Computational Biology

Group leader : B. Habermann

The Computational Biology group adresses biological problems using computational methods. We have two major research directions:

  1. we are interested in large-scale data integration, focusing on mitochondrial function in development and disease
  2. we are developing methods to work with remote sequence similarities, with the focus on de novo prediction of short, functional motifs in proteins.


Computer-driven analysis has complemented biological research for a long time. With the beginning of sequencing and deciphering the genetic code, methods were developed that help analyze this data. The sequencing of parts of or entire genomes has for instance enabled us to establish a fine-tuned view on the evolution of species.

In recent years, large-scale screens and next generation sequencing is generating a tremendous amount of data, which cannot be analyzed – or understood – without the help of computational techniques. Our lab is working in computer-assisted analysis of biological data.


The Computational Biology group is actively involved in two main research directions: the integration of large-scale data and working with remote protein sequence similarities.

Research interest 1: Integration of large-scale data

We work on the integration of large-scale data from different sources to extract meaningful biological information. We use mostly NGS-data, integrating differential expression with ChIP-seq or interactome data to provide biologists with testable hypothesis for further experimental studies. To this end, we develop data integration methods that are easy to use for non-experts.

To show feasibility of our methods, we have chosen the mitochondrial system, as it represents the central organelle for metabolic functions and energy production in the cell. It is experimentally very well characterized in terms of protein content and enzymatic pathways. Therefore, it enables us to look at changes in mitochondrial function in differing cellular conditions.

MitoXplorer: understanding mitochondrial function in health and disease

We are developing the MitoXplorer platform, an integrative web-tool to integrate large-scale expression and mutation data with the mitochondrial interactome and mitochondrial processes. Using specialized pipelines for NGS-data analysis, we extract mutation and expression data for all proteins localized to mitochondria and having mitochondrial function, irrespective of their genomic localization (mitochondrial or nuclear genome). We integrate expression and mutation data with a manually assembled and curated mitochondrial interactome and visualize observed changes in different experimental or disease conditions. This enables us to rapidly and visually compare different data-sets with respect to their mitochondrial functions.

This project is supported by DFG grant ‘Systems biological analysis of cancer genomes using deductive databases’ and the ANR grant ‘MITO-DYNAMICS’.

The pipeline of the mitoXplorer web-platform.

Biological networks for data analysis, integration and visualization
Biological networks such as protein-protein interaction networks or gene regulatory networks are an integral part to understand biological systems. We use such networks to interpret and integrate large-scale data coming from expression studies. We have developed several algorithms for network analysis and visualization:

1) miMerge and miScore for the generation of non-redundant protein interaction networks (Villaveces, et al., Database, 2015)

2) KEGGViewer (Villaveces, et al., F1000Res 3:43, 2014) for the visualization and integration of pathway data; and PsiquicGraph (Villaveces, et al., F1000Res 3:44, 2014) both available via the BioJS platform;

3) the Cytoscape plugins viPEr for generating focus networks based on -omics data and PEANUT for pathway enrichment of focus networks (Garmhausen et al., BMC Genomics 16:790, 2015)


viPEr-networks of Statin-treated human hepatocytes. (A) Focus network of all differentially regulated genes upon Atorvastatin treatement and direct Atorvastatin targets. (B) Focus networks based on same data between the Transcription Factors FoxA1/A2/A3 and all the main Atorvastatin target, HMGCR.

viPEr-networks of Statin-treated human hepatocytes. (A) Focus network of all differentially regulated genes upon Atorvastatin treatement and direct Atorvastatin targets. (B) Focus networks based on same data between the Transcription Factors FoxA1/A2/A3 and all the main Atorvastatin target, HMGCR.

Research interest 2: Working with remote sequence similarity – motif de novo prediction and orthology detection in the midnight zone of sequence similarity

Our Darwinian view on evolution states that evolution is the result of random changes of our genetic code combined with the process of natural selection. Many small changes over a long period of time have a major evolutionary impact. As a result, even true orthologs can share only low sequence similarity, which we refer to as conservation in the twilight or midnight zone.

Our group is interested in detecting sequence relationships in the twilight and midnight zone.

HH-MOTiF: de novo detection of functional short linear motifs in proteins
Protein motifs are defined as self-sufficient functional units. They are typically only between 3 and 23 amino acids long and have various functions in proteins. They can serve as cleavage sites, are required for proteasomal degradation, are involved in docking and ligand binding, serve as signals for post-translational modification or are signals for subcellular localization.

