The Kaderali Lab
Institute of Bioinformatics · University Medicine Greifswald · University of Greifswald
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Homepage of Stefan Simm
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Prof. Dr. Stefan SimmInstitute of BioinformaticsFelix-Hausdorff-Str. 8
D-17475 Greifswald
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Walther-Rathenau-Str. 48, Room A14.0.22
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+49 (0)3834 86-5443
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+49 (0)3834 86-5442
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- stefan.simm{at}uni-greifswald.de
Research interests
-
Classification via Machine Learning: Information extraction from sequence, structure and phenotypic features to classify transcriptome wide non-coding RNAs by their species
-
Integrated Multi-Omics approaches: Development of Machine Learning and visualization approaches to correlate different layer of -Omics and phenotypic features to identify putative biomarker and target
-
Evolutionary changes: Creation of
phylogenetic trees and orthologue search between families and kingdoms to
understand functional changes, operon conservation or evolution due to SNPs
and Indels. Further, the identification of epigenetic pattern to understand
regulatory mechanisms at a different level of evolution.
-
Microbial Communities: Interaction and Composition of microbial communities and their regulation on transcript, protein and metabolite level to understand their stress response and adaptation to biotic as well as abiotic stress
-
Development of cells and tissues: Diff. expression analysis (bulk or
single-cell) during time courses and the development in speicifc tissues and cells in eukaryotes on different level to understand priming principles and importance of regualtory mechanisms
Support consultation offers
-
Proteomics analysis: quantitative Proteomics, Protein-Protein interactions with cross-linking, Phospho-Proteomics
-
RNA-Seq analysis: Ribo-Seq, ncRNA-Seq, mRNA-Seq, Single-Cell Sequencing, CLIP-Seq,
ChIP-Seq, ATAC-Seq
-
Post-Hoc analyses: Gene Set Enrichment analysis, Clustering, Principal Component Analysis, Diff. expression analysis,
Neural Net archtiecture for classification
-
Structureprediction: RNA and protein structure prediction, functional domain prediction
-
Metagenomics analysis: Pan-genome, Microbiome, Phylogenomics
-
Integrated Multi-Omics: Visualization and Correlation of -Omics, Classification via Machine Learning
Ongoing Main Research Projects
Operon Prediction via Machine Learning approaches in Cyanobacteria using -Omics data (Christian Nitzsche)
Cyanobacteria are notorious nitrogen and carbon fertilizers, as well as producers of oxygen and a vast range of secondary metabolites. Their adaptation to environmental conditions is tightly linked to changes in the transcriptional profile and alterations of the composition of transcriptional units (TU). We want to systematically describe changes of cyanobacteria in communities in native-mimicking growth conditions to changing abiotic and biotic stresses. Different -Omics datasets will help to define features for machine learning approaches to classify operons and TUs. Further, the biosynthesis pathways of secondary metabolites as reaction mechanisms in microbial communities will be investigated to subsequent model regulatory networks. By this, reaction pattern should be described allowing to propose cyanobacterial applications for more efficient CO2 fixation, biofuel production or oxygen generation to improve enzymatic catalysis in sewage plants.
Correlation of -Omics data on phenotypic features in Community Medicine (Jan Oldenburg)
Community Medicine is of major importance to extract putative biomarker related to diseases and pinpoint to environmental conditions and other phenotypic features. In this respect, the usage of high-throughput methods for Transcriptomics, Proteomics and Metabolomics allow to create overall pictures of a specific level within the cell and can be used in combination to get more information on regualtory mechanisms explainging phenotypes. Beside the correlation and visualization of integrated Multi-Omics the other aim is to implement classifciation approaches via neural net, random forest and support vector machine methods to train based on given features the prediction for phenotypes like diseases.
Regulatory mechanisms in plants during abiotic stress including alternative splicing, RNA storage and protein modification (Jessica Bold)
Due to climate change the crop yield is influenced and for this reason it is important to understand stress response mechanisms in plants. Beside specific regulatory pathways affected by certain stresses also common stress response as an early indication for the plant is of major importance to understand the early and late steps in stress response of plants. Beside mRNA expression changes several post-transcriptional mechanisms like alterantive splicing, RNA storage, ncRNA regulation and protein modifications interconnect in this complex network. Using long and short reads as well as Ribo-Seq we want to understand the different levels of regulation in combination with phospho- and quantitative proteomics under gravitiy and heat stress.
Voronoi Treemaps for visualization of several -Omics data and functional assignment (alumnae Henning Wehrmann)
For the visualization of whole transcriptomics and proteomics and their functional assignment it is important to develop graphical user interfaces allowing a dynamic post-hoc analysis. For this reason we developed a standalone-toll based on Voronoi treemap creation to compare up to two similiar or different -Omics techniques in the context of functional hierarchies.
ncRNA and structure element prediction (alumane Henning Wehrmann, Heiko Dunkel, Robin Mayer)
Nowadays beside mRNA also the so called non-coding (ncRNA) become more and more important to understand the mechanisms within in the cell. Many ncRNA classes are understand bassed on their functionality, strcuture and sequence but most of them only in specific model organisms. For research topics in non-model organisms the identification and sub-classification of such ncRNA classes can lead to a more fine-grained regulatory network in such species. Further, these information can be used to predict novel ncRNAs and detect features improtant for their functionality.
