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Zhou B.,North Carolina State University | Zhou B.,Omicsoft Corporation | Bailey A.,Venganza Inc. | Niblett C.L.,Venganza Inc. | Qu R.,North Carolina State University
Plant Cell Reports | Year: 2016

Key message: Transgenic tall fescue plants expressing RNAi constructs of essential genes ofRhizoctonia solaniwere resistant toR. solani.Abstract: Tall fescue (Festuca arundinacea Schreb.) is an important turf and forage grass species widely used for home lawns and on golf courses in North Carolina and other transition zone states in the US. The most serious and frequently occurring disease of tall fescue is brown patch, caused by a basidiomycete fungus, Rhizoctonia solani. This research demonstrates resistance to brown patch disease achieved by the application of host induced gene silencing. We transformed tall fescue with RNAi constructs of four experimentally determined “essential” genes from R. solani (including genes encoding RNA polymerase, importin beta-1 subunit, Cohesin complex subunit Psm1, and a ubiquitin E3 ligase) to suppress expression of those genes inside the fungus and thus inhibit fungal infection. Four gene constructs were tested, and 19 transgenic plants were obtained, among which 12 plants had detectable accumulation of siRNAs of the target genes. In inoculation tests, six plants displayed significantly improved resistance against R. solani. Lesion size was reduced by as much as 90 %. Plants without RNAi accumulation did not show resistance. To our knowledge, this is the first case that RNAi constructs of pathogen genes introduced into a host plant can confer resistance against a necrotrophic fungus. © 2016 Springer-Verlag Berlin Heidelberg


Hu J.,North Carolina State University | Hu J.,Omicsoft Corporation | Tzeng J.-Y.,North Carolina State University | Tzeng J.-Y.,National Cheng Kung University
Bioinformatics | Year: 2014

Motivation: Gene set analysis is a popular method for large-scale genomic studies. Because genes that have common biological features are analyzed jointly, gene set analysis often achieves better power and generates more biologically informative results. With the advancement of technologies, genomic studies with multi-platform data have become increasingly common. Several strategies have been proposed that integrate genomic data from multiple platforms to perform gene set analysis. To evaluate the performances of existing integrative gene set methods under various scenarios, we conduct a comparative simulation analysis based on The Cancer Genome Atlas breast cancer dataset. Results: We find that existing methods for gene set analysis are less effective when sample heterogeneity exists. To address this issue, we develop three methods for multi-platform genomic data with heterogeneity: two non-parametric methods, multi-platform Mann-Whitney statistics and multi-platform outlier robust T-statistics, and a parametric method, multi-platform likelihood ratio statistics. Using simulations, we show that the proposed multi-platform Mann-Whitney statistics method has higher power for heterogeneous samples and comparable performance for homogeneous samples when compared with the existing methods. Our real data applications to two datasets of The Cancer Genome Atlas also suggest that the proposed methods are able to identify novel pathways that are missed by other strategies. © 2014 The Author 2014.


Lu J.,National Health Research Institute | Lu J.,SRA International, Inc. | Lu J.,Omicsoft Corporation | Bushel P.R.,National Health Research Institute
Gene | Year: 2013

RNA sequencing (RNA-Seq) allows for the identification of novel exon-exon junctions and quantification of gene expression levels. We show that from RNA-Seq data one may also detect utilization of alternative polyadenylation (APA) in 3' untranslated regions (3' UTRs) known to play a critical role in the regulation of mRNA stability, cellular localization and translation efficiency. Given the dynamic nature of APA, it is desirable to examine the APA on a sample by sample basis. We used a Poisson hidden Markov model (PHMM) of RNA-Seq data to identify potential APA in human liver and brain cortex tissues leading to shortened 3' UTRs. Over three hundred transcripts with shortened 3' UTRs were detected with sensitivity > 75% and specificity > 60%. Tissue-specific 3' UTR shortening was observed for 32 genes with a q-value ≤ 0.1. When compared to alternative isoforms detected by Cufflinks or MISO, our PHMM method agreed on over 100 transcripts with shortened 3' UTRs. Given the increasing usage of RNA-Seq for gene expression profiling, using PHMM to investigate sample-specific 3' UTR shortening could be an added benefit from this emerging technology. © 2013.


Hu J.,Omicsoft Corporation | Hu J.,North Carolina State University | Ge H.,Amgen Inc. | Newman M.,Omicsoft Corporation | Liu K.,Omicsoft Corporation
Bioinformatics | Year: 2012

Accurately mapping RNA-Seq reads to the reference genome is a critical step for performing downstream analysis such as transcript assembly, isoform detection and quantification. Many tools have been developed; however, given the huge size of the next generation sequencing datasets and the complexity of the transcriptome, RNA-Seq read mapping remains a challenge with the ever-increasing amount of data. We develop Omicsoft sequence aligner (OSA), a fast and accurate alignment tool for RNA-Seq data. Benchmarked with existing methods, OSA improves mapping speed 4-10-fold with better sensitivity and less false positives. © The Author 2012. Published by Oxford University Press. All rights reserved.


Ge H.,Amgen Inc. | Liu K.,Omicsoft Corporation | Juan T.,Amgen Inc. | Fang F.,University of Southern California | And 2 more authors.
Bioinformatics | Year: 2011

Motivation: Next generation sequencing technology generates highthroughput data, which allows us to detect fusion genes at both transcript and genomic levels. To detect fusion genes, the current bioinformatics tools heavily rely on paired-end approaches and overlook the importance of reads that span fusion junctions. Thus there is a need to develop an efficient aligner to detect fusion events by accurate mapping of these junction-spanning single reads, particularly when the read gets longer with the improvement in sequencing technology. Results: We present a novel method, FusionMap, which aligns fusion reads directly to the genome without prior knowledge of potential fusion regions. FusionMap can detect fusion events in both single- and paired-end datasets from either RNA-Seq or gDNA-Seq studies and characterize fusion junctions at base-pair resolution. We showed that FusionMap achieved high sensitivity and specificity in fusion detection on two simulated RNA-Seq datasets, which contained 75 nt paired-end reads. FusionMap achieved substantially higher sensitivity and specificity than the paired-end approach when the inner distance between read pairs was small. Using FusionMap to characterize fusion genes in K562 chronic myeloid leukemia cell line, we further demonstrated its accuracy in fusion detection in both single-end RNA-Seq and gDNA-Seq datasets. These combined results show that FusionMap provides an accurate and systematic solution to detecting fusion events through junction-spanning reads. © The Author 2011. Published by Oxford University Press. All rights reserved.

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