Drosophila melanogaster
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA295261 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR2337460.fastq.gz | Fastqsanger.gz | |||
| SRR2337461.fastq.gz | Fastqsanger.gz | |||
| SRR2337458.fastq.gz | Fastqsanger.gz | |||
| SRR2337459.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 4 of 4 |
Similar Datasets
Project description:Background High-throughput RNA-Sequencing (RNA-Seq) has become the preferred technique for studying gene expression differences between biological samples and for discovering novel isoforms, though the techniques to analyze the resulting data are still immature. One pre-processing step that is widely but heterogeneously applied is trimming, in which low quality bases, identified by the probability that they are called incorrectly, are removed. However, the impact of trimming on subsequent alignment to a genome could influence downstream analyses including gene expression estimation; we hypothesized that this might occur in an inconsistent manner across different genes, resulting in differential bias. Results To assess the effects of trimming on gene expression, we generated RNA-Seq data sets from four samples of larval Drosophila melanogaster sensory neurons, and used three trimming algorithmsâSolexaQA, Trimmomatic, and ConDeTriâto perform quality-based trimming across a wide range of stringencies. After aligning the reads to the D. melanogaster genome with TopHat2, we used Cuffdiff2 to compare the original, untrimmed gene expression estimates to those following trimming. With the most aggressive trimming parameters, over ten percent of genes had significant changes in their estimated expression levels. This trend was seen with two additional RNA-Seq data sets and with alternative differential expression analysis pipelines. We found that the majority of the expression changes could be mitigated by imposing a minimum length filter following trimming, suggesting that the differential gene expression was primarily being driven by spurious mapping of short reads. Slight differences with the untrimmed data set remained after length filtering, which were associated with genes with low exon numbers and high GC content. Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming results in the most biologically accurate gene expression estimates. Conclusions We find that aggressive quality-based trimming has a large impact on the apparent makeup of RNA-Seq-based gene expression estimates, and that short reads can have a particularly strong impact. We conclude that implementation of trimming in RNA-Seq analysis workflows warrants caution, and if used, should be used in conjunction with a minimum read length filter to minimize the introduction of unpredictable changes in expression estimates. Four biological replicates of 100 Drosophila melanogaster larval multi-dendritic sensory neurons were profiled by mRNA-Seq
2016-02-25 | E-GEOD-72884 | biostudies-arrayexpress
Project description:Background High-throughput RNA-Sequencing (RNA-Seq) has become the preferred technique for studying gene expression differences between biological samples and for discovering novel isoforms, though the techniques to analyze the resulting data are still immature. One pre-processing step that is widely but heterogeneously applied is trimming, in which low quality bases, identified by the probability that they are called incorrectly, are removed. However, the impact of trimming on subsequent alignment to a genome could influence downstream analyses including gene expression estimation; we hypothesized that this might occur in an inconsistent manner across different genes, resulting in differential bias. Results To assess the effects of trimming on gene expression, we generated RNA-Seq data sets from four samples of larval Drosophila melanogaster sensory neurons, and used three trimming algorithms—SolexaQA, Trimmomatic, and ConDeTri—to perform quality-based trimming across a wide range of stringencies. After aligning the reads to the D. melanogaster genome with TopHat2, we used Cuffdiff2 to compare the original, untrimmed gene expression estimates to those following trimming. With the most aggressive trimming parameters, over ten percent of genes had significant changes in their estimated expression levels. This trend was seen with two additional RNA-Seq data sets and with alternative differential expression analysis pipelines. We found that the majority of the expression changes could be mitigated by imposing a minimum length filter following trimming, suggesting that the differential gene expression was primarily being driven by spurious mapping of short reads. Slight differences with the untrimmed data set remained after length filtering, which were associated with genes with low exon numbers and high GC content. Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming results in the most biologically accurate gene expression estimates. Conclusions We find that aggressive quality-based trimming has a large impact on the apparent makeup of RNA-Seq-based gene expression estimates, and that short reads can have a particularly strong impact. We conclude that implementation of trimming in RNA-Seq analysis workflows warrants caution, and if used, should be used in conjunction with a minimum read length filter to minimize the introduction of unpredictable changes in expression estimates.
2016-02-25 | GSE72884 | GEO
Project description:Analysis of chimeric reads from RNA-seq data
| PRJNA218548 | ENA
Project description:RNA-seq analysis of genes involved in efferocytosis of S2 cell, Raw sequence reads
| PRJNA721223 | ENA
Project description:modENCODE RNA-Seq of Drosophila Kc167
| PRJNA119899 | ENA
Project description:RNA-seq: technical variability and sampling (female)
| PRJNA234514 | ENA
Project description:RNA-Seq of Drosophila cell line Dmel2
| PRJNA126589 | ENA
Project description:The 3' ends of most Drosophila melanogaster genes are poorly annotated or are determined by only a single EST or cDNA clone. To enhance the annotation of poly(A) site use in Drosophila, we performed deep sequencing on RNA isolated from 29 dissected tissues using an approach designed to enrich for poly(A) spanning reads. From these experiments, we identified 1.4 million poly(A) spanning reads leading to the identification of many new poly(A) sites and the identification of many tissue-specific poly(A) sites. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf RNA from 29 dissected Drosophila melanogaster tissues (in duplicate) were used to prepare polyA enriched RNA-Seq libraries. Briefly, total RNA was poly(A) selected, fragmented, and ligated to 5' and 3' RNA linkers. These libraries were amplified using Illumina paired-end primers, and subsequently reamplified using a 3' primer complementary to the 3' adapter but containing 6 Ts at the 3' end. The libraries were also multiplexed and up to 12 samples mixed per lane and sequenced on an Illumina GAIIx using paired-end 76 bp reads, or an illumina HiSeq 2000 using paired-end 100 bp reads. All reads were mapped to the Drosophila melanogaster genome to identify unmapped reads. Unmapped reads containing at least 10 A residues at the 3' end were identified, the terminal A residues trimmed, realigned to the genome to identify uniquely mapped reads. Such reads were identified as polyA spanning reads
2011-11-23 | E-GEOD-33905 | biostudies-arrayexpress
Project description:Drosophila melanogaster small RNA sequence from 14 libraries
| PRJNA106137 | ENA
Project description:Nascent-seq indicates widespread cotranscriptional RNA editing in Drosophila
| PRJNA159179 | ENA