OptiVag
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB37382 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR4704810_1.fastq.gz | Fastqsanger.gz | |||
| ERR4704810_2.fastq.gz | Fastqsanger.gz | |||
| ERR4704820_1.fastq.gz | Fastqsanger.gz | |||
| ERR4704820_2.fastq.gz | Fastqsanger.gz | |||
| ERR4704830_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 272 |
Similar Datasets
Project description:Evaluating different library preparation methods for Pool-seq
| PRJEB8948 | ENA
Project description:Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. Using common sets of reference samples, we evaluated the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. As part of this larger study, we assessed sequencing bias and reproducibility using an equimolar pool of 1,152 small RNA sequences ranging from 15-90 nt, and primarily comprised of annotated human microRNAs. We observed extensive protocol-specific and sequence-specific bias that was largely mitigated in protocols employing sequencing adapters with randomized end-nucleotides. We find that sequencing bias is highly reproducible across labs using the same library preparation technologies, and use the data to calculate inter-protocol bias correction factors. These results provide strong evidence for the feasibility of reproducible cross-laboratory small RNA-seq studies, even those involving analysis of data generated using different protocols.
2018-07-09 | GSE94584 | GEO
Project description:Performance comparison of small RNA-Seq library preparation methods for biofluid samples
| PRJNA628969 | ENA
Project description:Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. Using common sets of reference samples, we evaluated the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. As part of this larger study, we assessed reproducibility and diversity of sequencing of mature miRNAs, generating libraries from RNA extracted from human plasma that was pooled from 11 healthy individuals. We find that protocol-specific biases are largely recapitulated in the biological samples, and that inter-protocol bias correction factors estimated from the more comprehensive equimolar synthetic pools can be applied to the biological samples to get more comparable, and in some cases, more accurate estimates of miRNA abundance.
2018-07-09 | GSE94582 | GEO
Project description:Plant small RNAs are a diverse and complex set of molecules, ranging in length from 21 to 24 nt, involved in a wide range of essential biological processes. Nowadays, high-throughput sequencing is the most commonly used method for the discovery and quantification of small RNAs. However, it is known that several biases can occur during the preparation of small RNA libraries, especially using low input RNA. We used two types of plant biological samples to evaluate the performance of seven commercially available methods for small RNA library construction, using different RNA input amounts. We show that when working with plant material, library construction methods have differing capabilities to capture small RNAs, and that different library construction methods provide better results when applied to the detection of microRNAs, phased small RNAs, or tRNA-derived fragments.
2021-08-01 | GSE156973 | GEO
Project description:Analysis of RNA samples by massive parallel sequencing holds the promise to assay gene expression in both a quantitative and qualitative fashion and therefore allows for digital gene expression (DGE) profiling. We assessed the effect of different experimental approaches by generating small RNA libraries from a biological sample as well as an equimolar pool of synthetic miRNAs and analyzed the results using capillary dideoxy sequencing and next-generation sequencing platforms (Roche/454, AB/SOLiD and Illumina/Solexa). Whereas different sequencing platforms provided highly similar results, large differences in DGE profiles were observed depending on the library preparation method used. Nevertheless, our results indicate that the preferential nature of the library preparation methods is systematic and highly reproducible and we show that DGE is well suited for the quantification of relative expression differences between samples. Keywords: Transcriptome analysis
2009-07-29 | GSE16370 | GEO
Project description:Analysis of RNA samples by massive parallel sequencing holds the promise to assay gene expression in both a quantitative and qualitative fashion and therefore allows for digital gene expression (DGE) profiling. We assessed the effect of different experimental approaches by generating small RNA libraries from a biological sample as well as an equimolar pool of synthetic miRNAs and analyzed the results using capillary dideoxy sequencing and next-generation sequencing platforms (Roche/454, AB/SOLiD and Illumina/Solexa). Whereas different sequencing platforms provided highly similar results, large differences in DGE profiles were observed depending on the library preparation method used. Nevertheless, our results indicate that the preferential nature of the library preparation methods is systematic and highly reproducible and we show that DGE is well suited for the quantification of relative expression differences between samples. This SuperSeries is composed of the SubSeries listed below.
2009-07-29 | GSE16374 | GEO
Project description:Analysis of RNA samples by massive parallel sequencing holds the promise to assay gene expression in both a quantitative and qualitative fashion and therefore allows for digital gene expression (DGE) profiling. We assessed the effect of different experimental approaches by generating small RNA libraries from a biological sample as well as an equimolar pool of synthetic miRNAs and analyzed the results using capillary dideoxy sequencing and next-generation sequencing platforms (Roche/454, AB/SOLiD and Illumina/Solexa). Whereas different sequencing platforms provided highly similar results, large differences in DGE profiles were observed depending on the library preparation method used. Nevertheless, our results indicate that the preferential nature of the library preparation methods is systematic and highly reproducible and we show that DGE is well suited for the quantification of relative expression differences between samples. Keywords: Transcriptome analysis
2009-07-29 | GSE16369 | GEO