Their shortness and the fact that they typically lack substantial sequence conservation makes them very difficult to find de novo – i.e. without prior information on the localization or nature of the motif. We are using evolutionary restricted Hidden Markov Model (HMM) comparison in combination with a hierarchical model of motif trees to identify short functional motifs in proteins de novo (Prytuliak, et al., NAR 45 (W1):W470-W477, 2017). In collaboration with wet-lab researchers, we experimentally test our predicted motifs.

Representation of a motif tree

If you are interested, please visit the link.

morFeus: remote orthology detection
We are interested in discovering remote orthologs. Identifying orthologous proteins is one of the key tasks in computational biology: we need to know a protein’s orthologs to understand its evolution. Orthologs also tell us, whether the process a protein is involved in, is conserved beyond model species and across kingdoms.

Orthologs are equally important for wet-lab research: we transfer functional information across orthologous proteins and can therefore provide testable hypothesis for a protein’s function for uncharacterized proteins.

The level of sequence conservation even between orthologs is however sometimes below the detection limit of standard software and settings.

We have addressed this problem and developed a web-based method, morFeus (Wagner, et al., BMC Bioinformatics 15 (1), 263, 2014) for the detection of orthologs in the twilight and midnight zone of sequence similarity.

We compare weighted, binary representations of sequence alignments from a relaxed BLAST search and cluster hits based on their similarity to the query. Iterative reciprocal BLAST searches are carried out to verify orthology. Not only the query, but also other verified orthologs can establish orthology and include further hits for back-BLASTs. In a final step, a network of orthology (see figure) is created and a score independent of the BLAST E-value is calculated for putative orthologs using centrality scoring. We have tested morFeus against the state-of-the-art resources HomoloGene and Inparanoid and achieve significantly higher sensitivity with equal specificity.

morFeus network of orthology for fission yeast Apc13, a subunit of the anaphase Promoting Complex.

morFeus network of orthology for fission yeast Apc13, a subunit of the anaphase Promoting Complex.


Selected publications


mitoXplorer, a Visual Data Mining Platform to Systematically Analyze and Visualize Mitochondrial Expression Dynamics and Mutations

Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, Cecilia Garcia-Perez, Jose Villaveces, Salma Gamal, Giovanni Cardone, Fabiana Perocchi, Zuzana Storchova, Bianca H Habermann
Nucleic Acids Research PMID: 31799603


Whole-genome Comparison Between the Type Strain of Halobacterium Salinarum (DSM 3754 T ) and the Laboratory Strains R1 and NRC-1

Friedhelm Pfeiffer, Gerald Losensky, Anita Marchfelder, Bianca Habermann, Mike Dyall-Smith
Microbiologyopen PMID: 31797576


Phenotypic and genomic comparison of Photorhabdus luminescens subsp. laumondii TT01 and a widely used rifampicin-resistant Photorhabdus luminescens laboratory strain

Zamora-Lagos MA, Eckstein S, Langer A, Gazanis A, Pfeiffer F, Habermann B, Heermann R.
BMC Genomics. 2018 Nov 29;19(1):854. doi: 10.1186/s12864-018-5121-z. PMID: 30497380


Hypermethylation of gene body CpG islands predicts high dosage of functional oncogenes in liver cancer

Arechederra M, Daian F, Yim A, Bazai SK, Richelme S, Dono R, Saurin AJ, Habermann BH, Maina F.
Nat Commun. 2018 Aug 8;9(1):3164. doi: 10.1038/s41467-018-05550-5. PMID: 30089774


A transcriptomics resource reveals a transcriptional transition during ordered sarcomere morphogenesis in flight muscle.

Spletter ML, Barz C, Yeroslaviz A, Zhang X, Lemke SB, Bonnard A, Brunner E, Cardone G, Basler K, Habermann BH, Schnorrer F.
Elife. 2018 May 30;7. pii: e34058. doi: 10.7554/eLife.34058. PMID: 29846170


Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

Krempel R, Kulkarni P, Yim A, Lang U, Habermann B, Frommolt P.
BMC Bioinformatics. 2018 Apr 24;19(1):156. doi: 10.1186/s12859-018-2157-7. PMID: 29699486


The deregulated microRNAome contributes to the cellular response to aneuploidy.

Dürrbaum M, Kruse C, Nieken KJ, Habermann B, Storchová Z.
BMC Genomics. 2018 Mar 14;19(1):197. doi: 10.1186/s12864-018-4556-6. PMID: 29703144


SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data

Prytuliak R, Pfeiffer F, Habermann BH.
BMC Bioinformatics. 2018 Jan 26;19(1):24. doi: 10.1186/s12859-018-2020-x. PMID: 29373955


The axolotl genome and the evolution of key tissue formation regulators.

