليزية

bio-flow-cytometry-gating-analysis

Manual and automated gating for defining cell populations in flow cytometry. Covers rectangular, polygon, and data-driven gates. Use when identifying cell populations through hierarchical gating strategies.

لغة النص الأصلي: الإنجليزية

bio-format-conversion

Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when changing file formats or preparing data for different tools.

لغة النص الأصلي: الإنجليزية

bio-fragment-analysis

Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns…

لغة النص الأصلي: الإنجليزية

bio-gatk-variant-calling

Variant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller.

لغة النص الأصلي: الإنجليزية

bio-genome-engineering-base-editing-design

Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for…

لغة النص الأصلي: الإنجليزية

bio-genome-engineering-grna-design

Design guide RNAs for CRISPR-Cas9/Cas12a experiments using CRISPRscan and local scoring algorithms. Score guides for on-target activity using Rule Set 2 and Azimuth models. Use when designing sgRNAs for gene knockout, activation, or repression experiments.

لغة النص الأصلي: الإنجليزية

bio-genome-engineering-hdr-template-design

Design homology-directed repair donor templates for CRISPR knock-ins using primer3-py. Create ssODN, dsDNA, or plasmid templates with optimized homology arms. Use when designing donor templates for precise insertions, tagging, or allele replacement.

لغة النص الأصلي: الإنجليزية

bio-genome-engineering-off-target-prediction

Predict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity. Use when evaluating guide RNA specificity or selecting guides with minimal off-target risk.

لغة النص الأصلي: الإنجليزية

bio-genome-engineering-prime-editing-design

Design pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks. Use when designing prime editing experiments for precise insertions, deletions, or…

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-compartment-analysis

Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-contact-pairs

Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs.

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-hic-data-io

Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices.

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-hic-differential

Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-hic-visualization

Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-loop-calling

Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

لغة النص الأصلي: الإنجليزية

bio-hi-c-analysis-tad-detection

Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores.

لغة النص الأصلي: الإنجليزية

bio-imaging-mass-cytometry-cell-segmentation

Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.

لغة النص الأصلي: الإنجليزية

bio-imaging-mass-cytometry-data-preprocessing

Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization. Use when starting IMC analysis from raw MCD files or preparing images for segmentation.

لغة النص الأصلي: الإنجليزية

bio-imaging-mass-cytometry-interactive-annotation

Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping…

لغة النص الأصلي: الإنجليزية

bio-imaging-mass-cytometry-phenotyping

Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches. Use when assigning cell types to segmented IMC cells based on protein marker expression or when phenotyping cells in…

لغة النص الأصلي: الإنجليزية

bio-imaging-mass-cytometry-quality-metrics

Quality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC. Use when assessing data quality before analysis or troubleshooting problematic acquisitions.

لغة النص الأصلي: الإنجليزية

bio-imaging-mass-cytometry-spatial-analysis

Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying…

لغة النص الأصلي: الإنجليزية

bio-immunoinformatics-epitope-prediction

Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens. Use when designing vaccines, mapping antibody binding sites, or predicting immunogenic…

لغة النص الأصلي: الإنجليزية

bio-immunoinformatics-immunogenicity-scoring

Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or…

لغة النص الأصلي: الإنجليزية

bio-immunoinformatics-mhc-binding-prediction

Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.

لغة النص الأصلي: الإنجليزية

bio-immunoinformatics-neoantigen-prediction

Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy. Predict mutant peptides that bind patient HLA and may elicit T-cell responses. Use when identifying vaccine targets or checkpoint inhibitor response…

لغة النص الأصلي: الإنجليزية

bio-immunoinformatics-tcr-epitope-binding

Predict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition. Match TCRs to their cognate epitopes or predict TCR targets. Use when analyzing TCR repertoire specificity or identifying antigen-reactive T-cells.

لغة النص الأصلي: الإنجليزية

bio-isoform-switching

Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions. Use when investigating how splicing changes affect…

لغة النص الأصلي: الإنجليزية

bio-long-read-sequencing-clair3-variants

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.

لغة النص الأصلي: الإنجليزية

bio-long-read-sequencing-isoseq-analysis

Analyze PacBio Iso-Seq data for full-length isoform discovery and quantification. Use when characterizing transcript diversity or identifying novel splice variants.

لغة النص الأصلي: الإنجليزية

bio-long-read-sequencing-nanopore-methylation

Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.

لغة النص الأصلي: الإنجليزية