Camelina seed transcriptome: a tool for meal and oil improvement and translational research
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bio-inspired↗
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Results for “Transcriptomics”
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Temporal and spatial transcriptomic and micro RNA dynamics of CAM photosynthesis in pineapple
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Plant Sciences↗
A consensus-based ensemble approach to improve transcriptome assembly
Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.
59 BASIC BIOLOGICAL SCIENCES↗
Next-generation transcriptome assembly
Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalog of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies-along with some perspectives on transcriptome assembly in the near future.
59 BASIC BIOLOGICAL SCIENCES↗
Renal tubules transcriptome reveals metabolic maladaption during the progression of ischemia-induced acute kidney injury
Highlights: • We used the most common AKI model caused by renal ischemia/reperfusion injury (IRI) and established a severe AKI model with progressive renal pathology. • This study is the first to isolate renal tubules and charaterize tubular specific transcriptome during AKI progression. • Tubule specific transcriptome revealed that the deregulation of lipid metabolism occurred in multi-step wise, particularly in the process of lipid transport and FAO inhibition, which may result in TECs apoptosis, dedifferentiation and renal fibrogenesis. Renal tubular epithelial cells (TECs) play a critical role in driving acute kidney injury (AKI) progression, but the key molecular features in TECs during this process is not clear. To better understand the molecular characteristics in renal TECs during AKI and renal fibrogenesis, an irreversible AKI mouse model induced by ischemia/reperfusion injury (IRI) was used in this study. The renal tubules were isolated and tubule specific transcriptome was detected by RNA-seq at different stages in the progression of AKI in this model. The overall transcriptome indicated injury and repair process of TEC after renal IRI. In addition, metabolism maladaption was observed during AKI progression to chronic fibrosis. Particularly, we found dysregulation of multiple steps of lipid metabolism in tubule transcriptomes. Oil red O staining revealed massive lipid droplets accumulation in TECs at day 10, thus confirming the defect of lipid metabolism. This is the first study to charaterize renal tubule specific transcriptome during AKI progression. The results shed light on the molecular features in TECs for progressive AKI.
60 APPLIED LIFE SCIENCES↗
Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning
Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.
60 APPLIED LIFE SCIENCES↗
Comparing Transcriptomic Points of Departure to Apical Effect Concentrations For Larval Fathead Minnow Exposed to Chemicals with Four Different Modes Of Action
It is postulated that below a transcriptomic-based point of departure, adverse effects are unlikely to occur, thereby providing a chemical concentration to use in screening level hazard assessment. The present study extends previous work describing a high-throughput fathead minnow assay that can provide full transcriptomic data after exposure to a test chemical. One-day post-hatch fathead minnows were exposed to ten concentrations of three representatives of four chemical modes of action: organophosphates, ecdysone receptor agonists, plant photosystem II inhibitors, and estrogen receptor agonists for 24 h. Concentration response modeling was performed on whole body gene expression data from each exposure, using measured chemical concentrations when available. Transcriptomic points of departure in larval fathead minnow were lower than apical effect concentrations across fish species but not always lower than toxic effect concentrations in other aquatic taxa like crustaceans and insects. The point of departure was highly dependent on measured chemical concentration which were often lower than the nominal concentration. Differentially expressed genes between chemicals within modes of action were compared and often showed statistically significant overlap. In addition, reproducibility between identical exposures using a positive control chemical (CuSO 4 ) and variability associated with the transcriptomic point of departure using in silico sampling were considered. Results extend a transcriptomic-compatible fathead minnow high-throughput assay for possible use in ecological hazard screening.
59 BASIC BIOLOGICAL SCIENCES↗
Comparative transcriptomics and metabolomics reveal specialized metabolite drought stress responses in switchgrass ( Panicum virgatum )
Summary Switchgrass ( Panicum virgatum ) is a bioenergy model crop valued for its energy efficiency and drought tolerance. The related monocot species rice ( Oryza sativa ) and maize ( Zea mays ) deploy species‐specific, specialized metabolites as core stress defenses. By contrast, specialized chemical defenses in switchgrass are largely unknown. To investigate specialized metabolic drought responses in switchgrass, we integrated tissue‐specific transcriptome and metabolite analyses of the genotypes Alamo and Cave‐in‐Rock that feature different drought tolerance. The more drought‐susceptible Cave‐in‐Rock featured an earlier onset of transcriptomic changes and significantly more differentially expressed genes in response to drought compared to Alamo. Specialized pathways showed moderate differential expression compared to pronounced transcriptomic alterations in carbohydrate and amino acid metabolism. However, diterpenoid‐biosynthetic genes showed drought‐inducible expression in Alamo roots, contrasting largely unaltered triterpenoid and phenylpropanoid pathways. Metabolomic analyses identified common and genotype‐specific flavonoids and terpenoids. Consistent with transcriptomic alterations, several root diterpenoids showed significant drought‐induced accumulation, whereas triterpenoid abundance remained predominantly unchanged. Structural analysis verified select drought‐responsive diterpenoids as oxygenated furanoditerpenoids. Drought‐dependent transcriptome and metabolite profiles provide the foundation to understand the molecular mechanisms underlying switchgrass drought responses. Accumulation of specialized root diterpenoids and corresponding pathway transcripts supports a role in drought stress tolerance.
