Skill Claude CodeCodex
Merge sample metadata with count matrices and add gene annotations. Use when preparing data for differential expression analysis or visualization.
not rated 3 5mo ago A 31 tokens
original MIT
3Stars on the repository
200Mods indexed here, across every type
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Skill Claude CodeCodex
Work with sparse matrices for memory-efficient storage of count data. Use when dealing with single-cell data or large bulk RNA-seq datasets where most values are zero.
not rated 3 5mo ago A 41 tokens
original MIT
Skill Claude CodeCodex
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 precise nucleotide changes.
not rated 3 5mo ago A 61 tokens
original MIT
Skill Claude CodeCodex
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.
not rated 3 5mo ago A 66 tokens
original MIT
Skill Claude CodeCodex
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.
not rated 3 5mo ago A 63 tokens
original MIT
Skill Claude CodeCodex
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.
not rated 3 5mo ago A 55 tokens
original MIT
Skill Claude CodeCodex
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 point mutations.
not rated 3 5mo ago A 59 tokens
original MIT
Skill Claude CodeCodex
Create, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.
not rated 3 5mo ago A 61 tokens
original MIT
Skill Claude CodeCodex
Create and read bigWig browser tracks for visualizing continuous genomic data. Convert bedGraph to bigWig, extract signal values, and generate coverage tracks using UCSC tools and pyBigWig. Use when preparing coverage tracks for genome browsers or extracting signal at specific regions.
not rated 3 5mo ago A 67 tokens
original MIT
Skill Claude CodeCodex
Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.
not rated 3 5mo ago A 57 tokens
original MIT
Skill Claude CodeCodex
Parse, query, and convert GTF and GFF3 annotation files. Extract gene, transcript, and exon coordinates using gffread, gtfparse, and gffutils. Use when extracting specific features from gene annotations or converting between annotation formats.
not rated 3 5mo ago A 65 tokens
original MIT
Skill Claude CodeCodex
Core interval arithmetic operations including intersect, subtract, merge, complement, map, and groupby using bedtools and pybedtools. Use when finding overlapping regions, removing overlaps, combining adjacent intervals, or transferring annotations between interval files.
not rated 3 5mo ago A 57 tokens
original MIT
Skill Claude CodeCodex
Find nearest features, search within windows, and extend intervals using closest, window, flank, and slop operations. Use when performing TSS proximity analysis, assigning enhancers to genes, defining promoter regions, or finding nearby genomic features.
not rated 3 5mo ago A 58 tokens
original MIT
Skill Claude CodeCodex
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.
not rated 3 5mo ago A 61 tokens
original MIT
Skill Claude CodeCodex
Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.
not rated 3 5mo ago A 50 tokens
original MIT
Skill Claude CodeCodex
Visualize metagenomic profiles using R (phyloseq, microbiome) and Python (matplotlib, seaborn). Create stacked bar plots, heatmaps, PCA plots, and diversity analyses. Use when creating publication-quality figures from MetaPhlAn, Bracken, or other taxonomic profiling output.
not rated 3 5mo ago A 70 tokens
original MIT
Skill Claude CodeCodex
Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.
not rated 3 5mo ago A 51 tokens
original MIT
Skill Claude CodeCodex
Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.
not rated 3 5mo ago A 60 tokens
original MIT
Skill Claude CodeCodex
Multi-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.
not rated 3 5mo ago A 68 tokens
original MIT
Skill Claude CodeCodex
Similarity Network Fusion (SNF) for patient stratification using multi-omics data. Integrates multiple data types into a unified patient similarity network. Use when performing patient stratification or integrating multi-omics data into unified similarity networks.
not rated 3 5mo ago A 56 tokens
original MIT
Skill Claude CodeCodex
Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.
not rated 3 5mo ago A 58 tokens
original MIT
Skill Claude CodeCodex
Compute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, or generating bootstrap consensus trees.
not rated 3 5mo ago A 52 tokens
original MIT
Skill Claude CodeCodex
Build maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-ng. Use when inferring publication-quality trees with model selection, ultrafast bootstrap, or partitioned analyses from sequence alignments.
not rated 3 5mo ago A 52 tokens
original MIT
Skill Claude CodeCodex
Modify phylogenetic tree structure using Biopython Bio.Phylo. Use when rooting trees with outgroups or midpoint, pruning taxa, collapsing clades, ladderizing branches, or extracting subtrees.
not rated 3 5mo ago A 50 tokens
original MIT
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