Arbovirus sequencing platform comparison
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB47177 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR6588990.fastq.gz | Fastqsanger.gz | |||
| ERR6589000.fastq.gz | Fastqsanger.gz | |||
| ERR6589020_1.fastq.gz | Fastqsanger.gz | |||
| ERR6589020_2.fastq.gz | Fastqsanger.gz | |||
| ERR6589030_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 96 |
Similar Datasets
Project description:Spatio-temporal distribution analysis of arbovirus circulating in Quebec (Canada) between 2003 and 2016
| PRJNA1135176 | ENA
Project description:High-throughput systems for gene expression profiling have been developed and matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised regarding data comparability and agreement across technologies. Within an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing). The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery. Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive, and currently, both provide indispensable tools for transcriptome profiling. Keywords: biological replicates
2007-01-01 | GSE6313 | GEO
Project description:Microarray technology is a powerful tool able to measure RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross platform meta-analysis studies rapidly increase, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we notice that none of the previously published papers consider differences between labs. For this paper, a consortium of ten labs from the DC and Baltimore area was formed to compare three heavily used platforms using identical RNA samples. Each lab was given identical RNA samples (A1 and B1) which were processed according to what each lab considered best practice. Five of the labs used Affymetrix GeneChips, three used two-color spotted cDNA arrays, and two used two-color long oligo arrays. Samples 1 and 2 represent unique mixtures of total RNA from four knockout cell lines: PEX1, PEX6, PEX7, and PEX12. Each of the four cell lines is deficient for one of four PEX genes (required for peroxisome biogenesis/mutations cause peroxisome biogenesis disorders). Appropriate statistical analysis demonstrates that relatively large differences exist between labs using the same platform, but that the results from the best performing labs agree rather well. Keywords: other
2005-04-13 | GSE2521 | GEO
Project description:Here we describe CapTrap-Seq, an experimental workflow designed to address the problem of reduced transcript end detection by long-read RNA sequencing methods, especially at the 5' ends. We apply CapTrap-Seq to profile transcriptomes of the human heart and brain and we compared the obtained results with other library preparation approaches. CapTrap-Seq is a platform-agnostic method and here tested the method by using 3 different long-read sequencing platforms: MinION (ONT), Sequel (PacBaio) and Sequel II (PacBio).
2023-12-20 | E-MTAB-13063 | biostudies-arrayexpress
Project description:Microarray technology is a powerful tool able to measure RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross platform meta-analysis studies rapidly increase, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we notice that none of the previously published papers consider differences between labs. For this paper, a consortium of ten labs from the DC and Baltimore area was formed to compare three heavily used platforms using identical RNA samples. Each lab was given identical RNA samples (A1 and B1) which were processed according to what each lab considered best practice. Five of the labs used Affymetrix GeneChips, three used two-color spotted cDNA arrays, and two used two-color long oligo arrays. Samples 1 and 2 represent unique mixtures of total RNA from four knockout cell lines: PEX1, PEX6, PEX7, and PEX12. Each of the four cell lines is deficient for one of four PEX genes (required for peroxisome biogenesis/mutations cause peroxisome biogenesis disorders). Appropriate statistical analysis demonstrates that relatively large differences exist between labs using the same platform, but that the results from the best performing labs agree rather well. Keywords: other
2010-06-10 | E-GEOD-2521 | biostudies-arrayexpress
Project description:The aim of the present study was to compare, on a statistical basis, the performance of different microarray platforms to detect differences in gene expression in a realistic and challenging biological setting. Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina and home-spotted oligonucleotide arrays. We observed considerable overlap between the different platforms, the overlap being better detectable with significance level-based ranking than with a p-value based cut-off. Confirming the qualitative agreement between platforms, Pathway analysis consistently demonstrated aberrances in GABA-ergic signalling in the transgenic mice, even though pathways were represented by only partially overlapping genes on the different platforms. Keywords: microarray platform comparison
2008-03-25 | GSE8349 | GEO
Project description:Gene expression microarrays have made a profound impact in biomedical research. The diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and in house platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by QRT-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent pre-processing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms. This SuperSeries is composed of the SubSeries listed below.
2006-06-01 | GSE4854 | GEO
Project description:DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep and conventional machine learning models with respect to diagnostic accuracy and computational requirements while still being fully explainable. We use crossNN to train a pan-cancer classifier to discriminate more than 170 tumor types across all organ sites. Validation in an independent cohort of >5,000 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model with 99.1% and 97.8% precision for brain tumor and pan-cancer model, respectively.
2025-02-24 | GSE289246 | GEO
Project description:This study presents a comparison of small RNA sequencing libraries generated from the same cell lines but using different sequencing platforms and protocols. The samples were analyzed and compared at the level of miRNAs expression and as a population of small RNAs derived from repetitive elements. Despite a good correlation between sequencing platforms, there are qualitative and quantitative variations in the results depending on the protocol used.
2012-03-01 | GSE35368 | GEO