transcriptomics↗
Genome-wide analysis on Chlamydomonas reinhardtii reveals the impact of hydrogen peroxide on protein stress responses and overlap with other stress transcriptomes
Reactive oxygen species (ROS) are produced by and have the potential to be damaging to all aerobic organisms. In photosynthetic organisms, they are an unavoidable byproduct of electron transfer in both the chloroplast and mitochondrion. Here, in this paper, we employ the reference unicellular green alga Chlamydomonas reinhardtii to identify the effect of H 2 O 2 on gene expression by monitoring the changes in the transcriptome in a time-course experiment. Comparison of transcriptomes from cells sampled immediately prior to the addition of H 2 O 2 and 0.5 and 1 h subsequently revealed 1278 differentially abundant transcripts. Of those transcripts that increase in abundance, many encode proteins involved in ROS detoxification, protein degradation and stress responses, whereas among those that decrease are transcripts encoding proteins involved in photosynthesis and central carbon metabolism. In addition to these transcriptomic adjustments, we observe that addition of H 2 O 2 is followed by an accumulation and oxidation of the total intracellular glutathione pool, and a decrease in photosynthetic O 2 output. Additionally, we analyze our transcriptomes in the context of changes in transcript abundance in response to singlet O 2 (O 2 * ), and relate our H 2 O 2 -induced transcripts to a diurnal transcriptome, where we demonstrate enrichments of H 2 O 2 -induced transcripts early in the light phase, late in the light phase and 2 h prior to light. In conclusion, on this basis several genes that are highlighted in this work may be involved in previously undiscovered stress remediation pathways or acclimation responses.
59 BASIC BIOLOGICAL SCIENCES↗
UnigeneFinder: An Automated Pipeline for Gene Calling From Transcriptome Assemblies Without a Reference Genome
ABSTRACT For most species, transcriptome data are much more readily available than genome data. Without a reference genome, gene calling is cumbersome and inaccurate because of the high degree of redundancy in de novo transcriptome assemblies. To simplify and increase the accuracy of de novo transcriptome assembly in the absence of a reference genome, we developed UnigeneFinder. Combining several clustering methods, UnigeneFinder substantially reduces the redundancy typical of raw transcriptome assemblies. This pipeline offers an effective solution to the problem of inflated transcript numbers, achieving a closer representation of the actual underlying genome. UnigeneFinder performs comparably or better, compared with existing tools, on plant species with varying genome complexities. UnigeneFinder is the only available transcriptome redundancy solution that fully automates the generation of primary transcript, coding region, and protein sequences, analogous to those available for high‐quality reference genomes. These features, coupled with the pipeline’s cross‐platform implementation, focus on automation, and an accessible, user‐friendly interface, make UnigeneFinder a useful tool for many downstream sequence‐based analyses in nonmodel organisms lacking a reference genome, including differential gene expression analysis, accurate ortholog identification, functional enrichments, and evolutionary analyses. UnigeneFinder also runs efficiently both on high‐performance computing (HPC) systems and personal computers, further reducing barriers to use.
