Schulz Lab


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Alternative publication profile on google scholar here
*shared first authorship,^shared corresponding authorship


Compute interactions and TF scores with STARE [94] D Hecker, F Behjati Ardakani, MH Schulz
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data,

[93] F Drews, A Salhab, S Karunanithi, M Cheaib, M Jung, MH Schulz, M Simon
Broad domains of histone marks in the highly compact Paramecium macronuclear genome,

Graph-based transcript quantification and fusion-gene detection with Aeron [92] M Rautiainen, DA Durai , Y Chen, L Xin, H Meng Low, J Göke, T Marschall^, MH Schulz^
AERON: Transcript quantification and gene-fusion detection using long reads,

[91] J Grau^, F Schmidt, MH Schulz^
Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models,



[90] N Katsaouni, F Aul, L Krischker, S Schmalhofer, L Hedrich, MH Schulz
Energy efficient convolutional neural networks for arrhythmia detection,
Array, Vol 13, March 2022, 100127, full text

[89] R Schulze-Brüning, L Tombor, MH Schulz, S Dimmeler, D John
Comparative Analysis of common alignment tools for single cell RNA sequencing,
Gigascience. 2022 Jan 27;11:giac001, full text


[88] F Drews, S Karunanithi, U Götz, S Marker, R deWijn, M Pirritano, AM Rodrigues-Viana, M Jung, G Gasparoni, MH Schulz , M Simon
Two Piwis with Ago-like functions silence somatic genes at the chromatin level,
RNA Biology, Vol 18, pages 757-769, full text

Learning gene regulatory elements from epigenetics data STITCHIT [87] F Schmidt, A Marx, N Baumgarten, M Hebel, M Wegner, M Kaulich, M Leisegang, RP Brandes, J Göke, J Vreeken, MH Schulz
Integrative analysis of epigenetics data identifies gene-specific regulatory elements,
Nucleic Acids Res. 2021 Sep 11:gkab798, full text

[86] J Hoppstädter, A Dembek, M Höring, HS Schymik, C Dahlem, A Sultan, N Wirth, S Al-Fityan, B Diesel, G Gasparoni, J Walter, V Helms, H Huwer, M Simon, G Liebisch, MH Schulz, AK Kiemer
Dysregulation of cholesterol homeostasis in human lung cancer tissue and tumour-associated macrophages,
EBioMedicine. 2021 Sep 24;72:103578, full text

[85] B Serrano-Solano, MC Föll, C Gallardo-Alba, A Erxleben, H Rasche, S Hiltemann, M Fahrner, MJ Dunning, MH Schulz, B Scholtz, D Clements, A Nekrutenko, B Batut, BA Grüning
Fostering accessible online education using Galaxy as an e-learning platform,
PLoS Comput Biol . 2021 May 13;17(5):e1008923, full text

[84] N Katsaouni, A Tashkandi, L Wiese, MH Schulz
Machine learning based disease prediction from genotype data,
Biological Chemistry, 2021 Jul 5;402(8):871-885, full text

[83] J Fischer, F Behjati Ardakani, K Kattler, J Walter MH Schulz
CpG content-dependent associations between transcription factors and histone modifications,
PLoS One, 2021 Apr 15;16(4):e0249985, full text

[82] F Aul , N Katsaouni , L Krischker, S Schmalhofer, MH Schulz and L Hedrich
Schematic Generation of Programmable Analog Neural Networks for Signal Proccessing,
International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), Erlangen, Germany, July 2021

[81] N Baumgarten*, F Schmidt*, M Wegner, M Hebel, M Kaulich, MH Schulz
Computational prediction of CRISPR-impaired non-coding regulatory regions,
Biological Chemistry, 2021 Mar 2;402(8):973-982, full text | preprint

[80] T Warwick, MH Schulz, S Günther, R Gilsbach, A Neme, C Carlberg, RP Brandes, S Seuter
A hierarchical regulatory network analysis of the vitamin D induced transcriptome reveals novel regulators and complete VDR dependency in monocytes,
Scientific Reports, 6518(2021), full text

