Data on RNA-seq analysis of Drosophila melanogaster during ageing.
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ABSTRACT:
SUBMITTER: Bajgiran M
PROVIDER: S-EPMC8488473 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundRecent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have been found to play essential roles in biological processes. However, only limited information is available for lncRNAs in Drosophila melanogaster, an important model organism. Therefore, the characterization of lncRNAs and identification of new lncRNAs in D. melanogaster is an important area of research. Moreover, there is an increasing interest in the use of ChIP-seq data (H3K4me3, H3K36me3 and Pol II) to detect signatures of active transcription for reported lncRNAs.ResultsWe have developed a computational approach to identify new lncRNAs from two tissue-specific RNA-seq datasets using the poly(A)-enriched and the ribo-zero method, respectively. In our results, we identified 462 novel lncRNA transcripts, which we combined with 4137 previously published lncRNA transcripts into a curated dataset. We then utilized 61 RNA-seq and 32 ChIP-seq datasets to improve the annotation of the curated lncRNAs with regards to transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures. Furthermore, we used 30 time-course RNA-seq datasets and 32 ChIP-seq datasets to investigate whether the lncRNAs reported by RNA-seq have active transcription signatures. The results showed that more than half of the reported lncRNAs did not have chromatin signatures related to active transcription. To clarify this issue, we conducted RT-qPCR experiments and found that ~95.24% of the selected lncRNAs were truly transcribed, regardless of whether they were associated with active chromatin signatures or not.ConclusionsIn this study, we discovered a large number of novel lncRNAs, which suggests that many remain to be identified in D. melanogaster. For the lncRNAs that are known, we improved their characterization by integrating a large number of sequencing datasets (93 sets in total) from multiple sources (lncRNAs, RNA-seq and ChIP-seq). The RT-qPCR experiments demonstrated that RNA-seq is a reliable platform to discover lncRNAs. This set of curated lncRNAs with improved annotations can serve as an important resource for investigating the function of lncRNAs in D. melanogaster.
| S-EPMC4787191 | biostudies-literature
Project description:MicroRNAs (miRNAs) are short non-coding single-stranded RNAs with approximately 22 nucleotides in length that negatively regulate the mRNA translation of a target gene. MiR-2b-1 belongs to the largest miR-2 family in Drosophila melanogaster with 8 members and this miRNA family is conserved in invertebrates. miRNAs play key roles in gene regulation, cell proliferation, cell death, cell differentiation and cell developmental homeostasis in multicellular organisms. Its role in various human diseases is continuously being studied. miRNAs also found out to be crucial in maintaining stem cell niche in D. melanogaster gonads. We have identified that ectopic overexpression of miR-2b-1 of D. melanogaster causes testicular bulging (a tumour like phenotype) in 3-5 days old adult flies. Hence, we have performed a transcriptomic (RNA-seq) analysis to understand the role of miR-2b-1 in the development, maintenance, and differentiation of D. melanogaster adult testis stem cells. Data are available from GEO (accession number GSE211399).
| S-EPMC9679752 | biostudies-literature
Project description:A generally accepted approach to the analysis of RNA-Seq read count data does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic Reference Panel in order to quantify differences in gene expression among single flies. One of our experimental goals was to identify the optimal analysis approach for the detection of differential gene expression among the factors we varied in the experiment: genotype, environment, sex, and their interactions. Here we evaluate three different filtering strategies, eight normalization methods, and two statistical approaches using our data set. We assessed differential gene expression among factors and performed a statistical power analysis using the eight biological replicates per genotype, environment, and sex in our data set.We found that the most critical considerations for the analysis of RNA-Seq read count data were the normalization method, underlying data distribution assumption, and numbers of biological replicates, an observation consistent with previous RNA-Seq and microarray analysis comparisons. Some common normalization methods, such as Total Count, Quantile, and RPKM normalization, did not align the data across samples. Furthermore, analyses using the Median, Quantile, and Trimmed Mean of M-values normalization methods were sensitive to the removal of low-expressed genes from the data set. Although it is robust in many types of analysis, the normal data distribution assumption produced results vastly different than the negative binomial distribution. In addition, at least three biological replicates per condition were required in order to have sufficient statistical power to detect expression differences among the three-way interaction of genotype, environment, and sex.The best analysis approach to our data was to normalize the read counts using the DESeq method and apply a generalized linear model assuming a negative binomial distribution using either edgeR or DESeq software. Genes having very low read counts were removed after normalizing the data and fitting it to the negative binomial distribution. We describe the results of this evaluation and include recommended analysis strategies for RNA-Seq read count data.
| S-EPMC4702322 | biostudies-literature
Project description:FlyAtlas 2 (www.flyatlas2.org) is part successor, part complement to the FlyAtlas database and web application for studying the expression of the genes of Drosophila melanogaster in different tissues of adults and larvae. Although generated in the same lab with the same fly line raised on the same diet as FlyAtlas, the FlyAtlas2 resource employs a completely new set of expression data based on RNA-Seq, rather than microarray analysis, and so it allows the user to obtain information for the expression of different transcripts of a gene. Furthermore, the data for somatic tissues are now available for both male and female adult flies, allowing studies of sexual dimorphism. Gene coverage has been extended by the inclusion of microRNAs and many of the RNA genes included in Release 6 of the Drosophila reference genome. The web interface has been modified to accommodate the extra data, but at the same time has been adapted for viewing on small mobile devices. Users also have access to the RNA-Seq reads displayed alongside the annotated Drosophila genome in the (external) UCSC browser, and are able to link out to the previous FlyAtlas resource to compare the data obtained by RNA-Seq with that obtained using microarrays.
