gobics.de [ Thomas Lingner ]

Address: Universität Göttingen Institut für Mikrobiologie und Genetik Abteilung für Bioinformatik Goldschmidtstr. 1 37077 Göttingen Germany eMail: thomas [at] gobics [dot] de Phone: +49 551 39 13994 FAX: +49 551 39 14929

Short CV

• from 1999 to 2005 I studied "Naturwissenschaftliche Informatik" at the University of Bielefeld
• in October 2005 I joined the Department of Bioinformatics at the University of Göttingen as a PhD student.
• From March 2009 to February 2010 I spent a DAAD PostDoc year in Cedric Notredame's Group at the CRG in Barcelona
• since March 2010 I am Postdoctoral Fellow at the department of Bioinformatics in Göttingen
• since February 2011 I carry out peroxisomal protein research for my DFG-funded project "Machine learning methods for functional characterization of the peroxisome" (DFG Li-2050/1-1).

Research Interests:

• Bioinformatics, in particular protein/RNA classification, peroxisomal protein research, analysis of metagenomic data
• Machine learning and data mining methods

Publications:

• Klingenberg H, Martinjak R, GlöcknerFO, Daniel R, Lingner T, Meinicke P.
  Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences
  German Conference on Bioinformatics 80-89, 2013.
• Klingenberg H, Aßhauer KP, Lingner T, Meinicke P.
  Protein signature-based estimation of metagenomic abundances including all domains of life and viruses
  Bioinformatics, 29(8):973-980, 2013.
• Reumann S, Buchwald D, Lingner T
  PredPlantPTS1: A Web Server for the Prediction of Plant Peroxisomal Proteins.
  Frontiers in Plant Science 3:194, 2012.
• Chowdhary G, Kataya AR, Lingner T, Reumann S.
  Non-canonical peroxisome targeting signals: identification of novel PTS1 tripeptides and characterization of enhancer elements by computational permutation analysis.
  BMC Plant Biology 12(1):142, 2012.
• Lingner T, Kataya ARA, Reumann S
  Experimental and statistical post-validation of positive example EST sequences carrying peroxisome targeting signals type 1 (PTS1)
  Plant Signaling and Behaviour 7(2):263-268, 2012.
• Großekathöfer U, Sadeghipour A, Lingner T, Meinicke P, Hermann T, Kopp S.
  Low Latency Recognition and Reproduction of Natural Gesture Trajectories.
  ICPRAM (2):154-161, 2012.
• Lingner T, Aßhauer KP, Schreiber F, Meinicke P.
  CoMet--a web server for comparative functional profiling of metagenomes.
  Nucleic Acids Research, 39(Suppl. 2):W518, 2011.
• Meinicke P, Aßhauer KP, Lingner T.
  Mixture models for analysis of the taxonomic composition of metagenomes.
  Bioinformatics 27(12):1618-1624, 2011.
• Lingner T, Kataya AR, Antonicelli GE, Benichou A, Nilssen K, Chen XY, Siemsen T, Morgenstern B, Meinicke P, Reumann S.
  Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses.
  Plant Cell 23(4):1556-72, 2011.
• T. Lingner, S. Mühlhausen, T. Gabaldón, C. Notredame, P. Meinicke
  Predicting phenotypic traits of prokaryotes from protein domain frequencies
  BMC Bioinformatics 11:481, 2010.
• K. J. Hoff, T. Lingner, P. Meinicke, M. Tech
  Orphelia: predicting genes in metagenomic sequencing reads
  Nucleic Acids Research 37, W101-W105, 2009.
• A. Kaever, T. Lingner, K. Feussner, C. Gobel, I. Feussner, P. Meinicke
  MarVis: a tool for clustering and visualization of metabolic biomarkers
  BMC Bioinformatics 10:92, 2009.
• Thomas Lingner and Peter Meinicke
  Fast Target Set Reduction for Large-Scale Protein Function Prediction: A Multi-class Multi-label Machine Learning Approach
  WABI 2008, Lecture Notes in Computer Science, 5251 (Algorithms in Bioinformatics), 198-209.
• P. Meinicke, T. Lingner, A. Kaever, K. Feussner, C. Goebel, I. Feussner, P. Karlovsky, and B. Morgenstern.
  Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps.
  Algorithms for Molecular Biology, 3:9, 2008.
• T. Lingner and P. Meinicke
  Word correlation matrices for protein sequence analysis and remote homology detection
  BMC Bioinformatics, 9:259, 2008.
• D. Krefting, J. Bart, K. Beronow, O. Dzhimova, J. Falkner, M. Hartung, A. Hoheisel, T. Knoch, T. Lingner, Y. Mohammed, K. Peter, E. Rahm, U. Sax, D. Sommerfeld, T. Steinke, T. Tolxdorff, F. Viezens, M, Vossberg, A. Weisbecker
  MediGRID - A User Friendly Secured Grid Infrastructure
  Future Generation Computer Systems, 25:326-336, 2009.
• D. Sommerfeld, T. Lingner, M. Stanke, B. Morgenstern, H. Richter
  AUGUSTUS at MediGRID: Adaption of a Bioinformatics Application to Grid Computing for Efficient Genome Analysis
  Future Generation Computer Systems, 25:337-345, 2009.
• K. J. Hoff, M. Tech, T. Lingner, R. Daniel, B. Morgenstern, P. Meinicke
  Gene prediction in metagenomic fragments: a large scale machine learning approach
  BMC Bioinformatics, 9:217, 2008.
• Dietmar Sommerfeld, Thomas Lingner, and Harald Richter
  Stepwise Enabling of AUGUSTUS for MediGRID
  IfI-07-12, November 2007, 26 pp., Technical Report.
• T. Lingner and P. Meinicke
  Remote homology detection based on oligomer distances
  Bioinformatics 22, 2224-2231, 2006.
  Supplementary data (normalized kernel matrices in gzipped tab-delimited ASCII-Format): monomers / dimers / trimers
• Kaper M, Meinicke P, Großekathöfer U, Lingner T, Ritter H.
  BCI Competition 2003--Data set IIb: support vector machines for the P300 speller paradigm
  IEEE Trans Biomed Eng. 2004 Jun; 51(6):1073-6.

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• PhD thesis:
  Alignmentfreie Analyse von Proteinsequenzen mit Verfahren des maschinellen Lernens
  (german, with english abstract and articles)

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• Diploma thesis:
  Neue Ansätze zum maschinellen Lernen von Alignments
  (german, with Ulf Großekathöfer)

Download:

• Word Correlation kernel matrices for SCOP benchmark setup (tared and gzipped tab-delimited ASCII-Format, sequence order as in label file, 443 MB)
  
• Oligomer Distance Histogram kernel matrices gzipped (tab-delimited ASCII-Format, sequence order as in label file): monomers / dimers / trimers
• ODH toolbox for MATLAB^®