[79] M Scherer *, F Schmidt*, O Lazareva *, J Walter, J Baumbach, MH Schulz, M List.
Machine learning for deciphering cell heterogeneity and gene regulation,
Nature Computational Sciene, full text

[78] LS Tombor, D John, SF Glaser, G Luxán, E Forte, M Furtado, N Rosenthal, N Baumgarten, MH Schulz, J Wittig, EM Rogg, Y Manavski, A Fischer, M Muhly-Reinholz, K Klee, M Looso, C Selignow, T Acker, SI Bibli, I Fleming, R Patrick, RP Harvey, WT Abplanalp, S Dimmeler
Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction,
Nature Communications, 681(2021), full text

[77] M Hoffmann, E Pachl, M Hartung, V Stiegler, J Baumbach, MH Schulz, M List
SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions,
NAR Cancer, Vol.3, Issue 1,zcaa042, full text


[76] P Ebert, MH Schulz
Fast Detection of Differential Chromatin Domains with SCIDDO,
Bioinformatics, full text

Predict TF activity in single cells with Triangulate [75] F Behjati Ardakani, K Kattler, T Heinen, F Schmidt, D Feuerborn, G Gasparoni, K Lepikhov, P Nell, JG Hengstler, J Walter, MH Schulz
Prediction of single cell gene expression for transcription factor analysis,
Gigascience, 2020 Oct 30;9(11):giaa113., full text

[74] GK Buchmann, C Schürmann, T Warwick, MH Schulz, M Spaeth, OJ Müller, K Schröder, H Jo, N Weissmanng, RP Brandes
Deletion of NoxO1 limits atherosclerosis development in female mice,
Redox Biology, in press, full text

[73] F Aul, N Katsaouni, MH Schulz, L Hedrich
Synthesis of Power-Efficient Analog Neural Networks for Signal Processing,
Proceedings Analog 2020, in press, full text

Access enhancer-gene links at EpiRegio [72] N Baumgarten*, D Hecker*, S Karunanithi*, F Schmidt, M List, MH Schulz.
EpiRegio: Analysis and retrieval of regulatory elements linked to genes,
Nucleic Acids Research in press, full text

[71] JV Valbuena Perez, R Linnenberger, A Dembek, S Bruscoli, C Riccardi, MH Schulz, MR Meyer, AK Kiemer, J Hoppstädter.
Altered glucocorticoid metabolism represents a feature of macroph-aging,
Aging cell, in press, full text

[70] Karunanithi S, Oruganti V, de Wijn R, Drews F, Cheaib M, Nordström K, M Simon^ , MH Schulz^.
Feeding exogenous dsRNA interferes with endogenous sRNA accumulation in Paramecium,
DNA Res. 2020 Apr 27:dsaa005., full text

[69] B Pflüger-Müller, JA Oo, J Heering, T Warwick, E Proschak, S Günther, M Looso, F Rezende, C Fork, G Geisslinger, D Thomas, R Gurke, D Steinhilber, MH Schulz, MS Leisegang, RP Brandes.
The endocannabinoid anandamide has an anti-inflammatory effect on CCL2 expression in vascular smooth muscle cells,
Basic Res Cardiol. 2020 Apr 22;115(3):34, full text

[68] S Chakraborty, S Canzar, T Marschall^, MH Schulz^.
Chromatyping: Reconstructing Nucleosome Profiles from NOMe Sequencing Data,
J Comput Biol. 2020 Mar;27(3):330-341. , full text

[67] V Königs, C de Oliveira Freitas Machado, B Arnold, N Blümel, A Solovyeva, S Löbbert, M Schafranek, I Ruiz De Los Mozos, I Wittig, F McNicoll, MH Schulz, M Müller-McNicoll
SRSF7 maintains its homeostasis through the expression of Split-ORFs and nuclear body assembly,
Nat Struct Mol Biol. 2020 Mar;27(3):260-273. , full text

Use Hi-C or Hi-ChIP data with TEPIC [66] F Schmidt*, F Kern*, MH Schulz.
Integrative prediction of gene expression with chromatin accessibility and conformation data,
Epigenetics Chromatin. 2020 Feb 6;13(1):4. , full text