Xue, Bo [Plant Resilience Institute Michigan State↗
Integrated transcriptomic and proteomic evaluation of gentamicin nephrotoxicity in rats
Gentamicin is an aminoglycoside antibiotic, which induces renal tubular necrosis in rats. In the context of the European InnoMed PredTox project, transcriptomic and proteomic studies were performed to provide new insights into the molecular mechanisms of gentamicin-induced nephrotoxicity. Male Wistar rats were treated with 25 and 75 mg/kg/day subcutaneously for 1, 3 and 14 days. Histopathology observations showed mild tubular degeneration/necrosis and regeneration and moderate mononuclear cell infiltrate after long-term treatment. Transcriptomic data indicated a strong treatment-related gene expression modulation in kidney and blood cells at the high dose after 14 days of treatment, with the regulation of 463 and 3241 genes, respectively. Of note, the induction of NF-kappa B pathway via the p38 MAPK cascade in the kidney, together with the activation of T-cell receptor signaling in blood cells were suggestive of inflammatory processes in relation with the recruitment of mononuclear cells in the kidney. Proteomic results showed a regulation of 163 proteins in kidney at the high dose after 14 days of treatment. These protein modulations were suggestive of a mitochondrial dysfunction with impairment of cellular energy production, induction of oxidative stress, an effect on protein biosynthesis and on cellular assembly and organization. Proteomic results also provided clues for potential nephrotoxicity biomarkers such as AGAT and PRBP4 which were strongly modulated in the kidney. Transcriptomic and proteomic data turned out to be complementary and their integration gave a more comprehensive insight into the putative mode of nephrotoxicity of gentamicin which was in accordance with histopathological findings. -- Highlights: ► Gentamicin induces renal tubular necrosis in rats. ► The mechanisms of gentamicin nephrotoxicity remain still elusive. ► Transcriptomic and proteomic analyses were performed to study this toxicity in rats. ► Transcriptomic and proteomic data turned out to be complementary and are integrated. ► A more comprehensive putative model of nephrotoxicity of gentamicin is presented.
60 APPLIED LIFE SCIENCES↗
A survey of the sorghum transcriptome using single-molecule long reads
Alternative splicing and alternative polyadenylation (APA) of pre-mRNAs greatly contribute to transcriptome diversity, coding capacity of a genome and gene regulatory mechanisms in eukaryotes. Second-generation sequencing technologies have been extensively used to analyse transcriptomes. However, a major limitation of short-read data is that it is difficult to accurately predict full-length splice isoforms. Here we sequenced the sorghum transcriptome using Pacific Biosciences single-molecule real-time long-read isoform sequencing and developed a pipeline called TAPIS (Transcriptome Analysis Pipeline for Isoform Sequencing) to identify full-length splice isoforms and APA sites. Our analysis reveals transcriptome-wide full-length isoforms at an unprecedented scale with over 11,000 novel splice isoforms. Additionally, we uncover APA ofB11,000 expressed genes and more than 2,100 novel genes. Lastly, these results greatly enhance sorghum gene annotations and aid in studying gene regulation in this important bioenergy crop. The TAPIS pipeline will serve as a useful tool to analyse Iso-Seq data from any organism.
60 APPLIED LIFE SCIENCES↗
Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: missing value imputation using temporal datasets
Despite significant improvements in recent years, proteomic datasets currently available still suffer large number of missing values. Integrative analyses based upon incomplete proteomic and transcriptomic da-tasets could seriously bias the biological interpretation. In this study, we applied a non-linear data-driven stochastic gradient boosted trees (GBT) model to impute missing proteomic values for proteins experi-mentally undetected, using a temporal transcriptomic and proteomic dataset of Shewanella oneidensis. In this dataset, genes expression was measured after the cells were exposed to 1 mM potassium chromate for 5-, 30-, 60-, and 90-min, while protein abundance was measured only for 45- and 90-min samples. With the goal of elucidating the relationship between temporal gene expression and protein abundance data, and then using it to impute missing proteomic values for samples of 45-min (which does not have cognate transcriptomic data) and 90-min, we initially used nonlinear Smoothing Splines Curve Fitting (SSCF) to identify temporal relationships among transcriptomic data at different time points and then imputed missing gene expression measurements for the sample at 45-min. After the imputation was validated by biological constrains (i.e. operons), we used a data-driven Gradient Boosted Trees (GBT) model to uncover possible non-linear relationships between temporal transcriptomic and proteomic data, and to impute protein abundance for the proteins experimentally undetected in the 45- and 90-min sam-ples, based on relevant predictors such as temporal mRNA gene expression data, cellular roles, molecular weight, sequence length, protein length, guanine-cytosine (GC) content and triple codon counts. The imputed protein values were validated using biological constraints such as operon, regulon and pathway information. Finally, we demonstrated that such missing value imputation improved characterization of the temporal response of S. oneidensis to chromate.
