Project description:To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) rRNA was removed by using RNase H method, 2) QAIseq FastSelect RNA Removal Kit was used to remove the Globin RNA, 3) The purified fragmented cDNA was combined with End Repair Mix, then add A-Tailing Mix, mix well by pipetting, incubation, 4) PCR amplification, 5) Library quality control and pooling cyclization, 6) The RNA library was sequenced by MGI2000 PE100 platform with 100bp paired-end reads. Analysis steps: 1) RNA-seq raw sequencing reads were filtered by SOAPnuke (Li et al., 2008) to remove reads with sequencing adapter, with low-quality base ratio (base quality < 5) > 20%, and with unknown base (’N’ base) ratio > 5%. 2) Reads aligned to rRNA by Bowtie2 (v2.2.5) (Langmead and Salzberg, 2012) were removed. 3) The clean reads were mapped to the reference genome using HISAT2 (Kim et al., 2015). Bowtie2 (v2.2.5) was applied to align the clean reads to the transcriptome. 4)Then the gene expression level (FPKM) was determined by RSEM (Li and Dewey, 2011). Genes with FPKM > 0.1 in at least one sample were retained.

2021-03-24 | E-MTAB-10298 | biostudies-arrayexpress

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

Same disease, different outcomes: a retrospective cohort study of COVID-19-associated AKI across Brazil's dual-tiered healthcare system.

Project description: Not available

| S-EPMC12700444 | biostudies-literature

Salivary miRNA profiles in COVID-19 patients with different disease severity

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

2023-07-19 | GSE236017 | GEO

Longitudinal single-cell multiomics data for different severity of COVID-19

Project description:To trace immune responses in COVID-19 patients with severity, we performed in-depth, longitudinal single-cell multiomics involving T-cell receptor (TCR)/B-cell receptor (BCR) sequencing, feature barcoded antibody (Ab) panel detection (i.e., cellular indexing of transcriptomes and epitopes by sequencing, CITE-seq) followed by RNA sequencing in a single-cell resolution.

2025-01-27 | GSE196198 | GEO