Ontology highlight
ABSTRACT:
PROVIDER: PRJNA714677 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:To evaluate the gene expression profiling of peripheral leukocytes in different outcomes of SARS-CoV-2 infections, whole blood samples were collected from individuals with positive SARS-CoV-2 nasopharyngeal swab by RT-PCR (54 patients) and healthy uninfected individuals (12 volunteers). Infected patients were classified into mild, moderate, severe and critical groups according to a modified statement in the Novel Coronavirus Pneumonia Diagnosis and Treatment Guideline. Blood were collected into EDTA tubes and the buffy coat samples were stored in TRIzol reagent at -80 °C until use for RNA extraction. Affymetrix Clariom S array was used to perform the high-throughput gene expression profiling. Microarray analyses were performed using APT Affymetrix software, R packages and Bioconductor libraries. This systemic view of SARS-CoV-2 infections through blood transcriptomics will foster the understanding about molecular mechanisms and immunopathological processes involved in COVID-19 disease and its different outcomes.
2022-04-29 | E-MTAB-11240 | biostudies-arrayexpress
Project description:Set of microarray experiments used to identify an unknown coronavirus in a viral culture derived from a patient with SARS. March 2003. Keywords = SARS Keywords = coronavirus Keywords = viral discovery Keywords = viruses Keywords = respiratory infection
2010-06-05 | E-GEOD-546 | biostudies-arrayexpress
Project description:Differential expression was determined in Calu-3 cells between mock infected and infection with either Human coronavirus EMC and SARS coronavirus at different times post infection. Calu-3 2B4 cells were infected with Human Coronavirus EMC 2012 (HCoV-EMC) or mock infected. Samples were collected 0, 3, 7, 12, 18 and 24 hpi. There are 3 mock and 3 infected replicates for each time point, except for 12 hpi for which there are only 2 infected replicates (one replicate did not pass RNA quality check). There were no mock sampes at 18 hpi, and therefore infected samples at 18 hpi were compared with mocks at 24 hpi. For direct comparison with SARS-CoV infected cells, raw data from HCoV-EMC experiments were quantile normalized together with the SARS-CoV dataset (GEO Series accession number GSE33267).
2013-05-03 | E-GEOD-45042 | biostudies-arrayexpress
Project description:Short-read DNA sequencing technologies provide new tools to answer biological questions. However, high cost and low throughput limit their widespread use, particularly in organisms with smaller genomes such as S. cerevisiae. Although ChIP-Seq in mammalian cell lines is replacing array-based ChIP-chip as the standard for transcription factor binding studies, ChIP-Seq in yeast is still underutilized compared to ChIP-chip. We developed a multiplex barcoding system that allows simultaneous sequencing and analysis of multiple samples using Illumina’s platform. We applied this method to analyze the chromosomal distributions of three yeast DNA binding proteins (Ste12, Cse4 and RNA PolII) and a reference sample (input DNA) in a single experiment and demonstrate its utility for rapid and accurate results at reduced costs. We developed a barcoding ChIP-Seq method for the concurrent analysis of transcription factor binding sites for yeast. Our multiplex strategy generated high quality data that was indistinguishable from data obtained with non-barcoded libraries. None of the barcoded adapters induced differences relative to a non-barcoded adapter when applied to the same DNA sample. We used this method to map the binding sites for Cse4, Ste12 and Pol II throughout the yeast genome and we found 148 binding targets for Cse4, 823 targets for Ste12 and 2508 targets for PolII. Cse4 was strongly bound to all yeast centromeres as expected and the remaining non-centromeric targets correspond to highly expressed genes in rich media, the latter constituting a novel finding. We designed a multiplex short-read DNA sequencing method to perform efficient ChIP-Seq in yeast and other small genome model organisms. This method produces accurate results with higher throughput and reduced cost. Given constant improvements in high-throughput sequencing technologies, increasing multiplexing will be possible to further decrease costs per sample and to accelerate the completion of large consortium projects such as modENCODE.
2008-10-24 | GSE13322 | GEO
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent responsible for the ongoing pandemic of coronavirus disease 2019 (COVID-19). Single cell RNA sequencing (scRNAseq) studies have been valuable for studying SARS-CoV-2 pathogenesis, but the performance of these methods to detect and quantify viral RNAs has not been evaluated. Here we develop an analysis pipeline, single cell coronavirus sequencing (scCoVseq), to quantify unambiguous reads derived from coronavirus genomic (gRNA) and subgenomic mRNAs (sgmRNAs). We use this method to compare the ability of scRNAseq methods developed by 10X Genomics to detect and quantify SARS-CoV-2 RNAs, with particular focus on sgmRNAs. We find that while sequencing libraries from 10X Genomics Chromium Next Gem Single Cell V(D)J (10X 5') contain more unambiguous reads derived from sgmRNAs due to the presence of reads spanning junctions between the viral 5' leader and sgmRNA open reading frames. We demonstrate that by extending read 1 (R1) of 10X 5' leaders we can further increase the number of unambiguous reads from SARS-CoV-2 sgmRNA resulting in more UMIs per sgmRNA per cell. Using this method, which we call 10X 5' Extended R1, we are able quantify viral sgmRNA production per cell, which we believe will improve understanding regarding the dynamics of coronavirus RNA biogenesis over time and across cell types and viruses. We believe this will improve our understanding of coronavirus pathogenesis.
2023-07-30 | GSE189900 | GEO
Project description:We performed genome-wide CRISPR KO screens in human Huh7.5.1 cells to select for mutations that render host cells resistant to viral infection by SARS-CoV-2, human coronavirus 229E and OC43.
2020-10-16 | E-MTAB-9638 | biostudies-arrayexpress
Project description:Genomic RNA from SARS-Related Coronavirus 2
| PRJNA639551 | ENA