Browsing by Author "Yeh, Yuan-Ming"

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PARRoT - a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms
(2016-12-22)
Gan, Ruei-Chi
;
Chen, Ting-Wen
;
Wu, Timothy H.
;
Huang, Po-Jung
;
Lee, Chi-Ching
Background: Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. Results: Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. Conclusions: In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw.
PARRoT - a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms
(2016-12-22)
Gan, Ruei-Chi
;
Chen, Ting-Wen
;
Wu, Timothy H.
;
Huang, Po-Jung
;
Lee, Chi-Ching
Background: Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. Results: Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. Conclusions: In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw.
Phosphoproteome profiling of the sexually transmitted pathogen Trichomonas vaginalis
(2013-10-01)
Yeh, Yuan-Ming
;
Huang, Kuo-Yang
;
Gan, Ruei-Chi Richie
;
Huang, Hsien-Da
;
Wang, Tzu-Chien V.
Background/Purpose(s): Trichomoniasis caused by Trichomonas vaginalis is the most common non-viral sexually transmitted infection. Morphological transformation from the trophozoite stage to the amoeboid or pseudocyst stage is crucial for T. vaginalis infection and survival. Protein phosphorylation is a key post-translational modification involved in the regulation of several biological processes in various prokaryotes and eukaryotes. More than 880 protein kinases have been identified in the T. vaginalis genome. However, little is known about the phosphorylation of specific proteins and the distribution of phosphorylated proteins in different stages of the morphological transformation of T. vaginalis. Methods: To obtain a more comprehensive understanding of the T. vaginalis phosphoproteome, we analyzed phosphorylated proteins in the three morphological stages using titanium dioxide combined with LC-MS/MS. Results: A total of 93 phosphopeptides originating from 82 unique proteins were identified. Among these proteins, 21 were detected in all stages, 29 were identified in two different stages, and 32 were stage specific. Conclusion: Identification of stage-specific phosphorylated proteins indicates that phosphorylation of these proteins may play a key role in the morphological transformation of T. vaginalis. Copyright (C) 2012, Taiwan Society of Microbiology. Published by Elsevier Taiwan LLC. All rights reserved.