[65] S Gier, M Simon, G Gasparoni, S Khalifa, MH Schulz, MJ Schmitt, F Breinig
Yeast Viral Killer Toxin K1 Induces Specific Host Cell Adaptions via Intrinsic Selection Pressure,
Appl Environ Microbiol. 2020 Feb 3;86(4):e02446-19 , full text

Use ChIP-seq peak or TF-associated sequences to find a TF's motif MASSIF [64] N Baumgarten, F Schmidt, MH Schulz
Improved linking of motifs to their TFs using domain information,
Bioinformatics. 2020 Mar 1;36(6):1655-1662. , full text


[63] F Behjati*, F Schmidt*, MH Schulz
Predicting transcription factor binding using ensemble random forest models,
F1000 research, [version 2; peer review: 2 approved] , full text

[62] T Kröhler, SM Kessler, K Hosseini, M List, A Barghash, S Patial, S Laggai, K Gemperlein, J Haybaeck, R Müller , V Helms, MH Schulz, J Hoppstädter, PJ Blackshear, AK Kiemer
The mRNA-binding Protein TTP/ZFP36 in Hepatocarcinogenesis and Hepatocellular Carcinoma,
Cancers,2019, 11(11), 1754 , full text

[61] K Nordström, F Schmidt,Nina Gasparoni, A Salhab, G Gasparoni, K Kattler, F Müller, P Ebert, IG Costa, DEEP consortium, N Pfeifer, T Lengauer, MH Schulz^, J Walter^
Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data,
Nucleic Acids Research, gkz799, full text

[60] N Ritter, T Ali, N Kopitchinski, P Schuster, A Beisaw, DA Hendrix, MH Schulz, M Müller-McNicoll, S Dimmeler, P Grote
The lncRNA Locus Handsdown Regulates Cardiac Gene Programs and Is Essential for Early Mouse Development,
Developmenal Cell, 2019 Sep 9;50(5):644-657, full text

[59] E Elwakeel, M Brüggemann, AF Fink, MH Schulz, T Schmid, R Savai, B Brüne, K Zarnack, A Weigert
Phenotypic plasticity of fibroblasts during mammary carcinoma development
Int. J. Mol. Sci. 2019, 20(18), 4438, full text

SPONGE R package
for ceRNA detection
[58] M List, A Dehghani Amirabad, D Kostka, MH Schulz
Large-scale inference of competing endogenous RNA networks with sparse partial correlation,
Bioinformatics, 35:14:i596-i604, ISMB 2019 Proceedings, full text

[57] A Blum, S Khalifa, KN Nordström, M Simon, MH Schulz, MJ Schmitt
Transcriptomics of a KDELR1 knockout cell line reveals modulated cell adhesion properties,
Scientific Reports, 10611 (2019), full text

[56] S Karunanithi, V Oruganti, S Marker, AM Rodriguez-Viana, F Drews, M Pirritano, K Nordström, M Simon^, MH Schulz^
Exogenous RNAi mechanisms contribute to transcriptome adaptation by phased siRNA clusters in Paramecium,
Volume 47, Issue 15, 05 September 2019, Pages 8036–8049, full text

[55] S Gier, M Simon, KN Nordström, S Khalifa, MH Schulz, MJ Schmitt, F Breinig
Transcriptome Kinetics of Saccharomyces cerevisiae in Response to Viral Killer Toxin K1,
Front Microbiol, 2019 May 16;10:1102 full text

[54] DA Durai, MH Schulz
Improving in-silico normalization using read weights,
Scientific Reports, 5133 (2019) full text

[53] S Karunanithi, M Simon, MH Schulz
Automated analysis of small RNA datasets with RAPID,
PeerJ 7:e6710, full text

Better ATAC-seq footprinting due to bias correction HINT-ATAC [52] Z Li, MH Schulz, T Look, M Begemann, M Zenke, IG Costa Filho
Identification of transcription factor binding sites using ATAC-seq,
Genome Biology 2019, 20:45, full text

