Proceedings. IEEE Computational Systems Bioinformatics Conference最新文献

Mass spectrometry is a particularly useful technology for the rapid and robust identification of peptides and proteins in complex mixtures. Peptide sequences can be identified by correlating their observed tandem mass spectra (MS/MS) with theoretical spectra of peptides from a sequence database. Unfortunately, to perform this search the charge of the peptide must be known, and current chargestate- determination algorithms only discriminate singlyfrom multiply-charged spectra: distinguishing +2 from +3, for example, is unreliable. Thus, search software is forced to search multiply-charged spectra multiple times. To minimize this inefficiency, we present a support vector machine (SVM) that quickly and reliably classifies multiplycharged spectra as having either a +2 or +3 precursor peptide ion. By classifying multiply-charged spectra, we obtain a 40% reduction in search time while maintaining an average of 99% of peptide and 99% of protein identifications originally obtained from these spectra.

The Perfect Phylogeny Haplotyping (PPH) problem is one of the many computational approaches to the Haplotype Inference (HI) problem. Though there are many O(nm(2)) solutions to the PPH problem, the complexity of the PPH problem itself has remained an open question. In this paper, We introduce the FlexTree data structure that represents all the solutions for a PPH instance. We also introduce row-ordering that arranges the genotypes in a more manageable fashion. The column ordering, the FlexTree data structure and the row ordering together make the O(nm) OPPH algorithm possible. We also present some results on simulated data which demonstrate that the OPPH algorithm performs quiet impressively when compared to the earlier O(nm(2)) algorithms.

Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very useful concept to discover functionally related genes in multiple genomes. However, there are also many gene sets that do not preserve physical proximity. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraint. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. Especially, analysis of gene clusters that belong to B. subtilis and E. coli demonstrated that our model predicted many experimentally verified operons and functionally related clusters. Our program is fast enough to provide a sevice on the web at http://platcom. informatics.indiana.edu/platcom/. Users can select any combination of 97 genomes to predict gene teams.

With the rapid growth of biomedical research databases, opportunities for scientific inquiry have expanded quickly and led to a demand for computational methods that can extract biologically relevant patterns among vast amounts of data. A significant challenge is identifying temporal relationships among genotypic and clinical (phenotypic) data. Few software tools are available for such pattern matching, and they are not interoperable with existing databases. We are developing and validating a novel software method for temporal pattern discovery in biomedical genomics. In this paper, we present an efficient and flexible query algorithm (called TEMF) to extract statistical patterns from time-oriented relational databases. We show that TEMF - as an extension to our modular temporal querying application (Chronus II) - can express a wide range of complex temporal aggregations without the need for data processing in a statistical software package. We show the expressivity of TEMF using example queries from the Stanford HIV Database.

Specific topic search in the PubMed Database, one of the most important information resources for scientific community, presents a big challenge to the users. The researcher typically formulates boolean queries followed by scanning the retrieved records for relevance, which is very time consuming and error prone. We applied Support Vector Machines (SVM) for automatic retrieval of PubMed articles related to Human genome epidemiological research at CDC (Center for disease Control and Prevention). In this paper, we discuss various investigations into biomedical literature classification and analyze the effect of various issues related to the choice of keywords, training sets, kernel functions and parameters for the SVM technique. We report on the various factors above to show that SVM is a viable technique for automatic classification of biomedical literature into topics of interest such as epidemiology, cancer, birth defects etc. In all our experiments, we achieved high values of PPV, sensitivity and specificity.

Experimental processes to collect and process proteomics data are increasingly complex, while the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed a complete empirical Bayes model to analyze multi-protein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Our model considers not only bait-prey associations, but also prey-prey associations missed in previous work. Using our model and a yeast MPC proteomics data set, we estimated that there should be an average of 28 true associations per MPC, almost ten times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%.

