Human Genome U133 Plus 2.0 GeneChip (Affymetrix Inc., Santa Clara, CA, USA) was used for genome-wide mRNA expression profiling. Total RNA was isolated using TRIZOL Reagent (Invitrogen), and the quality of total RNA was assessed with the Agilent 2100 Bioanalyzer Platform. Double-stranded cDNA was synthesized by reverse transcription from total RNA, and then in vitro transcription was performed to produce biotin-labeled cRNA from the cDNA. The cRNA is fragmented before hybridization. After washing and staining with the GeneChip Fluidics Station 450 (Affymetrix), the genechips were scanned with the GeneChip Scanner 3000 (Affymetrix). Scanned output files were analyzed with GeneSpring GX 12.0 software (Agilent Technologies, Inc., Santa Clara, CA, USA). A fold difference of > 2.0 was defined as differentially expressed. IPA software (Ingenuity Systems, Redwood City, CA, USA) was used for pathway analysis. The Gene ID of the differentially expressed genes in KYSE410-I3 cells and the corresponding fold-change were uploaded into IPA software (Ingenuity Systems, Redwood City, CA, USA). The Core Analysis tool, Gene Ontology analysis, and the Fisher's Exact Test in IPA were used to identify statistically significant associations between differentially expressed genes and cellular/molecular pathways. We configured the core analysis to report Benjamini–Hochberg corrected P values.
Ipa software
Manufactured by Qiagen
Sourced in United States, Germany, China, Netherlands
IPA software is a bioinformatics application developed by Qiagen. It is designed to analyze and interpret complex biological and chemical data.
Automatically generated - may contain errors
Lab products found in correlation
Trizol reagent, by Thermo Fisher Scientific (7 mentions)
Rneasy mini kit, by Qiagen (5 mentions)
Nextseq 500, by Illumina (4 mentions)
Ingenuity pathway analysis (ipa), by Qiagen (4 mentions)
Rneasy plus micro kit, by Qiagen (3 mentions)
Agilent 2100 bioanalyzer, by Agilent Technologies (3 mentions)
Hiseq 4000 system, by Illumina (3 mentions)
Ingenuity pathway analysis software, by Qiagen (3 mentions)
Nanodrop nd 1000 spectrophotometer, by Thermo Fisher Scientific (3 mentions)
Microtube caps, by Thermo Fisher Scientific (2 mentions)
Rlt plus buffer, by Qiagen (2 mentions)
Affymetrix mouse gene 1.1 st array plates, by Thermo Fisher Scientific (2 mentions)
Lmd6500 system, by Leica (2 mentions)
Nugen ovation pico wta system v2, by Tecan (2 mentions)
Genechip scanner 3000, by Thermo Fisher Scientific (2 mentions)
Expression console, by Thermo Fisher Scientific (2 mentions)
Nanodrop 2000, by Thermo Fisher Scientific (2 mentions)
Ingenuity pathway analysis ipa, by Qiagen (2 mentions)
Novaseq 6000, by Illumina (2 mentions)
Hiseq 2500, by Illumina (2 mentions)
351 protocols using ipa software
1
Genome-wide mRNA expression profiling
2
Upstream miRNAs Regulate Nerve Injury
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Expression of mapped genes was determined using the reads per kilobase transcriptome per million mapped reads (RPKM) method (Mortazavi et al., 2008). Gene expression levels at 1, 4, 7, and 14 days after crush injury were compared with expression levels at 0 day to screen for differentially expressed genes with a fold change > 2 or < –2 with a false discovery rate ≤ 0.001 (Yi et al., 2015).
Differentially expressed genes were uploaded to IPA software (Ingenuity Systems Inc., Redwood City, CA, USA) for core analysis to determine upstream miRNAs. Discovered upstream miRNAs were subjected to Venn diagram analysis (Venny 2.1.0 online tool;http://bioinfogp.cnb.csic.es/tools/venny/index.html ). Expression levels of upstream miRNAs after PNI identified in a previously published miRNA profiling of a rat sciatic nerve transection injury model (Yu et al., 2011) were displayed in a heatmap. Interactions between miRNAs, their target genes, and involved Gene Ontology (GO) terms were identified using Cytoscape software (Shannon et al., 2003). Mechanistic networks between miRNAs and mRNAs were built using the built-in Ingenuity Pathways Knowledge Base (IPKB) in IPA software (Ingenuity Systems Inc.).
Differentially expressed genes were uploaded to IPA software (Ingenuity Systems Inc., Redwood City, CA, USA) for core analysis to determine upstream miRNAs. Discovered upstream miRNAs were subjected to Venn diagram analysis (Venny 2.1.0 online tool;
3
Differential Gene Expression Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
DEGs were identified using adjusted p < 0.01 (FDR) and FC > 1.3. The DEGs were visualized using a volcano plot generated with EnhancedVolcano R package (GitHub). DEGs were also subjected to network analysis using Ingenuity Pathway Analysis (IPA) software (QIAGEN). Genes that were identified with p < 0.05 were subjected to gene set enrichment analyses using IPA software (QIAGEN). The significant values for the canonical pathways were calculated by Fisher’s exact test, and the top 10 pathways were visualized.
