RNA-seq analysis of Huh7 cells based on NME7 knockdown
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE182142 | GEO | 2021/08/22
REPOSITORIES: GEO
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Project description:Purpose: Analysis of the regulatory network involved in PANK1 in liver cancer cells . Methods: mRNA profiles of Huh7-shPANK1 and Huh7-shNC group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 681 differential genes, of which 441 were up-regulated and 240 were down-regulated. In GO enrichment analysis, the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in the defense response to virus、defense response to other organism and receptor ligand activity. KEGG analysis shows that differential genes are enriched in Systemic lupus erythematosus , Alcoholism and influenza A pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in PANK1 in liver cancer is depicted.
2021-04-30 | GSE173621 | GEO
Project description:Purpose: Analysis of the regulatory network involved in PPDPF in colon cancer cells. Methods: mRNA profiles of HT8-cas1 and HT8 group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate .Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 3233 differential genes, of which 1877 were up-regulated and 1356 were down-regulated. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in viral life cycle,response to virus,neutrophil activation, and the transmembrane signaling receptor activity. KEGG analysis shows that differential genes are enriched in Epstein-Barr virus infection ,Glutathione metabolism and Proteasome pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in PPDPF in colon cancer .
2021-04-30 | GSE173575 | GEO
Project description:Purpose: Analysis of the regulatory network involved in SIK2 in breast cancer cells activated by the Wnt/β-catenin pathway. Methods: mRNA profiles of BT549-shSIK2 and BT549-Scrambled group that Wnt3a treated for 2 hours were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 1709 differential genes, of which 1147 were up-regulated and 562 were down-regulated. In GO enrichment analysis, the biological process is mainly in the cell ions homeostasis, the cell components are mainly enriched on the cell surface, and the molecular function is mainly in the transmembrane signaling receptor activity. KEGG analysis shows that differential genes are enriched in Axon guidance and Nicotinate and nicotinamide metabolism pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in SIK2 in breast cancer response to Wnt signaling is depicted.
2021-03-24 | GSE169344 | GEO
Project description:Purpose: Analysis of the regulatory network involved in DSTY in lung cancer cells. Methods: mRNA profiles of A549 knockdown cell group and A549 NC group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate .Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 2555 differential genes, of which 1444 were up-regulated and 1111 were down-regulated in A549 sh1 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in inflammation response,chemotaxis,innate immune response, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,JAK-STAT signaling pathway and PI3K-Akt signaling pathways.We identified 2600 differential genes, of which 1523 were up-regulated and 1077 were down-regulated in A549 sh4 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in chemokine receptor binding,nic hydroxy compound metabolic process,alcohol metabolic process, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,glutathione metabolism and chemical carcinogenesis. Conclusion: Based on RNA-seq analysis, the regulatory network involved in DSTY in Lung cancer .
2021-06-19 | GSE178487 | GEO
Project description:Skin mRNA profiles of wild-type (WT) and EGFR Inhibitors (Gefitinib and Afatinib) induced rash rats were generated by deep sequencing, in triplicate, using Illumina GAIIx. Raw sequences were mapped to the rat reference sequence by Hisat2 v2.0.5. FeatureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis was performed using the DESeq2 R package (1.16.1).
2022-04-04 | GSE199970 | GEO
Project description:To identify differentially expressed mRNAs after knockout of YTHDF2 in granule cells of mouse hippocampus dentate gyrus, we perfromed RNA-seq. Total RNA from DG deficient in YTHDF2 and the negative control DG were collected and subjected to construct sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB). After sequencencing, the filtered reads were mapped to the mouse reference genome (GRCm38) using HISAT2 v2.0.5 with default parameters. The resulting bam files were fed to featureCounts v1.5.0-p3 to count the number of RNA-seq reads, which was further normalized to calculate FPKM. Differential expression analysis of two groups (cKO vs NC) (three biological replicates per group) was performed using the DESeq2 package (version 1.20.0). Altogether 779 mRNA were differentially regulated, among which 536 mRNA were upregulated after YTHDF2 KD. This study provides a gene list which shows differentially expressed mRNAs after knockout of YTHDF2 in granule cells of mouse hippocampus dentate gyrus.
