Biomedical subjects

Alex T Adai

Publications and source records attributed to Alex T Adai.

4 recordsLinked to original sources

PubMed2006

CSRDB: a small RNA integrated database and browser resource for cereals.

Plant small RNAs (smRNAs), which include microRNAs (miRNAs), short interfering RNAs (siRNAs) and trans-acting siRNAs (ta-siRNAs), are emerging as significant components of epigenetic processes and of gene networks involved in development and in homeostasis. Here we present a bioinformatics resource for cereal crops, the Cereal Small RNA Database (CSRDB), consisting of large-scale datasets of maize and rice smRNA sequences generated by high-throughput pyrosequencing. The smRNA sequences have been mapped to the rice genome and to the available maize genome sequence and these results are presented in two genome browser datasets using the Generic Genome Browser. Potential RNA targets for the smRNAs have been predicted and access to the resulting smRNA/RNA target pair dataset has been made available through a MySQL based relational database. Various ways to access the data are provided including links from the genome browser to the target database. Data linking and integration are the main focus for this interface, and internal as well as external links are present. The resource is available at http://sundarlab.ucdavis.edu/smrnas/ and will be updated as more sequences become available.

Databases, Nucleic Acid↗

PubMed2006

Comparative whole genome transcriptome analysis of three Plasmodium falciparum strains.

Gene expression patterns have been demonstrated to be highly variable between similar cell types, for example lab strains and wild strains of Saccharomyces cerevisiae cultured under identical growth conditions exhibit a wide range of expression differences. We have used a genome-wide approach to characterize transcriptional differences between strains of Plasmodium falciparum by characterizing the transcriptome of the 48 h intraerythrocytic developmental cycle (IDC) for two strains, 3D7 and Dd2 and compared these results to our prior work using the HB3 strain. These three strains originate from geographically diverse locations and possess distinct drug sensitivity phenotypes. Our goal was to identify transcriptional differences related to phenotypic properties of these strains including immune evasion and drug sensitivity. We find that the highly streamlined transcriptome is remarkably well conserved among all three strains, and differences in gene expression occur mainly in genes coding for surface antigens involved in parasite-host interactions. Our analysis also detects several transcripts that are unique to individual strains as well as identifying large chromosomal deletions and highly polymorphic regions across strains. The majority of these genes are uncharacterized and have no homology to other species. These tractable transcriptional differences provide important phenotypes for these otherwise highly related strains of Plasmodium.

Animals↗

PubMed2004

A probabilistic functional network of yeast genes.

A conceptual framework for integrating diverse functional genomics data was developed by reinterpreting experiments to provide numerical likelihoods that genes are functionally linked. This allows direct comparison and integration of different classes of data. The resulting probabilistic gene network estimates the functional coupling between genes. Within this framework, we reconstructed an extensive, high-quality functional gene network for Saccharomyces cerevisiae, consisting of 4681 (approximately 81%) of the known yeast genes linked by approximately 34,000 probabilistic linkages comparable in accuracy to small-scale interaction assays. The integrated linkages distinguish true from false-positive interactions in earlier data sets; new interactions emerge from genes' network contexts, as shown for genes in chromatin modification and ribosome biogenesis.

Bayes Theorem↗

PubMed2004

LGL: creating a map of protein function with an algorithm for visualizing very large biological networks.

Networks are proving to be central to the study of gene function, protein-protein interaction, and biochemical pathway data. Visualization of networks is important for their study, but visualization tools are often inadequate for working with very large biological networks. Here, we present an algorithm, called large graph layout (LGL), which can be used to dynamically visualize large networks on the order of hundreds of thousands of vertices and millions of edges. LGL applies a force-directed iterative layout guided by a minimal spanning tree of the network in order to generate coordinates for the vertices in two or three dimensions, which are subsequently visualized and interactively navigated with companion programs. We demonstrate the use of LGL in visualizing an extensive protein map summarizing the results of approximately 21 billion sequence comparisons between 145579 proteins from 50 genomes. Proteins are positioned in the map according to sequence homology and gene fusions, with the map ultimately serving as a theoretical framework that integrates inferences about gene function derived from sequence homology, remote homology, gene fusions, and higher-order fusions. We confirm that protein neighbors in the resulting map are functionally related, and that distinct map regions correspond to distinct cellular systems, enabling a computational strategy for discovering proteins' functions on the basis of the proteins' map positions. Using the map produced by LGL, we infer general functions for 23 uncharacterized protein families.

Algorithms↗