4
Network Analysis of Differential Gene Expression
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
To further identify molecular sub-networks associated with each comparison (WT-C vs. WT-D and WT-C vs. KO-D), we used QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA) software (QIAGEN Inc., https://www.qiagenbio-informatics.com/products/ingenuity-pathway-analysis ), as an alternative network analysis approach. We uploaded a list of gene symbols along with their corresponding edgeR adjusted P values and log fold changes into the software, and set the analysis parameter to include genes with FDR < 0.01. Briefly, IPA uses the Ingenuity Pathways Knowledge Base to identify networks of the genes based on their connectivity and ranks networks based on the assigned scores. These scores take into the account the size of the network and the number of genes to predict if a network is relevant. Once the networks are ranked based on their assigned scores, they are presented by graphs that indicate the molecular relationships between genes or gene products. Here genes are presented by nodes, and the biological relationships between pairs of genes are represented by edges.
5
Molecular Subnetwork Identification via IPA
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
To further identify molecular sub-networks associated with each comparison (WT-C vs.
WT-D and WT-D vs. KO-D), we used QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA) software (QIAGEN Inc., https://www.qiagenbio-informatics.com/products/ingenuity-pathway-analysis), as an alternative network analysis approach. We uploaded a list of gene symbols along with their corresponding edgeR adjusted P values and log fold changes into the software, and set the analysis parameter to include genes with FDR < 0.01. Briefly, IPA uses the Ingenuity Pathways Knowledge Base to identify networks of the genes based on their connectivity and ranks networks based on the assigned scores.
These scores take into the account the size of the network and the number of genes to predict if a network is relevant. Once the networks are ranked based on their assigned scores, they are presented by graphs that indicate the molecular relationships between genes or gene products. Here genes are presented by nodes, and the biological relationships between pairs of genes are represented by edges.
WT-D and WT-D vs. KO-D), we used QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA) software (QIAGEN Inc., https://www.qiagenbio-informatics.com/products/ingenuity-pathway-analysis), as an alternative network analysis approach. We uploaded a list of gene symbols along with their corresponding edgeR adjusted P values and log fold changes into the software, and set the analysis parameter to include genes with FDR < 0.01. Briefly, IPA uses the Ingenuity Pathways Knowledge Base to identify networks of the genes based on their connectivity and ranks networks based on the assigned scores.
These scores take into the account the size of the network and the number of genes to predict if a network is relevant. Once the networks are ranked based on their assigned scores, they are presented by graphs that indicate the molecular relationships between genes or gene products. Here genes are presented by nodes, and the biological relationships between pairs of genes are represented by edges.
6
Transcriptome Analysis of FSCN1 Depletion
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Differentially expressed genes from the microarray data (2.0 FC, p < 0.05 Adj) were imported into the IPA Software (Ingenuity Systems Inc., Germantown, MD, USA) as we previously described [53 (link)]. Functional regulatory networks and canonical pathways were determined using upstream regulator analysis (URA), downstream effects analysis (DEA), mechanistic networks (MN), and casual network analysis (CNA) prediction algorithms. Disease and function analysis were used to identify the disease and functional categories affected by FSCN1 depletion based on alteration in transcriptome data. The biological functions assigned to each network are ranked according to the significance of that biological function to the network [54 (link)].
7
Pathway analysis of Ppt1 deficiency
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The IPA software (Ingenuity systems) contains a library of biological pathways published in the literature that are ranked by the significance of the association between the dataset and the canonical pathway. This significance is defined by two parameters: (a) the ratio of the number of proteins from the input dataset that are pertaining to a particular pathway divided by the total number of genes ascribed by the Ingenuity Knowledge Database to that canonical pathway and (b) a P value calculated using Fischer’s test that determines whether the probability of association between component proteins in the input dataset and the canonical pathway are due to chance. Prediction activation scores (z score) are a statistical measure of the match between an expected relationship direction and the observed protein expression within the input dataset. A positive z score indicates activation while a negative z score indicate inhibitiony16 (link),18 (link),68 (link). A 1.2 fold-change (20%) threshold filter was applied in IPA to the datasets for 3 month cortex, 3 month spinal cord and 7 month spinal cord (Ppt1−/− vs. wildtype respectively), analyzed and observed interactions were selected for this analysis.
8
Antibody Microarray Data Analysis Protocol
9
Comparative RNA-seq of MEST Overexpression
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
RNA-seq was performed to compare the gene profiles of MEST-overexpressing cells and parental cells at the Beijing Genomics Institute Tech (Shenzhen, China), and genes with a fold change of >2.0 were defined as differentially expressed. IPA software (Ingenuity Systems, Redwood City, CA, USA) was used for pathway analysis.
10
Proteomic Analysis of Pregnancy Blood Coagulation
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Biological processes (BPs) were obtained by GO analysis of the differential proteins using the DAVID website. Biological pathway analysis and disease/biological function analysis were performed with differential proteins by IPA software (Ingenuity Systems, Mountain View, CA, USA). Visual mapping and data preprocessing were conducted on the WuKong platform (https://www.omicsolution.org/wkomics/main/ accessed on 20 December). The graph was generated by graphpad Prism 8 to compare the expression level changes of related proteins involved in the blood coagulation process, which was enriched by the IPA biological pathway, and the number of samples at each pregnant time point was n = 3 when inputting parameters.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!