The distribution of measured serum parameters within the analyzed pig population was visualized using R version 3.3.1. A mixed linear model (JMP Genomics 7.0, SAS Institute, Cary, NC, USA) including sex as fixed effect and sire as random effect was applied to estimate impact of sex on analyzed serum parameters (Supplementary Table S1 ). The association between SNPs and serum parameters was analyzed using a mixed linear model (JMP Genomics 7.0, SAS Institute, Cary, NC, USA). The model included SNP genotype and sex as fixed effect, sire as random effect, and slaughter weight as a covariate. Least square means for genotypes were compared by Tukey’s range test. Results were considered significant at p ≤ 0.05. A tendency was considered at p ≤ 0.10. In order to account for type I errors, multiple-testing correction was performed with JMP Genomics 7.0, whereby the false discovery rate (q value) was calculated (Storey and Tibshirani 2003 (link)). Allele and genotype frequencies were calculated and tested for Hardy-Weinberg equilibrium (HWE) by chi-square analysis using the package genetics in R. Significant deviation from HWE was assumed at p ≤ 0.05.
Jmp genomics 7
Manufactured by SAS Institute
Sourced in United States, Germany
JMP Genomics 7.0 is a software tool for analyzing and visualizing genomic data. It provides a suite of statistical and data mining techniques for tasks such as gene expression analysis, genome-wide association studies, and sequence analysis.
Automatically generated - may contain errors
Lab products found in correlation
Nanodrop nd 1000 spectrophotometer, by Thermo Fisher Scientific (2 mentions)
Rneasy mini kit, by Qiagen (2 mentions)
Rna nano chip, by Agilent Technologies (2 mentions)
2100 bioanalyzer, by Agilent Technologies (2 mentions)
Quick amp labeling kit, by Agilent Technologies (2 mentions)
Feature extraction software version 10, by Agilent Technologies (2 mentions)
Rnase free dnase set, by Qiagen (2 mentions)
Masterpure complete dna and rna purification kit, by Illumina (2 mentions)
Spss 22.0 for window, by IBM (1 mentions)
Hiscansq, by Illumina (1 mentions)
Totalprep rna amplification kit, by Thermo Fisher Scientific (1 mentions)
Humanht 12 v4 expression beadchip, by Illumina (1 mentions)
Genomestudio software, by Illumina (1 mentions)
Gene expression module, by Illumina (1 mentions)
Porcine snowball microarray, by Thermo Fisher Scientific (1 mentions)
Infinium assay, by Illumina (1 mentions)
Hiseq 2000, by Illumina (1 mentions)
18 protocols using jmp genomics 7
1
Genetic Influence on Pig Serum Profiles
2
Microbial Abundance Analysis of Rice Bran Treatments
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The log CFU/ml for control (feed only, cecal only, feed + cecal) and experimental treatments (feed + cecal + Jasmine, Red Wells, or Calrose rice bran) were determined by averaging all biological replicates and one way analysis of variance (ANOVA) was conducted to compare differences of bacterial population or relative abundance among groups with JMP Genomics 7.0 (SAS Institute Inc., Cary, NC) at P < 0.05. A Student’s t-test was also used to compare mean abundances of metabolites between Calrose and the “no rice bran” control (NC). P < 0.05 in a two-tailed test was considered significant.
3
Batch Correction for Transcriptomics
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Probes were filtered out if the detection p value was greater than 0.01 for at least 100% of the samples. All data values <10 were set to 10 and then the data was log2 transformed. An additional filter selecting the 75% most variable transcripts was performed, leaving a total 18,004 probes for analysis. Principal variance component analysis (PVCA) was conducted to identify undesirable sources of technical variability within the data and batch correction was applied to correct for this technical variation. Both PVCA and batch correction were conducted using JMP Genomics 7.0 (SAS Institute) analysis software.
4
Filtering and Batch Correction for Transcriptomics
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Probes were filtered out if the detection P value was greater than 0.01 for at least 100% of the samples. All data values <10 was set to 10 and then the data were log2 transformed. An additional filter selecting the 75% most variable transcripts was performed, leaving a total 18,004 probes for analysis. Principal variance component analysis (PVCA) was conducted to identify undesirable sources of technical variability within the data and batch correction was applied to correct for this technical variation. Both PVCA and batch correction were conducted using JMP Genomics 7.0 (SAS Institute) analysis software.
5
Bacterial Population Analysis in Chicken
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The averages of plate counts were converted to log CFU/chicken. One way analysis of variance (ANOVA) was performed to compare differences of bacterial population or relative abundance among groups with JMP® Genomics 7.0 (SAS Institute Inc., Cary, NC) at P < 0.05.
