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MMseqs2: ultra fast and sensitive search and clustering suite
Benchmark and plotting scripts for the MERLoT paper
Remote protein homology detection suite.
Reconstruct the lineage topology of a scRNA-seq differentiation dataset.
Protein-Level ASSembler (PLASS): sensitive and precise protein assembler
A GUI for MMseqs2 to run on your workstation or servers
MMSeqs2 tutorial for metagenomics sequence data
data analysis scripts for degradation pathway story
PAR-CLIP data processing pipeline
Development branch of GxPRED
Predict prokaryotic host for phage metagenomic sequences
Bayesian Markov Model motif discovery tool version 2 - An expectation maximization algorithm for the de novo discovery of enriched motifs as modelled by higher-order Markov models.
Webserver for motif discovery with higher-order Bayesian Markov Models (BaMMs)
De-novo motif discovery and optimization
PRObabilistic Simulations of ScRNA-seq Tree-like Topologies
Contains plotting scripts, examples, and other small scripts relevant to CCMgen and the corresponding publication.
An R package with evaluation and visualization functions for the python PROSSTT package
PEnG-motif is an open-source software package for searching statistically overrepresented motifs (position specific weight matrices, PWMs) in a set of DNA sequences.
Bayesian multiple logistic regression for GWAS meta-analysis
Scripts to generate the pdb70 database for hh-suite on the basis of pdb's mmcif format
Modification of the scipy csgraph class to allow tracking of the visited nodes
Bayesian Markov Model motif discovery - An expectation maximization algorithm for the de novo discovery of enriched motifs as modelled by higher-order Markov models.
Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.
Scripts to generate the pfam database for hh-suite
XXmotif: eXhaustive, weight matriX-based motif discovery in nucleotide sequences
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 where each cluster is represented by its longest sequence (representative sequence).