Nowoshilow S, Schloissnig S, Fei JF, Dahl A, Pang AWC, Pippel M, Winkler S, Hastie AR, Young G, Roscito JG, Falcon F, Knapp D, Powell S, Cruz A, Cao H, Habermann B, Hiller M, Tanaka EM, Myers EW.
Nature. 2018 Jan 24. doi: 10.1038/nature25458. PMID: 29364872


The complete and fully assembled genome sequence of Aeromonas salmonicida subsp. pectinolytica and its comparative analysis with other Aeromonas species: investigation of the mobilome in environmental and pathogenic strains.

Pfeiffer F, Zamora-Lagos MA, Blettinger M, Yeroslaviz A, Dahl A, Gruber S, Habermann BH.
BMC Genomics. 2018 Jan 5;19(1):20. doi: 10.1186/s12864-017-4301-6. PMID: 29304740


HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons

Roman Prytuliak, Michael Volkmer, Markus Meier, Bianca H. Habermann
Nucleic Acids Res. 2017 Apr 29. PMID: 28460141


Revision and reannotation of the Halomonas elongata DSM 2581T genome.

Pfeiffer F, Bagyan I, Alfaro-Espinoza G, Zamora-Lagos MA, Habermann B, Marin-Sanguino A, Oesterhelt D, Kunte HJ.
Microbiologyopen. 2017 Aug;6(4). PMID: 28349658


A Guide to Computational Methods for Predicting Mitochondrial Localization.

Sun S, Habermann BH.
Methods Mol Biol. 2017;1567:1-14. PMID: 28276009


Oh Brother, Where Art Thou? Finding Orthologs in the Twilight and Midnight Zones of Sequence Similarity

Habermann BH.
Evolutionary Biology: Convergent Evolution, Evolution of Complex Traits, Concepts and Methods (Springer): pp393-419


Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways.

Garmhausen M, Hofmann F, Senderov V, Thomas M, Kandel BA, Habermann BH.
BMC Genomics. 2015 Oct 14;16:790. PMID: 26467653


Tools for visualization and analysis of molecular networks, pathways, and -omics data.

Villaveces JM, Koti P, Habermann BH.
Adv Appl Bioinform Chem. 2015 Jun 4;8:11-22. PMID: 26082651


Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study.

Villaveces JM, Jiménez RC, Porras P, Del-Toro N, Duesbury M, Dumousseau M, Orchard S, Choi H, Ping P, Zong NC, Askenazi M, Habermann BH, Hermjakob H.
Database (Oxford). 2015 Feb 4;2015. PMID: 25652942


morFeus: a web-based program to detect remotely conserved orthologs using symmetrical best hits and orthology network scoring.

Wagner I, Volkmer M, Sharan M, Villaveces JM, Oswald F, Surendranath V, Habermann BH.
BMC Bioinformatics. 2014 Aug 6;15:263. PMID: 25096057


KEGGViewer, a BioJS component to visualize KEGG Pathways.

Villaveces JM, Jimenez RC2, Habermann BH.
F1000Res. 2014 Feb 13;3:43. PMID: 24715980


PsicquicGraph, a BioJS component to visualize molecular interactions from PSICQUIC servers.

Villaveces JM, Jimenez RC, Habermann BH.
F1000Res. 2014 Feb 13;3:44. PMID: 25075287


Designing efficient and specific endoribonuclease-prepared siRNAs.

Surendranath V, Theis M, Habermann BH, Buchholz F.
Methods Mol Biol. 2013;942:193-204. PMID: 23027053


HMMerThread: detecting remote, functional conserved domains in entire genomes by combining relaxed sequence-database searches with fold recognition.

Bradshaw CR, Surendranath V, Henschel R, Mueller MS, Habermann BH.
PLoS One. 2011 Mar 10;6(3):e17568. PMID: 21423752


SeLOX--a locus of recombination site search tool for the detection and directed evolution of site-specific recombination systems.

Surendranath V, Chusainow J, Hauber J, Buchholz F, Habermann BH.
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W293-8. PMID: 20529878


Genome-wide resources of endoribonuclease-prepared short interfering RNAs for specific loss-of-function studies.

