Biology & Genomics

Quantify miRNAs and isomiRs fast with miRge3.0 against miRBase or MirGeneDB

Trim small RNA adapters and collapse miRNA and piRNA reads with cutadapt

Predict and rank miRNA target genes with seed-based and validated databases

Profile tRNA fragments, piRNAs and snoRNA-derived small RNAs with MINTmap

Build single-cell-like units from Visium HD and Stereo-seq spatial bins

Segment cells and extract image features from Xenium, MERFISH and CosMx data

Map ligand-receptor communication in spatial transcriptomics with Squidpy and COMMOT

Load Visium, Xenium, MERFISH and Slide-seq data into AnnData or SpatialData

Estimate cell type composition per spot in spatial data with cell2location and RCTD

Find spatially coherent tissue domains in spatial transcriptomics data

Combine spatial RNA with protein or ATAC data in multiomic spatial experiments

Build spatial neighbor graphs with kNN, Delaunay and Visium hex grids

Set QC thresholds and normalize spatial transcriptomics data without losing cells

Analyze CODEX, MIBI, IMC and CyCIF protein images as intensity data

Detect spatially variable genes and cell colocalization with Squidpy

Plot spatial expression and clusters on tissue with Squidpy and Scanpy

Predict whether a DNA variant alters splicing with SpliceAI and Pangolin

Check RNA-seq quality for splicing analysis with junction saturation and design audits

Quantify percent spliced in from RNA-seq with rMATS, SUPPA2 and MAJIQ

Download raw sequencing reads from SRA and ENA with sra-tools

Retrieve and read AlphaFold database models with pLDDT and PAE confidence

Detect ligand-binding pockets on apo protein structures with fpocket and P2Rank

Measure distances, angles, RMSD and SASA on protein structures with Bio.PDB

Map protein interfaces and buried surface area from structures with Bio.PDB

Predict protein and complex structures with ESMFold, AlphaFold and Boltz

Read, write and convert macromolecular structure files with Bio.PDB

Transform, renumber and clean protein structures in place with Bio.PDB

Navigate Bio.PDB structure hierarchies safely with altloc and insertion codes

Prepare protein structures for docking and MD with hydrogens and protonation

Judge protein model reliability from resolution, B-factors and MolProbity scores

Filter compound libraries with SMARTS substructure patterns and filter catalogs

Build and simulate multi-species metabolic community models with MICOM

Build tissue-specific metabolic models by integrating expression data

Run flux balance analysis and flux variability on genome-scale metabolic models

Predict gene essentiality and synthetic lethality in metabolic models with COBRApy

Build draft genome-scale metabolic models from genomes with CarveMe and gapseq

Validate and gap-fill genome-scale metabolic models with memote and COBRApy

Design metabolic engineering knockouts with OptKnock and minimal cut sets

Reconstruct B-cell clonal families and lineage trees with Immcantation

Assemble TCR and BCR clonotypes from V(D)J reads with MiXCR presets

Draw TCR and BCR repertoire figures like V-J chord plots and clonal tracking

Integrate single-cell TCR and BCR data with gene expression using scirpy

Map TCR and BCR sequences to candidate antigen specificity with clear caveats

Compute immune repertoire diversity and clonal overlap with VDJtools and immunarch

Test rhythmicity at 24-hour period in time-series omics with cosinor and JTK

Compare how rhythms change between conditions in circadian and time-series omics

Discover periodic signals of unknown period in irregular omics time series

Cluster temporally variable genes by trajectory shape with Mfuzz and tslearn

Infer time-delayed gene regulatory edges from bulk time-series expression