Proceedings. International Conference on Intelligent Systems for Molecular Biology最新文献

Untranslated regions (UTR) play important roles in the posttranscriptional regulation of mRNA processing. There is a wealth of UTR-related information to be mined from the rapidly accumulating EST collections. A computational tool, UTR-extender, has been developed to infer UTR sequences from genomically aligned ESTs. It can completely and accurately reconstruct 72% of the 3' UTRs and 15% of the 5' UTRs when tested using 908 functionally cloned transcripts. In addition, it predicts extensions for 11% of the 5' UTRs and 28% of the 3' UTRs. These extension regions are validated by examining splicing frequencies and conservation levels. We also developed a method called polyadenylation site scan (PASS) to precisely map polyadenylation sites in human genomic sequences. A PASS analysis of 908 genic regions estimates that 40-50% of human genes undergo alternative polyadenylation. Using EST redundancy to assess expression levels, we also find that genes with short 3' UTRs tend to be highly expressed.

Position-specific scoring matrices have been used extensively to recognize highly conserved protein regions. We present a method for accelerating these searches using a suffix tree data structure computed from the sequences to be searched. Building on earlier work that allows evaluation of a scoring matrix to be stopped early, the suffix tree-based method excludes many protein segments from consideration at once by pruning entire subtrees. Although suffix trees are usually expensive in space, the fact that scoring matrix evaluation requires an in-order traversal allows nodes to be stored more compactly without loss of speed, and our implementation requires only 17 bytes of primary memory per input symbol. Searches are accelerated by up to a factor of ten.

Signal finding (pattern discovery in unaligned DNA sequences) is a fundamental problem in both computer science and molecular biology with important applications in locating regulatory sites and drug target identification. Despite many studies, this problem is far from being resolved: most signals in DNA sequences are so complicated that we don't yet have good models or reliable algorithms for their recognition. We complement existing statistical and machine learning approaches to this problem by a combinatorial approach that proved to be successful in identifying very subtle signals.

Novel DNA microarray technologies enable the monitoring of expression levels of thousands of genes simultaneously. This allows a global view on the transcription levels of many (or all) genes when the cell undergoes specific conditions or processes. Analyzing gene expression data requires the clustering of genes into groups with similar expression patterns. We have developed a novel clustering algorithm, called CLICK, which is applicable to gene expression analysis as well as to other biological applications. No prior assumptions are made on the structure or the number of the clusters. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups of highly similar elements (kernels), which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clustering. CLICK has been implemented and tested on a variety of biological datasets, ranging from gene expression, cDNA oligo-fingerprinting to protein sequence similarity. In all those applications it outperformed extant algorithms according to several common figures of merit. CLICK is also very fast, allowing clustering of thousands of elements in minutes, and over 100,000 elements in a couple of hours on a regular workstation.

The repetitive structure of genomic DNA holds many secrets to be discovered. A systematic study of repetitive DNA on a genomic or inter-genomic scale requires extensive algorithmic support. The REPuter family of programs described herein was designed to serve as a fundamental tool in such studies. Efficient and complete detection of various types of repeats is provided together with an evaluation of significance, interactive visualization, and simple interfacing to other analysis programs.

Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding and/or scoring remote homology search. Here we focus on the prediction of residue contacts and show that this figure can be predicted with a neural network based method. The accuracy of the prediction is 12 percentage points higher than that of a simple statistical method. The neural network is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. When evolutionary information is taken into account, our method correctly predicts 69% of the residue states in the data base and it adds to the prediction of residue solvent accessibility. The predictor is available at htpp://www.biocomp.unibo.it

Predicting the secondary structure (alpha-helices, beta-sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike alpha-helices that are built up from one contiguous region of the polypeptide chain, beta-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel beta-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a beta-sheet structure. Variations on these architecture, including also profiles and ensembles, are trained and tested via five-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.

Mycobacterium tuberculosis (M. tb.) strains differ in the number and locations of a transposon-like insertion sequence known as IS6110. Accurate detection of this sequence can be used as a fingerprint for individual strains, but can be difficult because of noisy data. In this paper, we propose a non-parametric discriminant analysis method for predicting the locations of the IS6110 sequence from microarray data. Polymerase chain reaction extension products generated from primers specific for the insertion sequence are hybridized to a microarray containing targets corresponding to each open reading frame in M. tb. To test for insertion sites, we use microarray intensity values extracted from small windows of contiguous open reading frames. Rank-transformation of spot intensities and first-order differences in local windows provide enough information to reliably determine the presence of an insertion sequence. The nonparametric approach outperforms all other methods tested in this study.

A novel description of protein structure in terms of the generalized secondary structure elements (GSSE) is proposed. GSSE's are defined as fragments of the protein structure where the chain doesn't radically change its direction. In this new language, global protein topology becomes a particular arrangement of the relatively small number of large, rod like GSSE's. Protein topology can be described by an adjacency matrix giving information, which GSSE's are close in space to each other and defining a graph, where GSSE's are equivalent to vertices and interactions between them to edges. The information about the local structure is translated into the local density of pseudo-Calpha atoms along the chain and the curvature of the chain. This new description has a number of interesting and useful features. For instance, enumeration theorems of graph theory can be used to estimate a number of possible topologies for a protein built from a given number of elements. Different topologies, including novel ones, can be generated from the known by various permutations of elements. Many new regularities in protein structures become suddenly visible in a new description. A new local structure description is more amenable to predictions and easier to use in fold predictions.

We introduce a model based on stochastic context-free grammars (SCFGs) that can construct small subunit ribosomal RNA (SSU rRNA) multiple alignments. The method takes into account both primary sequence and secondary structure basepairing interactions. We show that this method produces multiple alignments of quality close to hand edited ones and outperforms several other methods. We also introduce a method of SCFG constraints that dramatically reduces the required computer resources needed to effectively use SCFGs on large problems such as SSU rRNA. Without such constraints, the required computer resources are infeasible for most computers. This work has applications to fields such as phylogenetic tree construction.