[51] SM Kessler, K Hosseini, UK Hussein, KM Kim, M List, CS Schultheiss, MH Schulz, S Laggai , KY Jang, AK Kiemer
Hepatocellular carcinoma and nuclear paraspeckles: induction in chemoresistance and prediction for poor survival,
Cell Physiol Biochem 2019;52:787-801, full text

Integrative analysis of epigenomic and expression time series
[50] D Gérard, F Schmidt, A Ginolhac, M Schmitz, R Halder, P Ebert, MH Schulz, T Sauter, L Sinkkonen
Temporal epigenomic profiling identifies AHR and GLIS1 as super-enhancer controlled regulators of mesenchymal multipotency,
Nucleic Acids Research, full text


Single cell analysis of bidirectional promoters. [49] F Behjati Ardakani, K Kattler, KN Nordström, N Gasparoni, G Gasparoni, S Fuchs, A Sinha, M Barann, P Ebert, J Fischer, B Hutter, G Zipprich, CD Imbusch, B Felder, J Eils, B Brors, T Lengauer, T Manke, P Rosenstiehl, J Walter, MH Schulz
Integrative analysis of single cell expression data reveals distinct regulatory states in bidirectional promoters,
Epigenetics & Chromatin, full text

Faster, more species, different workflows TEPIC 2.0 [48] F Schmidt, F Kern, P Ebert, N Baumgarten, MH Schulz
TEPIC 2 - An extended framework for transcription factor binding prediction and integrative epigenomic analysis,
Bioinformatics, bty856, full text

Access the OntologyEval software [47] F Schmidt, M List, E Cukuroglu, S Köhler, J Göke, MH Schulz
An ontology-based method for assessing the performance of batch effects adjustment methods in heterogeneous data sets,
Bioinformatics, Volume 34, Issue 17, i908–i916, Proceedings of ECCB 2018,full text

New features have been added to TEPIC [46] F Schmidt, MH Schulz
On the problem of confounders in modelling gene expression,
Bioinformatics, full text

[45] E Rogg, WT Abplanalp, C Bischof, D John, MH Schulz, J Krishnan, A Fischer, C Poluzzi, L Schaefer, A Bonauer, AM Zeiher, S Dimmeler
Analysis of cell type-specific effects of miR-92a provides novel insights into target regulation and mechanism of action ,
Circulation, full text

[44] A Dehghani Amirabad*, P Ramasamy*, M Wierz*, KN Nordström, SM Kessler^, MH Schulz^, M Simon^
Transgenic expression of the RNA binding protein IMP2 stabilizes miRNA targets in murine microsteatosis ,
BBA - Molecular Basis of Disease, full text

The ORNA software can be downloaded here. [43] DA Durai, MH Schulz
In-silico read normalization with set multicover optimization,
Bioinformatics, full text presented at Recomb-seq 2017

The JAMI software can be accessed here. [42] A Hornakova*, M List*, J Vreeken, MH Schulz
JAMI - Fast computation of Conditional Mutual Information for ceRNA network analysis
Bioinformatics, full text

ChromaClique software can be accessed here. [41] S Chakraborty, S Canzar , Marschall T, MH Schulz
Chromatyping: Reconstructing Nucleosome Profiles from NOMe Sequencing Data
Proceedings RECOMB 2018, in press

[40] M Pirritano, U Götz, S Karunanithi, K Nordström, MH Schulz, M Simon
Environmental temperature controls accumulation of transacting siRNAs involved in heterochromatin formation
Genes 2018, 9(2), 117full text

[39] K Grosser, P Ramasamy, A Dehghani Amirabad, G Gasparoni, MH Schulz, M Simon, M Schrallhammer
More than the ‘killer trait’: infection with the bacterial endosymbiont Caedibacter taeniospiralis causes transcriptomic modulation in Paramecium host
Genome Biology and Evolution, full text


The RegulatorTrail webserver can be accessed here. [38] T Kehl, L Schneider, F Schmidt, D Stöckel, N Gerstner, C Backes,E Meese, A Keller, MH Schulz, HP Lenhof
RegulatorTrail: a web service for the identification of key transcriptional regulators.
Nucleic Acids Research (2017), full text

