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    ABSTRACT: Candida glabrata is the second most common pathogenic Candida species and has emerged as a leading cause of nosocomial fungal infections. Its reduced susceptibility to antifungal drugs and its close relationship to Saccharomyces cerevisiae make it an interesting research focus. Although its genome sequence was published in 2004, little is known about its transcriptional dynamics. Here, we provide a detailed RNA-Seq-based analysis of the transcriptomic landscape of C. glabrata in nutrient-rich media, as well as under nitrosative stress and during pH shift. Using RNA-Seq data together with state-of-the-art gene prediction tools, we refined the annotation of the C. glabrata genome and predicted 49 novel protein-coding genes. Of these novel genes, 14 have homologs in S. cerevisiae and six are shared with other Candida species. We experimentally validated four novel protein-coding genes of which two are differentially regulated during pH shift and interaction with human neutrophils, indicating a potential role in host-pathogen interaction. Furthermore, we identified 58 novel non-protein-coding genes, 38 new introns and condition-specific alternative splicing. Finally, our data suggest different patterns of adaptation to pH shift and nitrosative stress in C. glabrata, Candida albicans and S. cerevisiae and thus further underline a distinct evolution of virulence in yeast.
    Nucleic Acids Research 03/2015; 43(3). DOI:10.1093/nar/gku1357 · 8.81 Impact Factor
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    ABSTRACT: Background Alternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals.ResultsThis study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies.Conclusions No single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events.
    BMC Bioinformatics 12/2014; 15(1):364. DOI:10.1186/s12859-014-0364-4 · 2.67 Impact Factor
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    ABSTRACT: Transcriptomes are routinely compared in term of a list of differentially expressed genes followed by functional enrichment analysis. Due to the technology limitations of microarray, the molecular mechanisms of differential expression is poorly understood. Using RNA-seq data, we propose a generalized dSpliceType framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. We applied the method to two public RNA-seq data sets and compared the transcriptomes between treatment and control conditions. The generalized dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model. We compared dSpliceType with two other methods in terms of five most common types of differential splicing events between two conditions using RNA-Seq. dSpliceType is free available from http://orleans.cs.wayne.edu/dSpliceType/
    IEEE Transactions on NanoBioscience 02/2015; 14(2). DOI:10.1109/TNB.2015.2388593 · 1.77 Impact Factor

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