Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RNA-seq of chemostat grown yeast (Y11335 BY4742 )

ABSTRACT: We profiled total-mRNA from yeast cells grown in identical conditions to match samples submitted for proteome-wide absolute quantification. Matching mRNA profiles to absolute protein copies numbers enables a more accurate comparison of transcript to final protein levels. Here we were able to show direct comparison results in a correlation of Rsquared of 0.58, show suggesting as much as ~40% of final protein levels in yeast cannot be determined from mRNA levels alone. Reads were mapped to a reference genome of S. cerevisiae, downloaded from the Saccharomyces Genome Database (SGD), using Bowtie version 1 (74). Mapped sequences were then assembled into transcripts and quantified using Cufflinks version 2.0 (75) (using the SGD reference genome GTF file).

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE73898 | GEO | 2015/10/10

SECONDARY ACCESSION(S): PRJNA298358

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Shared Molecules

Only show the datasets with similarity scores above: 0.5

Threshold

0.5

Similar Datasets

Evaluation of mRNA expression in the equine chorioallantoic membrane

Project description:Purpose:The goal of this study was to evalute gene expression patterns of equine chorioallantoic membrane during different stages of the pregnancy Method: mRNA profile of equine chorioallantoic membrane (CAM) from 45days, 4months, 6months and 10months (4 samples for each time points) generated by RNA-sequencing,using a Illumina HiSeq 4000 ( HiSeq 4000 sequencing kit version 1). The sequence reads were trimmed for adapters and quality using TrimGalore Version 0.4.4,and then mapped to EquCab2.0 using STAR-2.5.2b. Final quantification at the gen level was performed by analyzing the BAM files in cufflinks using the Equus_caballus_ENSEMBL_88 gtf file as Guide.

2018-11-19 | GSE108279 | GEO

Constant production of FlhG ensures proper function of the flagellar counter in Shewanella putrefaciens

Project description:Purpose : Identification of the regulons directly and indirectly affected by the main regulators of flagellation in Shewanella putrefaciens CN-32 Methods : mRNA profiles were generated for Shewanella putrefaciens CN-32 samples by deep sequencing. The removal of ribosomal RNA was performed using the Ribo-Zero Bacteria Kit (Illumina) and cDNA libraries were generated with the ScriptSeq v2 Kit (Illumina) . The samples were sequenced in single end mode on an Illumina HiSeq 2500 device and mRNA reads were trimmed using the tool ‘cutadapt’ (version 3.5) with default settings and mapped to the NC_009438.1 (Shewanella putrefaciens CN-32) reference genome from NCBI using ‘bowtie2’ (version 2.3.5.1) with default settings for single-end sequencing.

2022-12-21 | GSE207531 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and non-antibiotic pharmaceuticals stressed E. coli K-12 LE392, P. putida KT2440, and RP4 plasmid Transcriptomes [experiment 2]

Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of E. coli K-12 LE392, P. putida KT2440 and IncPα RP4 plasmid, and their mRNA response under the exposure of CuO NPs and Cu2+. The concentrations were 5 μmol/L for CuO NPs and Cu2+. The group without dosing CuO NPs or Cu2+ was the control group. Each concentration was conducted in triplicate. By comparing the mRNA profiles of experimental groups and control group, the effects of these CuO NPs and Cu2+ on transcriptional levels can be revealed. Illumina HiSeq 2500 was applied. The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913), P.putida reference genome (NC_002947), and IncPα plasmid reference genome (NC_00) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.

2022-05-15 | GSE131228 | GEO

Absolute quantification of B cell transcriptome using ERCC spiked-in RNA

Project description:RNA-seq analysis of with known quantity of cell numbers and reference RNA (ERCC) to determine absolute abundance of endogenous mRNA abundances in B cells

2017-03-08 | GSE83734 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and carbamazepine-stressed E. coli K-12 LE392, P. putida KT2440, and RP4 plasmid Transcriptomes

Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of wild-type E. coli K-12 LE392, P. putida KT2440 and IncPα RP4 plasmid, and their mRNA response under the exposure of antiepileptic drug carbamazepine. Three concentrations of carbamazepine were applied, which were 0.05 mg/L, 10.0 mg/L and 50.0 mg/L (refer to low, medium and high, respectively). The group without dosing carbamazepine was the control group. Each concentration was conducted in triplicate. By comparing the mRNA profiles of experimental groups and control group, the effects of carbamazepine on transcriptional levels can be revealed. Illumina HiSeq 2500 was applied. The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913), P.putida reference genome (NC_002947), and IncPα plasmid reference genome (NC_00) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.

