Postdoctoral /MS Level Researcher position available!

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Patents / Copyrights

Optimal RNAi Design

Nucleosome Mapping

Istvan  (Steve) Ladunga, Ph.D.
Professor of Computational Biology
Head of the Computational Biology Research Lab
Department of Statistics
University of Nebraska - Lincoln

E145 Beadle Center, University of Nebraska-Lincoln
1901 Vine St., Lincoln, NE 68588-0665
Phone: (402) 472-6074    

Research

My Laboratory conducts research in the computational biology (you can call it bioinformatics) of:

•   Transcriptional regulation in the alga Chlamydomonas reinhardtii and in the yeast Saccharomyces cerevisiae
•   Lipid production and carbon conentrating mechanisms in algae
•   Structural principles of nucleosomes and nucleosome mapping,
•   Next generation sequencing including RNA-seq and ChIP-Seq
•   Optimal design of short interfering RNA duplexes,
•   Pattern analysis, and
•   Physical sequence analysis.

Featured publications

Nucleosomes:

Transcription is affected by nucleosomal resistance against polymerase passage. In turn, nucleosomal resistance is determined by DNA sequence, histone chaperones and remodeling enzymes. The contributions of these factors are widely debated: one recent title claims " DNA-encoded nucleosome organization" while another title states that "histone-DNA interactions are not the major determinant of nucleosome positions." These opposing conclusions were drawn from similar experiments analyzed by idealized methods. We attempt to resolve this controversy to reveal nucleosomal competency for transcription.

To this end, we analyzed 26 in vivo, nonlinked, and in vitro genome-wide nucleosome maps/replicates by new, rigorous methods. Individual H2A nucleosomes are reconstituted inaccurately by transcription, chaperones and remodeling enzymes. At gene centers, weakly positioned nucleosome arrays facilitate rapid histone eviction and remodeling, easing polymerase passage. Fuzzy positioning is not due to artefacts. At the regional level, transcriptional competency is strongly influenced by intrinsic histone-DNA affinities. This is confirmed by reproducing the high in vivo occupancy of translated regions and the low occupancy of intergenic regions in reconstitutions from purified DNA and histones. Regional level occupancy patterns are protected from invading histones by nucleosome excluding sequences and barrier nucleosomes at gene boundaries and within genes.

Dense arrays of weakly positioned nucleosomes appear to be necessary for transcription. Weak positioning at exons facilitates temporary remodeling, polymerase passage and hence the competency for transcription. At regional levels, the DNA sequence plays a major role in determining these features but positions of individual nucleosomes are typically modified by transcription, chaperones and enzymes. This competency is reduced at intergenic regions by sequence features, barrier nucleosomes, and proteins, preventing accessibility regulation of untargeted genes. This combination of DNA- and protein-influenced positioning regulates DNA accessibility and competence for regulatory protein binding and transcription. Interactive nucleosome displays are offered at http://chromatin.unl.edu/cgi-bin/skyline.cgi.

Istvan Ladunga: More Complete Gene Silencing by Fewer siRNAs: Transparent Optimized Design and Biophysical Signature
Nucleic Acids Res. 2007;35(2):433-40. Epub 2006 Dec 14.

Highly accurate knockdown functional analyses based on RNA interference (RNAi) requires the most complete hydrolysis of the targeted mRNA while avoiding the degradation of untargeted genes (off-target effects). This in turn requires significant improvements to target selection for two reasons. First, the average silencing activity of randomly selected siRNAs is as low as 62%. Second, applying more than five different siRNAs may lead to saturation of the RNA-induced silencing complex (RISC) and to the degradation of untargeted genes. Therefore, selecting a small number of highly active siRNAs is critical for obtaining the most complete knockdown and minimizing off-target effects. To satisfy these needs, a publicly available and transparent machine-learning tool is presented that ranks all possible siRNAs for each targeted gene. Support vector machines with polynomial kernels and constrained optimization models select and utilize the most predictive effective combinations from 572 sequence, thermodynamic, accessibility, and self-hairpin features over 2,200 published siRNAs. This tool reaches an accuracy of 92.3% in cross-validation experiments. We fully present the underlying biophysical signature that involves free energy, accessibility, and dinucleotide characteristics. We show that while complete silencing is possible at certain structured target sites, accessibility information improves the prediction of the 90% active siRNA target sites.

