Computational systems bioinformatics. Computational Systems Bioinformatics Conference最新文献

Recent genome-wide studies on nucleosome positioning in model organisms have shown strong evidence that nucleosome landscapes in the proximity of protein-coding genes exhibit regular characteristic patterns. Here, we propose a computational framework to discover novel genes in the human malaria parasite genome P. falciparum using nucleosome positioning inferred from MAINE-seq data. We rely on a classifier trained on the nucleosome landscape profiles of experimentally verified genes, and then used to discover new genes (without considering the primary DNA sequence). Cross-validation experiments show that our classifier is very accurate. About two thirds of the locations reported by the classifier match experimentally determined expressed sequence tags in GenBank, for which no gene has been annotated in the human malaria parasite.

The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. Although it has been demonstrated in the literature that methods using protein-protein interaction data alone can successfully predict a large number of protein complexes, the incorporation of gene expression profiles could help refine the putative complexes and hence improve the accuracy of the computational methods. By combining protein-protein interaction data and microarray gene expression profiles, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network and then breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our extensive tests on three widely used protein-protein interaction datasets and comparisons with the latest methods for protein complex identification demonstrate the superior performance of our method in terms of accuracy, efficiency, and capability in predicting novel protein complexes. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes.

Along with the wide application of mass spectrometry in proteomics, more and more mass spectrometry data are becoming publicly available. Several public mass spectrometry data repositories have been built on the Internet. However, most of these repositories are devoid of effective searching methods. In this paper we describe a new mass spectrometry data library, and a novel method to efficiently index and search in the library for spectra that are similar to a query spectrum. A public online server have been set up and demonstrated outstanding speed and scalability of our methods. Together with the mass spectrometry library, our searching method can improve the protein identification confidence by comparing a spectrum with the ones that are already characterized in the database. The searching method can also be used alone to cluster the similar spectra in a mass spectrometry dataset together, in order to to improve the speed and accuracy of the protein identification or quantification.

Kinship analysis using genetic data is important for many biological applications, including many in conservation biology. Wide availability of microsatellites has boosted studies in wild populations that rely on the knowledge of kinship, particularly sibling relationships (sibship). While there exist many methods for reconstructing sibling relationships, almost none account for errors and mutations in microsatellite data, which are prevalent and affect the quality of reconstruction. We present an error-tolerant method for reconstructing sibling relationships based on the concept of consensus methods. We test our approach on both real and simulated data, with both pre-existing and introduced errors. Our method is highly accurate on almost all simulations, giving over 90% accuracy in most cases. Ours is the first method designed to tolerate errors while making no assumptions about the population or the sampling.

Structural similarity between proteins gives us insights on the evolutionary relationship between proteins which have low sequence similarity. In this paper, we present a novel approach called STSA for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process, a superposition step and an alignment step, until convergence. Given two superposed structures, we propose a novel greedy algorithm to construct both sequential and non-sequential alignments. The quality of STSA alignments is evident in the high agreement it has with the reference alignments in the challenging-to-align RPIC set. Moreover, on a dataset of 4410 protein pairs selected from the CATH database, STSA has a high sensitivity and high specificity values and is competitive with state-of-the-art alignment methods and gives longer alignments with lower rmsd. The STSA software along with the data sets will be made available on line at http://www.cs.rpi.edu/-zaki/software/STSA.