Ontology highlight
ABSTRACT:
PROVIDER: PRJNA778188 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR16811200_1.fastq.gz | Fastqsanger.gz | |||
| SRR16811200_2.fastq.gz | Fastqsanger.gz | |||
| SRR16811194_1.fastq.gz | Fastqsanger.gz | |||
| SRR16811194_2.fastq.gz | Fastqsanger.gz | |||
| SRR16811195_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 14 |
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Project description:We established three new rhesus embryonic stem cells lines and conducted their microRNA profilings by Solexa sequencing. Sequencing of small RNA libraries yielded 12.66 million, 13.12 million and 11.57 million raw reads from IVF1.2, IVF3.2 and IVF3.3, respectively. After filtering, we obtained 10.89 million (IVF1.2), 10.60 million (IVF3.2) and 9.26 million (IVF3.3) clean reads (18-30nt).
2011-06-20 | GSE27886 | GEO
Project description:The goal of this study is to identify the pathway alterations driving the adaptive resistance to PI3K inhibition in GBM. We generated the resistant cell lines through a patient-derived in vivo glioma sphere-forming cell (GSC) model. We performed RNA-seq on the paired GSC samples including the parental and resistant groups. Libraries were sequenced with an average coverage for each tumor of 50x on the Hiseq4000 platform from Illumina, using 76 nt pair-ended reads. RNA-seq raw data were pre-processed using PRADA. PRADA aligned RNA-seq reads to a composite reference database composed of whole genome and transcriptome sequences; we used the hg19 human genome assembly, together with the Ensembl64 transcriptome version. Transcripts were filtered for size and protein-coding genes. Expression data were normalized to reads per kilobase per million reads, and these values were log2-transformed for further analyses.
2018-12-14 | GSE123821 | GEO
Project description:The fungal skin disease chytridiomycosis has caused the devastating decline and extinction of hundreds of amphibian species globally, yet the potential for evolving resistance, and the underlying pathophysiological mechanisms remain poorly understood. We exposed 406 naïve, captive-raised alpine tree frogs (Litoria verreauxii alpina) to the aetiological agent Batrachochytrium dendrobatidis in two concurrent and controlled infection experiments. We investigated (A) survival outcomes and clinical pathogen burdens between populations and clutches, and (B) individual host tissue responses to chytridiomycosis. Here we present multiple interrelated datasets associated with these exposure experiments, including animal signalment, survival and pathogen burden of 355 animals from Experiment A, and the following datasets related to 61 animals from Experiment B: animal signalment and pathogen burden; raw RNA-Seq reads from skin, liver and spleen tissues; de novo assembled transcriptomes for each tissue type; raw gene expression data; annotation data for each gene; and raw metabolite expression data from skin and liver tissues. These data provide an extensive baseline for future analyses.
2018-01-09 | MTBLS457 | MetaboLights
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived PG and their healthy progenitor lines transcriptome profiling (RNA-seq) to proteomic methods (iTRAQ) and to evaluate these protocols for optimal high-throughput data analysis Methods: The raw RNA-Seq reads for each sample were aligned to the reference human genome browser (GRCh38.p12 assembly) using Bowtie2 and Tophat2. Results: An average of 23 million paired-end 100-bp reads was obtained per sample. After alignment, raw sequence read depths were converted to estimate transcript abundance measured as fragments per kilobase of exons per million (FPKM), and the cuffinks of differentially expressed genes and transcripts were calculate with Cuffdidd. Conclusions: Our study represents a detailed analysis of human PG lines transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell pathological line. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2018-04-30 | GSE113648 | GEO
Project description:We used complementing high-throughput sequencing technologies, including RNA-seq, DNase I-seq, and ChIP-seq, to examine the distribution of DNase I hypersensitive sites and H3K4me2 histone marks, to infer transcription factor binding sites, to identify key pathways in Gefinitib resistant and sensitive cell lines, PC9R and PC9 respectively, and to verify that some of up-regulated genes in PC9R can be found in Gefinitib resistant patients, which indicated that our study has valid clinical relevance. Total mRNA profiles of PC9 and PC9R cell lines were generated by deep sequencing, in duplicate, using Illumina Hiseq2000.The mapping results of RNA-seq reads by the TopHat algorithm (version 1.1.4) to the hg19 reference genome were used to count the number of reads per gene with the hgseq-count algorithm. Then, DESeq was used to detect the genes differentially expressed between the PC9 and PC9R samples, with a p-value of 0.05 as the threshold. DNase-Seq and ChIP-Seq raw reads were aligned with Bowtie0.12.9 to hg19. MACS2 (https://github.com/taoliu/MACS/downloads) was employed to call PC9-specific regions using PC9 cells as the treatment and PC9R cells as the control, with a q-value threshold (DNase-Seq: 10-2; ChIP-Seq: 10-3) to minimise false positives; then, the process was repeated with the treatment and control cell lines switched.
2017-11-20 | GSE63356 | GEO
Project description:We established three new rhesus embryonic stem cells lines and conducted their microRNA profilings by Solexa sequencing. Sequencing of small RNA libraries yielded 12.66 million, 13.12 million and 11.57 million raw reads from IVF1.2, IVF3.2 and IVF3.3, respectively. After filtering, we obtained 10.89 million (IVF1.2), 10.60 million (IVF3.2) and 9.26 million (IVF3.3) clean reads (18-30nt). Examination of 3 different small RNA expression profilings in 3 rhesus embryonic stem cell lines
2011-06-19 | E-GEOD-27886 | biostudies-arrayexpress
Project description:Morus alba L. Raw protein sequence reads and sequence
2024-02-08 | PXD049451 |
Project description:Subcellular RNA-seq datasets are used for genome-wide analysis of circRNAs in 293FT cells. Here we knocked out circRNAs by base editor (BE)-mediated nucleotide changes.
2021-12-01 | GSE172193 | GEO
Project description:Bordel2018 - GSMM for Human Metabolic
Reactions (HMR database)
This model is described in the article:
Constraint based modeling of
metabolism allows finding metabolic cancer hallmarks and
identifying personalized therapeutic windows
Sergio Bordel
Oncotarget. 2018; 9:19716-19729
Abstract:
In order to choose optimal personalized anticancer
treatments, transcriptomic data should be analyzed within the
frame of biological networks. The best known human biological
network (in terms of the interactions between its different
components) is metabolism. Cancer cells have been known to have
specific metabolic features for a long time and currently there
is a growing interest in characterizing new cancer specific
metabolic hallmarks. In this article it is presented a method
to find personalized therapeutic windows using RNA-seq data and
Genome Scale Metabolic Models. This method is implemented in
the python library, pyTARG. Our predictions showed that the
most anticancer selective (affecting 27 out of 34 considered
cancer cell lines and only 1 out of 6 healthy mesenchymal stem
cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
all the considered cell lines can be selectively affected by
targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
of drugs or siRNAs combined in patient specific manners could
be at the core of metabolism based personalized treatments.
This model is hosted on
BioModels Database
and identified by:
MODEL1707250000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
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| MODEL1707250000 | BioModels