Online RNAi target selection can be performed on our web-server.

Education

Ph.D. in Molecular Biology, summa cum laude, Budapest (Eotvos) University of Sciences, Hungary.
MS and BS in Biology, Budapest University of Sciences.

Professional Experience

Department of Statistics, University of Nebraska-Lincoln

• Large-scale analysis of nucleosome-depleted regions, transcription factor binding sites and histone modifications using chromatin immunoprecipitation and next-generation (Illumina/Solexa) sequencing
• Reconstruction of transcriptional regulatory networks.
• Prediction of transcription factor binding sites
• Optimization of siRNA selection for the highest knockdown activity using machine learning and biophysical analysis
• Education: graduate/undergraduate course STAT/BIOC 842/442 Computational Molecular Biology, Fall 2010, every Fall semester
• Education: graduate course STAT 892 Computational Analyses of Next-Generation Sequencing and Biological Networks, Spring 2011

Celera Genomics/Applied Biosystems, Foster City, CA
Senior Staff Scientist

• Led a group of 12 professionals, identified and patented novel and splice-variant secreted proteins from genomic and EST sequences.
• Directed the creation of a pipeline utilizing Celera Genomics"s proprietary OTTO automatic prediction tool, the full repertoire of BLAST, sim4, lap over public and patent nucleic acid and protein databases using Celera"s Compute Farm by the Load Sharing Facility, translated SW on a Paracel-Dell computer, novelty checks, GeneWise searches, signal peptide and domain predictions, and alternative transcripts.
• Managed the large-scale annotations of secreted proteins from the Celera genome.
• Mentored & trained coworkers on bioinformatics and annotation, .
• Identified candidate novel microRNAs in humans.

AGY Therapeutics, Inc., South San Francisco, CA

• Reconstructed major post-stroke events of neuronal regeneration, memory and learning using systems biology, pathways and ontologies applied to microarray and in situ hybridization experiments in rat models of stroke.

SmithKline Beecham (now GlaxoSmithKine) Pharmaceuticals, King of Prussia, PA 1996 - 2000
Principal Investigator, Bioinformatics Dept.

1. Developed physical sequence analysis for the prediction of secretory proteins, neuropeptides and membrane proteins.
2. Analyzed gene expression in prostate cancer.
3. Adapted and refined algorithms for the maximization of correct classifications using mathematical programming.

Baylor College of Medicine, Human Genome Center, Houston
1994 - 1996 Visiting Research Associate

1. Developed the FASTA-SWAP and FASTA-PAT pattern database search tools. Increased speed 100x by creating and implementing BINREP, a compact representation of multiple alignments. Encoded 10 databases of multiple alignments including 2 novel ones.
2. Created scoring theory for substitution patterns, implemented in pattern-to-single-residue scoring matrices.
3. Revealed conservation of biological, physical, sterical, and chemical features of amino acids.

Department of Mathematics, Stanford University
1993 - 1994 Visiting Research Associate

1. Developed genomic comparison methods.
2. Performed research in mathematical statistics and computational biology with Samuel Karlin, member National Academy of Sciences, principal author of the probability theory applied in BLAST.

Department of Genetics, Budapest University of Sciences
1989 - 1993 Research Associate

Lectured computational molecular biology, molecular evolution and genetics.
Co-organized a Conference of the European Society for Evolutionary Biology. Predicted signal peptides by computational neural networks.
Developed a preliminary classification of proteins.

Other work experience

Headed the Department of International Cooperation at the National Committee for Technological Development, Budapest.
Research Associate at the Institute for Computer Science, Academy of Sciences, Budapest. Working in the Team of Istvan Lang, Secretary General of the Hungarian Academy of Sciences, contributed to the reduction of nationwide crop yield losses by ~1.6 percent by optimized culture allocation.

