Transcriptome Of Organisms Research Articles

Transcriptome analysis of a springtail, Folsomia candida, reveals energy constraint and oxidative stress during petroleum hydrocarbon exposure

Petroleum hydrocarbon (PHC) contamination in soil is ubiquitous and poses harmful consequences to many organisms. The toxicity of PHC-impacted soil is difficult to predict due to variations in mixture composition and the impacts of natural weathering processes. Hence, high-throughput methods to assess PHC-impacted soils is required to expedite land management decisions. Next-generation sequencing is a robust tool that allows researchers to investigate the effects of contaminants on the transcriptome of organisms and identify molecular biomarkers. In this study, the effects of PHCs on conventional endpoints (i.e., survival and reproduction) and gene expression rates of a model springtail species, Folsomia candida were investigated. Age-synchronized F. candida were exposed to ecologically-relevant concentrations of soils spiked with fresh crude oil to calculate the reproductive EC25 and EC50 values using conventional toxicity testing. Soils spiked to these concentrations were then used to evaluate effects on the F. candida transcriptome over a 7-day exposure period. RNA-seq analysis found 98 and 132 differentially expressed genes when compared to the control for the EC25 and EC50 treatment groups, respectively. The majority of up-regulated genes were related to xenobiotic biotransformation reactions and oxidative stress response, while down-regulated genes coded for carbohydrate and peptide metabolic processes. Promotion of the pentose phosphate pathway was also found. Results suggest that the decreased reproduction rates of F. candida exposed to PHCs is due to energy constraints caused by inhibition of carbohydrate metabolic processes and allocation of remaining energy to detoxify xenobiotics. These findings provide insights into the molecular effects in F. candida following exposure to crude oil for seven days and highlight their potential to be used as a high-throughput screening test for PHC-contaminated sites. Adverse molecular effects can be measured as early as 24 h following exposure, whereas conventional toxicity tests may require a minimum of four weeks.

Transcriptome analysis in Parhyale hawaiensis reveal sex-specific responses to AgNP and AgCl exposure

Analysis of the transcriptome of organisms exposed to toxicants offers new insights for ecotoxicology, but further research is needed to enhance interpretation of results and effectively incorporate them into useful environmental risk assessments. Factors that must be clarified to improve use of transcriptomics include assessment of the effect of organism sex within the context of toxicant exposure. Amphipods are well recognized as model organisms for toxicity evaluation because of their sensitivity and amenability to laboratory conditions. To investigate whether response to metals in crustaceans differs according to sex we analyzed the amphipod Parhyale hawaiensis after exposure to AgCl and Ag nanoparticles (AgNP) via contaminated food. Gene specific analysis and whole genome transcriptional profile of male and female organisms were performed by both RT-qPCR and RNA-seq. We observed that expression of transcripts of genes glutathione transferase (GST) did not differ among AgCl and AgNP treatments. Significant differences between males and females were observed after exposure to AgCl and AgNP. Males presented twice the number of differentially expressed genes in comparison to females, and more differentially expressed were observed after exposure to AgNP than AgCl treatments in both sexes. The genes that had the greatest change in expression relative to control were those genes related to peptidase and catalytic activity and chitin and carbohydrate metabolic processes. Our study is the first to demonstrate sex specific differences in the transcriptomes of amphipods upon exposure to toxicants and emphasizes the importance of considering gender in ecotoxicology.

TransFlow: a modular framework for assembling and assessing accurate de novo transcriptomes in non-model organisms