Site projects and future Collaborations
Site projects to join
-
Microbiome of Wildlings (Christian, Simon, Neetika):
WGS and Amplicon sequencing from mouse microbiome to differentiate human, wild mouse, lab mouse and wildling mouse
-
Medical features correlated to microbiome (Jan, Fabian, Neetika):
SHIP data from microbiome and disease related medical features to detect via ML regressions and correlations
-
Outdoor event modelling via ABM for COVID-19 (Christian):
Changing parameters for events to see the behavior of the spreading within an ABM
-
Alcoholmeasurement prediction in the air (Jan, industry):
Using neural nets to create a robust prediction of the alcohol level from a hardware
-
Genemodel adaptation in different tissues (Mladen, Simon):
Checking the alternative spliceforms of a gene in different tissues and races of dogs to adapt the reference model
-
ncRNA detection in introns (Mladen, Angelique):
Functional annotation and strcuture prediction of intronic regions to detect novel ncRNAs
-
Single-cell analysis in cancer (Prof. Schmidt, Piotr, Neetika):
Analyzing in one patient and different cell cultures the changes between cancer and non-cancer tissue on a single-cell level
-
Single-cell analysis in the skin (Beiersdorf, Neetika, Sergio):
Analyzing the differences of young and old skin tissues focusing on immune cell types and their development
-
Riboswitch detection in plants (Robin):
Developing a CNN to predict riboswitches in a genome seqeunces based on known trained riboswitch families in bacteria
-
Diff. expression due to cyanobacterial adaptation (Niclas):
Analyzing the changes of short and long-term diff. expression changes based on starvation conditions in Anabaena sp. PCC7120
Future Collaborations
-
Epigenetic priming: Cross-Priming of different stresses based on
epigenetic regulatory mechanisms in plants
-
Microbiome niches: WGS and Amplicon analysis of different tissues to
detect changes based on environmental features
-
Inter-individual variance in RNA expression:
Analysis of changes in the expression within one species but
different biological replicates to detect inter-individual changes
-
ncRNA Expression Atlas: Creation and
analysis of regulatory networks of ncRNA classes and their involvement in
different tissues under diseases
-
Microbial Evolution: Adaptation of SNPs and InDels during hospital
stay in realtion to antibiotics resistance
Publications
2020
Streit D, Shanmugam T, Garbelyanski A, Simm S, Schleiff E (2020)
The Existence and Localization of Nuclear snoRNAs in Arabidopsis thaliana Revisited
Plants (Basel) 9(8):2016
Gross LE, Spies N, Simm S, Schleiff E (2020)
Toc75-V/OEP80 is processed during translocation into chloroplasts, and the membrane-embedded form exposes its POTRA domain to the intermembrane space
FEBS Open Bio 10(3):444-454
Keller M, Schleiff E, Simm S (2020)
miRNAs involved in transcriptome remodeling during pollen development and heat stress response in Solanum lycopersicum
Scientific Reports 10(1):10694
Hu Y, Fragkostefanakis S, Schleiff E, Simm S (2020)
Transcriptional Basis for Differential Thermosensitivity of Seedlings of Various Tomato Genotypes
Genes (Basel) 11(6):655
Bodensohn US, Simm S, Fischer K, Jäschke M, Groß LE, Kramer K, Ehmann C, Rensing SA, Ladig R, Schleiff E (2019)
The intracellular distribution of the components of the GET system in vascular plants
Biochim Biophys Acta Mol Cell Res. 2019 Jun 21. pii: S0167-4889(19)30104-1. doi: 10.1016/j.bbamcr.2019.06.012. [Epub ahead of print] PubMed PMID: 31233800.
Wiesemann K, Simm S, Mirus O, Ladig R, Schleiff E (2019)
Regulation of two GTPases Toc159 and Toc34 in the translocon of the outer envelope of chloroplasts
Biochim Biophys Acta Proteins Proteom. 2019 Jun;1867(6):627-636. doi: 10.1016/j.bbapap.2019.01.002. Epub 2019 Jan 3. PubMed PMID: 30611779.
Kovacevic J, Palm D, Jooss D, Bublak D, Simm S, Schleiff E (2019)
Co-orthologues of ribosome biogenesis factors in A. thaliana are differentially regulated by
transcription factors
Plant Cell Rep. 2019 May 13. doi: 10.1007/s00299-019-02416-y. [Epub ahead of print] PubMed PMID: 31087154.
Fragkostefanakis S, Simm S, El-Shershaby A, Hu Y, Bublak D, Mesihovic A, Darm K, Mishra SK, Tschiersch B, Theres K, Scharf C, Schleiff E, Scharf KD (2019)
The repressor and co-activator HsfB1 regulates the major heat stress transcription factors in tomato
Plant Cell Environ. 2019 Mar;42(3):874-890. doi: 10.1111/pce.13434. Epub 2018 Oct 11. PubMed PMID: 30187931.
