Söding Lab

MMseqs2: ultra fast and sensitive search and clustering suite

soedinglab

Remote protein homology detection suite.

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MetaEuk - sensitive, high-throughput gene discovery and annotation for large-scale eukaryotic metagenomics

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sensitive and precise assembly of short sequencing reads

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Bayesian Markov Model motif discovery - An expectation maximization algorithm for the de novo discovery of enriched motifs as modelled by higher-order Markov models.

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Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.

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kClust is a fast and sensitive clustering method for the clustering of protein sequences. It is able to cluster large protein databases down to 20-30% sequence identity. kClust generates a clustering...

soedinglab

Reconstruct the lineage topology of a scRNA-seq differentiation dataset.

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MMseqs2 app to run on your workstation or servers

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