Source

smithery.ai

37,462 skills · 110K combined installs

Skills from this source

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Skill

Source

8W Activity

Installs

bio-copy-number-cnv-annotation Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, pop…

bio-copy-number-cnv-visualization Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACET…

bio-copy-number-cnvkit-analysis Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a rea…

bio-copy-number-gatk-cnv Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, Denoi…

bio-crispr-screens-base-editing-analysis Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchm…

bio-crispr-screens-crispresso-editing Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR),…

bio-crispr-screens-hit-calling Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK…

bio-crispr-screens-jacks-analysis Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA …

bio-crispr-screens-screen-qc Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson an…

bio-data-visualization-color-palettes Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency…

bio-data-visualization-genome-browser-tracks Install the bio-data-visualization-genome-browser-tracks skill for your AI agent. Published on smithery.ai.

bio-data-visualization-genome-tracks Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screensho…

bio-data-visualization-heatmaps-clustering Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/sc…

bio-data-visualization-interactive-visualization Build interactive HTML/web visualizations with plotly (Python/R), bokeh (Python), and gganimate/plotly frames for ani…

bio-data-visualization-multipanel-figures Compose multi-panel publication figures with patchwork, cowplot, gridExtra (R), or matplotlib GridSpec/subfigures (Py…

bio-data-visualization-specialized-omics-plots Install the bio-data-visualization-specialized-omics-plots skill for your AI agent. Published on smithery.ai.

bio-data-visualization-upset-plots Build UpSet plots to visualize set intersections beyond 4 sets (where Venn fails) using ComplexUpset (modern, ggplot2…

bio-data-visualization-volcano-customization Install the bio-data-visualization-volcano-customization skill for your AI agent. Published on smithery.ai.

bio-differential-expression-batch-correction Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for vi…

bio-differential-expression-de-results Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling o…

bio-differential-expression-de-visualization Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot…

bio-differential-expression-deseq2-basics Performs differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing…

bio-differential-expression-edger-basics Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F…

bio-differential-expression-timeseries-de Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT…

bio-duplicate-handling Mark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant …

bio-entrez-fetch Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary).

bio-entrez-link Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink).

bio-entrez-search Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell).

bio-epitranscriptomics-m6a-differential Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-b…

bio-epitranscriptomics-m6a-peak-calling Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-correcte…

bio-epitranscriptomics-m6anet-analysis Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning o…

bio-epitranscriptomics-merip-preprocessing Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splic…

bio-epitranscriptomics-modification-visualization Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools comp…

bio-experimental-design-batch-design Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, p…

bio-experimental-design-multiple-testing Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods …

bio-experimental-design-power-analysis Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, …

bio-experimental-design-sample-size Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experime…

bio-expression-matrix-counts-ingest Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or …

bio-expression-matrix-gene-id-mapping Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, bioma…

bio-expression-matrix-sparse-handling Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatri…

bio-fastq-quality Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-positio…

bio-filter-sequences Filter and select sequences by criteria (length, ID, GC content, N content, motifs, patterns, description) using Biop…

bio-flow-cytometry-bead-normalization Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-elemen…

bio-flow-cytometry-clustering-phenotyping Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSO…

bio-flow-cytometry-compensation-transformation Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies varian…

bio-flow-cytometry-cytometry-qc Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowC…

bio-flow-cytometry-differential-analysis Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell popul…

bio-flow-cytometry-doublet-detection Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. …

bio-flow-cytometry-fcs-handling Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTO…

bio-flow-cytometry-gating-analysis Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) …