Palm D, Streit D, Shanmugam T, Weis BL, Ruprecht M, Simm S, Schleiff E (2019)
Plant-specific ribosome biogenesis factors in Arabidopsis thaliana with essential
function in rRNA processing
Nucleic Acids Res. 2019 Feb 28;47(4):1880-1895. doi: 10.1093/nar/gky1261. PubMed PMID: 30576513; PubMed Central PMCID: PMC6393314.
Berz J, Simm S, Schuster S, Scharf KD, Schleiff E, Ebersberger I (2019)
HEATSTER: A Database and Web Server for Identification and Classification of Heat Stress
Transcription Factors in Plants
Bioinform Biol Insights. 2019 Jan 7;13:1177932218821365. doi: 10.1177/1177932218821365. eCollection 2019. PubMed
PMID: 30670918; PubMed Central PMCID: PMC6327235.
2018
Palm D, Streit D, Ruprecht M, Simm S, Scharf C, Schleiff E (2018)
Late ribosomal protein localization in Arabidopsis thaliana differs to that in Saccharomyces cerevisiae
FEBS Open Bio. 2018 Jul 25;8(9):1437-1444. doi: 10.1002/2211-5463.12487. eCollection 2018 Sep. PubMed PMID: 30186745; PubMed Central PMCID: PMC6120241.
Keller M, SPOT-ITN Consortium, Simm S (2018)
The coupling of transcriptome and proteome adaptation during development and heat stress response of tomato pollen
BMC Genomics. 2018 Jun 8;19(1):447. doi: 10.1186/s12864-018-4824-5. PubMed PMID: 29884134; PubMed Central PMCID: PMC5994098.
Lampe S, Kunze M, Scholz A, Brauß TF, Winslow S, Simm S, Keller M, Heidler J, Wittig I, Brüne B, Schmid T (2018)
Identification of the TXNIP IRES and characterization of the impact of regulatory IRES trans-acting factors
Biochim Biophys Acta Gene Regul Mech. 2018 Feb;1861(2):147-157. doi: 10.1016/j.bbagrm.2018.01.010. Epub 2018 Jan 31. PubMed PMID: 29378331.
2017 - 2010
Keller M, Hu Y, Mesihovic A, Fragkostefanakis S, Schleiff E, Simm S (2017)
Alternative splicing in tomato pollen in response to heat stress
DNA Res. 2017 Apr 1;24(2):205-217. doi: 10.1093/dnares/dsw051. PubMed PMID: 28025318; PubMed Central PMCID: PMC5397606.
Simm S, Scharf KD, Jegadeesan S, Chiusano ML, Firon N, Schleiff E (2016)
Survey of Genes Involved in Biosynthesis, Transport, and Signaling of Phytohormones with
Focus on Solanum lycopersicum
Bioinform Biol Insights. 2016 Sep 26;10:185-207. eCollection 2016. PubMed PMID: 27695302; PubMed Central PMCID: PMC5038615.
Simm S, Einloft J, Mirus O, Schleiff E (2016)
50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification
Biol Res. 2016 Jul 4;49(1):31. doi: 10.1186/s40659-016-0092-5. PubMed PMID: 27378087; PubMed Central PMCID: PMC4932767.
Fragkostefanakis S, Mesihovic A, Simm S, Paupière MJ, Hu Y, Paul P, Mishra SK, Tschiersch B, Theres K, Bovy A, Schleiff E, Scharf KD (2016)
HsfA2 Controls the Activity of Developmentally and Stress-Regulated Heat Stress Protection
Mechanisms in Tomato Male Reproductive Tissues
Plant Physiol. 2016 Apr;170(4):2461-77. doi: 10.1104/pp.15.01913. Epub 2016 Feb 25. PubMed PMID:
26917685; PubMed Central PMCID: PMC4825147.
Sagor GH, Zhang S, Kojima S, Simm S, Berberich T, Kusano T (2016)
Reducing Cytoplasmic Polyamine Oxidase Activity in Arabidopsis Increases Salt and Drought
Tolerance by Reducing Reactive Oxygen Species Production and Increasing Defense Gene Expression
Front Plant Sci. 2016 Feb 29;7:214. doi: 10.3389/fpls.2016.00214. eCollection 2016. PubMed PMID: 26973665; PubMed Central PMCID: PMC4770033.
Paul P, Chaturvedi P, Selymesi M, Ghatak A, Mesihovic A, Scharf KD, Weckwerth W, Simm S, Schleiff E (2016)
The membrane proteome of male gametophyte in Solanum lycopersicum
J Proteomics. 2016 Jan 10;131:48-60. doi: 10.1016/j.jprot.2015.10.009. Epub 2015 Oct 9. PubMed PMID: 26455813.
Palm D, Simm S, Darm K, Weis BL, Ruprecht M, Schleiff E, Scharf C (2016)
Proteome distribution between nucleoplasm and nucleolus and its relation to ribosome
biogenesis in Arabidopsis thaliana
RNA Biol. 2016;13(4):441-54. doi: 10.1080/15476286.2016.1154252. Epub 2016 Mar 16. PubMed PMID: 26980300; PubMed
Central PMCID: PMC5038169.