BackgroundThe advances in high-throughput sequencing technologies are allowing more and more de novo assembling of transcriptomes from many new organisms. Some degree of automation and evaluation is required to warrant reproducibility, repetitivity and the selection of the best possible transcriptome. Workflows and pipelines are becoming an absolute requirement for such a purpose, but the issue of assembling evaluation for de novo transcriptomes in organisms lacking a sequenced genome remains unsolved. An automated, reproducible and flexible framework called TransFlow to accomplish this task is described.ResultsTransFlow with its five independent modules was designed to build different workflows depending on the nature of the original reads. This architecture enables different combinations of Illumina and Roche/454 sequencing data, and can be extended to other sequencing platforms. Its capabilities are illustrated with the selection of reliable plant reference transcriptomes and the assembling six transcriptomes (three case studies for grapevine leaves, olive tree pollen, and chestnut stem, and other three for haustorium, epiphytic structures and their combination for the phytopathogenic fungus Podosphaera xanthii). Arabidopsis and poplar transcriptomes revealed to be the best references. A common result regarding de novo assemblies is that Illumina paired-end reads of 100 nt in length assembled with OASES can provide reliable transcriptomes, while the contribution of longer reads is noticeable only when they complement a set of short, single-reads.ConclusionsTransFlow can handle up to 181 different assembling strategies. Evaluation based on principal component analyses allows its self-adaptation to different sets of reads to provide a suitable transcriptome for each combination of reads and assemblers. As a result, each case study has its own behaviour, prioritises evaluation parameters, and gives an objective and automated way for detecting the best transcriptome within a pool of them. Sequencing data type and quantity (preferably several hundred millions of 2×100 nt or longer), assemblers (OASES for Illumina, MIRA4 and EULER-SR reconciled with CAP3 for Roche/454) and strategy (preferably scaffolding with OASES, and probably merging with Roche/454 when available) arise as the most impacting factors.

Extension of Partial Gene Transcripts by Iterative Mapping of RNA-Seq Raw Reads.

Many non-model organisms lack reference genomes and the sequencing and de novo assembly of an organism's transcriptome is an affordable means by which to characterize the coding component of its genome. Despite the advances that have made this possible, assembling a transcriptome without a known reference usually results in a collection of full-length and partial gene transcripts. The downstream analysis of genes represented as partial transcripts then often requires further experimental work in the laboratory in order to obtain full- length sequences. We have explored whether partial transcripts, encoding genes of interest present in de novo assembled transcriptomes of a model and non-model insect species, could be further extended by iterative mapping against the raw transcriptome sequencing reads. Partial sequences encoding cytochrome P450s and carboxyl/cholinesterase were used in this analysis because they are large multigene families and exhibit significant variation in expression. We present an effective method to improve the continuity of partial transcripts in silico that, in the absence of a reference genome, maybe a quick and cost-effective alternative to their extension by laboratory experimentation. Our approach resulted in the successful extension of incompletely assembled transcripts, often to full length. We experimentally validated these results \textit{in silico} and using real-time PCR and sequencing.

Plasticity and evolutionary divergence in gene expression associated with alternative habitat use in larvae of the European Fire Salamander.

Transcriptomes of organisms reveal differentiation associated with the use of different habitats. However, this leaves open how much of the observed differentiation can be attributed to genetic differences or to transcriptional plasticity. In this study, we disentangle causes of differential gene expression in larvae of the European fire salamander from the Kottenforst forest in Germany. Larvae inhabit permanent streams and ephemeral ponds and represent an example of a young evolutionary split associated with contrasting ecological conditions. We hypothesized that adaptation towards differences in water temperature plays a role because the thermal regime between stream and pond habitats differs notably. Tissue samples from tail fins of larvae were collected to study gene expression using microarrays. We found ample evidence for differentiation among larvae occupying different habitats in nature with 2,800 of 11,797 genes being differentially expressed. We then quantified transcriptional plasticity towards temperature and genetic differentiation based on controlled temperature laboratory experiments. Gene-by-environment interactions modelling revealed that 28% of the gene expression divergence observed among samples in nature could be attributed to plasticity related to water temperature. Expression patterns of only a small number of 101 genes were affected by the genotype. Our analysis demonstrates that effects of environmental factors must be taken into account to explain variation of gene expression in salamanders in nature. Notwithstanding, it provides first evidence that genetic factors determined gene expression divergence between pond and stream ecotypes and could be involved in adaptive evolution.

Examining De Novo Transcriptome Assemblies via a Quality Assessment Pipeline

New de novo transcriptome assembly and annotation methods provide an incredible opportunity to study the transcriptome of organisms that lack an assembled and annotated genome. There are currently a number of de novo transcriptome assembly methods, but it has been difficult to evaluate the quality of these assemblies. In order to assess the quality of the transcriptome assemblies, we composed a workflow of multiple quality check measurements that in combination provide a clear evaluation of the assembly performance. We presented novel transcriptome assemblies and functional annotations for Pacific Whiteleg Shrimp (Litopenaeus vannamei ), a mariculture species with great national and international interest, and no solid transcriptome/genome reference. We examined Pacific Whiteleg transcriptome assemblies via multiple metrics, and provide an improved gene annotation. Our investigations show that assessing the quality of an assembly purely based on the assembler's statistical measurements can be misleading; we propose a hybrid approach that consists of statistical quality checks and further biological-based evaluations.