[37] CS Schultheiss, S Laggai, B Czepukojc, UK Hussein, M List, A Barghash, S Tierling, K Hosseini, N Golob-Schwarz, J Pokorny, N Hachenthal, MH Schulz, V Helms, J Walter, V Zimmer, F Lammert, RM Bohle, L Dandolo, J Haybaeck, AK Kiemer, SM Kessler
The long non-coding RNA H19 suppresses carcinogenesis and chemoresistance in hepatocellular carcinoma.
Cell Stress (2017), full text

[36] MH Schulz, Z Bar-Joseph
Probabilistic models for error correction of non-uniform sequencing data
Algorithms for Next-Generations Sequencing Data: Techniques, Approaches and Applications, Springer Book chapter, full text

[35] D Weese, MH Schulz, H Richard
DNA-Seq error correction based on substring indices
Algorithms for Next-Generations Sequencing Data: Techniques, Approaches and Applications, Springer Book chapter, full text


The TEPIC software can be downloaded here. [34] F Schmidt , N Gasparoni, G Gasparoni, K Gianmoena, C Cadenas, JK Polansky, P Ebert, KJV Nordström, M Barann, A Sinha, S Fröhler, J Xiong, A Dehghani Amirabad, F Behjati Ardakani, B Hutter, G Zipprich, B Felder, E Jürgen Eils, B Brors, W Chen, JG Hengstler, A Hamann, T Lengauer, P Rosenstiel, J Walter, MH Schulz
Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction,
Nucleic Acids Research, 29 November 2016, full text

[33] Durek P, Nordström KJV, Gasparoni G, Salhab A, Kressler C, de Almeida M, Bassler K, Ulas T, Schmidt F, Xiong J, Glažar P, Klironomos F, Sinha A, Kinkley S, Yang X, Arrigoni L, Dehghani Amirabad A, Behjati Ardakani F , Feuerbach L, Gorka O, Ebert P, Müller F, Li N, Frischbutter S, Schlickeiser S, Cendon C, Fröhler S, Felder B, Gasparoni N, Imbusch CD, Hutter B, Zipprich G, Tauchmann Y, Reinke S, Wassilew G, Hoffmann U, Richter AS, Sieverling L; DEEP Consortium., Chang HD, Syrbe U, Kalus U, Eils J, Brors B, Manke T, Ruland J, Lengauer T, Rajewsky N, Chen W, Dong J, Sawitzki B, Chung HR, Rosenstiel P, Schulz MH, Schultze JL, Radbruch A, Walter J, Hamann A, Polansky JK
Epigenomic Profiling of Human CD4+ T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development
Cell Immunity, Volume 45, Issue 5, 15 November 2016, full text

all IHEC papers DEEP press release [32] HG Stunnenberg, The International Human Epigenome Consortium (including F Schmidt, M Schulz), Martin Hirst
The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery
Cell, Volume 167, Issue 5, 17 November 2016, full text

[31] A Dehghani Amirabad, MH Schulz
Multitask regression for condition-specific prioritization of miRNA targets in transcripts
Proceedings of the German Conference for Bioinformatics, 2016 [full text], presented at Recomb-CCB 2016 and ML for Health at UAI 2016

Optimize your assembly with KREATION [30] D Durai, MH Schulz
Informed kmer selection for de novo transcriptome assembly
Bioinformatics, 32 (11): 1670-1677, 2016 [pdf]

[29] U Götz*, S Marker*, M Cheaib*, K Andresen, S Shrestha, D Durai, KJV Nordström, MH Schulz, M Simon
Two sets of RNAi components are required for heterochromatin formation in trans triggered by truncated transgenes
Nucleic Acids Research, 44 (12): 5908-5923, 2016 [pdf]


[28] X He*, AE Cicek*, Y Wang*, MH Schulz , HS Le, Z Bar-Joseph
De novo ChIP-seq Analysis
Genome Biology, 16:205 [full text]

