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Analyzes experimental RNA structure probing data from SHAPE-MaP and DMS-MaPseq experiments using ShapeMapper2. Converts mutation rates to per-nucleotide reactivity profiles that constrain structure pr…
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UM…
Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq,…
Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use wh…
Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial p…
Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expressi…
Detects circadian and ultradian rhythms in time-series omics data using CosinorPy cosinor models, MetaCycle (JTK_CYCLE, ARSER), and RAIN non-parametric tests. Fits cosine models to estimate phase and …
Models continuous temporal trajectories from bulk or time-resolved omics data using generalized additive models (mgcv), spline regression, and changepoint detection (segmented, ruptures). Fits smooth …
Find patterns, motifs, and subsequences in biological sequences using Biopython. Use when searching for transcription factor binding sites, regulatory elements, or any sequence pattern. For restrictio…
Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when…
Infer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudoti…
Predict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs.
Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when comput…
Analyze single-cell TCR and BCR data integrated with gene expression using scirpy. Use when working with 10x Genomics VDJ data alongside scRNA-seq or when integrating immune receptor information with …
Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep le…
End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use…
Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types…
Infer cell-cell communication networks from scRNA-seq data using CellChat, NicheNet, and LIANA for ligand-receptor interaction analysis. Use when inferring ligand-receptor interactions between cell ty…
Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction n…
Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use wh…
Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).
Clusters genes by temporal expression profile shape using Mfuzz soft clustering, TCseq, and DEGreport degPatterns. Groups co-regulated genes into shared trajectory patterns via fuzzy c-means or hierar…
Discovers periodic signals of unknown period in time-series omics data using Lomb-Scargle periodograms (scipy), autocorrelation, and wavelet time-frequency decomposition (pywt). Identifies dominant fr…
End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when proces…