Peter Meinicke

BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm

We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.

Metabolic priming by a secreted fungal effector

Maize smut caused by the fungus Ustilago maydis is a widespread disease characterized by the development of large plant tumours. U. maydis is a biotrophic pathogen that requires living plant tissue for its development and establishes an intimate interaction zone between fungal hyphae and the plant plasma membrane. U. maydis actively suppresses plant defence responses by secreted protein effectors. Its effector repertoire comprises at least 386 genes mostly encoding proteins of unknown function and expressed exclusively during the biotrophic stage. The U. maydis secretome also contains about 150 proteins with probable roles in fungal nutrition, fungal cell wall modification and host penetration as well as proteins unlikely to act in the fungal-host interface like a chorismate mutase. Chorismate mutases are key enzymes of the shikimate pathway and catalyse the conversion of chorismate to prephenate, the precursor for tyrosine and phenylalanine synthesis. Root-knot nematodes inject a secreted chorismate mutase into plant cells likely to affect development. Here we show that the chorismate mutase Cmu1 secreted by U. maydis is a virulence factor. The enzyme is taken up by plant cells, can spread to neighbouring cells and changes the metabolic status of these cells through metabolic priming. Secreted chorismate mutases are found in many plant-associated microbes and might serve as general tools for host manipulation.

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

BackgroundHorizontal 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.ResultsWe 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.ConclusionSIGI-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.

Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data

Motivation: The characterization of phylogenetic and functional diversity is a key element in the analysis of microbial communities. Amplicon-based sequencing of marker genes, such as 16S rRNA, is a powerful tool for assessing and comparing the structure of microbial communities at a high phylogenetic resolution. Because 16S rRNA sequencing is more cost-effective than whole metagenome shotgun sequencing, marker gene analysis is frequently used for broad studies that involve a large number of different samples. However, in comparison to shotgun sequencing approaches, insights into the functional capabilities of the community get lost when restricting the analysis to taxonomic assignment of 16S rRNA data. Results: Tax4Fun is a software package that predicts the functional capabilities of microbial communities based on 16S rRNA datasets. We evaluated Tax4Fun on a range of paired metagenome/16S rRNA datasets to assess its performance. Our results indicate that Tax4Fun provides a good approximation to functional profiles obtained from metagenomic shotgun sequencing approaches. Availability and implementation: Tax4Fun is an open-source R package and applicable to output as obtained from the SILVAngs web server or the application of QIIME with a SILVA database extension. Tax4Fun is freely available for download at http://tax4fun.gobics.de/. Contact: kasshau@gwdg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Orphelia: predicting genes in metagenomic sequencing reads

Metagenomic sequencing projects yield numerous sequencing reads of a diverse range of uncultivated and mostly yet unknown microorganisms. In many cases, these sequencing reads cannot be assembled into longer contigs. Thus, gene prediction tools that were originally developed for whole-genome analysis are not suitable for processing metagenomes. Orphelia is a program for predicting genes in short DNA sequences that is available through a web server application (http://orphelia.gobics.de). Orphelia utilizes prediction models that were created with machine learning techniques on the basis of a wide range of annotated genomes. In contrast to other methods for metagenomic gene prediction, Orphelia has fragment length-specific prediction models for the two most popular sequencing techniques in metagenomics, chain termination sequencing and pyrosequencing. These models ensure highly specific gene predictions.

Identification of Novel Plant Peroxisomal Targeting Signals by a Combination of Machine Learning Methods and in Vivo Subcellular Targeting Analyses

Two different prediction methods for Arabidopsis proteins carrying peroxisome targeting signals type 1 (PTS1) are described. Validation of many novel targeting signals and Arabidopsis PTS1 proteins by in vivo localization experiments demonstrates a high prediction accuracy of the new methods. In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

Critical assessment of metagenome interpretation − a benchmark of computational metagenomics software

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

Gene prediction in metagenomic fragments: A large scale machine learning approach

BackgroundMetagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.ResultsWe introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability.ConclusionLarge scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).

Principal surfaces from unsupervised kernel regression

We propose a nonparametric approach to learning of principal surfaces based on an unsupervised formulation of the Nadaraya-Watson kernel regression estimator. As compared with previous approaches to principal curves and surfaces, the new method offers several advantages: first, it provides a practical solution to the model selection problem because all parameters can be estimated by leave-one-out cross-validation without additional computational cost. In addition, our approach allows for a convenient incorporation of nonlinear spectral methods for parameter initialization, beyond classical initializations based on linear PCA. Furthermore, it shows a simple way to fit principal surfaces in general feature spaces, beyond the usual data space setup. The experimental results illustrate these convenient features on simulated and real data.

Mixture models for analysis of the taxonomic composition of metagenomes

Motivation: Inferring the taxonomic profile of a microbial community from a large collection of anonymous DNA sequencing reads is a challenging task in metagenomics. Because existing methods for taxonomic profiling of metagenomes are all based on the assignment of fragmentary sequences to phylogenetic categories, the accuracy of results largely depends on fragment length. This dependence complicates comparative analysis of data originating from different sequencing platforms or resulting from different preprocessing pipelines. Results: We here introduce a new method for taxonomic profiling based on mixture modeling of the overall oligonucleotide distribution of a sample. Our results indicate that the mixture-based profiles compare well with taxonomic profiles obtained with other methods. However, in contrast to the existing methods, our approach shows a nearly constant profiling accuracy across all kinds of read lengths and it operates at an unrivaled speed. Availability: A platform-independent implementation of the mixture modeling approach is available in terms of a MATLAB/Octave toolbox at http://gobics.de/peter/taxy. In addition, a prototypical implementation within an easy-to-use interactive tool for Windows can be downloaded. Contact: pmeinic@gwdg.de; thomas@gobics.de Supplementary Information: Supplementary data are available at Bioinformatics online.