We present a novel unsupervised method for extracting meaningful motifs from biological sequence data. This de novo motif extraction (MEX) algorithm is data driven, finding motifs that are not necessarily over-represented in the data. Applying MEX to the oxidoreductases class of enzymes, containing approximately 7000 enzyme sequences, a relatively small set of motifs is obtained. This set spans a motif-space that is used for functional classification of the enzymes by an SVM classifier. The classification based on MEX motifs surpasses that of two other SVM based methods: SVMProt, a method based on the analysis of physical-chemical properties of a protein generated from its sequence of amino acids, and SVM applied to a Smith-Waterman distances matrix. Our findings demonstrate that the MEX algorithm extracts relevant motifs, supporting a successful sequence-to-function classification.

Nuclear Overhauser effect (NOE) distance restraints are the main experimental data from protein nuclear magnetic resonance (NMR) spectroscopy for computing a complete three dimensional solution structure including sidechain conformations. In general, NOE restraints must be assigned before they can be used in a structure determination program. NOE assignment is very time-consuming to do manually, challenging to fully automate, and has become a key bottleneck for high-throughput NMR structure determination. The difficulty in automated NOE assignment is ambiguity: there can be tens of possible different assignments for an NOE peak based solely on its chemical shifts. Previous automated NOE assignment approaches rely on an ensemble of structures, computed from a subset of all the NOEs, to iteratively filter ambiguous assignments. These algorithms are heuristic in nature, provide no guarantees on solution quality or running time, and are slow in practice. In this paper we present an accurate, efficient NOE assignment algorithm. The algorithm first invokes the algorithm in [30, 29] to compute an accurate backbone structure using only two backbone residual dipolar couplings (RDCs) per residue. The algorithm then filters ambiguous NOE assignments by merging an ensemble of intra-residue vectors from a protein rotamer database, together with internuclear vectors from the computed backbone structure. The protein rotamer database was built from ultra-high resolution structures (<1.0 A) in the Protein Data Bank (PDB). The algorithm has been successfully applied to assign more than 1,700 NOE distance restraints with better than 90% accuracy on the protein human ubiquitin using real experimentally-recorded NMR data. The algorithm assigns these NOE restraints in less than one second on a single-processor workstation.

Motivation: The availability of genome-wide location analyses based on chromatin immunoprecipitation (ChIP) data gives a new insight for in silico analysis of transcriptional regulations.

Results: We propose a novel discriminative discovery framework for precisely identifying transcriptional regulatory motifs from both positive and negative samples (sets of upstream sequences of both bound and unbound genes by a transcription factor (TF)) based on the genome-wide location data. In this framework, our goal is to find such discriminative motifs that best explain the location data in the sense that the motifs precisely discriminate the positive samples from the negative ones. First, in order to discover an initial set of discriminative substrings between positive and negative samples, we apply a decision tree learning method which produces a text-classification tree. We extract several clusters consisting of similar substrings from the internal nodes of the learned tree. Second, we start with initial profile-HMMs constructed from each cluster for representing putative motifs and iteratively refine the profile-HMMs to improve the discrimination accuracies. Our genome-wide experimental results on yeast show that our method successfully identifies the consensus sequences for known TFs in the literature and further presents significant performances for discriminating between positive and negative samples in all the TFs, while most other motif detecting methods show very poor performances on the problem of discriminations. Our learned profile-HMMs also improve false negative predictions of ChIP data.

LTR retrotransposons constitute one of the most abundant classes of repetitive elements in eukaryotic genomes. In this paper, we present a new algorithm for detection of full-length LTR retrotransposons in genomic sequences. The algorithm identifies regions in a genomic sequence that show structural characteristics of LTR retrotransposons. Three key components distinguish our algorithm from that of current software - (i) a novel method that preprocesses the entire genomic sequence in linear time and produces high quality pairs of LTR candidates in running time that is constant per pair, (ii) a thorough alignment-based evaluation of candidate pairs to ensure high quality prediction, and (iii) a robust parameter set encompassing both structural constraints and quality controls providing users with a high degree of flexibility. Validation of both our serial and parallel implementations of the algorithm against the yeast genome indicates both superior quality and performance results when compared to existing software.