Ontology highlight
ABSTRACT:
PROVIDER: PRJNA714737 | ENA |
REPOSITORIES: ENA
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Project description:The Affymetrix oligonucleotide microarrays measure gene expression by quantifying intensity of fluorescently labeled gene fragments that bind to sets of 25-mer oligonucleotide probes on the chip with specific sequences tailored to be complementary to the target genes. Each gene is associated with a "probe set" containing several pairs (usually 11) of "perfect match" (perfectly complementary to target sequence) and "mismatch" (different base at position 13 of 25) probes. The raw measurements of each probe set consist of a set of intensities from the probes, which require in silico preprocessing by (1) correcting for background variability, (2) normalizing intensities across samples, and (3) summarizing intensities across the probe set into a single expression value. The output of the summarization step corresponds to the background-adjusted value for the mRNA of interest. We preprocess using GCRMA, which corrects for background variability by accounting for optical noise, probe affinity, and mismatch probe adjustment; normalizes intensities by quantile normalization; and summarizes intensities using a median polish method. To minimize preprocessing batch effects, it is desirable to preprocess all samples in the dataset together. However, preprocessing across multple platforms requires a consolidation of probes with identical sequences, precluding global preprocessing on datasets with multiple platforms using the standard preprocessing pipelines. To address this problem, we have developed and applied a custom preprocessing pipeline to combine the raw .CEL files from multiple platforms that share the same probe sets.
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Project description:Purpose: The goal of this study are to investigate the TRPM1-regulated genes using RNAseq to compare the transcriptome profiling between 661W cells expressing TRPM1 and control vectors Methods: RNAs were isolatedusing the RNeasy Mini kit (Qiagen). RNA-seq libraries were prepared using the KAPA mRNA HyperPrep Kit (KAPA Biosystems, Roche, Basel, Switzerland) and validated using the Qsep 100 DNA/RNA Analyzer (BiOptic Inc., Taiwan). Libraries were sequenced on a NovaSeq 6000 sequencer (Illumina, CA, USA). Clean reads were aligned to the mouse genome (GRCm38) using HISAT2 (version 2.1.0) after removing low-quality reads. The differential expression of genes between TRPM1-overexpression and control cells was computed using the fragments per kilobase of transcript per million mapped reads calculated by featureCounts (version 2.0.0). Raw read counts were imported into edgeR (version 3.28.1) and analyzed by using R package of DESeq (version 1.40.0). Genes with false discovery rate (FDR) p-value < 0.05 adjusted by using Benjamini–Hochberg (BH) method were considered as differentially expressed genes (DEGs). Gene set enrichment analysis of the genes differentially expressed upon TRPM1 expression was done using the Gene Set Knowledgebase（GSKB）hallmark gene sets. Results: We had 40,922,040 clean reads in the control group and 44,244,608 clean reads in the TRPM1-overexpressing group. We mapped 43,253, 668 (97.76%) sequence reads in the control group and 39,942,649 (97.6%) sequence reads in the TRPM1-overexpressing group. We identified 16,014 transcripts. 76 transcripts showed differential expression between the vector control group and TRPM1-expressing group, with a fold change ≥1.5 and p value <0.01. Gene set enrichment analysis of the genes differentially expressed upon TRPM1 expression uncovered several TRPM1-regulated genes that may contribute to photoreceptor function, such as retina morphogenesis and JAK-STAT cascade. Conclusions: Our study represents the genes associated with TRPM1 overexpression in 661W photoreceptor cells using RNA-seq approach. The overexpression of TRPM1 may contribute to regulate the photoreceptor morphogenesis and function
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Project description:To better understand the mechanism by which MAGEA3 contributes to HCC progression, we conducted RNA sequencing in the PLC5 HCC cell line after 72 hours of treatment with scramble or sh8375 in triplicate. RNA-sequencing was conducted on poly-A enriched RNA, 100 bp single reads using an Illumina HiSeq2500 instrument. Libraries were constructed using the TruSeq RNA Library Prep Kit v2. Raw sequencing reads were mapped to the GRCh38 reference genome (USCS) using STAR (2.4.2g1). Aligned reads were mapped to GRCh38 genetic features using featureCounts from the subRead package with default settings. Data analysis included differential gene expression between conditions and gene set enrichment analysis.
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Project description:Purpose: identify global changes in gene expression in C2C12 myotubes caused by an increase in Crebrf. Methods: Triplicate replicates of differentiated C2C12 myotubes expressing either Gfp or Crebrf for 3 days after transduction. After assessing RNA quality with Agilent Bioanalyzer, mRNAs were enriched by poly-A pull-down. Then, sequencing libraries constructed with Illumina TruSeq RNA prep kit were sequenced using. We multiplexed samples in each lane, which yields targeted number of single-end 75 bp reads for each sample, as a fraction of 180 million reads for the whole lane. Sequence reads were mapped back to the Drosophila genome (flybase genome annotation version r6.30) using STAR. With the uniquely mapped reads, we quantified gene expression levels using Cufflinks (FPKM values). Next, differentially expressed genes between experimental and control data were analyzed with DESeq2. Results: Gene set enrichment analysis reveals oxidative phosphorylation and interferon response as the top up- and down-regulated gene sets, respectively, upon Crebrf expression. The glycolysis gene set is also reduced. These gene expression findings parallel our metabolic observations in Seahorse analysis. Conclusions: Our study indicates that CREBRF is a strong regulator of muscle metabolism in C2C12 myotubes
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