Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (reproducibility)

ABSTRACT: The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to assess the reproducibility of small RNA datasets produced using Illumina sequencing-by-synthesis technology (SBS).

ORGANISM(S): Arabidopsis thaliana

PROVIDER: GSE14694 | GEO | 2009/06/03

SECONDARY ACCESSION(S): PRJNA114411

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (reproducibility)

Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to assess the reproducibility of small RNA datasets produced using Illumina sequencing-by-synthesis technology (SBS). We analyzed the reproducibility of small RNA SBS datasets by comparing libraries generated for biological replicates (rep1 and rep2) and technical replicates (rep2 and rep3).

2009-06-02 | E-GEOD-14694 | biostudies-arrayexpress

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards)

2009-06-03 | GSE14695 | GEO

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing

Project description:This SuperSeries is composed of the following subset Series: GSE14694: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (reproducibility) GSE14695: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards) Refer to individual Series

2009-06-02 | E-GEOD-14696 | biostudies-arrayexpress

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards)

Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to develop synthetic oligoribonucleotides as spike-in standards. We assessed the use of synthetic oligoribonucleotide standards as spike-in controls. These standards can be used to set an objective standard against which to compare samples. Standards were added to the total RNA (100 ug) in the following amounts: Std2 (TATATGCAAGTCCGGCCATAC) 0.01 pmol, Std3 (TAGCTAACGCATATCCGCATC) 0.1 pmol, Std6 (TGAAGCTGACATCGGTCATCC) 1.0 pmol.

2009-06-02 | E-GEOD-14695 | biostudies-arrayexpress

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing

Project description:This SuperSeries is composed of the SubSeries listed below.

2009-06-03 | GSE14696 | GEO

bigSCale: An Analytical Framework for Big-Scale Single Cell Data

Project description:Single-cell RNA sequencing significantly deepened our insights into complex tissues and latest techniques are capable to analyze ten-thousands of cells simultaneously. With bigSCale, we provide an analytical framework being scalable to analyze millions of cells, addressing challenges of future large data sets. Unlike other methods, bigSCale does not constrain data to fit an a priori-defined distribution and instead uses an accurate numerical model of noise. We evaluate the performance of bigSCale using a biological model of aberrant gene expression and simulated data sets, which underlined its speed and accuracy. We further apply bigSCale to analyze 1.3 million cells from the mouse developing forebrain. The directed down-sampling strategy identified rare populations, such as Reelin positive Cajal-Retzius neurons, for which we determined a previously not recognized heterogeneity associated to distinct differentiation stages, spatial organization and cellular function. Together, bigSCale presents a perfect solution to address future challenges of large single-cell data sets.

2018-04-15 | GSE102934 | GEO

Analytical validation of SKY92 for the identification of high-risk Multiple Myeloma

Project description:Multiple myeloma is an incurable plasma cell cancer with a large variability in survival. Multiple myeloma patients classified high risk by the SKY92 gene expression classifier are at high risk of relapse and short survival. We performed analytical validation of the SKY92 assay with primary bone marrow specimens from 12 multiple myeloma patients and 7 reference cell line specimens. The SKY92 results were 100% concordant - with the reference and/or their expected result - for sensitivity, specificity, microarray stability and RLT buffer stability; The SKY92 results were 90% concordant for primary specimen stability, 96.4% for intermediate precision and 80%-100% for RNA stability. For the cell-line reproducibility the concordance was at least 92.9%, except for one near-cut point specimen. For the clinical specimen reproducibility, the concordance was 100%, except for two near-cut point specimens. Three independent laboratories were concordant in ≥ 77.8% and ≥ 92.9% of experiments (respectively patient specimens and cell lines). Statistical acceptance thresholds were developed as Delta ≤ 1.48 (change in SKY92 score) and standard deviation ≤ 0.45 (SD across SKY92 scores). Using the CLSI method of choice (EP05-A2/A3), restricted maximum likelihood or REML, the observed Deltas (0 - 1.14) and standard deviations (0.22-0.31) passed acceptance criteria. The analytical validation for the SKY92 assay as a prognostic molecular test for individual multiple myeloma patients is therefore successfully proven.

2020-10-28 | GSE159289 | GEO

Biospecimen Pre-analytical Variables (BPV)

Project description:Biospecimen Pre-analytical Variables (BPV)

| PRJNA376469 | ENA

Biospecimen Pre-analytical Variables (BPV)

Project description:Biospecimen Pre-analytical Variables (BPV)

| PRJNA932917 | ENA

SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators

Project description:The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. Recently single-cell technologies such as CITE-seq have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. The predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN greatly enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states.

2021-08-16 | GSE172155 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data