estimateBackground

estimateBackground,TranscriptionDataSet-method

A <code><a href="/link/TranscriptionDataSet?package=transcriptR&version=1.0.2" rd-options="" data-mini-rdoc="transcriptR::TranscriptionDataSet">TranscriptionDataSet</a></code> object.

object

<code>Numeric</code>. False Discovery Rate cutoff value.
Default: 0.05.

fdr.cutoff

Gene expression is a stochastic process, which often results in substantial
expression noise. To obtain a putative set of transcribed regions, it is
necessary to identify those regions that are expressed significantly above
the background level. Using a Poisson-based approach for estimating
the noise distribution from the frequency of the transcribed regions with the
low fragments coverage, <code><a href="/link/estimateBackground?package=transcriptR&version=1.0.2" rd-options="" data-mini-rdoc="transcriptR::estimateBackground">estimateBackground</a></code> function returns a
coverage cutoff value for a specific
<href>https://en.wikipedia.org/wiki/False_discovery_rate</href>{False Discovery Rate (FDR)}.

The differences in the RNA types being sequenced have an
        impact on the resulting sequencing profiles. mRNA-seq data is
        enriched with reads derived from exons, while GRO-, nucRNA- and
        chrRNA-seq demonstrate a substantial broader coverage of both
        exonic and intronic regions. The presence of intronic reads in
        GRO-seq type of data makes it possible to use it to
        computationally identify and quantify all de novo continuous
        regions of transcription distributed across the genome. This
        type of data, however, is more challenging to interpret and
        less common practice compared to mRNA-seq. One of the
        challenges for primary transcript detection concerns the
        simultaneous transcription of closely spaced genes, which needs
        to be properly divided into individually transcribed units. The
        R package transcriptR combines RNA-seq data with ChIP-seq data
        of histone modifications that mark active Transcription Start
        Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this
        challenge. The advantage of this approach over the use of, for
        example, gene annotations is that this approach is data driven
        and therefore able to deal also with novel and case specific
        events. Furthermore, the integration of ChIP- and RNA-seq data
        allows the identification all known and novel active
        transcription start sites within a given sample.

Armen R Karapetyan

transcriptR

An Integrative Tool for ChIP- And RNA-Seq Based Primary
        Transcripts Detection and Quantification

estimateBackground function

A <code><a href="TranscriptionDataSet" rd-options="">TranscriptionDataSet</a></code> object.

Gene expression is a stochastic process, which often results in substantial
expression noise. To obtain a putative set of transcribed regions, it is
necessary to identify those regions that are expressed significantly above
the background level. Using a Poisson-based approach for estimating
the noise distribution from the frequency of the transcribed regions with the
low fragments coverage, <code><a href="estimateBackground" rd-options="">estimateBackground</a></code> function returns a
coverage cutoff value for a specific
<href>https://en.wikipedia.org/wiki/False_discovery_rate</href>{False Discovery Rate (FDR)}.

estimateBackground: estimateBackground

Description

Usage

Arguments

Value

See Also

Examples