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dc.contributor.authorGan, Ruei-Chien_US
dc.contributor.authorChen, Ting-Wenen_US
dc.contributor.authorWu, Timothy H.en_US
dc.contributor.authorHuang, Po-Jungen_US
dc.contributor.authorLee, Chi-Chingen_US
dc.contributor.authorYeh, Yuan-Mingen_US
dc.contributor.authorChiu, Cheng-Hsunen_US
dc.contributor.authorHuang, Hsien-Daen_US
dc.contributor.authorTang, Petrusen_US
dc.date.accessioned2019-04-03T06:37:04Z-
dc.date.available2019-04-03T06:37:04Z-
dc.date.issued2016-12-22en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://dx.doi.org/10.1186/s12859-016-1366-1en_US
dc.identifier.urihttp://hdl.handle.net/11536/145939-
dc.description.abstractBackground: 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.en_US
dc.language.isoen_USen_US
dc.subjectComparative transcriptomeen_US
dc.subjectTranscriptome quantificationen_US
dc.subjectDe novo transcriptome assemblyen_US
dc.subjectNon-model transcriptomeen_US
dc.subjectWeb serviceen_US
dc.titlePARRoT - a homology-based strategy to quantify and compare RNA-sequencing from non-model organismsen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s12859-016-1366-1en_US
dc.identifier.journalBMC BIOINFORMATICSen_US
dc.citation.volume17en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000392533100006en_US
dc.citation.woscount3en_US
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