Fragkostefanakis S, Simm S, Paul P, Bublak D, Scharf KD, Schleiff E (2015)
Chaperone network composition in Solanum lycopersicum explored by transcriptome
profiling and microarray meta-analysis
Plant Cell Environ. 2015 Apr;38(4):693-709. doi: 10.1111/pce.12426. Epub 2014 Oct 1. PubMed PMID: 25124075.
Simm S, Keller M, Selymesi M, Schleiff E (2015)
The composition of the global and feature specific cyanobacterial core-genomes
Front Microbiol. 2015 Mar 19;6:219. doi: 10.3389/fmicb.2015.00219. eCollection 2015. PubMed PMID: 25852675; PubMed
Central PMCID: PMC4365693.
Simm S, Fragkostefanakis S, Paul P, Keller M, Einloft J, Scharf KD, Schleiff E (2015)
Identification and Expression Analysis of Ribosome Biogenesis Factor Co-orthologs in Solanum lycopersicum
Bioinform Biol Insights. 2015 Feb 4;9:1-17. doi: 10.4137/BBI.S20751. eCollection 2015. PubMed PMID: 25698879; PubMed Central PMCID: PMC4325683.
Sloan KE, Leisegang MS, Doebele C, RamÃrez AS, Simm S, Safferthal C, Kretschmer J, Schorge T, Markoutsa S, Haag S, Karas M, Ebersberger I, Schleiff E, Watkins NJ, Bohnsack MT (2014)
The association of late-acting snoRNPs with human pre-ribosomal complexes requires the RNA helicase DDX21
Nucleic Acids Res. 2015 Jan;43(1):553-64. doi: 10.1093/nar/gku1291. Epub 2014 Dec 4. PubMed PMID: 25477391; PubMed Central PMCID: PMC4288182.
Martin R, Hackert P, Ruprecht M, Simm S, Brüning L, Mirus O, Sloan KE, Kudla G, Schleiff E, Bohnsack MT (2014)
A pre-ribosomal RNA interaction network involving snoRNAs and the Rok1 helicase
RNA. 2014 Aug;20(8):1173-82. doi: 10.1261/rna.044669.114. Epub 2014 Jun 19. PubMed PMID: 24947498; PubMed Central
PMCID: PMC4105744.
Paul P, Simm S, Mirus O, Scharf KD, Fragkostefanakis S, Schleiff E (2014)
The complexity of vesicle transport factors in plants examined by orthology search
PLoS One. 2014 May 20;9(5):e97745. doi: 10.1371/journal.pone.0097745. eCollection 2014. PubMed PMID: 24844592; PubMed Central PMCID: PMC4028247.
Ebersberger I, Simm S, Leisegang MS, Schmitzberger P, Mirus O, von Haeseler A, Bohnsack MT, Schleiff E (2014)
The evolution of the ribosome biogenesis pathway from a yeast perspective
Nucleic Acids Res. 2014 Feb;42(3):1509-23. doi: 10.1093/nar/gkt1137. Epub 2013 Nov 14. PubMed PMID: 24234440; PubMed Central PMCID: PMC3919561.
Paul P, Simm S, Blaumeiser A, Scharf KD, Fragkostefanakis S, Mirus O, Schleiff E (2013)
The protein translocation systems in plants - composition and variability on
the example of Solanum lycopersicum
BMC Genomics. 2013 Mar 18;14:189. doi: 10.1186/1471-2164-14-189. PubMed PMID: 23506162; PubMed Central PMCID:
PMC3610429.
Simm S, Papasotiriou DG, Ibrahim M, Leisegang MS, Müller B, Schorge T, Karas M, Mirus O, Sommer MS, Schleiff E (2013)
Defining the core proteome of the chloroplast envelope membranes
Front Plant Sci. 2013 Feb 6;4:11. doi: 10.3389/fpls.2013.00011. eCollection 2013. PubMed PMID: 23390424; PubMed Central
PMCID: PMC3565376.
Missbach S, Weis BL, Martin R, Simm S, Bohnsack MT, Schleiff E (2013)
40S ribosome biogenesis co-factors are essential for gametophyte and embryo development
PLoS One. 2013;8(1):e54084. doi: 10.1371/journal.pone.0054084. Epub 2013 Jan 30. PubMed PMID: 23382868; PubMed Central PMCID: PMC3559688.
Bionda T, Tillmann B, Simm S, Beilstein K, Ruprecht M, Schleiff E (2010)
Chloroplast import signals: the length requirement for translocation in vitro and in vivo
J Mol Biol. 2010 Sep 24;402(3):510-23. doi: 10.1016/j.jmb.2010.07.052. Epub 2010 Aug 3. PubMed PMID: 20688079.
