Algorithms for locating extremely conserved elements in multiple sequence alignments

<p>Abstract</p> <p>Background</p> <p>In 2004, Bejerano <it>et al</it>. announced the startling discovery of hundreds of "ultraconserved elements", long genomic sequences perfectly conserved across human, mouse, and rat. Their announcement stimulated a flurry of subsequent research.</p> <p>Results</p> <p>We generalize the notion of ultraconserved element in a natural way from extraordinary human-rodent conservation to extraordinary conservation over an arbitrary set of species. We call these "Extremely Conserved Elements". There is a linear time algorithm to find all such Extremely Conserved Elements in any multiple sequence alignment, provided that the conservation is required to be across all the aligned species. For the general case of conservation across an arbitrary subset of the aligned species, we show that the question of whether there exists an Extremely Conserved Element is <it>NP</it>-complete. We illustrate the linear time algorithm by cataloguing all 177 Extremely Conserved Elements in the currently available 44-vertebrate whole-genome alignment, and point out some of the characteristics of these elements.</p> <p>Conclusions</p> <p>The <it>NP</it>-completeness in the case of conservation across an arbitrary subset of the aligned species implies that it is unlikely an efficient algorithm exists for this general case. Despite this fact, for the interesting case of conservation across all or most of the aligned species, our algorithm is efficient enough to be practical. The 177 Extremely Conserved Elements that we catalog demonstrate many of the characteristics of the original ultraconserved elements of Bejerano <it>et al</it>.</p

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment

Discovery and Applications of Bacterial Noncoding RNAs

Thesis (Ph.D.)--University of Washington, 2012Noncoding RNAs (ncRNAs) are functional transcripts that do not code for proteins. Many of them play indispensible roles in the cell. For example, the ribosomal RNAs make up the ribosome that is the factory for making proteins and riboswitches bind to small metabolites in the cell and regulate gene expression. Computational discovery of ncRNAs is challenging, however, because ncRNAs evolve rapidly on the nucleotide level while preserving secondary structure. In the first part of this thesis, we develop two clustering algorithms that are robust to weak sequence homology signals and are applicable on the genomic scale. We show that both algorithms can recover most known ncRNA families and as few as 5 homologous sequences are needed to predict a strong motif. In the second part of the thesis, we investigate whether secondary structure in- formation improves maximum likelihood tree inference for ncRNAs. An accurate phylogenetic tree has important biological and clinical applications: it can be used to infer the function of novel organisms and understand the evolutionary history of species. We show that using structure information, a more realistic gap model, and a maximum likelihood approach improves phylogenetic tree inference. In the third part of the thesis, we develop a method for profiling human gut microbial communities using high-throughput sequencing. Our method works on Illumina short reads and does not require assembly or taxonomic identification. We show that it can differentiate between the gut microbiota of healthy individuals at low sequencing depth, making it a cost-effective screening tool for large population studies. In the final part of the thesis, we use a standard additions experiment to examine sequencing bias and errors in Illumina HiSeq. We identify features associated with systematic errors and develop an error correction pipeline. We show that our method reduces base errors and produces better species diversity estimates

POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome

One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

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