Selected Publications

• Ladunga, I. (2010) (Editor) Computational Biology of Transcription Factor Binding.In the series: Methods in Molecular Biology. Springer, Berlin.ISBN: 978-1-60761-853-9
• Belch Y, Yang J, Liu Y, Malkaram SA, Liu R, Riethoven JJ, Ladunga I. (2010) Weakly positioned nucleosomes enhance the transcriptional competency of chromatin. PloS One 5(9):e12984. Full text
• Ladunga, I. (2010) An overview of the computational analyses and discovery of transcription factor binding sites. Methods Mol Biol. 674:1-22.
• Jiao S, Bailey CP, Zhang S, Ladunga I. (2010) Probabilistic peak calling and controlling false discovery rate estimations in transcription factor binding site mapping from ChIP-seq. Methods Mol Biol. 674:161-177.
• Gonzalez, M, Sealls, W., Jesch, E.D., Brosnan, M.J., Ladunga, I., Ding, X, Black, P.N., DiRusso, C.C. (2010) Defining a relationship between dietary fatty acids and the cytochrome P450 system in a mouse model of fatty liver disease. Physiol Genomics doi:10.1152/physiolgenomics.00209.2010
• Ruscher, K.; Shamloo, M.; Rickhag, M.; Ladunga, I.; Soriano, L.; Gisselsson, L.; Toresson, H.; Ruslim-Litrus, L.; Oksenberg, D.; Urfer, R.; Johansson, B.; Nikolich, K.; Wieloch, T. (2010) The sigma-1 receptor enhances brain plasticity and functional recovery after experimental stroke. Brain Accepted for publication.
• van Dijk K, Ding Y, Malkaram S, Riethoven JJ, Liu R, Yang J, Laczko P, Chen H, Xia Y, Ladunga I, Avramova Z, Fromm M.(2010) Dynamic Changes in Genome-Wide Histone H3 Lysine 4 Methylation Patterns in Response to Dehydration Stress in Arabidopsis thaliana. BMC Plant Biol. 10(1):238.
• Prokupek AM, Kachman SD, Ladunga I, Harshman LG. (2009) Transcriptional profiling of the sperm storage organs of Drosophila melanogaster. Insect Mol Biol. 18:465-475
• Ladunga I. (2009) Finding similar nucleotide sequences using network BLAST searches. Curr Protoc Bioinformatics. Chapter 3:Unit 3.3.
• Ladunga I. (2009) Finding homologs in amino acid sequences using network BLAST searches. Curr Protoc Bioinformatics. Chapter 3:Unit 3.4.
• Y. Wang, I. Ladunga, A.R. Miller, K. M. Horken, T. Plucinak, D. P. Weeks, and C. P. Bailey. (2008) The Small Ubiquitin-like Modifier (SUMO) and SUMO Conjugating System of Chlamydomonas reinhardtii. Genetics,179(1):177-192.
• Abdelaty S, Alvarez-Venegas R, Yilmaz M, Le O, Hou Q, Sader M, Al-Abdallat A, Xia Y, Lu G, Ladunga I, Avramova Z. (2008). The highly similar Arabidopsis homologs of Trithorax atx1 and atx2 encode divergent biochemical functions. Plant Cell, 20(3):568-579.
• Ladunga, I. (2007) More Complete Gene Silencing by Fewer siRNAs: Transparent Optimized Design and Biophysical Signature. Nucleic Acids Res.35(2):433-440 Epub: 2006 Dec 14.
• Ladunga, I. (1999) PHYSEAN: PHYsical SEquence ANalysis of Proteins. Bioinformatics, 15: 128-138.
• Ladunga, I. (2000) Large-scale discovery of secretory proteins from nucleic acid sequences. Current Opinion in Biotechnology,11:13-18, invited paper.
• Ladunga, I., and Smith, R.F.(1997) Amino acid substitution patterns conserve folding of proteins by preserving steric and hydrophobicity properties. Protein Engineering 10: 187-196.
• Ladunga, I., Wiese, B.A. and Smith, R.F. (1996) FASTA-SWAP and FASTA-PAT: pattern database searches using combinations of aligned amino acids and a novel scoring theory. Journal of Molecular Biology. 259: 840-854.
• Ladunga, I. (2002) Finding homologs in amino acid sequences using network BLAST searches. Curr. Prot. Bioinf., Chapter 3.4. Published online November 2002
• Ladunga, I. (2002) Finding homologs in nucleic acid sequences using network BLAST searches. Curr. Prot. Bioinf., Chapter 3.3. Published online August 2002
• Karlin, S., and Ladunga, I. (1994). Novel comparisons of genomic sequences in Eukaryotes. Proc. Natl. Acad. Sci. U.S.A., 91: 2832-12836.
• Karlin, S., Ladunga, I., and Blaisdell, B.E.(1994). Heterogeneity of genomes: measures and values. Proc. Natl. Acad. Sci. U.S.A., 91: 12837-12841.
• Ladunga, I. (1992) Phylogenetic continuum indicates "galaxies" in the protein universe. Journal of Molecular Evolution, 34: 358-375.
• Ladunga, I., Czakó, F, Csabai, I., and Geszti, T. (1991) Improving prediction accuracy of signal peptides by simulated neural networks. Computer Applications in the BioSciences, 7: 485-487.
• Ladunga, I. (1999). OLIGOPAT: Oligopeptide Pattern Analysis and Prediction of Proteins by Mathematical Programming Protein Sci,.8, Suppl. 1., 157.
• Ladunga, I. (1998) A Novel Sequence Analysis Based on Weighted Physicochemical Properties of Amino Acid Residues. Protein Sci. 7: 75.
• Thomas, P.D., Kejariwal, A., Campbell, M.J., Mi, H., Diemer, K., Guo, N., Ladunga, I., Ulitsky-Lazareva, B., Muruganujan, A., Rabkin, S., Vandergriff, J.A., and Doremieux, O. (2003) PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids. Res., 31: 334-341.

