Biomedical subjects

Shawn M Gomez

Publications and source records attributed to Shawn M Gomez.

8 recordsLinked to original sources

PubMed2006

SAGE analysis of mosquito salivary gland transcriptomes during Plasmodium invasion.

Invasion of the vector salivary glands by Plasmodium is a critical step for malaria transmission. To describe salivary gland cellular responses to sporozoite invasion, we have undertaken the analysis of Anopheles gambiae salivary gland transcriptome using Serial Analysis of Gene Expression (SAGE). Statistical analysis of the more than 160000 sequenced tags generated from four libraries, two from glands infected by Plasmodium berghei, two from glands of controls, revealed that at least 57 Anopheles genes are differentially expressed in infected salivary glands. Among the 37 immune-related genes identified by SAGE tags, four (Defensin1, GNBP, Serpin6 and Cecropin2) were found to be upregulated during salivary gland invasion, while five genes encoding small secreted proteins display induction patterns strongly reminiscent of that of Cecropin2. Invasion by Plasmodium has also an impact on the expression of genes involved in transport, lipid and energy metabolism, suggesting that the sporozoite may exploit the metabolism of its host. In contrast, protein composition of saliva is predicted to be only slightly modified after infection. This study, which is the first transcriptome analysis of the salivary gland response to Plasmodium infection, provides a basis for a better understanding of Plasmodium/Anopheles salivary gland interactions.

Animals↗

PubMed2006

Anopheles gambiae genome reannotation through synthesis of ab initio and comparative gene prediction algorithms.

BACKGROUND: Complete genome annotation is a necessary tool as Anopheles gambiae researchers probe the biology of this potent malaria vector. RESULTS: We reannotate the A. gambiae genome by synthesizing comparative and ab initio sets of predicted coding sequences (CDSs) into a single set using an exon-gene-union algorithm followed by an open-reading-frame-selection algorithm. The reannotation predicts 20,970 CDSs supported by at least two lines of evidence, and it lowers the proportion of CDSs lacking start and/or stop codons to only approximately 4%. The reannotated CDS set includes a set of 4,681 novel CDSs not represented in the Ensembl annotation but with EST support, and another set of 4,031 Ensembl-supported genes that undergo major structural and, therefore, probably functional changes in the reannotated set. The quality and accuracy of the reannotation was assessed by comparison with end sequences from 20,249 full-length cDNA clones, and evaluation of mass spectrometry peptide hit rates from an A. gambiae shotgun proteomic dataset confirms that the reannotated CDSs offer a high quality protein database for proteomics. We provide a functional proteomics annotation, ReAnoXcel, obtained by analysis of the new CDSs through the AnoXcel pipeline, which allows functional comparisons of the CDS sets within the same bioinformatic platform. CDS data are available for download. CONCLUSION: Comprehensive A. gambiae genome reannotation is achieved through a combination of comparative and ab initio gene prediction algorithms.

Algorithms↗

PubMed2005

Pilot Anopheles gambiae full-length cDNA study: sequencing and initial characterization of 35,575 clones.

We describe the preliminary analysis of over 35,000 clones from a full-length enriched cDNA library from the malaria mosquito vector Anopheles gambiae. The clones define nearly 3,700 genes, of which around 2,600 significantly improve current gene definitions. An additional 17% of the genes were not previously annotated, suggesting that an equal percentage may be missing from the current Anopheles genome annotation.

Animals↗

PubMed2004

The genomic sequence of the accidental pathogen Legionella pneumophila.

We present the genomic sequence of Legionella pneumophila, the bacterial agent of Legionnaires' disease, a potentially fatal pneumonia acquired from aerosolized contaminated fresh water. The genome includes a 45-kilobase pair element that can exist in chromosomal and episomal forms, selective expansions of important gene families, genes for unexpected metabolic pathways, and previously unknown candidate virulence determinants. We highlight the genes that may account for Legionella's ability to survive in protozoa, mammalian macrophages, and inhospitable environmental niches and that may define new therapeutic targets.

DNA, Bacterial↗

PubMed2003

Learning to predict protein-protein interactions from protein sequences.

In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.

Algorithms↗

PubMed2003

Network approaches to genome annotation and the challenge of Anopheles gambiae.

The rapid advancement of current sequencing projects has provided significant challenges for the swift and accurate processing, integration and analysis of biological data. Such challenges will only become greater as a new round of organisms, both well known and obscure, await sequencing and further study. While perhaps less well studied at the molecular level than Drosophila melanogaster, the recently sequenced mosquito, Anopheles gambiae, provides an example of what will be faced by many of these awaiting projects. This review presents a brief overview of some of the challenges associated with these future genome projects (including Anopheles) as well as a description of recent computational approaches that utilize genome context for the prediction of gene and/or protein function. Such approaches show promise not only for the study of Anopheles, but also in the study of biological processes in general.

Animals↗

PubMed2002

Of truth and pathways: chasing bits of information through myriads of articles.

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

Algorithms↗

PubMed2002

Towards the prediction of complete protein--protein interaction networks.

We present a statistical method for the prediction of protein--protein interactions within an organism. This approach is based on the treatment of proteins as collections of conserved domains, where each domain is responsible for a specific interaction with another domain. By characterizing the frequency with which specific domain--domain interactions occur within known interactions, our model can assign a probability to an arbitrary interaction between any two proteins with defined domains. Domain interaction data is complemented with information on the topology of a network and is incorporated into the model by assigning greater probabilities to networks displaying more biologically realistic topologies. We use Markov chain Monte Carlo techniques for the prediction of posterior probabilities of interaction between a set of proteins; allowing its application to large data sets. In this work we attempt to predict interactions in a set of 40 human proteins, known to form a connected network, and discuss methods for future improvement.

Markov Chains↗