Kittler R*, Surendranath V*, Heninger AK, Slabicki M, Theis M, Putz G, Franke K, Caldarelli A, Grabner H, Kozak K, Wagner J, Rees E, Korn B, Frenzel C, Sachse C, Sönnichsen B, Guo J, Schelter J, Burchard J, Linsley PS, Jackson AL, Habermann B#, Buchholz F#. * - equal contribution; # - co-corresponding authors
Nat Methods. 2007 Apr;4(4):337-44. PMID: 17351622


ProFAT: a web-based tool for the functional annotation of protein sequences.

Bradshaw CR, Surendranath V, Habermann B.
BMC Bioinformatics. 2006 Oct 23;7:466. PMID: 17059594


An Ambystoma mexicanum EST sequencing project: analysis of 17,352 expressed sequence tags from embryonic and regenerating blastema cDNA libraries.

Habermann B#, Bebin AG, Herklotz S, Volkmer M, Eckelt K, Pehlke K, Epperlein HH, Schackert HK, Wiebe G, Tanaka EM#. # - co-corresponding authors
Genome Biol. 2004;5(9):R67. Epub 2004 Aug 13. PMID: 15345051


DEQOR: a web-based tool for the design and quality control of siRNAs.

Henschel A, Buchholz F, Habermann B.
Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W113-20. PMID: 15215362


The BAR-domain family of proteins: a case of bending and binding?

Habermann B.
EMBO Rep. 2004 Mar;5(3):250-5. PMID: 14993925


The power and the limitations of cross-species protein identification by mass spectrometry-driven sequence similarity searches.

Habermann B, Oegema J, Sunyaev S, Shevchenko A.
Mol Cell Proteomics. 2004 Mar;3(3):238-49. PMID: 14695901


Evaluating the landscape of gene cooperativity with receptor tyrosine kinases in liver tumorigenesis using transposon-mediated mutagenesis

Fan Y, Bazai SK, Daian F, Arechederra M, Richelme S, Temiz NA, Yim A, Habermann BH, Dono R, Largaespada DA, Maina F.
J Hepatol. 2018 Dec 6. pii: S0168-8278(18)32578-9. doi: 10.1016/j.jhep.2018.11.027. PMID: 30529386


High-resolution TADs reveal DNA sequences underlying genome organization in flies.

Ramírez F, Bhardwaj V, Arrigoni L, Lam KC, Grüning BA, Villaveces J, Habermann B, Akhtar A, Manke T.
Nat Commun. 2018 Jan 15;9(1):189. doi: 10.1038/s41467-017-02525-w. PMID: 29335486


Structure of a Cytoplasmic 11-Subunit RNA Exosome Complex.

Kowalinski E, Kögel A, Ebert J, Reichelt P, Stegmann E, Habermann B, Conti E.
Mol Cell. 2016 Jul 7;63(1):125-34. doi: 10.1016/j.molcel.2016.05.028. PMID: 27345150


Secretory cargo sorting by Ca2+-dependent Cab45 oligomerization at the trans-Golgi network.

Crevenna AH, Blank B, Maiser A, Emin D, Prescher J, Beck G, Kienzle C, Bartnik K, Habermann B, Pakdel M, Leonhardt H, Lamb DC, von Blume J.
J Cell Biol. 2016 May 9;213(3):305-14. doi: 10.1083/jcb.201601089. PMID: 27138253


Human Holliday junction resolvase GEN1 uses a chromodomain for efficient DNA recognition and cleavage.

Lee SH, Princz LN, Klügel MF, Habermann B, Pfander B, Biertümpfel C.
Elife. 2015 Dec 18;4. pii: e12256.


The RNA-binding protein Arrest (Bruno) regulates alternative splicing to enable myofibril maturation in Drosophila flight muscle.

Spletter ML, Barz C, Yeroslaviz A, Schönbauer C, Ferreira IR, Sarov M, Gerlach D, Stark A, Habermann BH, Schnorrer F.
EMBO Rep. 2015 Feb;16(2):178-91. PMID: 25532219


MTERF1 binds mtDNA to prevent transcriptional interference at the light-strand promoter but is dispensable for rRNA gene transcription regulation.

Terzioglu M, Ruzzenente B, Harmel J, Mourier A, Jemt E, López MD, Kukat C, Stewart JB, Wibom R, Meharg C, Habermann B, Falkenberg M, Gustafsson CM, Park CB, Larsson NG.
Cell Metab. 2013 Apr 2;17(4):618-26. doi: 10.1016/j.cmet.2013.03.006. PMID: 23562081

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Stephen Chapman

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Paul De Boissier

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Paulina Garcia Gonzalez

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Sébastien Tempel

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