| S-EPMC5753349 | biostudies-literature
Project description:The Drosophila melanogaster embryo has been widely utilized as a model for genetics and developmental biology due to its small size, short generation time, and large brood size. Information on embryonic metabolism during developmental progression is important for further understanding the mechanisms of Drosophila embryogenesis. Therefore, the aim of this study is to assess the changes in embryos' metabolome that occur at different stages of the Drosophila embryonic development. Time course samples of Drosophila embryos were subjected to GC/MS-based metabolome analysis for profiling of low molecular weight hydrophilic metabolites, including sugars, amino acids, and organic acids. The results showed that the metabolic profiles of Drosophila embryo varied during the course of development and there was a strong correlation between the metabolome and different embryonic stages. Using the metabolome information, we were able to establish a prediction model for developmental stages of embryos starting from their high-resolution quantitative metabolite composition. Among the important metabolites revealed from our model, we suggest that different amino acids appear to play distinct roles in different developmental stages and an appropriate balance in trehalose-glucose ratio is crucial to supply the carbohydrate source for the development of Drosophila embryo.
| S-EPMC4133167 | biostudies-literature
Project description:Embryogenesis is one of the most important processes in the life of an animal. During this dynamic process, progressive cell division and cellular differentiation are accompanied by significant changes in protein expression at the level of the proteome. However, very few studies to date have described the dynamics of the proteome during the early development of an embryo in any organism. In this dataset, we monitor changes in protein expression across a timecourse of more than 20 h of Drosophila melanogaster embryonic development. Mass-spectrometry data were produced using a SWATH acquisition mode on a Sciex Triple-TOF 6600. A spectral library built in-house was used to analyse these data and more than 1950 proteins were quantified at each embryonic timepoint. The files presented here are a permanent digital map and can be reanalysed to test against new hypotheses. The data have been deposited with the ProteomeXchange Consortium with the dataset identifier PRIDE: PXD0031078.
| S-EPMC5097952 | biostudies-literature
Project description:Similar to humans, insects lose their physical and physiological capacities with age, which makes them a convenient study system for human ageing. Although insects have an efficient oxygen-transport system, we know little about how their flight capacity changes with age and environmental oxygen conditions. We measured two types of locomotor performance in ageing Drosophila melanogaster flies: the frequency of wing beats and the capacity to climb vertical surfaces. Flight performance was measured under normoxia and hypoxia. As anticipated, ageing flies showed systematic deterioration of climbing performance, and low oxygen impeded flight performance. Against predictions, flight performance did not deteriorate with age, and younger and older flies showed similar levels of tolerance to low oxygen during flight. We suggest that among different insect locomotory activities, flight performance deteriorates slowly with age, which is surprising, given that insect flight is one of the most energy-demanding activities in animals. Apparently, the superior capacity of insects to rapidly deliver oxygen to flight muscles remains little altered by ageing, but we showed that insects can become oxygen limited in habitats with a poor oxygen supply (e.g., those at high elevations) during highly oxygen-demanding activities such as flight.
| S-EPMC8070683 | biostudies-literature
Project description:SummaryWe have developed an RNA-Seq analysis workflow for single-ended Illumina reads, termed RseqFlow. This workflow includes a set of analytic functions, such as quality control for sequencing data, signal tracks of mapped reads, calculation of expression levels, identification of differentially expressed genes and coding SNPs calling. This workflow is formalized and managed by the Pegasus Workflow Management System, which maps the analysis modules onto available computational resources, automatically executes the steps in the appropriate order and supervises the whole running process. RseqFlow is available as a Virtual Machine with all the necessary software, which eliminates any complex configuration and installation steps.Availability and implementationhttp://genomics.isi.edu/rnaseqContactwangying@xmu.edu.cn; knowles@med.usc.edu; deelman@isi.edu; tingchen@usc.eduSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:By measuring messenger RNA levels for all genes in a sample, RNA-seq provides an attractive option to characterize the global changes in transcription. RNA-seq is becoming the widely used platform for gene expression profiling. However, real transcription signals in the RNA-seq data are confounded with measurement and sequencing errors and other random biological/technical variation. To extract biologically useful transcription process from the RNA-seq data, we propose to use the second ODE for modeling the RNA-seq data. We use differential principal analysis to develop statistical methods for estimation of location-varying coefficients of the ODE. We validate the accuracy of the ODE model to fit the RNA-seq data by prediction analysis and 5-fold cross validation. To further evaluate the performance of the ODE model for RNA-seq data analysis, we used the location-varying coefficients of the second ODE as features to classify the normal and tumor cells. We demonstrate that even using the ODE model for single gene we can achieve high classification accuracy. We also conduct response analysis to investigate how the transcription process responds to the perturbation of the external signals and identify dozens of genes that are related to cancer.
| S-EPMC4539434 | biostudies-literature
Project description:Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. We demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. We find that the transcriptome maps generated by our algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and we validate our system's ability to identify novel small RNAs, operons and transcription start sites. Our results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.
| S-EPMC3737546 | biostudies-literature