Dataset Information

FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data.

ABSTRACT: 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.

SUBMITTER: Leader DP

PROVIDER: S-EPMC5753349 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data.

Leader David P DP Krause Sue A SA Pandit Aniruddha A Davies Shireen A SA Dow Julian A T JAT

Nucleic acids research 20180101 D1

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 expre ...[more]

PMID: 29069479

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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: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.

Project description:R-loops are three-stranded nucleotide structures consisting of a DNA:RNA hybrid and a displaced ssDNA non-template strand. Previous work suggests that R-loop formation is primarily determined by the thermodynamics of DNA:RNA binding, which are governed by base composition (e.g., GC skew) and transcription-induced DNA superhelicity. However, R-loops have been described at genomic locations that lack these properties, suggesting that they may serve other context-specific roles. To better understand the genetic determinants of R-loop formation, we have characterized the Drosophila melanogaster R-loop landscape across strains and between sexes using DNA:RNA immunoprecipitation followed by high-throughput sequencing (DRIP-seq). We find that R-loops are associated with sequence motifs that are G-rich or exhibit G/C skew, as well as highly expressed genes, tRNAs, and small nuclear RNAs, consistent with a role for DNA sequence and torsion in R-loop specification. However, we also find motifs associated with R-loops that are A/T-rich and lack G/C skew as well as a subset of R-loops that are enriched in polycomb-repressed chromatin. Differential enrichment analysis reveals a small number of sex-biased R-loops: while non-differentially enriched and male-enriched R-loops form at similar genetic features and chromatin states and contain similar sequence motifs, female-enriched R-loops form at unique genetic features, chromatin states, and sequence motifs and are associated with genes that show ovary-biased expression. Male-enriched R-loops are most abundant on the dosage-compensated X chromosome, where R-loops appear stronger compared to autosomal R-loops. R-loop-containing genes on the X chromosome are dosage-compensated yet show lower MOF binding and reduced H4K16ac compared to R-loop-absent genes, suggesting that H4K16ac or MOF may attenuate R-loop formation. Collectively, these results suggest that R-loop formation in vivo is not fully explained by DNA sequence and topology and raise the possibility that a distinct subset of these hybrid structures plays an important role in the establishment and maintenance of epigenetic differences between sexes.

Project description:The X chromosome constitutes a unique genomic environment because it is present in one copy in males, but two copies in females. This simple fact has motivated several theoretical predictions with respect to how standing genetic variation on the X chromosome should differ from the autosomes. Unmasked expression of deleterious mutations in males and a lower census size are expected to reduce variation, while allelic variants with sexually antagonistic effects, and potentially those with a sex-specific effect, could accumulate on the X chromosome and contribute to increased genetic variation. In addition, incomplete dosage compensation of the X chromosome could potentially dampen the male-specific effects of random mutations, and promote the accumulation of X-linked alleles with sexually dimorphic phenotypic effects. Here we test both the amount and the type of genetic variation on the X chromosome within a population of Drosophila melanogaster, by comparing the proportion of X linked and autosomal trans-regulatory SNPs with a sexually concordant and discordant effect on gene expression. We find that the X chromosome is depleted for SNPs with a sexually concordant effect, but hosts comparatively more SNPs with a sexually discordant effect. Interestingly, the contrasting results for SNPs with sexually concordant and discordant effects are driven by SNPs with a larger influence on expression in females than expression in males. Furthermore, the distribution of these SNPs is shifted towards regions where dosage compensation is predicted to be less complete. These results suggest that intrinsic properties of dosage compensation influence either the accumulation of different types of trans-factors and/or their propensity to accumulate mutations. Our findings document a potential mechanistic basis for sex-specific genetic variation, and identify the X as a reservoir for sexually dimorphic phenotypic variation. These results have general implications for X chromosome evolution, as well as the genetic basis of sex-specific evolutionary change.

Dataset Information

FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data.

Publications

FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data.

Similar Datasets

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