Biology & Genomics

Extract and deduplicate reads by UMI to count molecules accurately with umi_tools

Parse FASTA, FASTQ, GenBank and ABI sequence files with Biopython SeqIO

Find translated ORFs including uORFs and novel ORFs from Ribo-seq data

Trim, size select and align ribosome profiling reads before translation analysis

Check ribosome profiling 3 nucleotide periodicity and P site offsets

Detect ribosome pause sites and stalling at codon resolution from Ribo-seq

Calculate translation efficiency by comparing ribosome occupancy with mRNA levels

Check RNA-seq libraries for rRNA contamination, strandedness and coverage bias

Draw sashimi plots of RNA-seq coverage and splice junction read counts

Compute N50, length distributions and GC content for sequence datasets

Find structural analogs by Tanimoto similarity and cluster compounds with Butina

Integrate scRNA-seq batches with Harmony, scVI and fastMNN while keeping biology

Label single cell types automatically with CellTypist, SingleR and Azimuth

Infer ligand receptor cell communication networks with CellChat and LIANA

Cluster single cell data with PCA, Leiden and UMAP in Seurat or Scanpy

Load 10X data and convert between h5ad, RDS, Seurat and AnnData objects

Remove doublet cells from scRNA-seq data with Scrublet and scDblFinder

Reconstruct cell lineage trees from CRISPR barcodes or mitochondrial mutations

Find cluster marker genes and assign cell type labels in Seurat or Scanpy

Infer metabolite mediated signaling between cell types from scRNA-seq with MeboCost

Analyze CITE-seq, Multiome and other multi modal single cell datasets

Analyze Perturb-seq and CROP-seq pooled CRISPR screens with single cell RNA

Filter, normalize and select genes for single cell RNA-seq in Seurat or Scanpy

Analyze single cell ATAC-seq with Signac or ArchR including peaks and motifs

Analyze alternative splicing per cell with BRIE2 or leafcutter2

Infer developmental trajectories and RNA velocity from single cell data

Segment cells and extract features from H&E or IF tissue images in spatial data

Infer ligand receptor signaling between cells in spatial transcriptomics data

Load Visium, Xenium and MERFISH spatial data into Python objects

Estimate cell type mixtures in spatial spots with cell2location, RCTD or Tangram

Find tissue domains by clustering spots with expression and spatial context

Analyze high resolution spatial data from Slide-seq, Stereo-seq and Visium HD

Build kNN, Delaunay and radius neighbor graphs for spatial transcriptomics

Run QC, filtering and normalization on spatial transcriptomics spots and cells

Analyze CODEX, IMC and MIBI spatial proteomics with segmentation and colocalization

Compute Moran I, Geary C and co-occurrence statistics for spatial data

Plot gene expression and clusters over histology images for spatial data

Check RNA-seq data quality for splicing with junction saturation and RSeQC

Quantify splicing PSI values from RNA-seq with SUPPA2 or rMATS-turbo

Access AlphaFold predicted structures and read their pLDDT confidence scores

Predict protein and complex structures with AlphaFold3, ESMFold, Chai-1 and Boltz

Find compounds that contain a substructure using SMARTS patterns in RDKit

Analyze B cell receptor mutation, clonal lineages and phylogenetics with Immcantation

Assemble TCR and BCR clonotypes from raw repertoire reads with MiXCR

Draw circos, clone tracking and diversity figures for immune repertoires

Combine single cell TCR and BCR data with gene expression using scirpy

Measure immune repertoire diversity and shared clonotypes with VDJtools

Annotate variants with consequences and clinical data using VEP and SnpEff

Call SNPs and indels from aligned reads with bcftools mpileup and call