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Schematic overview of the modular structure underlying procedures for gene set enrichment analysis. Gene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with different phenotypes (e.g ...
One analysis method, known as gene set enrichment analysis, identifies coregulated gene networks rather than individual genes that are up- or down-regulated in different cell populations. [1] Although microarray studies can reveal the relative amounts of different mRNAs in the cell, levels of mRNA are not directly proportional to the expression ...
Central Motif Enrichment Analysis (CentriMo) is a tool for inferring direct DNA binding from ChIP-seq data. CentriMo is based on the observation that the positional distribution of binding sites matching the direct-binding motif tends to be unimodal, well centered and maximal in the precise center of the ChIP-seq peak regions. CentriMo takes a ...
Methods differ in the use of transcript enrichment, fragmentation, amplification, single or paired-end sequencing, and whether to preserve strand information. [ 65 ] The sensitivity of an RNA-Seq experiment can be increased by enriching classes of RNA that are of interest and depleting known abundant RNAs.
The premise behind the test is the activation of downstream reporter gene(s) by the binding of a transcription factor onto an upstream activating sequence (UAS). For two-hybrid screening, the transcription factor is split into two separate fragments, called the DNA-binding domain (DBD or often also abbreviated as BD) and activating domain (AD).
The human genome contains on the order of 20,000 genes which work in concert to produce roughly 1,000,000 distinct proteins. This is due to alternative splicing, and also because cells make important changes to proteins through posttranslational modification after they first construct them, so a given gene serves as the basis for many possible versions of a particular protein.
The development of high-throughput RNA sequencing (RNA-seq) and microarrays has made gene expression analysis a routine. RNA analysis was previously limited to tracing individual transcripts by Northern blots or quantitative PCR. Higher throughput and speed allow researchers to frequently characterize the expression profiles of populations of ...
As with any scientific experiment, it is prudent to conduct RNA-Seq in a well controlled setting. If this is not possible or the study is a meta-analysis, another solution is to detect technical artifacts by inferring latent variables (typically principal component analysis or factor analysis) and subsequently correcting for these variables. [58]