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Data on RNA-seq analysis of Drosophila melanogaster during ageing.

ABSTRACT: Ageing is defined as gradual decline of physiological, cellular and molecular state of an organism with time. The age-associated cell dysfunctions usually cause chronic diseases such as diabetes, cancers and other age-related diseases. Many of the genes and pathways involved in ageing are conserved in different species. These genes and pathways have been categorised into nine cellular and molecular hallmarks, namely, genomic instability, telomere attrition, loss of proteostasis, mitochondrial dysfunction, epigenetic alterations, deregulated nutrient sensing, stem cell exhaustion, cellular senescence and altered intercellular communication. Despite countless studies on ageing, the molecular mechanism of ageing is poorly understood. Here, we performed genome wide transcriptome mapping of ageing process in D. melanogaster. In which, transcriptomic analysis conducted on the 1 day and 60 days flies. Illumina Hiseq platform were used to generate raw data. Afterwards, further analysis including differential expression analysis, GO classification and KEGG pathway enrichment analysis were performed. The raw data were uploaded to SRA database and the BioProject ID is PRJNA718442. These data provide the basis for future research in order to discover the genes and pathways involved in ageing.

SUBMITTER: Bajgiran M

PROVIDER: S-EPMC8488473 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Data on RNA-seq analysis of Drosophila melanogaster during ageing.

Bajgiran Morteza M Azlan Azali A Shamsuddin Shaharum S Azzam Ghows G Halim Mardani Abdul MA

Data in brief 20210922

Ageing is defined as gradual decline of physiological, cellular and molecular state of an organism with time. The age-associated cell dysfunctions usually cause chronic diseases such as diabetes, cancers and other age-related diseases. Many of the genes and pathways involved in ageing are conserved in different species. These genes and pathways have been categorised into nine cellular and molecular hallmarks, namely, genomic instability, telomere attrition, loss of proteostasis, mitochondrial dy ...[more]

PMID: 34632013

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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.

Project description:BackgroundA 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.ResultsWe 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.ConclusionsThe 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.

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Data on RNA-seq analysis of Drosophila melanogaster during ageing.

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Data on RNA-seq analysis of Drosophila melanogaster during ageing.

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