Comparison of RNA-seq and microarray platforms for splice.

I've gotten a number of microarray datasets from GEO and arrayexpress, each of which looks at similar chemical treatments of mammalian cells. I downloaded the processed data, and from this I can easily generate log2 fold change values for each probe between the treated sample and the untreated sample.

Choosing between microarray and RNA-seq for gene expression studies and more? Hi experts, Since RNA-seq with NGS technology is changing gene expression studies with great advantages.

Male F344 rats were exposed for 13 weeks to 5 doses of bromobenzene, and liver gene expression was measured using both microarrays and RNA-seq. Multiple normalization methods were evaluated for each technology, and gene expression changes were statistically analyzed using both analysis of variance and benchmark dose (BMD).

Targeted RNA-sequencing (RNA-Seq) is a highly accurate method for selecting and sequencing specific transcripts of interest. It offers both quantitative and qualitative information. Targeted RNA-Seq can be achieved via either enrichment or amplicon-based approaches, both of which enable gene expression analysis in a focused set of genes of interest.

Comparison of RNA-Seq and Affymetrix microarray in detection of genes expressed at low levels. Expression of MYCL1 was at low levels in CD4 T cells and the subtle change in MYCL1 expression in the process of T cell activation cannot be detected by microarray approach (center and right panels).

Comparing RNA seq and Microarray data. How do I go about it? So basically I am trying to compare RNA Seq data with Microarray data of different serotypes of say any bacteria or virus. I am a total beginner in the bioinformatics field and it's just a hypothesis. What I mean is is this comparison even possible?

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The result of directly comparing RNA-Seq and microarray data is shown in the movie at right. It is clear that the sensitivity of RNA-Seq (HiSeq2000) is inferior to that of Agilent microarrays. The dynamic range of RNA-Seq is narrower than Agilent, and the sensitivity of differential expression is weaker than Agilent.

Moreover, CEL-seq (cell expression by linear amplification) put the T7 promoter to the 5’ end of the first strand of the c DNA and amplified it in a less biased way. In order to characterize small non-coding RNA, small RNA-SEQ or miRNA microarrays are used. 3P-seq, PAS-seq and 3’RACE are used to detect alternative polyadenylation.

RNA-Sequencing Vs. Microarrays. When, if ever, is using microarrays better than RNA-seq? Gene expression Analysis is at the core of most of our projects at Genevia Technologies (more about our expression analyses here).There are two main assays yielding transcriptome-wide gene expression profiles of a tissue or culture, namely expression microarrays and next-generation RNA-sequencing.

We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and.

Here, we present datasets for the whole transcriptional response verocytoxigenic Escherichia coli (VTEC) obtained from RNA-seq and microarray platforms in response to spinach, together with a comparison between the datasets (available at Array Express: E-MTAB-3249, E-MTAB-4120, E-MTAB-7441).