6
RNA Extraction, Labeling, and Microarray Analysis
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Total RNA was isolated using the RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol, including on-column DNA digestion (RNase-Free DNase Set; Qiagen Hombrechtikon, Switzerland). The quality of each RNA sample was measured on an RNA Nanochip with a Bioanalyzer 2100 (Agilent Technologies), and only high-quality RNA (RNA integrity number > 7.0) was used for gene expression analysis. RNA quantification was performed spectrophotometrically using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Fluorescently labeled cRNA was produced from 500 ng of total RNA using the Quick Amp Labeling Kit (Agilent Technologies) according to the manufacturer’s protocol. Differential gene expression profiling was performed by competitive dual-color hybridization on whole mouse genome oligo microarrays (mouse: G4846A, Agilent Technologies). Array slides were XDR-scanned and analyzed using the Feature Extraction Software Version 10.7.3.1 (Agilent Technologies). Statistical analysis and visualization were performed using JMP Genomics 7.0 (SAS Institute, Boeblingen, Germany). Full gene array data were submitted to the Gene Expression Omnibus.
7
Comprehensive RNA Profiling Workflow
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Total RNA was isolated using the RNeasy Mini Kit according to the manufacturer's instructions, which included an on‐column DNA digestion step (RNase‐Free DNase Set; Qiagen Hombrechtikon, Switzerland). To ensure that only high‐quality RNA (RNA integrity number >7.0) was used for gene expression analysis, each RNA sample was checked on an RNA Nanochip with a Bioanalyzer 2100 (Agilent Technologies). RNA was quantified spectrophotometrically with a NanoDrop ND‐1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Fluorescently labeled cRNA was generated from 500 ng total RNA with the Quick Amp Labeling Kit (Agilent Technologies) according to the manufacturer's protocol, and differential gene expression profiling was performed by competitive dual‐color hybridization on whole mouse or human Genome Oligo Microarrays (mouse: G4846A, human: G4845A, 4 × 44 K, Agilent Technologies). Array slides were XDR‐scanned and analyzed with Feature Extraction Software Version 10.7.3.1 (Agilent Technologies). Statistical analysis and visualization was performed with JMP Genomics 7.0 (SAS Institute, Boeblingen, Germany). Full gene array data were submitted to the gene expression omnibus.
8
TM6SF2 SNP and Hepatitis C Viral Load
9
Pharmacokinetic Analysis of Genetic Variants
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The data were analyzed with the statistical programs JMP Genomics 7.0 (SAS Institute, Inc., Cary, NC) and IBM SPSS 22.0 for Windows (Armonk, NY). The pharmacokinetic variables were logarithmically transformed before analysis. Sex, body weight, lean body weight,47 and body surface area48 were tested as demographic covariates for pharmacokinetic data using stepwise linear regression analysis, with P value thresholds of 0.05 for entry and 0.10 for removal. Possible effects of genetic variants on pharmacokinetic variables were investigated using linear regression analysis fixed for significant demographic covariates with a stepwise approach. A Bonferroni‐corrected P value threshold of 1.09 × 10−6 was employed for the 379 genes and thresholds of 0.05 for entry and 0.10 for removal for the candidate gene analysis. Additive coding was employed for genetic variants, and multiallelic variants were expanded. CYP2D6 data were included as the activity scores in the candidate gene analysis. Haplotype computations for SLCO1B1 were performed with PHASE v2.1.1.49 , 50 Statistical comparisons of in vitro data were done using independent samples Student's t‐test, with logarithmic transformation as appropriate.
10
Gene Expression Analysis of iBET72 Treated Cells
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Gene expression analysis was carried out in the Research Technologies Branch, NIAID. RNA integrity was verified using a Bioanalyzer. Five hundred nanograms of RNA was amplified and labeled using the Illumina TotalPrep RNA amplification kit (Applied Biosystems), hybridized to Illumina HumanHT-12 v4 Expression BeadChip, and scanned using the Illumina HiScan-SQ. Signal data were extracted from image files with the Gene Expression module (v.1.9.0) of the GenomeStudio software (v.2011.1) from Illumina, Inc. Signal intensities were converted to log2 scale. Analysis of variance (ANOVA) was performed on the normalized signals to test mRNA expression differences between 20861 and 20863 cells treated with iBET72+ and iBET72−. Significance was determined by a false discovery rate at 0.05 to account for multiple comparisons. Statistical analysis was performed primarily in JMP/Genomics 7.0 (SAS Institute Inc., Cary, NC).
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