Patents and copyrights

134 patents and copyrights listed >here<.
Selected patents/copyrights:

1. Ladunga, I., Smith, R.F and Wiese B.A.: FASTA-SWAP and FASTA-PAT: pattern database searches using combinations, filed in June, 1996. Copyright.
   
2. Ladunga, I. Methods and systems for identification of protein classes
   Patent Number: US5987390, 1999, Industry, United States of America.
3. A browsable database for biological use
   Patent Number: WO2004053769 (A3) , 2004, Industry, United States of America.
4. A browsable database for biological use
   Patent Number: US2005149269, 2005, Industry, United States of America.
5. A browsable database for biological use
   Patent Number: WO2004053769 (A2) , 2004, Industry, United States of America.
6. A browsable database for biological use
   Patent Number: EP1576524 (A3), 2004, Industry, United States of America.
7. A browsable database for biological use
   Patent Number: EP1576524 (A2), 2004, Industry, United States of America.
8. A browsable database for biological use
   Patent Number: EP1576524 (A0), 2004, Industry, United States of America.
9. A browsable database for biological use
   Patent Number: AU2003299589 (A1), 2004, Industry, United States of America.
10. A browsable database for biological use
    Patent Number: WO2004053769, 2004, Industry, United States of America.
11. Isolated human secreted proteins, nucleic acid molecules encoding human secreted proteins, and uses thereof
    Patent Number: US2005048560, 2005, Industry, United States of America.
12. Isolated human secreted proteins, nucleic acid molecules encoding human secreted proteins, and uses thereof
    Patent Number: US2005043229, 2005, Industry, United States of America.

Book chapters and dissertations

Ladunga, I. (1982) Molecular evolution. In: Vida G.(ed.): The Genetic Basis of Evolution. (in Hungarian), pp. 157-207, Natura, Budapest.
Ladunga, I. Computer Analyses of Protein Evolution. Ph.D. Thesis (in Hungarian). Budapest University of Sciences.
Ladunga I. Computer Analysis and Simulation of Protein Evolution. Master$-1òùs Thesis (in Hungarian). Budapest University of Sciences.-A

Professional Organizations

International Society for Computational Biology
Institute for Electric and Electronic Engineers, Computer Society
American Association for the Advancement of Science
National Committee for Technological Development, Hungary
European Society for Evolutionary Biology