Teaching Experience
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WS 2016-2019:
Strukturelle Bioinformatik
(Goethe Universitaet)
-
SS 2015-2019:
Algorithmen der Sequenzanalyse
(Goethe Universitaet)
-
Since WS 2019:
Biometrie fuer Humanbiologen
(UniversitaetsMedizin Greifswald)
-
Since SS 2019:
Bioinformatik Seminar fuer Humanbiologen
(UniversitaetsMedizin Greifswald)
-
Since SS 2020:
Wahlfach medizinische Bioinformatik
(UniversitaetsMedizin Greifswald)
-
Since WS 2020:
Wissenschaftlichkeit2 für Medizinstudenten
(UniversitaetsMedizin Greifswald)
|
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Research interests
-
Classification via Machine Learning: Information extraction from sequence, structure and phenotypic features to classify transcriptome wide non-coding RNAs by their species
-
Integrated Multi-Omics approaches: Development of Machine Learning and visualization approaches to correlate different layer of -Omics and phenotypic features to identify putative biomarker and target
-
Evolutionary changes: Creation of phylogenetic trees and orthologue search between families and kingdoms to understand functional changes, operon conservation or evolution due to SNPs and Indels. Further, the identification of epigenetic pattern to understand regulatory mechanisms at a different level of evolution.
-
Microbial Communities: Interaction and Composition of microbial communities and their regulation on transcript, protein and metabolite level to understand their stress response and adaptation to biotic as well as abiotic stress
-
Development of cells and tissues: Diff. expression analysis (bulk or single-cell) during time courses and the development in speicifc tissues and cells in eukaryotes on different level to understand priming principles and importance of regualtory mechanisms
Support consultation offers
-
Proteomics analysis: quantitative Proteomics, Protein-Protein interactions with cross-linking, Phospho-Proteomics
-
RNA-Seq analysis: Ribo-Seq, ncRNA-Seq, mRNA-Seq, Single-Cell Sequencing, CLIP-Seq, ChIP-Seq, ATAC-Seq
-
Post-Hoc analyses: Gene Set Enrichment analysis, Clustering, Principal Component Analysis, Diff. expression analysis, Neural Net archtiecture for classification
-
Structureprediction: RNA and protein structure prediction, functional domain prediction
-
Metagenomics analysis: Pan-genome, Microbiome, Phylogenomics
-
Integrated Multi-Omics: Visualization and Correlation of -Omics, Classification via Machine Learning
Ongoing Main Research Projects
Operon Prediction via Machine Learning approaches in Cyanobacteria using -Omics data (Christian Nitzsche)
Cyanobacteria are notorious nitrogen and carbon fertilizers, as well as producers of oxygen and a vast range of secondary metabolites. Their adaptation to environmental conditions is tightly linked to changes in the transcriptional profile and alterations of the composition of transcriptional units (TU). We want to systematically describe changes of cyanobacteria in communities in native-mimicking growth conditions to changing abiotic and biotic stresses. Different -Omics datasets will help to define features for machine learning approaches to classify operons and TUs. Further, the biosynthesis pathways of secondary metabolites as reaction mechanisms in microbial communities will be investigated to subsequent model regulatory networks. By this, reaction pattern should be described allowing to propose cyanobacterial applications for more efficient CO2 fixation, biofuel production or oxygen generation to improve enzymatic catalysis in sewage plants.
Correlation of -Omics data on phenotypic features in Community Medicine (Jan Oldenburg)
Community Medicine is of major importance to extract putative biomarker related to diseases and pinpoint to environmental conditions and other phenotypic features. In this respect, the usage of high-throughput methods for Transcriptomics, Proteomics and Metabolomics allow to create overall pictures of a specific level within the cell and can be used in combination to get more information on regualtory mechanisms explainging phenotypes. Beside the correlation and visualization of integrated Multi-Omics the other aim is to implement classifciation approaches via neural net, random forest and support vector machine methods to train based on given features the prediction for phenotypes like diseases.
Regulatory mechanisms in plants during abiotic stress including alternative splicing, RNA storage and protein modification (Jessica Bold)
Due to climate change the crop yield is influenced and for this reason it is important to understand stress response mechanisms in plants. Beside specific regulatory pathways affected by certain stresses also common stress response as an early indication for the plant is of major importance to understand the early and late steps in stress response of plants. Beside mRNA expression changes several post-transcriptional mechanisms like alterantive splicing, RNA storage, ncRNA regulation and protein modifications interconnect in this complex network. Using long and short reads as well as Ribo-Seq we want to understand the different levels of regulation in combination with phospho- and quantitative proteomics under gravitiy and heat stress.
Voronoi Treemaps for visualization of several -Omics data and functional assignment (alumnae Henning Wehrmann)
For the visualization of whole transcriptomics and proteomics and their functional assignment it is important to develop graphical user interfaces allowing a dynamic post-hoc analysis. For this reason we developed a standalone-toll based on Voronoi treemap creation to compare up to two similiar or different -Omics techniques in the context of functional hierarchies.
ncRNA and structure element prediction (alumane Henning Wehrmann, Heiko Dunkel, Robin Mayer)
Nowadays beside mRNA also the so called non-coding (ncRNA) become more and more important to understand the mechanisms within in the cell. Many ncRNA classes are understand bassed on their functionality, strcuture and sequence but most of them only in specific model organisms. For research topics in non-model organisms the identification and sub-classification of such ncRNA classes can lead to a more fine-grained regulatory network in such species. Further, these information can be used to predict novel ncRNAs and detect features improtant for their functionality.
