RNASEQR—a streamlined and accurate RNA-seq sequence analysis program

Chen, Leslie Y; Wei, Kuo-Chen; Huang, Abner C. Y.; Wang, Kai; Huang, Chiung-Yin; Yi, Danielle; Tang, Chuan Yi; Galas, David J.; Hood, Leroy

doi:10.1093/nar/gkr1248

Cited by 28 publications

(23 citation statements)

References 44 publications

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“…PARRoT is also the first web-service designed for comparing expression profiles between transcriptomes without requiring the prior knowledge for reference genome. Although there are web applications available for analyzing RNA-Seq data [24, 33], few of them were designed for analyzing de novo transcriptome for non-model organisms [26]. Meanwhile, most of the available de novo assemblers were designed for local execution only, lacking downstream analyses such as aligning the RNA-Seq reads back to de novo assembled transcripts, analyzing differentially expressed transcripts between datasets and annotating the de novo assembled transcripts.…”

Section: Discussionmentioning

confidence: 99%

PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms

Gan

Chen

Wu³

et al. 2016

BMC Bioinformatics

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BackgroundNext-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.ResultsHere, 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.ConclusionsIn 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.

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Section: Discussionmentioning

confidence: 99%

PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms

Gan

Chen

Wu³

et al. 2016

BMC Bioinformatics

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“…2,3 Despite some controversy, it is clear this technique can form a basis for most studies and unveil potential directions that can be further explored using conventional techniques, including microarrays and the real-time quantitative polymerase chain reaction. 12 Advances in information technology combined with accurate mapping of RNA-Seq reads 13,14 has aided the identification of SNPs in diseases such as cancer. [4][5][6] For example, it has recently been employed to show how cyclosporine A (a drug frequently used to rescue glucocorticoid-resistant diseases) can promote a genome-wide shift attenuating the responsiveness of a cell type prone to promoting resistance.…”

Section: Hi G H -Throug Hput S Equen Cing and S Teroid Recep Tor Acmentioning

confidence: 99%

“…In a study using palmitate treated human islets of Langerhans, more than 3000 splice variants that had not been detected in previous microarray studies were identified. 12 Advances in information technology combined with accurate mapping of RNA-Seq reads 13,14 has aided the identification of SNPs in diseases such as cancer. 15,16 In addition to RNA-Seq, a technique referred to as "Drop-seq" analyses mRNA transcripts from individual cells and determines the cell of origin for transcripts, which is incredibly useful for expression profiling of heterogenous cell populations in vivo.…”

Section: Hi G H -Throug Hput S Equen Cing and S Teroid Recep Tor Acmentioning

confidence: 99%

Thirty years of neuroendocrinology: Technological advances pave the way for molecular discovery

Stubbs

Conway-Campbell

Lightman

2018

J Neuroendocrinology

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Since the 1950s, the systems level interactions between the hypothalamus, pituitary and end organs such as the adrenal, thyroid and gonads have been well known; however, it is only over the last three decades that advances in molecular biology and information technology have provided a tremendous expansion of knowledge at the molecular level. Neuroendocrinology has benefitted from developments in molecular genetics, epigenetics and epigenomics, and most recently optogenetics and pharmacogenetics. This has enabled a new understanding of gene regulation, transcription, translation and post‐translational regulation, which should help direct the development of drugs to treat neuroendocrine‐related diseases.

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“…Most recent mappers are capable of using reference annotation files to deal with known exonexon junctions and to predict new splice sites, which is essential when analyzing RNA-seq data from most eukaryotes. GSNAP, SOAP-splice, RNASEQR [107], STAR and TopHat2 are some recommended options for spliced alignments, but for intronless species, miRNA and transcriptomes, unspliced aligners can be used. Comparative evaluations showed that FMindex-based mappers are preferable [108] and that, again, no tool is the best for every performance parameters like speed, alignment yield, exon discovery and accuracy [109].…”

Section: Mapping To a Referencementioning

confidence: 99%

RNA‐seq: Applications and Best Practices

Pereira¹,

Imada²,

MunizGuedes³

2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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RNA-sequencing (RNA-seq) is the state-of-the-art technique for transcriptome analysis that takes advantage of high-throughput next-generation sequencing. Although being a powerful approach, RNA-seq imposes major challenges throughout its steps with numerous caveats. There are currently many experimental options available, and a complete comprehension of each step is critical to make right decisions and avoid getting into inconclusive results. A complete workflow consists of: (1) experimental design; (2) sample and library preparation; (3) sequencing; and (4) data analysis. RNA-seq enables a wide range of applications such as the discovery of novel genes, gene/transcript quantification, and differential expression and functional analysis. This chapter will encompass the main aspects from sample preparation to downstream data analysis. It will be discussed how to obtain high-quality samples, replicates amount, library preparation, sequencing platforms and coverage, focusing on best recommended practices based on specialized literature. Basic techniques and well-known algorithms are presented and discussed, guiding both beginners and experienced users in the implementation of reliable experiments.Sanger sequencing [6], but with advances in next-generation sequencing technology (NGS), transcriptomic studies have evolved considerably and RNA-seq [7,8] became the state-of-art for transcriptome analysis.RNA-seq consists of the direct sequencing of transcripts by NGS. Several NGS platforms [9][10][11] are commercially available nowadays. In general, an RNA set of interest is converted to a library of complementary DNA (cDNA) fragments and sequenced in a high-throughput manner. Compared to ESTs, RNA-seq provides better resolution and representativeness, whereas when compared to microarrays, the independence of reference sequences facilitates the discovery of novel genes and isoforms [8].RNA-seq experiments harbors challenges from the experimental design to data analysis. Since a complete comprehension of each step is critical to make right decision, this chapter will encompass essential principles required for a successful RNA-seq experiment, focusing on best recommended practices based on specialized and recent literature. Basic techniques and well-known algorithms are presented and discussed, guiding both beginners and experienced users in the implementation of reliable experiments. Experimental designIn order to obtain a successful RNA-seq experiment, it is critical to have a good experimental design. Despite its importance, a proper planning is not always done. There are many experimental options available, and to fully comprehend each step, it is essential to make right decisions, avoiding inconclusive results. These choices depend on extrinsic (e.g., cost, time, samples availability) and intrinsic (e.g., experimental design complexity, transcriptional variability among tissues, samples and organisms) factors. The amount of available resources is usually the main extrinsic limiting factor driving researchers' decisio...

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RNASEQR—a streamlined and accurate RNA-seq sequence analysis program

Cited by 28 publications

References 44 publications

PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms

PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms

Thirty years of neuroendocrinology: Technological advances pave the way for molecular discovery

RNA‐seq: Applications and Best Practices

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