@soedinglab

Söding Lab

Quantitative and Computational Biology

Pinned repositories

  1. MMseqs2

    MMseqs2: ultra fast and sensitive search and clustering suite

    C++ 167 25

  2. hh-suite

    Remote protein homology detection suite.

    C++ 82 47

  3. WIsH

    Predict prokaryotic host for phage metagenomic sequences

    C++ 7 3

  4. BaMMmotif2

    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.

    C++ 3 3

  5. PEnG-motif

    PEnG-motif is an open-source software package for searching statistically overrepresented motifs (position specific weight matrices, PWMs) in a set of DNA sequences.

    C++ 3 3

  6. b-lore

    Bayesian multiple logistic regression for GWAS meta-analysis

    Python 2

  • MMseqs2: ultra fast and sensitive search and clustering suite

    C++ 167 25 GPL-3.0 Updated Oct 17, 2018
  • Benchmark and plotting scripts for the MERLoT paper

    R Updated Oct 15, 2018
  • Remote protein homology detection suite.

    C++ 82 47 Updated Oct 10, 2018
  • Reconstruct the lineage topology of a scRNA-seq differentiation dataset.

    R 8 3 GPL-3.0 Updated Oct 9, 2018
  • Protein-Level ASSembler (PLASS): sensitive and precise protein assembler

    C++ 11 1 GPL-3.0 Updated Sep 24, 2018
  • A GUI for MMseqs2 to run on your workstation or servers

    JavaScript 2 GPL-3.0 Updated Sep 19, 2018
  • MMSeqs2 tutorial for metagenomics sequence data

    TeX 1 Updated Sep 8, 2018
  • data analysis scripts for degradation pathway story

    Jupyter Notebook Updated Aug 20, 2018
  • PAR-CLIP data processing pipeline

    Python 1 GPL-3.0 Updated Aug 20, 2018
  • Development branch of GxPRED

    Python Updated Aug 15, 2018
  • Predict prokaryotic host for phage metagenomic sequences

    C++ 7 3 GPL-3.0 Updated Aug 14, 2018
  • 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.

    C++ 3 3 GPL-3.0 Updated Aug 10, 2018
  • Webserver for motif discovery with higher-order Bayesian Markov Models (BaMMs)

    HTML 2 2 AGPL-3.0 Updated Aug 9, 2018
  • De-novo motif discovery and optimization

    Python 1 1 GPL-3.0 Updated Aug 8, 2018
  • C 4 1 Updated Aug 2, 2018
  • PRObabilistic Simulations of ScRNA-seq Tree-like Topologies

    Python 8 3 GPL-3.0 Updated Aug 1, 2018
  • HTML 4 1 AGPL-3.0 Updated Jul 5, 2018
  • Contains plotting scripts, examples, and other small scripts relevant to CCMgen and the corresponding publication.

    Python Updated Jul 4, 2018
  • An R package with evaluation and visualization functions for the python PROSSTT package

    HTML Updated Jun 21, 2018
  • PEnG-motif is an open-source software package for searching statistically overrepresented motifs (position specific weight matrices, PWMs) in a set of DNA sequences.

    C++ 3 3 GPL-3.0 Updated Jun 13, 2018
  • Bayesian multiple logistic regression for GWAS meta-analysis

    Python 2 GPL-3.0 Updated Jun 12, 2018
  • Scripts to generate the pdb70 database for hh-suite on the basis of pdb's mmcif format

    Shell 1 3 Updated May 31, 2018
  • Shell 2 1 Updated Apr 24, 2018
  • Modification of the scipy csgraph class to allow tracking of the visited nodes

    Python 1 Updated Apr 11, 2018
  • Jupyter Notebook Updated Mar 27, 2018
  • Bayesian Markov Model motif discovery - An expectation maximization algorithm for the de novo discovery of enriched motifs as modelled by higher-order Markov models.

    C++ 15 2 GPL-3.0 Updated Oct 5, 2017
  • Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.

    C 29 12 AGPL-3.0 Updated Sep 23, 2017
  • Scripts to generate the pfam database for hh-suite

    Shell Updated Jul 18, 2017
  • XXmotif: eXhaustive, weight matriX-based motif discovery in nucleotide sequences

    Perl GPL-3.0 Updated Apr 21, 2017
  • 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).

    C++ 7 1 Updated Mar 1, 2017