Expressed Sequence Tag Data Mining Research Articles

Phylogenomics of Annelida revisited: a cladistic approach using genome-wide expressed sequence tag data mining and examining the effects of missing data.

We present phylogenomic analyses of the most comprehensive molecular character set compiled for Annelida and its constituent taxa, including over 347000 aligned nucleotide sites for 39 taxa. The nucleotide data set was recovered using a pre-existing amino acid data set of almost 48000 aligned sites as a backbone for tBLASTn searches against NCBI. In addition, orthology determinations of the loci in the original amino acid data set were scrutinized using an All vs All Reciprocal Best Hit approach, employing BLASTp, and examining for statistical interdependency among the loci. This approach revealed considerable sequence redundancy among the loci in the original data set and a new data set was compiled, with the redundancy removed. The newly compiled nucleotide data set, the original amino acid data set, and the new reduced amino acid data set were subjected to parsimony analyses and two forms of bootstrap resampling. The last-named data set also was analysed using a maximum-likelihood approach. There were two main objectives to these analyses: (i) to examine the general topology, including support, resulting from the analyses of the new data sets and (ii) to assess the consistency of the branching patterns across optimality criteria by comparison with previous probabilistic approaches. The phylogenetic hypotheses resulting from analyses of the three data sets are largely unsupported, reflecting the continued difficulty of finding numerous, reliable, and suitable loci for a group as ancient as Annelida. Resulting parsimonious hypotheses disagree, in some respects, with the previous probabilistic approaches; Sedentaria and, in most cases, Errantia are not supported as monophyletic groups but Pleistoannelida is recovered as a (unsupported) monophyletic group in one of the three parsimony analyses as well as the likelihood analysis. In addition, we performed missing data titration studies to estimate the impact of missing data on overall support and support for specific clades.

The α- and β-expansin and xyloglucan endotransglucosylase/hydrolase gene families of wheat: Molecular cloning, gene expression, and EST data mining

Expansins and xyloglucan endotransglucosylase/hydrolases (XTHs) are families of extracellular proteins with members that have been shown to play an important role in cell wall growth. In this study, three, six, and five members of the wheat α-expansin ( TaEXPA1 to TaEXPA3), β-expansin ( TaEXPB1 to TaEXPB6), and XTH ( TaXTH1 to TaXTH5) gene families, respectively, were isolated from a dwarf wheat line. The mRNA expression analysis by real-time RT-PCR indicates that these genes display different transcription levels in different stages/organs/treatments, possibly suggesting their functional roles in the cell wall expansion process. Moreover, the comparison of the expression levels reveals that most of the expansins show lower expression than the XTHs. Finally, we present the analysis of wheat α- and β-expansins and XTH families by expressed sequence tag data mining.

A semiautomated approach to gene discovery through expressed sequence tag data mining: discovery of new human transporter genes.

Identification and functional characterization of the genes in the human genome remain a major challenge. A principal source of publicly available information used for this purpose is the National Center for Biotechnology Information database of expressed sequence tags (dbEST), which contains over 4 million human ESTs. To extract the information buried in this data more effectively, we have developed a semiautomated method to mine dbEST for uncharacterized human genes. Starting with a single protein input sequence, a family of related proteins from all species is compiled. This entire family is then used to mine the human EST database for new gene candidates. Evaluation of putative new gene candidates in the context of a family of characterized proteins provides a framework for inference of the structure and function of the new genes. When applied to a test data set of 28 families within the major facilitator superfamily (MFS) of membrane transporters, our protocol found 73 previously characterized human MFS genes and 43 new MFS gene candidates. Development of this approach provided insights into the problems and pitfalls of automated data mining using public databases.

The Merck Gene Index browser: an extensible data integration system for gene finding, gene characterization and EST data mining.

To make effective use of the vast amounts of expressed sequence tag (EST) sequence data generated by the Merck-sponsored EST project and other similar efforts, sequences must be organized into gene classes, and scientists must be able to 'mine' the gene class data in the context of related genomic data. This paper presents the Merck Gene Index browser, an easily extensible, World Wide Web-based system for mining the Merck Gene Index (MGI) and related genomic data. The MGI is a non-redundant set of clones and sequences, each representing a distinct gene, constructed from all high-quality 3' EST sequences generated by the Merck-sponsored EST project. The MGI browser integrates data from a variety of sources and storage formats, both local and remote, using an eclectic integration strategy, including a federation of relational databases, a local data warehouse and simple hypertext links. Data currently integrated include: LENS cDNA clone and EST data, dbEST protein and non-EST nucleic acid similarity data, WashU sequence chromatograms. Entrez sequence and Medline entries, and UniGene gene clusters. Flatfile sequence data are accessed using the Bioapps server, an internally developed client-server system that supports generic sequence analysis applications. Browser data are retrieved and formatted by means of the Bioinformatics Data Integration Toolkit (B-DIT), a new suite of Perl routines.