SBR mercury↗
Revealing systematic changes in the transcriptome during the transition from exponential growth to stationary phase
ABSTRACT The composition of bacterial transcriptomes is determined by the transcriptional regulatory network (TRN). The TRN regulates the transition from one physiological state to another. Here, we use independent component analysis to monitor the composition of the transcriptome during the transition from the exponential growth phase to the stationary phase. With Escherichia coli K-12 MG1655 as a model strain, we trigger the transition using carbon, nitrogen, and sulfur starvation. We find that (i) the transition to the stationary phase accompanies common transcriptome changes, including increased stringent responses and reduced production of cellular building blocks and energy regardless of the limiting element; (ii) condition-specific changes are strongly associated with transcriptional regulators ( e.g. , Crp, NtrC, CysB, Cbl) responsible for metabolizing the limiting element; and (iii) the shortage of each limiting element differentially affects the production of amino acids and extracellular polymers. This study demonstrates how the combination of genome-scale datasets and new data analytics reveals the fundamental characteristics of a key transition in the life cycle of bacteria. IMPORTANCE Nutrient limitations are critical environmental perturbations in bacterial physiology. Despite its importance, a detailed understanding of how bacterial transcriptomes are adjusted has been limited. By utilizing independent component analysis (ICA) to decompose transcriptome data, this study reveals key regulatory events that enable bacteria to adapt to nutrient limitations. The findings not only highlight common responses, such as the stringent response, but also condition-specific regulatory shifts associated with carbon, nitrogen, and sulfur starvation. The insights gained from this work advance our knowledge of bacterial physiology, gene regulation, and metabolic adaptation.
Lim, Hyun Gyu (ORCID:0000000204692388)↗
Analysis of Transcriptome and Epitranscriptome in Plants Using PacBio Iso-Seq and Nanopore-Based Direct RNA Sequencing
Nanopore sequencing from Oxford Nanopore Technologies (ONT) and Pacific BioSciences (PacBio) single-molecule real-time (SMRT) long-read isoform sequencing (Iso-Seq) are revolutionizing the way transcriptomes are analyzed. These methods offer many advantages over most widely used high-throughput short-read RNA sequencing (RNA-Seq) approaches and allow a comprehensive analysis of transcriptomes in identifying full-length splice isoforms and several other post-transcriptional events. In addition, direct RNA-Seq provides valuable information about RNA modifications, which are lost during the PCR amplification step in other methods. Here, we present a comprehensive summary of important applications of these technologies in plants, including identification of complex alternative splicing (AS), full-length splice variants, fusion transcripts, and alternative polyadenylation (APA) events. Furthermore, we discuss the impact of the newly developed nanopore direct RNA-Seq in advancing epitranscriptome research in plants. Additionally, we summarize computational tools for identifying and quantifying full-length isoforms and other co/post-transcriptional events and discussed some of the limitations with these methods. Sequencing of transcriptomes using these new single-molecule long-read methods will unravel many aspects of transcriptome complexity in unprecedented ways as compared to previous short-read sequencing approaches. Analysis of plant transcriptomes with these new powerful methods that require minimum sample processing is likely to become the norm and is expected to uncover novel co/post-transcriptional gene regulatory mechanisms that control biological outcomes during plant development and in response to various stresses.
alternative polyadenylation↗
Synechococcus sp. Strain PCC 7002 Transcriptome: Acclimation to Temperature, Salinity, Oxidative Stress, and Mixotrophic Growth Conditions
Synechococcus sp. strain PCC 7002 is a unicellular, euryhaline cyanobacterium. It is a model organism for studies of cyanobacterial metabolism and has great potential for biotechnological applications. It exhibits an exceptional tolerance of high-light irradiation and shows very rapid growth.The habitats from which this and closely related strains were isolated are subject to changes in several environmental factors, including light, nutrient supply, temperature, and salinity. In this study global transcriptome profiling via RNAseq has been used to perform a comparative and integrated study of global changes in cells grown at different temperatures, at different salinities, and under mixotrophic conditions, when a metabolizable organic carbon source was present. Furthermore, the transcriptomes were investigated for cells that were subjected to a heat shock and that were exposed to oxidative stress. Lower growth temperatures caused relatively minor changes of the transcriptome; the most prominent changes affected fatty acid desaturases. A heat shock caused severe changes of the transcriptome pattern; transcripts for genes associated with major metabolic pathways declined and those for different chaperones increased dramatically. Oxidative stress, however, left the transcript pattern almost unaffected. When grown at high salinity, Synechococcus sp. PCC 7002 had increased expression of genes involved in compatible solute biosynthesis and showed increased mRNA levels for several genes involved in electron transport. Transcripts of two adjacent genes dramatically increased upon growth at high salinity; the respective proteins are putatively involved in coping with oxidative stress and in triggering ion channels. Only minor changes were observed when cells were grown at low salinity or when the growth medium was supplemented with glycerol. However, the transcriptome data suggest that cells must acclimate to excess reducing equivalents when a reduced C-source is present.
