2016
Gan, R. -C.; Chen, T. -W.; Wu, T. H.; Huang, P. -J.; Lee, C. -C.; Yeh, Y. -M.; Chiu, C. -H.; Huang, H. -D.; Tang, P.
PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms Journal Article
In: BMC Bioinformatics, 17 , 2016, ISSN: 14712105, (cited By 3).
Abstract | Links | BibTeX | 標籤: animal; biological model; Cnidaria; comparative study; gene expression profiling; genetics; genomics; high throughput sequencing; Internet; molecular genetics; procedures; sequence analysis; software, Animals; Cnidaria; Gene Expression Profiling; Genomics; High-Throughput Nucleotide Sequencing; Internet; Models, Biological; Molecular Sequence Annotation; Sequence Analysis, Birds; Gene expression; RNA; Websites, Functional annotation; Gene expression profiles; Next-generation sequencing; Quantification methods; Transcriptome assemblies; Transcriptome profiles; Transcriptome quantifications; Transcriptomes, RNA; Software; Transcriptome, transcriptome, Web services
@article{Gan2016,
title = {PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms},
author = {R. -C. Gan and T. -W. Chen and T. H. Wu and P. -J. Huang and C. -C. Lee and Y. -M. Yeh and C. -H. Chiu and H. -D. Huang and P. Tang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006892898&doi=10.1186%2fs12859-016-1366-1&partnerID=40&md5=4991defff4e25eeca679425824ef6437},
doi = {10.1186/s12859-016-1366-1},
issn = {14712105},
year = {2016},
date = {2016-01-01},
journal = {BMC Bioinformatics},
volume = {17},
publisher = {BioMed Central Ltd.},
abstract = {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. © 2016 The Author(s).},
note = {cited By 3},
keywords = {animal; biological model; Cnidaria; comparative study; gene expression profiling; genetics; genomics; high throughput sequencing; Internet; molecular genetics; procedures; sequence analysis; software, Animals; Cnidaria; Gene Expression Profiling; Genomics; High-Throughput Nucleotide Sequencing; Internet; Models, Biological; Molecular Sequence Annotation; Sequence Analysis, Birds; Gene expression; RNA; Websites, Functional annotation; Gene expression profiles; Next-generation sequencing; Quantification methods; Transcriptome assemblies; Transcriptome profiles; Transcriptome quantifications; Transcriptomes, RNA; Software; Transcriptome, transcriptome, Web services},
pubstate = {published},
tppubtype = {article}
}
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. © 2016 The Author(s).