• Journal of Microbiology and Biotechnology
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• v.24 no.12
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• pp.1609-1621
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• 2014

The plant pathogen Burkholderia gladioli, which has a broad host range that includes rice and onion, causes bacterial panicle blight and sheath rot. Based on the complete genome sequence of B. gladioli BSR3 isolated from infected rice sheaths, the genome of B. gladioli BSR3 contains the luxI/luxR family of genes. Members of this family encode N-acyl-homoserine lactone (AHL) quorum sensing (QS) signal synthase and the LuxR-family AHL signal receptor, which are similar to B. glumae BGR1. In B. glumae, QS has been shown to play pivotal roles in many bacterial behaviors. In this study, we compared the QS-dependent gene expression between B. gladioli BSR3 and a QS-defective B. gladioli BSR3 mutant in two different culture states (10 and 24 h after incubation, corresponding to an exponential phase and a stationary phase) using RNA sequencing (RNA-seq). RNA-seq analyses including gene ontology and pathway enrichment revealed that the B. gladioli BSR3 QS system regulates genes related to motility, toxin production, and oxalogenesis, which were previously reported in B. glumae. Moreover, the uncharacterized polyketide biosynthesis is activated by QS, which was not detected in B. glumae. Thus, we observed not only common QS-dependent genes between B. glumae BGR1 and B. gladioli BSR3, but also unique QS-dependent genes in B. gladioli BSR3.

• Genomics & Informatics
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• v.17 no.3
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• pp.26.1-26.12
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• 2019

Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.

• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.25-26
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• 2010

• BMB Reports
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• v.50 no.6
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• pp.283-284
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• 2017

Single cell transcriptome analysis is a powerful tool for defining cell types or sub-populations within a heterogeneous bulk population. Tumor-associated microenvironment is a complex ecosystem consisting of numerous cell types that support tumor growth, angiogenesis, immune evasion, and metastasis. With the success of checkpoint inhibitors targeting the immune cell compartment, tumor microenvironment is emerging as a potential anti-cancer target, and understanding it has become an imminent subject in cancer biology.

• Genomics & Informatics
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• v.18 no.3
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• pp.26.1-26.6
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• 2020

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type-specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

• Molecules and Cells
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• v.40 no.10
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• pp.714-730
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• 2017

Pre-mRNA splicing further increases protein diversity acquired through evolution. The underlying driving forces for this phenomenon are unknown, especially in terms of gene expression. A rice alternatively spliced transcript detection microarray (ASDM) and RNA sequencing (RNA-Seq) were applied to differentiate the transcriptome of 4 representative organs of Oryza sativa L. cv. Ilmi: leaves, roots, 1-cm-stage panicles and young seeds at 21 days after pollination. Comparison of data obtained by microarray and RNA-Seq showed a bell-shaped distribution and a co-lineation for highly expressed genes. Transcripts were classified according to the degree of organ enrichment using a coefficient value (CV, the ratio of the standard deviation to the mean values): highly variable (CVI), variable (CVII), and constitutive (CVIII) groups. A higher index of the portion of loci with alternatively spliced transcripts in a group (IAST) value was observed for the constitutive group. Genes of the highly variable group showed the characteristics of the examined organs, and alternatively spliced transcripts tended to exhibit the same organ specificity or less organ preferences, with avoidance of 'organ distinctness'. In addition, within a locus, a tendency of higher expression was found for transcripts with a longer coding sequence (CDS), and a spliced intron was the most commonly found type of alternative splicing for an extended CDS. Thus, pre-mRNA splicing might have evolved to retain maximum functionality in terms of organ preference and multiplicity.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.3
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• pp.343-351
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• 2016

Although the chemical, physical, and nutritional properties of bovine milk have been extensively studied, only a few studies have attempted to characterize milk-synthesizing genes using RNA-seq data. RNA-seq data was collected from 21 Holstein samples, along with group information about milk production ability; milk yield; and protein, fat, and solid contents. Meta-analysis was employed in order to generally characterize genes related to milk production. In addition, we attempted to investigate the relationship between milk related traits, parity, and lactation period. We observed that milk fat is highly correlated with lactation period; this result indicates that this effect should be considered in the model in order to accurately detect milk production related genes. By employing our developed model, 271 genes were significantly (false discovery rate [FDR] adjusted p-value<0.1) detected as milk production related differentially expressed genes. Of these genes, five (albumin, nitric oxide synthase 3, RNA-binding region (RNP1, RRM) containing 3, secreted and transmembrane 1, and serine palmitoyltransferase, small subunit B) were technically validated using quantitative real-time polymerase chain reaction (qRT-PCR) in order to check the accuracy of RNA-seq analysis. Finally, 83 gene ontology biological processes including several blood vessel and mammary gland development related terms, were significantly detected using DAVID gene-set enrichment analysis. From these results, we observed that detected milk production related genes are highly enriched in the circulation system process and mammary gland related biological functions. In addition, we observed that detected genes including caveolin 1, mammary serum amyloid A3.2, lingual antimicrobial peptide, cathelicidin 4 (CATHL4), cathelicidin 6 (CATHL6) have been reported in other species as milk production related gene. For this reason, we concluded that our detected 271 genes would be strong candidates for determining milk production.

• Journal of Microbiology and Biotechnology
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• v.26 no.7
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• pp.1242-1251
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• 2016

The ethanol production of Trichoderma reesei was improved by genome shuffling in our previous work. Using RNA-Seq, the transcriptomes of T. reesei wild-type CICC40360 and recombinant strain HJ48 were compared under fermentation conditions. Based on this analysis, we defined a set of T. reesei genes involved in ethanol production. Further expression analysis identified a series of glycolysis enzymes, which are upregulated in the recombinant strain HJ48 under fermentation conditions. The differentially expressed genes were further validated by qPCR. The present study will be helpful for future studies on ethanol fermentation as well as the roles of the involved genes. This research reveals several major differences in metabolic pathways between recombinant strain HJ48 and wild-type CICC40360, which relates to the higher ethanol production on the former, and their further research could promote the development of techniques for increasing ethanol production.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.1-8
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• 2020

In this paper, we propose a gene family based RNA-seq read distribution method in means to accelerate the overal transcriptome assembly computation time. To measure the performance of our transcriptome sequence data distribution method, we evaluated the performance by testing four types of data sets of the Arabidopsis thaliana genome (Whole Unclassified Reads, Family-Classified Reads, Model-Classified Reads, and Randomly Classified Reads). As a result of de novo transcript assembly in distributed nodes using model classification data, the generated gene contigs matched 95% compared to the contig generated by WUR, and the execution time was reduced by 4.2 times compared to a single node environment using the same resources.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.225-243
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• 2016

Thanks to recent advance of next generation sequencing techniques, RNA-seq enabled to have an unprecedented opportunity to identify transcript variants with isoform diversity and allelic imbalance (Anders et al., 2012) by different transcriptional rates. To date, it is well known that those features might be associated with the aberrant patterns of disease complexity such as tissue (Anders and Huber, 2010; Anders et al., 2012; Nariai et al., 2014) specific differential expression at isoform levels or tissue specific allelic imbalance in mal-functionality of disease processes, etc. Nevertheless, the knowledge of post-transcriptional modification and AI in transcriptomic and genomic areas has been little known in the traditional platforms due to the limitation of technology and insufficient resolution. We here stress the potential of isoform variability and allelic specific expression that are relevant to the abnormality of disease mechanisms in transcriptional genetic regulatory networks. In addition, we systematically review how robust Bayesian approaches in RNA-seq have been developed and utilized in this regard in the field.