Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MRNA Sequencing of Human PromoCells Using Random Primed mRNA-Sequencing Technique

ABSTRACT: The differential gene expression of human cardiomyocytes induced by kinase inhibitors sorafenib and sunitinib is measured by a high-throughput mRNA-sequencing approach called 3'-DGE, that is based on a 3' end-focused reference sequence library and a transcript molecule counting method with unique molecular identifiers (UMI) for correcting PCR bias.

ORGANISM(S): Homo sapiens

PROVIDER: GSE98428 | GEO | 2017/11/08

SECONDARY ACCESSION(S): PRJNA385095

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

mRNA Sequencing of Human PromoCells Using 3'-directed Digital Gene Expression (3'-DGE) Technique

Project description:The differential gene expression of human cardiomyocytes induced by kinase inhibitors sorafenib and sunitinib is measured by a high-throughput mRNA-sequencing approach called 3'-DGE, that is based on a 3' end-focused reference sequence library and a transcript molecule counting method with unique molecular identifiers (UMI) for correcting PCR bias.

2017-11-08 | GSE98431 | GEO

Single-cell RNA sequencing with molecular spikes to determine RNA counting accuracy

Project description:Molecule counting is central to single-cell sequencing, yet no experimental strategy to evaluate counting performance exist. Here, we introduce RNA spike-ins containing inbuilt unique molecular identifiers (molecular spikes) that we use to monitor single-cell RNA counting performance across methods and to identify experimental steps essential for accurate counting. In this dataset, we add molecular spikes to popular single-cell RNA-seq protocols: SCRB-seq, Smart-seq3 and 10x Genomics (v2). For SCRB-seq and Smart-seq3, we also include variations of the library preparation procedure that are suspected to lead to changes in the UMI counting accuracy.

2022-02-16 | E-MTAB-10372 | biostudies-arrayexpress

Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules

Project description:Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules

| PRJNA906109 | ENA

miRNA-Seq of human blood with different disease state of COVID-19

Project description:To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) Small RNA enrichment and purification, 2) Adaptor ligation and Unique molecular identifiers (UMI) labeled Primer addition, 3) RT-PCR, Library quantitation and pooling cyclization, 4) Library quality control, 5) Small RNAs were sequenced by BGI500 platform with 50bp single-end reads resulting in at least 20M reads for each sample. Analysis steps: 1) Small RNA raw sequencing reads with low quality tags (which have more than four bases whose quality is less than ten, or have more than six bases with a quality less than thirteen.), the reads with poly A tags, and the tags without 3’ primer or tags shorter than 18nt were removed. 2) After data filtering, the clean reads were mapped to the reference genome and other sRNA database including miRbase, siRNA, piRNA and snoRNA using Bowtie2 (Langmead and Salzberg, 2012). Particularly, cmsearch (Nawrocki and Eddy, 2013) was performed for Rfam mapping. 3) The small RNA expression level was calculated by counting absolute numbers of molecules using unique molecular identifiers (UMI, 8-10nt). MiRNA with UMI count lager than 1 in at least one sample were considered as expressed.

2021-03-24 | E-MTAB-10299 | biostudies-arrayexpress

Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [RNA-seq]

Project description:Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [RNA-seq]

| PRJNA906113 | ENA

Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [scRNA-Seq]

Project description:Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [scRNA-Seq]

| PRJNA906114 | ENA

Anchor-Enhanced Bead Design for Reduced Oligonucleotide Synthesis Errors in Single-cell sequencing

Project description:Single-cell transcriptomics, reliant on the incorporation of barcodes and unique molecular identifiers (UMIs) into captured polyA+ mRNA, faces a significant challenge due to synthesis errors in oligonucleotide capture sequences. These inaccuracies, which are especially problematic in long-read sequencing, impair the precise identification of sequences and result in inaccuracies in UMI deduplication. To mitigate this issue, we have modified the oligonucleotide capture design, which integrates an interposed anchor between the barcode and UMI, and a 'V' base anchor adjacent to the polyA capture region. This configuration is devised to ensure compatibility with both short and long-read sequencing technologies, facilitating improved UMI recovery and enhanced feature detection, thereby improving the efficacy of droplet-based sequencing methods.

2024-04-09 | GSE263458 | GEO

Sequencing Universal Human Reference RNA by Smart-seq and early barcoding library preparation methods

Project description:Many library preparation methods are available for gene expression quantification. Here, we sequenced and analysed Universal Human Reference RNA (UHRR) prepared using Smart-Seq2, TruSeq (public data) and a protocol using unique molecular identifiers (UMIs) that all include the ERCC spike-in mRNAs to investigate the effects of amplification bias on expression quantification. UHRR 10 and 12 replicates for Smart-seq2 and UMI-seq library preparation methods, respectively.

2016-05-13 | E-GEOD-75823 | biostudies-arrayexpress

Transcriptome profiling of drug resistant cell lines (SKOV3).

Project description:The aim of this research was to uncover transcriptome changes in SKOV3 cells during the acquisition of resistance to anticancer drugs Sorafenib, Sunitinib, Pazopanib, Everolimus, Temsirolimus.

2017-04-14 | GSE97751 | GEO

Transcriptome profiling of drug resistant cell lines (NGP-127).

Project description:The aim of this research was to uncover transcriptome changes in NGP-127 cells during the acquisition of resistance to anticancer drugs Sorafenib, Sunitinib, Pazopanib, Everolimus, Temsirolimus.

2017-04-14 | GSE97750 | 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