[27] M Cheaib, A Dehghani Amirabad, KJV Nordström, MH Schulz, M Simon
Epigenetic regulation of serotype expression antagonizes transcriptome dynamics in Paramecium tetraurelia
DNA Research, Oxford Journals [full text]

[26] P Ebert, F Müller, K Nordström, T Lengauer, MH Schulz
A General Concept for Consistent Documentation of Computational Analyses
Database, Oxford Journals, 2015 Jun 8 [full text]


Easy to use error correction of genomic reads for indel-prone technologies. Software can be found here . [25] MH Schulz^,* ,D Weese*, M Holtgrewe*, V Dimitrova,S Niu, K Reinert, H Richard^,*
Fiona: a parallel and automatic strategy for read error correction
Bioinformatics 17 (30): i356-i363, ECCB 2014 proceedings [full text]

[24] MH Schulz
Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly
Genome Biology, 10 15: 498 [full text]

[23] T Steijger, JF Abril, PG Engström, F Kokocinski, The RGASP Consortium (including MH Schulz), TJ Hubbard, R Guigó, J Harrow, P Bertone
Assessment of transcript reconstruction methods for RNA-seq
Nature Methods [full text]


On the PNAS cover with commentary by Uwe Ohler [22] MH Schulz, KV Pandit, CL Lino Cardenas, N Ambalavanan, N Kaminski and Z Bar-Joseph
Reconstructing dynamic microRNA-regulated interaction networks
PNAS [full text]

Download SEECER and improve your RNA-seq data! [21] H Le, MH Schulz*, BM MCcauley, V Hinman, and Z Bar-Joseph
Probabilistic error correction for RNA sequencing
Nucleic Acids Research [full text]


Details about the new features of DREM 2.0. Check it out!
[20] MH Schulz*, WE Devanny*, A Gitter, S Zhong, J Ernst and Z Bar-Joseph
DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data
BMC Systems Biology [full text]

An improved method to query for diseases with phenotypes. Test BOQA! [19] S Bauer, S Köhler, MH Schulz and PN Robinson
Bayesian Ontology Querying for Accurate and Noise-Tolerant Semantic Searches
Bioinformatics [full text]

Assemble your Transcriptome with Oases. Subscribe to the mailing list for regular updates. [18] MH Schulz*,DR Zerbino*, M Vingron and E Birney
Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels
Bioinformatics 28 (8): 1086-1092 [full text]

Fast alignment free similarity computation for DNA sequences with ALF [17] J Göke, MH Schulz, J Lasserre and M Vingron
Estimation of Pairwise Sequence Similarity of Mammalian Enhancers with Word Neighbourhood Counts
Bioinformatics 28 (5): 656-663 [full text]

Sensitive detection of structural variations from resequencing data with SplazerS [16] AK Emde, MH Schulz, D Weese, R and Sun, M Vingron, VM Kalscheuer, SA Haas and K Reinert
Detecting genomic indel variants with exact breakpoints in single-and paired-end sequencing data using SplazerS
Bioinformatics 28 (5): 656-663. [full text]


[15] S Roepcke*, S Stahlberg*, H Klein, MH Schulz, L Theobald, S Gohlke, M Vingron and DJ Walther
A tandem sequence motif acts as a distance-dependent enhancer in a set of genes involved in translation by binding the proteins NonO and SFPQ
BMC Genomics, 12:624 [full text]

Exact P-values improve similarity searches in ontologies. Maybe for your problem as well? Try here!
[14] MH Schulz, S Köhler, S Bauer and PN Robinson
Exact score distribution computation for ontological similarity searches
BMC Bioinformatics, 12:441 [full text]

The software can be downloaded here. [13] P Huggins*, S Zhong*, I Shiff, R Beckerman, O Laptenko, C Prives, MH Schulz , I Simon and
Z Bar-Joseph
DECOD: fast and accurate discriminative DNA motif finding
Bioinformatics, 27 (17):2361-67 [full text]


