Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

SUBMITTER: Ding Ma

PROVIDER: E-MTAB-10298 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Purpose: To explore myokines and signaling pathways in skeletal muscle dysfunction in a Cigarette Smoke-induced Model of Chronic Obstructive Pulmonary Disease Methods: Total RNA was extracted from the muscle tissues using Trizol (Invitrogen, Carlsbad, California, USA) according to manual instruction. About 60 mg of tissues were ground into powder by liquid nitrogen in a 2 mL tube, followed by being homogenized for 2 minutes and rested horizontally for 5 minutes. The mix was centrifuged for 5 minutes at 12,000×g at 4°C, and then the supernatant was transferred into a new EP tube with 0.3 mL chloroform/isoamyl alcohol (24:1). The mix was shacked vigorously for 15s, and then centrifuged at 12,000×g for 10 minutes at 4°C. After centrifugation, the upper aqueous phase where RNA remained was transferred into a new tube with equal volume of supernatant of isopropyl alcohol, then centrifuged at 13,600 rpm for 20 minutes at 4°C. After deserting the supernatant, the RNA pellet was washed twice with 1 mL 75% ethanol, and then the mix was centrifuged at 13,600 rpm for 3 minutes at 4°C to collect residual ethanol, followed by the pellet air dry for 5-10 minutes in the biosafety cabinet. Finally, 25μL~100μL of DEPC-treated water was added to dissolve the RNA. Subsequently, total RNA was qualified and quantified using a Nano Drop and Agilent 2100 bioanalyzer (Thermo Fisher Scientific, MA, USA). Results: The sequencing data were filtered with SOAPnuke (v1.5.2) (Li et al., 2008)by (1) Removing reads containing sequencing adapter; (2) Removing reads whose low-quality base ratio (base quality less than or equal to 5) was more than 20%; (3) Removing reads whose unknown base ('N' base) ratio was more than 5%, and afterwards clean reads were obtained and stored in FASTQ format. The clean reads were mapped to the reference genome using HISAT2 (v2.0.4)(Kim et al., 2015). Bowtie2 (v2.2.5)(Langmead and Salzberg, 2012) was applied to align the clean reads to the reference coding gene set，and then expression level of gene was calculated by RSEM (v1.2.12)(Li and Dewey, 2011). The heatmap was drawn by pheatmap (v1.0.8) according to the gene expression in different samples. Essentially, differential expression analysis was performed using the DESeq2(v1.4.5) with Q value ≤ 0.05. To take insight to the change of phenotype, GO (http://www.geneontology.org/) and KEGG (https://www.kegg.jp/) enrichment analysis of annotated different expressed gene was performed by Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) based on Hypergeometric test. The significant levels of terms and pathways were corrected by Q value with a rigorous threshold (Q value ≤ 0.05) by Bonferroni. Conclusions: Our study represents the first detailed analysis of retinal 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 or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

Project description:The goals of this study are to comprehensively identify genes controlled by Jmjd6 in the thymic stroma, and to identify a novel alternative splicing mechanism. Methods: Samples were WT and Jmjd6-/- fetal thymus organ culture with (2 samples for each category) or without (1 sample for each category) RANKL stimulation for 4 days under 2-DG. One Âµg of total RNA was used for library construction with TruSeq RNA Sample Prep Kit v2. THe ligated products were amplified using 8 cycles of PCR to generate RNA-seq library. Library integrity was verified by Bioanalyzer DNA1000 assay. Sequencing was performed in 101-bp paired-end mode using an Illumina HiSeq.Technical duplicate has done. Results: A total of 177,060,020 reads were obtained for 6 samples. Filtered reads were mapped to the UCSC mm10 using the TopHat program(v2.0.10) with the default parameters. The Cufflinks program (v2.1.1) was then used to assemble 22,448 transcripts and to calculate the fragments per kilobase of exon per million mapped fragments(FPKM) values, which are normalized measurement of gene expression levels(= genes-FPKM file).To identify differentially expressed genes, the ratio of the maximum FPKM to the minimum FPKM was compared among 6 samples. When the ratio was more than 3, the gene was regarded as being significantly altered in expression level. We added 0.1 to the FPKM value to avoid division by zero. This led us to identify 3212 genes with differential expression. Among these, the expression levels of 2536 genes were significantly associated with the RANKL treatment or Jmjd6 expression ( P value <0.05). Analysis for intron retention was performed as follows. According to the current gene annotation ("known genes" in UCSC mm10), there are 188,208 introns in total. As intron retention events should be observed in the genes with relatively high expression, we only focused on the genes with the maximum FPKM value more than 10 at least in one of the six samples. As a result, we obtained 84,708 introns. The reads mapped to these intronic regions were counted by the intersectBed program in the BEDTools utilities with -c option, and the counts are converted into the FPKM values for each intron (intronic FPKM). There are 1051 introns with intronic FPKM more than 10 in at last on of the six samples, and the degree of intron retention was calculated by dividing intronic FPKM value by conventional FPKM value for each gene (intron-FPKM file). Samples were WT and Jmjd6-/- fetal thymus organ culture with (2 samples for each category) or without (1 sample for each category) RANKL stimulation for 4 days under 2-DG. One Âµg of total RNA was used for library construction with TruSeq RNA Sample Prep Kit v2. THe ligated products were amplified using 8 cycles of PCR to generate RNA-seq library. Library integrity was verified by Bioanalyzer DNA1000 assay. Sequencing was performed in 101-bp paired-end mode using an Illumina HiSeq.Technical duplicate has done.

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

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