Dataset Information

STACK: Sequence Tag Alignment and Consensus Knowledgebase.

ABSTRACT: STACK is a tool for detection and visualisation of expressed transcript variation in the context of developmental and pathological states. The datasystem organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index and is accessible via WWW at http://www.sanbi.ac.za/Dbases.html. STACK represents a broadly applicable resource, as it is the only reconstructed transcript database for which the tools for its generation are also broadly available (http://www.sanbi.ac.za/CODES).

SUBMITTER: Christoffels A

PROVIDER: S-EPMC29830 | biostudies-literature | 2001 Jan

REPOSITORIES: biostudies-literature

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STACK: Sequence Tag Alignment and Consensus Knowledgebase.

Christoffels A A van Gelder A A Greyling G G Miller R R Hide T T Hide W W

Nucleic acids research 20010101 1

STACK is a tool for detection and visualisation of expressed transcript variation in the context of developmental and pathological states. The datasystem organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect t ...[more]

PMID: 11125101

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Project description:BackgroundIn recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.Methodology/principal findingsWe employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.ConclusionsFor the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.

Project description:BACKGROUND AND AIMS:Banana (Musa acuminata) is a crop contributing to global food security. Many varieties lack resistance to biotic stresses, due to sterility and narrow genetic background. The objective of this study was to develop an expressed sequence tag (EST) database of transcripts expressed during compatible and incompatible banana-Mycosphaerella fijiensis (Mf) interactions. Black leaf streak disease (BLSD), caused by Mf, is a destructive disease of banana. Microsatellite markers were developed as a resource for crop improvement. METHODOLOGY:cDNA libraries were constructed from in vitro-infected leaves from BLSD-resistant M. acuminata ssp. burmaniccoides Calcutta 4 (MAC4) and susceptible M. acuminata cv. Cavendish Grande Naine (MACV). Clones were 5'-end Sanger sequenced, ESTs assembled with TGICL and unigenes annotated using BLAST, Blast2GO and InterProScan. Mreps was used to screen for simple sequence repeats (SSRs), with markers evaluated for polymorphism using 20 diploid (AA) M. acuminata accessions contrasting in resistance to Mycosphaerella leaf spot diseases. PRINCIPAL RESULTS:A total of 9333 high-quality ESTs were obtained for MAC4 and 3964 for MACV, which assembled into 3995 unigenes. Of these, 2592 displayed homology to genes encoding proteins with known or putative function, and 266 to genes encoding proteins with unknown function. Gene ontology (GO) classification identified 543 GO terms, 2300 unigenes were assigned to EuKaryotic orthologous group categories and 312 mapped to Kyoto Encyclopedia of Genes and Genomes pathways. A total of 624 SSR loci were identified, with trinucleotide repeat motifs the most abundant in MAC4 (54.1 %) and MACV (57.6 %). Polymorphism across M. acuminata accessions was observed with 75 markers. Alleles per polymorphic locus ranged from 2 to 8, totalling 289. The polymorphism information content ranged from 0.08 to 0.81. CONCLUSIONS:This EST collection offers a resource for studying functional genes, including transcripts expressed in banana-Mf interactions. Markers are applicable for genetic mapping, diversity characterization and marker-assisted breeding.

Dataset Information

STACK: Sequence Tag Alignment and Consensus Knowledgebase.

Publications

STACK: Sequence Tag Alignment and Consensus Knowledgebase.

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