2023-01-19 | GSE171790 | GEO
Project description:Purpose: Conditional knockout of Zfp36l1 Zfp36l2 early in lymphocyte development leads to a bypass of beta-selection and subsequently T cell acute lymphoblastic leukemia. This RNA seq experiment aimed to determine the molecular pathways affected by loss of Zfp36l1 and Zfp36l2, and to deduce direct targets of these RNA binding proteins. Methods: RNA was isolated from sorted Zfp36l1fl/fl; Zfp36l2fl/fl DN3a (Lineage-negative, CD44-, Kitlow, CD25+, CD98low) and DN3b (Lineage-negative, CD44-, Kitlow, CD25intermediate, CD98+) cells as well as Zfp36l1fl/fl; Zfp36l2fl/fl; CD2cre DN3 (Lineage-negative, CD44-, Kitlow, CD25+) cells with the RNeasy Micro Kit (Qiagen). RNAseq libraries were prepared from 20-200ng RNA using the TruSeq Stranded Total RNA and rRNA Removal Mix â?? Gold from Illumina. Libraries were sequenced by Hiseq in 100bp single-end reads. The reads were trimmed to remove adapter sequences using Trim Galore then mapped using Tophat (version 2.0.12) to the GRCm38 mouse assembly; reads with an identical sequence to more than one genomic locus were not mapped. Quality control analysis was carried out with FastQC. Reads were counted using htseq-count tool and mouse gtf file version 78. Results: Differences in the abundance of transcripts between DCKO and control samples were calculated in the R/Bioconductor program DESeq2 (version 1.6.3). Adjusted P values for differential expression were calculated in DESeq2 using a Benjamini-Hochberg correction: genes with an adjusted p-value of less than 5% were considered significant. Differentially expressed mouse transcripts identified using DESeq2 were analyzed for gene set enrichment using Toppfun. Conclusions: We identified an enrichment of mRNAs involved in cell cycle progression within Zfp36l1 Zfp36l2 double conditional knockouts. 4 biological replicates of control DN3a, control DN3b and DCKO DN3-like cells were analyzed
2016-09-05 | E-GEOD-79174 | biostudies-arrayexpress
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare the difference of mRNA between si-nc and si-SRSF11 in LOVO cell line Methods: LOVO mRNA profiles of si-nc and si-SRSF11 were generated by deep sequencing, in triplicate, using Illumina Novaseq . The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Sample collection and preparation RNA quantification and qualification 1RNA degradation and contamination was monitored on 1% agarose gels. 2RNA purity was checked using the NanoPhotometer spectrophotometer (IMPLEN, CA, USA) . 3RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Library preparation for Transcriptome sequencing A total amount of 1 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer(5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase(RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 250~300 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. Clustering and sequencing (Genchem Experimental Department) The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. Data Analysis Quality control Raw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. All the downstream analyses were based on the clean data with high quality. Reads mapping to the reference genome Reference genome and gene model annotation files were downloaded from genome website directly. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. We selected Hisat2 as the mapping tool for that Hisat2 can generate a database of splice junctions based on the gene model annotation file and thus a better mapping result than other non-splice mapping tools. Novel transcripts prediction The mapped reads of each sample were assembled by StringTie (v1.3.3b) (Mihaela Pertea.et al. 2015) in a reference-based approach. StringTie uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate fulllength transcripts representing multiple splice variants for each gene locus. Quantification of gene expression level featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels. Differential expression analysis (For DESeq2 with biological replicates) Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). DESeq2 provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. (For edgeR without biological replicates) Prior to differential gene expression analysis, for each sequenced library, the read counts were adjusted by edgeR program package through one scaling normalized factor. Differential expression analysis of two conditions was performed using the edgeR R package (3.18.1). The P values were adjusted using the Benjamini & Hochberg method. Corrected P-value of 0.05 and absolute foldchange of 2 were set as the threshold for significantly differential expression. GO and KEGG enrichment analysis of differentially expressed genes Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the clusterProfiler R package, in which gene length bias wascorrected. GO terms with corrected Pvalue less than 0.05 were considered significantly enriched by differential expressed genes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-through put experimental technologies (http://www.genome.jp/kegg/). We used clusterProfiler R package to test the statistical enrichment of differential expression genes in KEGG pathways. SNP analysis GATK2 (v3.7) software was used to perform SNP calling. Raw vcf files were filtered with GATK standard filter method and other parameters (cluster:3; WindowSize:35; QD < 2.0 o; FS > 30.0; DP < 10 and SnpEff software was used to annotation for the Variablesite. AS analysis Alternative Splicing is an important mechanism for regulate the expression of genes and the variable of protein. rMATS(3.2.5) software was used to analysis the AS event. PPI analysis of differentially expressed genes PPI analysis of differentially expressed genes was based on the STRING database, which known and predicted Protein-Protein Interactions. Weighted correlation network analysis WGCNA (Weighted correlation network analysis) is a systematic biological method used to describe the gene association modes among different samples. it can be used to identify gene sets that are highly synergistic changed, and identify candidate biomarkers or therapeutic targets based on the coherence of gene sets and the correlation between gene sets and phenotypes. The R package WGCNA is a set of functions used to calculate various weighted association analysis, which can be used for network construction, gene screening, gene cluster identification, topological feature calculation, data simulation and visualization. WGCNA is suitable for multi- sample data. Generally, more than 15 samples are required. One input file is sample information, that is, a matrix describing the traits of the sample: the traits used for association analysis must be numeric; If it is a regional or categorical variable, it needs to be converted to a 0-1 matrix. The other is gene expression data. For transcriptome sequencing ,FPKM can be used as gene expression data.