Site projects and future Collaborations
-
Microbiome of Wildlings (Christian, Simon, Neetika): WGS and Amplicon sequencing from mouse microbiome to differentiate human, wild mouse, lab mouse and wildling mouse
-
Medical features correlated to microbiome (Jan, Fabian, Neetika): SHIP data from microbiome and disease related medical features to detect via ML regressions and correlations
-
Outdoor event modelling via ABM for COVID-19 (Christian): Changing parameters for events to see the behavior of the spreading within an ABM
-
Alcoholmeasurement prediction in the air (Jan, industry): Using neural nets to create a robust prediction of the alcohol level from a hardware
-
Genemodel adaptation in different tissues (Mladen, Simon): Checking the alternative spliceforms of a gene in different tissues and races of dogs to adapt the reference model
-
ncRNA detection in introns (Mladen, Angelique): Functional annotation and strcuture prediction of intronic regions to detect novel ncRNAs
-
Single-cell analysis in cancer (Prof. Schmidt, Piotr, Neetika): Analyzing in one patient and different cell cultures the changes between cancer and non-cancer tissue on a single-cell level
-
Single-cell analysis in the skin (Beiersdorf, Neetika, Sergio): Analyzing the differences of young and old skin tissues focusing on immune cell types and their development
-
Riboswitch detection in plants (Robin): Developing a CNN to predict riboswitches in a genome seqeunces based on known trained riboswitch families in bacteria
-
Diff. expression due to cyanobacterial adaptation (Niclas): Analyzing the changes of short and long-term diff. expression changes based on starvation conditions in Anabaena sp. PCC7120
Site projects to join
-
Epigenetic priming: Cross-Priming of different stresses based on epigenetic regulatory mechanisms in plants
-
Microbiome niches: WGS and Amplicon analysis of different tissues to detect changes based on environmental features
-
Inter-individual variance in RNA expression: Analysis of changes in the expression within one species but different biological replicates to detect inter-individual changes
-
ncRNA Expression Atlas: Creation and analysis of regulatory networks of ncRNA classes and their involvement in different tissues under diseases
-
Microbial Evolution: Adaptation of SNPs and InDels during hospital stay in realtion to antibiotics resistance
Future Collaborations
Publications
2020
Streit D, Shanmugam T, Garbelyanski A, Simm S, Schleiff E (2020)
The Existence and Localization of Nuclear snoRNAs in Arabidopsis thaliana Revisited
Plants (Basel) 9(8):2016
Gross LE, Spies N, Simm S, Schleiff E (2020)
Toc75-V/OEP80 is processed during translocation into chloroplasts, and the membrane-embedded form exposes its POTRA domain to the intermembrane space
FEBS Open Bio 10(3):444-454
Keller M, Schleiff E, Simm S (2020)
miRNAs involved in transcriptome remodeling during pollen development and heat stress response in Solanum lycopersicum
Scientific Reports 10(1):10694
Hu Y, Fragkostefanakis S, Schleiff E, Simm S (2020)
Transcriptional Basis for Differential Thermosensitivity of Seedlings of Various Tomato Genotypes
Genes (Basel) 11(6):655
Bodensohn US, Simm S, Fischer K, Jäschke M, Groß LE, Kramer K, Ehmann C, Rensing SA, Ladig R, Schleiff E (2019)
The intracellular distribution of the components of the GET system in vascular plants
Biochim Biophys Acta Mol Cell Res. 2019 Jun 21. pii: S0167-4889(19)30104-1. doi: 10.1016/j.bbamcr.2019.06.012. [Epub ahead of print] PubMed PMID: 31233800.
Wiesemann K, Simm S, Mirus O, Ladig R, Schleiff E (2019)
Regulation of two GTPases Toc159 and Toc34 in the translocon of the outer envelope of chloroplasts
Biochim Biophys Acta Proteins Proteom. 2019 Jun;1867(6):627-636. doi: 10.1016/j.bbapap.2019.01.002. Epub 2019 Jan 3. PubMed PMID: 30611779.
Kovacevic J, Palm D, Jooss D, Bublak D, Simm S, Schleiff E (2019)
Co-orthologues of ribosome biogenesis factors in A. thaliana are differentially regulated by
transcription factors
Plant Cell Rep. 2019 May 13. doi: 10.1007/s00299-019-02416-y. [Epub ahead of print] PubMed PMID: 31087154.
Fragkostefanakis S, Simm S, El-Shershaby A, Hu Y, Bublak D, Mesihovic A, Darm K, Mishra SK, Tschiersch B, Theres K, Scharf C, Schleiff E, Scharf KD (2019)
The repressor and co-activator HsfB1 regulates the major heat stress transcription factors in tomato
Plant Cell Environ. 2019 Mar;42(3):874-890. doi: 10.1111/pce.13434. Epub 2018 Oct 11. PubMed PMID: 30187931.
Palm D, Streit D, Shanmugam T, Weis BL, Ruprecht M, Simm S, Schleiff E (2019)
Plant-specific ribosome biogenesis factors in Arabidopsis thaliana with essential
function in rRNA processing
Nucleic Acids Res. 2019 Feb 28;47(4):1880-1895. doi: 10.1093/nar/gky1261. PubMed PMID: 30576513; PubMed Central PMCID: PMC6393314.
