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  • 1
    Publication Date: 2014-11-07
    Description: Motivation: A proper target or marker is essential in any diagnosis (e.g. an infection or cancer). An ideal diagnostic target should be both conserved in and unique to the pathogen. Currently, these targets can only be identified manually, which is time-consuming and usually error-prone. Because of the increasingly frequent occurrences of emerging epidemics and multidrug-resistant ‘superbugs’, a rapid diagnostic target identification process is needed. Results: A new method that can identify uniquely conserved regions (UCRs) as candidate diagnostic targets for a selected group of organisms solely from their genomic sequences has been developed and successfully tested. Using a sequence-indexing algorithm to identify UCRs and a k -mer integer-mapping model for computational efficiency, this method has successfully identified UCRs within the bacteria domain for 15 test groups, including pathogenic, probiotic, commensal and extremophilic bacterial species or strains. Based on the identified UCRs, new diagnostic primer sets were designed, and their specificity and efficiency were tested by polymerase chain reaction amplifications from both pure isolates and samples containing mixed cultures. Availability and implementation: The UCRs identified for the 15 bacterial species are now freely available at http://ucr.synblex.com . The source code of the programs used in this study is accessible at http://ucr.synblex.com/bacterialIdSourceCode.d.zip Contact: yazhousun@synblex.com Supplementary Information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 2
    Publication Date: 2013-08-13
    Description: Motivation: Protein domain classification is an important step in functional annotation for next-generation sequencing data. For RNA-Seq data of non-model organisms that lack quality or complete reference genomes, existing protein domain analysis pipelines are applied to short reads directly or to contigs that are generated using de novo sequence assembly tools. However, these strategies do not provide satisfactory performance in classifying short reads into their native domain families. Results: We introduce SALT, a protein domain classification tool based on profile hidden Markov models and graph algorithms. SALT carefully incorporates the characteristics of reads that are sequenced from the domain regions and assembles them into contigs based on a supervised graph construction algorithm. We applied SALT to two RNA-Seq datasets of different read lengths and quantified its performance using the available protein domain annotations and the reference genomes. Compared with existing strategies, SALT showed better sensitivity and accuracy. In the third experiment, we applied SALT to a non-model organism. The experimental results demonstrated that it identified more transcribed protein domain families than other tested classifiers. Availability: The source code and supplementary data are available at https://sourceforge.net/projects/salt1/ Contact: yannisun@msu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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