Drosophila simulans
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA234526 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1107940_1.fastq.gz | Fastqsanger.gz | |||
| SRR1107940_2.fastq.gz | Fastqsanger.gz | |||
| SRR1107941_1.fastq.gz | Fastqsanger.gz | |||
| SRR1107941_2.fastq.gz | Fastqsanger.gz | |||
| SRR1107942_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 12 |
Similar Datasets
Project description:RNA-seq: technical variability and sampling (female)
| PRJNA234514 | ENA
Project description:RNA-seq: technical variability and sampling
| PRJNA234515 | ENA
Project description:RNA-seq of male genital development in Drosophila simulnas
| PRJDB6923 | ENA
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54067 | GEO
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54068 | GEO
Project description:We generated RNA-seq data of Drosophila simulans and Drosophila mauritiana developing male genitalia in order to identify expression level differences between these species. These species are closely related, yet have dramatic differences in their male genital morphologies. Three independent RNA-seq library replicates were generated for Dsim w501 and Dmau D1 developing male genitalia. Flies were reared under the above conditions, and white pre-pupae collected. Males were selected using gonad size and allowed to develop in a humid container at 25ºC until stages 2 and 4.5 (see staging guide in (Hagen et al., 2019); tartan underlies the evolution of Drosophila male genital morphology). Between these stages, the claspers develop from a ridge structure to a distinct appendage separate from the surrounding tissue, and the posterior lobe has begun to extend outwards from the lateral plate primordia (Hagen et al., 2019). The heads of pupae were impaled with a needle onto a charcoal agar plate and submerged in 1xPBS. Dissection scissors were used to remove the distal tip of the pupal case and the outer membrane, and pressure applied to the abdomen to allow the developing genitalia to be quickly expelled from the pupal case and dissected away from the abdomen. Note that the entire genital arch, including internal genital organs (but not including abdominal tissue), was isolated for RNA extraction. The genitalia from fifteen males from each stage were collected and placed directly into TRIzol. RNA was then extracted using standard procedures. Quality and quantity of RNA was verified using a Qubit, and samples were sent to the Centre for Genomic Research at the University of Liverpool where dual-indexed, strand-specific RNA-seq libraries were prepared using NEBNext polyA selection and Ultra Directional RNA preparation kits. Samples were then sequenced using Illumina HiSeq 4000 (paired-end, 2x150 bp sequencing). Dsim w501 and Dmau D1 reads were mapped against reannotated reference coding sequences (Torres-Oliva et al., 2016).
2020-08-29 | E-MTAB-9465 | biostudies-arrayexpress
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases. Three independent samples of D. simulans male heads were collected with each sample representing a unique pool of biological material. Each sample was prepared according to manufacturer's instructions and then the same library was run on two lanes of a Solexa/Illumina flow cell, resulting in two technical replicates for each biological replicate, runs were 36 base-pair paired end.
2014-01-15 | E-GEOD-54068 | biostudies-arrayexpress
Project description:Assessment of sources of non-biological (technical) variability in the processing of RNA samples and gene chips during microarray analysis. The purpose of this experiment was to measure technical variability in microarray data introduced by various steps in preparation of the RNA, amplification/labeling of the RNA and hybridization of the sample to the arrays on separate days. The experimental design utilized a single tissue source - homogenized brain tissue pooled from 3 eight week old male C57/Bl mice.
2010-12-11 | GSE26008 | GEO
Project description:RNA-seq of Drosophila simulans and Drosophila mauritiana developing male genitalia
| PRJEB39952 | ENA
Project description:Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity amongst single cells. Here we identify two major sources of technical variability, sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this and after validation by single-molecule FISH experiments, we apply these models to demonstrate that growing mES cells in 2i instead of serum/LIF globally reduces gene expression variability.
2014-04-20 | GSE54695 | GEO