Berz J, Simm S, Schuster S, Scharf KD, Schleiff E, Ebersberger I (2019)
HEATSTER: A Database and Web Server for Identification and Classification of Heat Stress
Transcription Factors in Plants
Bioinform Biol Insights. 2019 Jan 7;13:1177932218821365. doi: 10.1177/1177932218821365. eCollection 2019. PubMed
PMID: 30670918; PubMed Central PMCID: PMC6327235.
2018
Palm D, Streit D, Ruprecht M, Simm S, Scharf C, Schleiff E (2018)
Late ribosomal protein localization in Arabidopsis thaliana differs to that in Saccharomyces cerevisiae
FEBS Open Bio. 2018 Jul 25;8(9):1437-1444. doi: 10.1002/2211-5463.12487. eCollection 2018 Sep. PubMed PMID: 30186745; PubMed Central PMCID: PMC6120241.
Keller M, SPOT-ITN Consortium, Simm S (2018)
The coupling of transcriptome and proteome adaptation during development and heat stress response of tomato pollen
BMC Genomics. 2018 Jun 8;19(1):447. doi: 10.1186/s12864-018-4824-5. PubMed PMID: 29884134; PubMed Central PMCID: PMC5994098.
Lampe S, Kunze M, Scholz A, Brauß TF, Winslow S, Simm S, Keller M, Heidler J, Wittig I, Brüne B, Schmid T (2018)
Identification of the TXNIP IRES and characterization of the impact of regulatory IRES trans-acting factors
Biochim Biophys Acta Gene Regul Mech. 2018 Feb;1861(2):147-157. doi: 10.1016/j.bbagrm.2018.01.010. Epub 2018 Jan 31. PubMed PMID: 29378331.
2017 - 2010
Keller M, Hu Y, Mesihovic A, Fragkostefanakis S, Schleiff E, Simm S (2017)
Alternative splicing in tomato pollen in response to heat stress
DNA Res. 2017 Apr 1;24(2):205-217. doi: 10.1093/dnares/dsw051. PubMed PMID: 28025318; PubMed Central PMCID: PMC5397606.
Simm S, Scharf KD, Jegadeesan S, Chiusano ML, Firon N, Schleiff E (2016)
Survey of Genes Involved in Biosynthesis, Transport, and Signaling of Phytohormones with
Focus on Solanum lycopersicum
Bioinform Biol Insights. 2016 Sep 26;10:185-207. eCollection 2016. PubMed PMID: 27695302; PubMed Central PMCID: PMC5038615.
Simm S, Einloft J, Mirus O, Schleiff E (2016)
50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification
Biol Res. 2016 Jul 4;49(1):31. doi: 10.1186/s40659-016-0092-5. PubMed PMID: 27378087; PubMed Central PMCID: PMC4932767.
Fragkostefanakis S, Mesihovic A, Simm S, Paupière MJ, Hu Y, Paul P, Mishra SK, Tschiersch B, Theres K, Bovy A, Schleiff E, Scharf KD (2016)
HsfA2 Controls the Activity of Developmentally and Stress-Regulated Heat Stress Protection
Mechanisms in Tomato Male Reproductive Tissues
Plant Physiol. 2016 Apr;170(4):2461-77. doi: 10.1104/pp.15.01913. Epub 2016 Feb 25. PubMed PMID:
26917685; PubMed Central PMCID: PMC4825147.
Sagor GH, Zhang S, Kojima S, Simm S, Berberich T, Kusano T (2016)
Reducing Cytoplasmic Polyamine Oxidase Activity in Arabidopsis Increases Salt and Drought
Tolerance by Reducing Reactive Oxygen Species Production and Increasing Defense Gene Expression
Front Plant Sci. 2016 Feb 29;7:214. doi: 10.3389/fpls.2016.00214. eCollection 2016. PubMed PMID: 26973665; PubMed Central PMCID: PMC4770033.
Paul P, Chaturvedi P, Selymesi M, Ghatak A, Mesihovic A, Scharf KD, Weckwerth W, Simm S, Schleiff E (2016)
The membrane proteome of male gametophyte in Solanum lycopersicum
J Proteomics. 2016 Jan 10;131:48-60. doi: 10.1016/j.jprot.2015.10.009. Epub 2015 Oct 9. PubMed PMID: 26455813.
Palm D, Simm S, Darm K, Weis BL, Ruprecht M, Schleiff E, Scharf C (2016)
Proteome distribution between nucleoplasm and nucleolus and its relation to ribosome
biogenesis in Arabidopsis thaliana
RNA Biol. 2016;13(4):441-54. doi: 10.1080/15476286.2016.1154252. Epub 2016 Mar 16. PubMed PMID: 26980300; PubMed
Central PMCID: PMC5038169.
Fragkostefanakis S, Simm S, Paul P, Bublak D, Scharf KD, Schleiff E (2015)
Chaperone network composition in Solanum lycopersicum explored by transcriptome
profiling and microarray meta-analysis
Plant Cell Environ. 2015 Apr;38(4):693-709. doi: 10.1111/pce.12426. Epub 2014 Oct 1. PubMed PMID: 25124075.
