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ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest.
The cost and accessibility of ChIP-seq is a major disadvantage, which has led to the more predominant use of ChIP-chip in laboratories across the world. [2] This photo compares the efficacy of the two experimental techniques, ChIP-seq and ChIP-chip. Table 1 Advantages and disadvantages of NChIP and XChIP
The Carlson curve is a term to describe the rate of DNA sequencing or cost per sequenced base as a function of time. [1] It is the biotechnological equivalent of Moore's law . Carlson predicted that the doubling time of DNA sequencing technologies (measured by cost and performance) would be at least as fast as Moore's law.
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The ChIA-PET method combines ChIP-based methods, [2] and Chromosome conformation capture (3C) based methods, [3] to extend the capabilities of both approaches. ChIP-Sequencing (ChIP-Seq) is a popular method used to identify transciption factor binding sites (TFBS) while 3C has been used to identify long-range chromatin interactions.
The cost must also take into account personnel costs, data processing costs, legal, communications and other costs. One way to assess this is via commercial offerings. The first such whole diploid genome sequencing (6 billion bp, 3 billion from each parent) was from Knome and their price dropped from $350,000 in 2008 to $99,000 in 2009.
Wilbanks and colleagues [3] is a survey of the ChIP-seq peak callers, and Bailey et al. [4] is a description of practical guidelines for peak calling in ChIP-seq data. Peak calling may be conducted on transcriptome/exome as well to RNA epigenome sequencing data from MeRIPseq [ 5 ] or m6Aseq [ 6 ] for detection of post-transcriptional RNA ...
Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail. Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes – in many cases, an organism's entire genome – in a ...