Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

BACKGROUND: Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands. RESULTS: We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU) of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. CONCLUSION: SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired genes

P-value based visualization of codon usage data

Two important and not yet solved problems in bacterial genome research are the identification of horizontally transferred genes and the prediction of gene expression levels. Both problems can be addressed by multivariate analysis of codon usage data. In particular dimensionality reduction methods for visualization of multivariate data have shown to be effective tools for codon usage analysis. We here propose a multidimensional scaling approach using a novel similarity measure for codon usage tables. Our probabilistic similarity measure is based on P-values derived from the well-known chi-square test for comparison of two distributions. Experimental results on four microbial genomes indicate that the new method is well-suited for the analysis of horizontal gene transfer and translational selection. As compared with the widely-used correspondence analysis, our method did not suffer from outlier sensitivity and showed a better clustering of putative alien genes in most cases

PAC-Learning for insolvency-prediction and hotspot-identification

Die Methoden des maschinellen Lernens erweisen sich nicht nur in der Bioinformatik als sehr effektiv, sondern auch in anderen Bereichen, vor allem wegen ihrer Universalität. Im Falle des überwachten Lernens haben alle Ansätze eines gemeinsam: Es wird versucht, anhand von Daten bestimmte, generalisierbare Verteilungseigenschaften zu lernen (Training), um somit auch auf ungesehenen Daten zuverlässige Vorhersagen treffen zu können. Ein weiteres Modell im Bereich des maschinellen Lernens ist das Konzept-Lernen (hier PAC-Lernen).In dieser Dissertation wird im Rahmen des PAC-Lernens eine für die Praxis relevante Konzeptklasse und die dafür notwendigen Lernalgorithmen entwickelt und analysiert. Das PAC-Lernen wird in zwei verschiedenen Anwendungsbereichen durch einen Vergleich gegenüber bewährten Verfahren validiert, zum einen in der Betriebswirtschaft, zur Insolvenzvorhersage und zum anderen in der Bioinformatik, zur Erkennung von Hotspots in Protein-Protein-Wechselwirkungen.Bei der Insolvenzvorhersage wird versucht, eine Aussage über eine bevorstehende Insolvenz eines deutschen Unternehmens zu treffen. Es existieren bereits zahlreiche Ansätze zu diesem Thema, jedoch sind die Vorhersagen noch nicht zuverlässig genug. Die Grundlage für die Insolvenzvorhersage bilden in dieser Arbeit Jahresabschlüsse des Verbandes der Vereine Creditreform. Die Ergebnisse des PAC-Lernens werden anschließend mit bewährten Verfahren zur Insolvenzvorhersage (z.B. neuronale Netze, multivariate Diskriminanzanalyse, Kendall-Verfahren) verglichen.Bei der Hotspot-Identifikation in Protein-Protein-Wechselwirkungen wird versucht, Aminosäuren, die in Interfaces (Bindestellen) lokalisiert sind, zu charakterisieren. Es wird davon ausgegangen, dass Hotpots die Bindung zwischen zwei Proteinen, die zu einem größeren Komplex aggregieren, maßgeblich stabilisieren. Als Datenbasis dient eine Auswahl von Homo- und Heterodimeren aus der PDB-Datenbank.Die entwickelten Methoden werden schließlich in das Tool InSolve (http://www.tcs.informatik.uni-goettingen.de/insolve) mit grafischer Oberfläche zur einfachen Anwendung integriert.Durch dieses Vorgehen werden zwei unterschiedliche Anwendungsbereiche (Bioinformatik und Betriebswirtschaft) über dieselbe Methode (PAC-Lernen) miteinander verknüpft

A Generalized Model of PAC Learning and its Applicability

We combine a new data model, where the random classification is subjected to rather weak restrictions which in turn are based on the Mammen−Tsybakov [E. Mammen and A.B. Tsybakov, Ann. Statis. 27 (1999) 1808–1829; A.B. Tsybakov, Ann. Statis. 32 (2004) 135–166.] small margin conditions, and the statistical query (SQ) model due to Kearns [M.J. Kearns, J. ACM 45 (1998) 983–1006] to what we refer to as PAC + SQ model. We generalize the class conditional constant noise (CCCN) model introduced by Decatur [S.E. Decatur, in ICML ’97: Proc. of the Fourteenth Int. Conf. on Machine Learn. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA (1997) 83–91] to the noise model orthogonal to a set of query functions. We show that every polynomial time PAC + SQ learning algorithm can be efficiently simulated provided that the random noise rate is orthogonal to the query functions used by the algorithm given the target concept. Furthermore, we extend the constant-partition classification noise (CPCN) model due to Decatur [S.E. Decatur, in ICML ’97: Proc. of the Fourteenth Int. Conf. on Machine Learn. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA (1997) 83–91] to what we call the constant-partition piecewise orthogonal (CPPO) noise model. We show how statistical queries can be simulated in the CPPO scenario, given the partition is known to the learner. We show how to practically use PAC + SQ simulators in the noise model orthogonal to the query space by presenting two examples from bioinformatics and software engineering. This way, we demonstrate that our new noise model is realistic