Transcriptomics technologies.

Transcriptomics technologies are the techniques used to study an organism’s transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell.The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline. Transcriptomics has been defined by repeated technological innovations that transform the field. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences.Measuring the expression of an organism’s genes in different tissues, conditions, or time points gives information on how genes are regulated and reveals details of an organism’s biology. It can also help to infer the functions of previously unannotated genes. Transcriptomic analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.

The Anopheles gambiae transcriptome - a turning point for malaria control.

Mosquitoes are important vectors of several pathogens and thereby contribute to the spread of diseases, with social, economic and public health impacts. Amongst the approximately 450 species of Anopheles, about 60 are recognized as vectors of human malaria, the most important parasitic disease. In Africa, Anopheles gambiae is the main malaria vector mosquito. Current malaria control strategies are largely focused on drugs and vector control measures such as insecticides and bed-nets. Improvement of current, and the development of new, mosquito-targeted malaria control methods rely on a better understanding of mosquito vector biology. An organism's transcriptome is a reflection of its physiological state and transcriptomic analyses of different conditions that are relevant to mosquito vector competence can therefore yield important information. Transcriptomic analyses have contributed significant information on processes such as blood-feeding parasite-vector interaction, insecticide resistance, and tissue- and stage-specific gene regulation, thereby facilitating the path towards the development of new malaria control methods. Here, we discuss the main applications of transcriptomic analyses in An. gambiae that have led to a better understanding of mosquito vector competence.

PR2S2Clust: Patched RNA-seq read segments' structure-oriented clustering.

RNA-seq, the next generation sequencing platform, enables researchers to explore deep into the transcriptome of organisms, such as identifying functional non-coding RNAs (ncRNAs), and quantify their expressions on tissues. The functions of ncRNAs are mostly related to their secondary structures. Thus by exploring the clustering in terms of structural profiles of the corresponding read-segments would be essential and this fuels in our motivation behind this research. In this manuscript we proposed PR2S2Clust, Patched RNA-seq Read Segments' Structure-oriented Clustering, which is an analysis platform to extract features to prepare the secondary structure profiles of the RNA-seq read segments. It provides a strategy to employ the profiles to annotate the segments into ncRNA classes using several clustering strategies. The system considers seven pairwise structural distance metrics by considering short-read mappings onto each structure, which we term as the "patched structure" while clustering the segments. In this regard, we show applications of both classical and ensemble clusterings of the partitional and hierarchical variations. Extensive real-world experiments over three publicly available RNA-seq datasets and a comparative analysis over four competitive systems confirm the effectiveness and superiority of the proposed system. The source codes and dataset of PR2S2Clust are available at the http://biomecis.uta.edu/~ashis/res/PR2S2Clust-suppl/ .

Deconvolution of base pair level RNA-Seq read counts for quantification of transcript expression levels

RNA-Seq has emerged as the method of choice for profiling the transcriptomes of organisms. In particular, it aims to quantify the expression levels of transcripts using short nucleotide sequences or short reads generated from RNA-Seq experiments. In real experiments, the label of the transcript, from which each short read is generated, is missing, and short reads are mapped to the genome rather than the transcriptome. Therefore, the quantification of transcript expression levels is an indirect statistical inference problem. In this article, we propose to use individual exonic base pairs as observation units and, further, to model nonzero as well as zero counts at all base pairs at both the transcript and gene levels. At the transcript level, two-component Poisson mixture distributions are postulated, which gives rise to the Convolution of Poisson mixture (CPM) distribution model at the gene level. The maximum likelihood estimation method equipped with the EM algorithm is used to estimate model parameters and quantify transcript expression levels. We refer to the proposed method as CPM-Seq. Both simulation studies and real data demonstrate the effectiveness of CPM-Seq, showing that CPM-Seq produces more accurate and consistent quantification results than Cufflinks.

Whole organism transcriptome analysis of zebrafish models of Bardet-Biedl Syndrome and Alström Syndrome provides mechanistic insight into shared and divergent phenotypes.