2022-12-01 | GSE199094 | GEO
Project description:Purpose: The goal of this study is to compare NGS-derived wild type and Hnrnpul1 knockout (Hnrnpul1-/-) RAW 264.7 cells transcriptomes with or without LPS stimulation. Methods: Sequancing was performed by Novogene China Co. Ltd. RNA profiles of wild type and Hnrnpul1-/- RAW 264.7 cells as well as LPS stimulated (10 h) wild type and Hnrnpul1-/- RAW 264.7 cells were generated by deep sequencing using Illumina Novaseq 6000. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Method of TMM was used to normalize the readcount. Negative binomial distribution model was used to calculate the P value, and FDR was calculated by the method of Benjaminiand Hochberg. Results: Using an optimized data analysis workflow, we mapped about 40 million sequence reads per sample to the mouse genome (GRCm38/mm10). Comparing to wild type RAW 264.7 cells, 237 genes were up-regulated and 181 genes were down-regulated in Hnrnpul1-/- cells. At 10 h following LPS stimulation, 341 genes were up-regulated and 288 genes were down-regulated in Hnrnpul1-/- cells. Genes were pre-ranked according to log2FoldChange(KO/WT) followed by GSEA and 6 gene sets were significantly enriched. Significantly differential genes were undergone GO analysis (biological process) and biological process including cell-cell adhesion, positive regulation of cell activation and regulation of response to external stimulus were enriched. Conclusions: Lacking Hnrnpul1 promotes the expression of inflammatory cytokines in LPS stimulated RAW 264.7 cells.
2022-06-01 | GSE196232 | GEO
Project description:Purpose: To obtain an overview of the metabolic changes associated to the absence of KpsM in Synechocystis, in particular on carbon-related metabolic pathways Methods: The trancriptomes of Synechocystis wild type and kpsM mutant were analyzed by RNA sequencing, using three biological replicates. Cultures were grown until reaching an OD730nm of 1.5. Sequencing libraries were generated using NEBNext® Ultra TM RNA Library Prep Kit for Illumina®. PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina). Raw data (raw reads) of FASTQ format were firstly processed through in-house scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter and poly-N sequences and reads with low quality from raw data. Paired-end clean reads were mapped to the reference genome using HISAT2 software.HTSeq was used to count the read numbers mapped of each gene, including known and novel genes. And then RPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. RPKM, Reads Per Kilobase of exon model per Million mapped reads, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels. Differential expression analysis between two conditions/groups (three biological replicates per condition) was performed using DESeq2 R package. DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochberg’s approach for controlling the False Discovery Rate (FDR). Genes with an adjusted P value < 0.05 found by DESeq2 were assigned as differentially expressed. Results: Approximately 700 genes (out of the 3636 identified and 215 quantified, representing a coverage of 85,12%) were significantly differentially expressed (P 216 value < 0.05) in the kpsM mutant compared to the wild type. Of those, 406 were down217 regulated, while 297 were up-regulated. Conclusions: Overall, our transcriptomic data indicates alterations in the mechanisms of energy production and conversion in the kpsM mutant compared to the wild type. The approach resulted in the identification of altered levels of transcripts belonging to a variety of functional categories and highlighting a number of key metabolic processes affected by the disruption of kpsM, namely photosynthesis, oxidative phosphorylation and carbon metabolism.
2021-01-20 | GSE165073 | GEO