Simm S, Keller M, Selymesi M, Schleiff E (2015)
The composition of the global and feature specific cyanobacterial core-genomes
Front Microbiol. 2015 Mar 19;6:219. doi: 10.3389/fmicb.2015.00219. eCollection 2015. PubMed PMID: 25852675; PubMed
Central PMCID: PMC4365693.
Simm S, Fragkostefanakis S, Paul P, Keller M, Einloft J, Scharf KD, Schleiff E (2015)
Identification and Expression Analysis of Ribosome Biogenesis Factor Co-orthologs in Solanum lycopersicum
Bioinform Biol Insights. 2015 Feb 4;9:1-17. doi: 10.4137/BBI.S20751. eCollection 2015. PubMed PMID: 25698879; PubMed Central PMCID: PMC4325683.
Sloan KE, Leisegang MS, Doebele C, RamÃrez AS, Simm S, Safferthal C, Kretschmer J, Schorge T, Markoutsa S, Haag S, Karas M, Ebersberger I, Schleiff E, Watkins NJ, Bohnsack MT (2014)
The association of late-acting snoRNPs with human pre-ribosomal complexes requires the RNA helicase DDX21
Nucleic Acids Res. 2015 Jan;43(1):553-64. doi: 10.1093/nar/gku1291. Epub 2014 Dec 4. PubMed PMID: 25477391; PubMed Central PMCID: PMC4288182.
Martin R, Hackert P, Ruprecht M, Simm S, Brüning L, Mirus O, Sloan KE, Kudla G, Schleiff E, Bohnsack MT (2014)
A pre-ribosomal RNA interaction network involving snoRNAs and the Rok1 helicase
RNA. 2014 Aug;20(8):1173-82. doi: 10.1261/rna.044669.114. Epub 2014 Jun 19. PubMed PMID: 24947498; PubMed Central
PMCID: PMC4105744.
Paul P, Simm S, Mirus O, Scharf KD, Fragkostefanakis S, Schleiff E (2014)
The complexity of vesicle transport factors in plants examined by orthology search
PLoS One. 2014 May 20;9(5):e97745. doi: 10.1371/journal.pone.0097745. eCollection 2014. PubMed PMID: 24844592; PubMed Central PMCID: PMC4028247.
Ebersberger I, Simm S, Leisegang MS, Schmitzberger P, Mirus O, von Haeseler A, Bohnsack MT, Schleiff E (2014)
The evolution of the ribosome biogenesis pathway from a yeast perspective
Nucleic Acids Res. 2014 Feb;42(3):1509-23. doi: 10.1093/nar/gkt1137. Epub 2013 Nov 14. PubMed PMID: 24234440; PubMed Central PMCID: PMC3919561.
Paul P, Simm S, Blaumeiser A, Scharf KD, Fragkostefanakis S, Mirus O, Schleiff E (2013)
The protein translocation systems in plants - composition and variability on
the example of Solanum lycopersicum
BMC Genomics. 2013 Mar 18;14:189. doi: 10.1186/1471-2164-14-189. PubMed PMID: 23506162; PubMed Central PMCID:
PMC3610429.
Simm S, Papasotiriou DG, Ibrahim M, Leisegang MS, Müller B, Schorge T, Karas M, Mirus O, Sommer MS, Schleiff E (2013)
Defining the core proteome of the chloroplast envelope membranes
Front Plant Sci. 2013 Feb 6;4:11. doi: 10.3389/fpls.2013.00011. eCollection 2013. PubMed PMID: 23390424; PubMed Central
PMCID: PMC3565376.
Missbach S, Weis BL, Martin R, Simm S, Bohnsack MT, Schleiff E (2013)
40S ribosome biogenesis co-factors are essential for gametophyte and embryo development
PLoS One. 2013;8(1):e54084. doi: 10.1371/journal.pone.0054084. Epub 2013 Jan 30. PubMed PMID: 23382868; PubMed Central PMCID: PMC3559688.
Bionda T, Tillmann B, Simm S, Beilstein K, Ruprecht M, Schleiff E (2010)
Chloroplast import signals: the length requirement for translocation in vitro and in vivo
J Mol Biol. 2010 Sep 24;402(3):510-23. doi: 10.1016/j.jmb.2010.07.052. Epub 2010 Aug 3. PubMed PMID: 20688079.
Teaching Experience
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WS 2016-2019: Strukturelle Bioinformatik (Goethe Universitaet)
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SS 2015-2019: Algorithmen der Sequenzanalyse (Goethe Universitaet)
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Since WS 2019: Biometrie fuer Humanbiologen (UniversitaetsMedizin Greifswald)
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Since SS 2019: Bioinformatik Seminar fuer Humanbiologen (UniversitaetsMedizin Greifswald)
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Since SS 2020: Wahlfach medizinische Bioinformatik (UniversitaetsMedizin Greifswald)
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Since WS 2020: Wissenschaftlichkeit2 für Medizinstudenten (UniversitaetsMedizin Greifswald)