BackgroundBardet-Biedl Syndrome (BBS) and Alström Syndrome are two pleiotropic ciliopathies with significant phenotypic overlap between them across many tissues. Although BBS and Alström genes are necessary for the proper function of primary cilia, their role in defects across multiple organ systems is unclear.MethodsTo provide insight into the pathways underlying BBS and Alström phenotypes, we carried out whole organism transcriptome analysis by RNA sequencing in established zebrafish models of the syndromes.ResultsWe analyzed all genes that were significantly differentially expressed and found enrichment of phenotypically significant pathways in both models. These included multiple pathways shared between the two disease models as well as those unique to each model. Notably, we identified significant downregulation of genes in pathways relevant to visual system deficits and obesity in both disorders, consistent with those shared phenotypes. In contrast, neuronal pathways were significantly downregulated only in the BBS model but not in the Alström model. Our observations also suggested an important role for G-protein couple receptor and calcium signaling defects in both models.DiscussionPathway network analyses of both models indicate that visual system defects may be driven by genetic mechanisms independent of other phenotypes whereas the majority of other phenotypes are a result of genetic players that contribute to multiple pathways simultaneously. Additionally, examination of genes differentially expressed in opposing directions between the two models suggest a deficit in pancreatic function in the Alström model, that is not present in the BBS model.ConclusionsThese findings provide important novel insight into shared and divergent phenotypes between two similar but distinct genetic syndromes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2679-1) contains supplementary material, which is available to authorized users.

Optimization and comparison of bottom-up proteomic sample preparation for early-stage Xenopus laevis embryos

Xenopus laevis is an important model organism in developmental biology. While there is a large literature on changes in the organism's transcriptome during development, the study of its proteome is at an embryonic state. Several papers have been published recently that characterize the proteome of X. laevis eggs and early-stage embryos; however, proteomic sample preparation optimizations have not been reported. Sample preparation is challenging because a large fraction (~90% by weight) of the egg or early-stage embryo is yolk. We compared three common protein extraction buffer systems, mammalian Cell-PE LB(TM) lysing buffer (NP40), sodium dodecyl sulfate (SDS), and 8M urea, in terms of protein extraction efficiency and protein identifications. SDS extracts contained the highest concentration of proteins, but this extract was dominated by a high concentration of yolk proteins. In contrast, NP40 extracts contained ~30% of the protein concentration as SDS extracts, but excelled in discriminating against yolk proteins, which resulted in more protein and peptide identifications. We then compared digestion methods using both SDS and NP40 extraction methods with one-dimensional reverse-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS). NP40 coupled to a filter-aided sample preparation (FASP) procedure produced nearly twice the number of protein and peptide identifications compared to alternatives. When NP40-FASP samples were subjected to two-dimensional RPLC-ESI-MS/MS, a total of 5171 proteins and 38,885 peptides were identified from a single stage of embryos (stage 2), increasing the number of protein identifications by 23% in comparison to other traditional protein extraction methods.

Homologous PNA Hybridization to Noncanonical DNA G-Quadruplexes.

Potential guanine (G) quadruplex-forming sequences (QFSs) found throughout the genomes and transcriptomes of organisms have emerged as biologically relevant structures. These G-quadruplexes represent novel opportunities for gene regulation at the DNA and RNA levels. Recently, the definition of functional QFSs has been expanding to include a variety of unconventional motifs, including relatively long loop sequences (i.e., >7 nucleotides) separating adjacent G-tracts. We have identified a QFS within the 25S rDNA gene from Saccharomyces cerevisae that features a long loop separating the two 3'-most G-tracts. An oligonucleotide based on this sequence, QFS3, folds into a stable G-quadruplex in vitro. We have studied the interaction between QFS3 and several loop mutants with a small, homologous (G-rich) peptide nucleic acid (PNA) oligomer that is designed to form a DNA/PNA heteroquadruplex. The PNA successfully invades the DNA quadruplex target to form a stable heteroquadruplex, but with surprisingly high PNA:DNA ratios based on surface plasmon resonance and mass spectrometric results. A model for high stoichiometry PNA-DNA heteroquadruplexes is proposed, and the implications for quadruplex targeting by G-rich PNA are discussed.

Trans and cis factors affecting A-to-I RNA editing efficiency of a noncoding editing target in C. elegans.

