Detection of consensus modules proceeds by defining a suitable consensus dissimilarity (Methods, Eq. 22) and using it as input to hierarchical clustering.
This feature is an advantage of the LDSS method over other methods of detection of consensus sequences among promoter populations, such as Gibbs Sampling method.
Several approaches have been developed for this detection of consensus sequences in a co-regulated promoter set (Gibbs Motif Sampling [ 14, 15], MEME [ 16]), and detection of over-represented sequence in co-regulated promoters with a set of reference sequences [ 17, 18].
Here, we describe the generation of almost 9 billion nt bases using Illumina RNA-Seq technology, and the detection of 42,062 consensus sequences.
Therefore, sequencing of PCR product directly (after gel purification) has advantage in detection of the consensus sequence of the majority population in the amplicon but has the disadvantage in competency to detect the minority point mutations.
The detection of Dsx consensus binding sites associated with the p53, synj, geko, cdk4/6, and rab6 genes (Additional file 2), all of which have sexually dimorphic expression in the pupal brain (Additional file 1, Figures 4, 6, 7, 11) that is disrupted by dsx knockdown, suggests that Dsx directly regulates expression of these genes.
These methods have allowed the quantitative detection of frequencies of consensus to alternate SNP bases at any particular SNP locus.
MicroRNAs (miRNAs) are a new promising class of circulating biomarkers for cancer detection, but lack of consensus on data normalization methods has affected the diagnostic potential of circulating miRNAs.
What the development of a programme theory can achieve is to shift improvement practitioners' thinking from the implicit to the explicit, thus giving voice to the (often unrecognised) assumptions that guide their interventions, enabling the detection of any lack of consensus among team members, and surfacing weakness or incoherence in the proposed intervention's causal logic.
By binary consensus[5], we refer to a subset of detection consensus problems where the network is trying to reach consensus over a binary parameter.
Fig. 4 shows that as the height cutoff for the detection of branches in the consensus dendrogram increases, the probability of finding spurious consensus modules (and genes therein) increases; for excessively high branch cutoffs levels, the probability of finding as many genes in permuted data sets as in the unpermuted becomes unacceptably high.
