Sentence examples for measuring the transcript from inspiring English sources

RNA-Seq technology has revolutionized the face of gene expression profiling by generating read count data measuring the transcript abundances for each queried gene on multiple experimental subjects.

For all these experiments, the efficacy of poly I C signaling on primary mouse hepatocytes was checked by measuring the transcript levels of genes typically induced (TLR3, PKR, Mx1, and OAS1c).

Next, the influence of natural genetic variation on gene expression was studied by measuring the transcript levels of all genes of all strains, corrected for differential hybridization.

The results were validated by randomly selecting eight genes and measuring the transcript levels by qRT-PCR (Additional file 1: Table S1).

To validate that the reduction was not an artefact of measuring the transcript levels using array technology, we selected 60 samples (10 normals and 50 randomly selected cancers) from the HG_U133 plus data set for qRT PCR analysis.

Quantitative real-time PCR (qPCR) was used to validate transcriptome expression levels for each library by measuring the transcript and gene expression levels separately for each crab used in the exposure experiment.

To validate the MPGR expression data, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to measure the transcript levels of CrWRKY49, CrWRKY50, CrWRKY51, and CrWRKY52.

Thus, we measured the transcript levels of four PR genes, PR1a, PR1b, PR10 and PBZ1, by reverse transcription polymerase chain reaction (RT-PCR) analysis.

A short-term experiment was designed to measure the transcript levels of downstream genes contributing to the biosynthesis of steviol glycosides.

Therefore, we measured the transcript levels of representatives of meiosis-specific genes (Figure 3A).

To determine if AMPs were induced during CrPV infection, we measured the transcript levels of several AMPs at 6, 12, and 24 hpi using qRT-PCR.