Adenosine-to-inosine RNA editing by ADARs affects thousands of adenosines in an organism's transcriptome. However, adenosines are not edited at equal levels nor do these editing levels correlate well with ADAR expression levels. Therefore, additional mechanisms are utilized by the cell to dictate the editing efficiency at a given adenosine. To examine cis- and trans-acting factors that regulate A-to-I editing levels specifically in neural cells, we utilized the model organism Caenorhabditis elegans. We demonstrate that a double-stranded RNA (dsRNA) binding protein, ADR-1, inhibits editing in neurons, which is largely masked when examining editing levels from whole animals. Furthermore, expression of ADR-1 and mRNA expression of the editing target can act synergistically to regulate editing efficiency. In addition, we identify a dsRNA region within the Y75B8A.8 3′ UTR that acts as a cis-regulatory element by enhancing ADR-2 editing efficiency. Together, this work identifies mechanisms that regulate editing efficiency of noncoding A-to-I editing sites, which comprise the largest class of ADAR targets.

LEMONS - A Tool for the Identification of Splice Junctions in Transcriptomes of Organisms Lacking Reference Genomes.

RNA-seq is becoming a preferred tool for genomics studies of model and non-model organisms. However, DNA-based analysis of organisms lacking sequenced genomes cannot rely on RNA-seq data alone to isolate most genes of interest, as DNA codes both exons and introns. With this in mind, we designed a novel tool, LEMONS, that exploits the evolutionary conservation of both exon/intron boundary positions and splice junction recognition signals to produce high throughput splice-junction predictions in the absence of a reference genome. When tested on multiple annotated vertebrate mRNA data, LEMONS accurately identified 87% (average) of the splice-junctions. LEMONS was then applied to our updated Mediterranean chameleon transcriptome, which lacks a reference genome, and predicted a total of 90,820 exon-exon junctions. We experimentally verified these splice-junction predictions by amplifying and sequencing twenty randomly selected genes from chameleon DNA templates. Exons and introns were detected in 19 of 20 of the positions predicted by LEMONS. To the best of our knowledge, LEMONS is currently the only experimentally verified tool that can accurately predict splice-junctions in organisms that lack a reference genome.

RNA CoMPASS: A Dual Approach for Pathogen and Host Transcriptome Analysis of RNA-Seq Datasets

High-throughput RNA sequencing (RNA-seq) has become an instrumental assay for the analysis of multiple aspects of an organism's transcriptome. Further, the analysis of a biological specimen's associated microbiome can also be performed using RNA-seq data and this application is gaining interest in the scientific community. There are many existing bioinformatics tools designed for analysis and visualization of transcriptome data. Despite the availability of an array of next generation sequencing (NGS) analysis tools, the analysis of RNA-seq data sets poses a challenge for many biomedical researchers who are not familiar with command-line tools. Here we present RNA CoMPASS, a comprehensive RNA-seq analysis pipeline for the simultaneous analysis of transcriptomes and metatranscriptomes from diverse biological specimens. RNA CoMPASS leverages existing tools and parallel computing technology to facilitate the analysis of even very large datasets. RNA CoMPASS has a web-based graphical user interface with intrinsic queuing to control a distributed computational pipeline. RNA CoMPASS was evaluated by analyzing RNA-seq data sets from 45 B-cell samples. Twenty-two of these samples were derived from lymphoblastoid cell lines (LCLs) generated by the infection of naïve B-cells with the Epstein Barr virus (EBV), while another 23 samples were derived from Burkitt's lymphomas (BL), some of which arose in part through infection with EBV. Appropriately, RNA CoMPASS identified EBV in all LCLs and in a fraction of the BLs. Cluster analysis of the human transcriptome component of the RNA CoMPASS output clearly separated the BLs (which have a germinal center-like phenotype) from the LCLs (which have a blast-like phenotype) with evidence of activated MYC signaling and lower interferon and NF-kB signaling in the BLs. Together, this analysis illustrates the utility of RNA CoMPASS in the simultaneous analysis of transcriptome and metatranscriptome data. RNA CoMPASS is freely available at http://rnacompass.sourceforge.net/.