2019-01-23 | GSE112064 | GEO

Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of wild-type E. coli K-12 LE392, P. putida KT2440 and IncPα RP4 plasmid, and their mRNA response under the exposure of six non-antibiotic pharmaceuticals, including ibuprofen, naproxen, gemfibrozil, diclofenac, propanolol, and iopromide. The concentrations were 0.5 mg/L for ibuprofen, naproxen, gemfibrozil, diclofenac, propanolol, and 1.0 mg/L for iopromide. The group without dosing pharmaceutical was the control group. Each concentration was conducted in triplicate. By comparing the mRNA profiles of experimental groups and control group, the effects of these six non-antibiotic pharmaceuticals on transcriptional levels can be revealed. Illumina HiSeq 2500 was applied. The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913), P.putida reference genome (NC_002947), and IncPα plasmid reference genome (NC_00) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.

2021-09-01 | GSE130562 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and disinfectants stressed Acinetobacter baylyi ADP1 Transcriptomes

Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of wild-type A.baylyi ADP1, and its mRNA response under the exposure of two disinfectants, chloramine and free chlorine. The concentrations 10 mg/L. The group without dosing disinfectants was the control group. Each concentration was conducted in triplicate. By comparing the mRNA profiles of experimental groups and control group, the effects of these two disinfectants on transcriptional levels can be revealed. Illumina HiSeq 2500 was applied. The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the A.baylyi ADP1 reference genome (NC_005966.1), using SeqAlto (version 0.5). Cufflinks (version 2.2.1) to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R. (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.

2021-05-12 | GSE142062 | GEO

Stepwise reconstitution of genomic nucleosome organization with pure proteins

Project description:Nucleosome arrays begin at nucleosome-free promoter regions (NFRs) and regulate gene expression. Reconstituting such organization throughout a genome with purified proteins is a critical challenge in establishing biochemical mechanisms for chromosome assembly. Here we establish a four-step hierarchical building plan for yeast genomic nucleosome organization using only purified components: genomic DNA, histones, site-specific organizing factors Abf1 and Reb1, and the chromatin remodelers RSC, ISW2, INO80, and ISW1a. First, RSC makes NFRs by translating promoter poly(dA:dT) tracts into directional nucleosome removal. Second, +1 nucleosomes are positioned by INO80 at most genes potentially involving DNA shape, or by ISW2 using gene-specific Abf1 and Reb1. Third, INO80 or ISW2 create arrays with wide spacing. Fourth, ISW1a tightens the spacing and creates properly positioned arrays. We conclude that entire genomes use a simple set of rules and proteins, without transcription, to build a common chromatin architecture. In this study, nucleosomes were assembled using Salt Gradient Dialysis (SGD) on yeast genomic DNA library. Assembled nucleosomes were either left untreated (labelled as "SGD", control), treated with whole cell extract (WCE), mutant extracts (rsc3ts WCE, isw1 isw2 chd1 WCE), purified remodelers; singly or in combinations (RSC, ISW1a, ISW1b, ISW2, INO80, CHD1, SWI/SNF), combinations of mutant extracts and chromatin remodelers or combination of General Regulatory Factors (Abf1, Reb1) and chromatin remodelers. The resulting nucleosome positions were mapped genome-wide using MNase-(anti-H3-ChIP)-Seq.

2016-08-14 | E-GEOD-72106 | biostudies-arrayexpress

Transposon expression in the Drosophila brain is driven by neighboring genes and diversifies the neural transcriptome

Project description:Bulk mRNA sequencing reads, mapped to masked Drosophila reference genome

2020-09-17 | GSE150413 | GEO

Intron evolution in Saccharomycetaceae

Project description:Purpose: Saccharomyceatacea yeast are intron-poor species and they contain on average 300 introns in their genomes. We designed RNAseq experiment to investigate if splicing patterns in related yeast species are similar. Methods: Total RNA was extracted from wild type cells and processed by the RiboMinus Transcriptome Isolation Kit for Yeast and Bacteria (Invitrogen) to deplete the rRNA. cDNA libraries were prepared according to manufacturer's protocol and sequenced by SOLiD. Sequence reads were filtered and processed by TopHat. Results: We found 216, 163, 200 and 155 predicted introns with canonical splice signals in S. cerevisiae, S. kudriavzevii, S. bayanus and N. castellii respectively. Three introns in S. cerevisiae, four in S. bayanus and ten in S. castellii are novel compared to Saccharomyces Genome Database (SGD) annotations. The expression of introns and splicing shows very high correlation between species. Conclusion: Transcripts with introns in yeast species tested show similar levels of expression and splicing. We found few novel introns, which are conserved in yeast genomes.

2014-09-15 | GSE58884 | 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