59 BASIC BIOLOGICAL SCIENCES↗
Transcriptomics Processing Pipelines for Space Biology: An Open Source and Consensus-Driven Approach
Transcriptomics holds significant value in elucidating the relationship between gene expression, experimental factors, biological factors, and various types of omics data. Enhancing our understanding of these connections is paramount for foundational biology, which plays a pivotal role in devising solutions for challenges pertinent to both space travel and terrestrial life. The NASA GeneLab project, part of the Open Science Data Repository (OSDR.nasa.gov), seeks to accelerate space biology research through cataloging and democratizing ‘omics data, including transcriptomics. Since raw omics data are largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community via the Open Science Analysis Working Groups (AWGs) to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data have greater immediate value to diverse users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. As of June 2023, transcriptomics studies comprise over half of GeneLab datasets hosted on the OSDR, including data from bulk RNA-seq and Affymetrix or Agilent 1-Channel DNA microarray assays. In collaboration with the AWGs, GeneLab developed consensus processing pipelines for these transcriptomics data types that includes quality control, background correction (microarray only), data normalization and quantification, culminating in the detection and annotation of differentially expressed genes. The work presented here describes Nextflow implementations of GeneLab’s consensus transcriptomics pipelines that automates and accelerates processing of these datasets. In addition to the core data processing, these workflows also include raw data staging and a robust verification and validation program to identify errors in real-time, stop additional downstream computation, and preserve computational resources. These workflows are used to generate GeneLab processed data hosted on the OSDR, and are publicly available as open source software for others to use at: https://github.com/nasa/GeneLab_Data_Processing.
Jonathan Oribello↗
The developmental transcriptome of Drosophila melanogaster
Drosophila melanogaster is one of the most well studied genetic model organisms; nonetheless, its genome still contains unannotated coding and non-coding genes, transcripts, exons and RNA editing sites. Full discovery and annotation are pre-requisites for understanding how the regulation of transcription, splicing and RNA editing directs the development of this complex organism. Here we used RNA-Seq, tiling microarrays and cDNA sequencing to explore the transcriptome in 30 distinct developmental stages. We identified 111,195 new elements, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events, and inferred protein isoforms that previously eluded discovery using established experimental, prediction and conservation-based approaches. These data substantially expand the number of known transcribed elements in the Drosophila genome and provide a high-resolution view of transcriptome dynamics throughout development. Drosophila melanogaster is an important non-mammalian model system that has had a critical role in basic biological discoveries, such as identifying chromosomes as the carriers of genetic information and uncovering the role of genes in development. Because it shares a substantial genic content with humans, Drosophila is increasingly used as a translational model for human development, homeostasis and disease. High-quality maps are needed for all functional genomic elements. Previous studies demonstrated that a rich collection of genes is deployed during the life cycle of the fly. Although expression profiling using microarrays has revealed the expression of, 13,000 annotated genes, it is difficult to map splice junctions and individual base modifications generated by RNA editing using such approaches. Single-base resolution is essential to define precisely the elements that comprise the Drosophila transcriptome. Estimates of the number of transcript isoforms are less accurate than estimates of the number of genes. Whereas, 20% of Drosophila genes are annotated as encoding alternatively spliced premRNAs, splice-junction microarray experiments indicate that this number is at least 40% (ref. 7). Determining the diversity of mRNAs generated by alternative promoters, alternative splicing and RNA editing will substantially increase the inferred protein repertoire. Non-coding RNA genes (ncRNAs) including short interfering RNAs (siRNAs) and microRNAS (miRNAs) (reviewed in ref. 10), and longer ncRNAs such as bxd (ref. 11) and rox (ref. 12), have important roles in gene regulation, whereas others such as small nucleolar RNAs (snoRNAs)and small nuclear RNAs (snRNAs) are important components of macromolecular machines such as the ribosome and spliceosome. The transcription and processing of these ncRNAs must also be fully documented and mapped. As part of the modENCODE project to annotate the functional elements of the D. melanogaster and Caenorhabditis elegans genomes, we used RNA-Seq and tiling microarrays to sample the Drosophila transcriptome at unprecedented depth throughout development from early embryo to ageing male and female adults. We report on a high-resolution view of the discovery, structure and dynamic expression of the D. melanogaster transcriptome.
60 APPLIED LIFE SCIENCES↗