The first paper to predict enhancer-target associations with up to 2Mb distance to the enhancer [12] C Rödelsperger, G Guo, M Kolanczyk, A Pletschacher, S Köhler, S Bauer, MH Schulz, and
PN Robinson
Integrative analysis of genomic, functional and protein interaction data predicts long-range enhancer-target gene interactions
Nucleic Acids Research, 40 (7) [full text]

[11] H Richard*, MH Schulz*, M Sultan*, A Nürnberger, S Schrinner, D Balzereit, E Dagand, A Rasche, H Lehrach, M Vingron, SA Haas, and ML Yaspo
Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments
Nucleic Acids Research, 38 (10):e112 [full text]


Best paper selection IMIA Yearbook of Medical Informatics 2010

[10] S Köhler, MH Schulz, P Krawitz, S Bauer, S Doelken, CE Ott, C Mundlos, D Horn, S Mundlos and PN Robinson
Clinical Diagnostics with Semantic Similarity Searches in Ontologies
The American Journal of Human Genetics, 85 (4):457-64 [full text]

First paper for exact p-value computation in ontology similarity searches. [9] MH Schulz, S Köhler, S Bauer, M Vingron and PN Robinson
Exact Score Distribution Computation for Similarity Searches in Ontologies
Proceedings WABI 2009 , Springer LNCS, Volume 5724, 2009 [full text]

[8] C Rödelsperger, S Köhler, MH Schulz, T Manke, S Bauer and PN Robinson
Short Ultraconserved Promoter Regions Delineate a Class of Ancient, Preferentially Expressed Alternatively Spliced Transcripts
Genomics,94 (5):308-16 [full text]

Best paper award ISMB-SIG Next-Generation Sequencing 2009

[7] K Ye, MH Schulz, Q Long, R Apweiler and Z Ning
Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short read data
Bioinformatics, 25 (21):2865-71 [full text]


Reported in Genome News and evaluated by Faculty of 1000 [6] M Sultan*, MH Schulz*, H Richard*, A Magen, A Klingenhoff, M Scherf, M Seifert, T Borodina, A Soldatov, D Parkhomchuk, D Schmidt, S O'Keeffe, S Haas, M Vingron, H Lehrach and ML Yaspo (2008)
A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome
Science, 321 (5891):956-60 [full text]

[5] MH Schulz, D Weese, T Rausch, A Döring, K Reinert and M Vingron
Fast and adaptive variable order Markov chain construction
Proceedings WABI 2008, Springer LNCS, Volume 5251 [full text]

[4] D Weese and MH Schulz
Efficient string mining under constraints via the deferred frequency index
Industrial Conference for Data Mining (ICDM 2008), LNAI 5077, pp. 374-388 [full text]

[3] W Chen, V Kalscheu, A Tzschach, C Menzel, R Ullmann, MH Schulz, F Erdogan, N Li, Z Kijas, G Arkesteijn, IL Pajares, M Goetz-Sothmann, U Heinrich, I Rost, A Dufke, U Grasshoff, BG Glaeser, M Vingron and HH Ropers
Mapping translocation breakpoints by next-generation sequencing
Genome Research 18: 1143-1149 [full text]

[2] G Guo, S Bauer, J Hecht, MH Schulz, A Busche and PN Robinson (2008)
A short ultraconserved sequence drives transcription from an alternate FBN1 promoter
The International Journal of Biochemistry & Cell Biology, 40(4):638-50 [full text]

[1] MH Schulz*, S Bauer* and PN Robinson (2008)
The generalised k-Truncated Suffix Tree for time- and space- efficient searches in multiple DNA or protein sequences
International Journal of Bioinformatics Research and Applications, 4(1):81-95 [full text]


    MH Schulz
    Data Structures and Algorithms for Analysis of Alternative Splicing with RNA-seq Data (August 2010) [pdf]
    Phd thesis at Max Planck Institute for Molecular Genetics (Dep. Computational Molecular Biology) and the Freie Universität Berlin

    MH Schulz
    Characterisation of Mitochondrial Ribosomal Protein Gene Promoters (2005)
    Bachelor's thesis at Max Planck Institute for Molecular Genetics (Dep. Computational Molecular Biology) and the Freie Universität Berlin