Combining Different mRNA Capture Methods to Analyze the Transcriptome: Analysis of the Xenopus laevis Transcriptome

mRNA sequencing (mRNA-seq) is a commonly used technique to survey gene expression from organisms with fully sequenced genomes. Successful mRNA-seq requires purification of mRNA away from the much more abundant ribosomal RNA, which is typically accomplished by oligo-dT selection. However, mRNAs with short poly-A tails are captured poorly by oligo-dT based methods. We demonstrate that combining mRNA capture via oligo-dT with mRNA capture by the 5’ 7-methyl guanosine cap provides a more complete view of the transcriptome and can be used to assay changes in mRNA poly-A tail length on a genome-wide scale. We also show that using mRNA-seq reads from both capture methods as input for de novo assemblers provides a more complete reconstruction of the transcriptome than either method used alone. We apply these methods of mRNA capture and de novo assembly to the transcriptome of Xenopus laevis, a well-studied frog that currently lacks a finished sequenced genome, to discover transcript sequences for thousands of mRNAs that are currently absent from public databases. The methods we describe here will be broadly applicable to many organisms and will provide insight into the transcriptomes of organisms with sequenced and unsequenced genomes.

Studies on the transcriptomes of non-model organisms

The transcriptome represents the whole complement of RNA transcripts in cells or tissues and reflects the expressed genes at various life stages, tissue types, physiological states, and environmental conditions. Transcriptome analysis provides a comprehensive understanding of gene expression and its regulation. Non-model organism has many interesting traits of which model organisms lack, and the study of its transcriptome has great significance in solving the questions of genetic evolution, genetic breeding, ecology and so on. Because of absence of reference genome information, and traditional transcriptome research methods which are complicated to operate, long experimental period and costly, slow progress has been made in the research of non-model organism transcriptome. Fortunately, RNA sequencing (RNA-seq), the next-generation sequencing technology, has completely changed the way of transcriptome study, becoming an advanced technology on the investigation of non-model organism transcriptome. In this paper, we give a summary of the non-model organism transcriptome research using RNA-seq in recent years and briefly describe its general flow and principles from aspects of sample preparation, high throughput DNA sequencing, and bioinformatics analysis. Finally, questions that are still open and awaiting further research are also discussed.

Optimization of De Novo Short Read Assembly of Seabuckthorn (Hippophae rhamnoides L.) Transcriptome

Seabuckthorn ( Hippophae rhamnoides L.) is known for its medicinal, nutritional and environmental importance since ancient times. However, very limited efforts have been made to characterize the genome and transcriptome of this wonder plant. Here, we report the use of next generation massive parallel sequencing technology (Illumina platform) and de novo assembly to gain a comprehensive view of the seabuckthorn transcriptome. We assembled 86,253,874 high quality short reads using six assembly tools. At our hand, assembly of non-redundant short reads following a two-step procedure was found to be the best considering various assembly quality parameters. Initially, ABySS tool was used following an additive k-mer approach. The assembled transcripts were subsequently subjected to TGICL suite. Finally, de novo short read assembly yielded 88,297 transcripts (> 100 bp), representing about 53 Mb of seabuckthorn transcriptome. The average length of transcripts was 610 bp, N50 length 1198 BP and 91% of the short reads uniquely mapped back to seabuckthorn transcriptome. A total of 41,340 (46.8%) transcripts showed significant similarity with sequences present in nr protein databases of NCBI (E-value < 1E-06). We also screened the assembled transcripts for the presence of transcription factors and simple sequence repeats. Our strategy involving the use of short read assembler (ABySS) followed by TGICL will be useful for the researchers working with a non-model organism’s transcriptome in terms of saving time and reducing complexity in data management. The seabuckthorn transcriptome data generated here provide a valuable resource for gene discovery and development of functional molecular markers.

The bench scientist's guide to statistical analysis of RNA-Seq data

BackgroundRNA sequencing (RNA-Seq) is emerging as a highly accurate method to quantify transcript abundance. However, analyses of the large data sets obtained by sequencing the entire transcriptome of organisms have generally been performed by bioinformatics specialists. Here we provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.FindingsWhen comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, respectively identified 11,995 and 11,317 differentially expressed genes from an RNA-seq dataset generated from soybean leaf tissue grown in elevated O3. However, the number of genes in common between these two methods was only 10,535, resulting in 2,242 genes determined to be differentially expressed by only one method. Upon analysis of the non-significant genes, several limitations of these analytic tools were revealed, including evidence for overly stringent parameters for determining statistical significance of differentially expressed genes as well as increased type II error for high abundance transcripts.ConclusionsBecause of the high variability between methods for determining differential expression of RNA-Seq data, we suggest using several bioinformatics tools, as outlined here, to ensure that a conservative list of differentially expressed genes is obtained. We also conclude that despite these analytical limitations, RNA-Seq provides highly accurate transcript abundance quantification that is comparable to qRT-PCR.