Restriction-based methodologies such as RADSeq are most commonly used for population studies, and targeted sequencing methods, usually based on target enrichment via synthesized RNA probes, have gained traction at higher levels for phylogenetics (McCormack et al., 2013, table 1; Cronn et al., 2012, table 5).
To characterize the small RNA transcriptome, the new 'deep-sequencing' technologies, such as Roche 454 and Illumina Solexa, have been employed which have significant advantages over previous hybridization-based methodologies, such as microarray and PCR-based assays [6], [7].
However, older chip-based methodologies such as comparative genomic hybridization and single-nucleotide polymorphism arrays have benefited from this technological explosion and are now so accessible that they can be employed in increasingly larger cohorts of patients.
Out of the all the downstream processing steps, the selective purification steps represent 70% of the total costs and are dominated by chromatography-based methodologies such as those outlined above [ 134].
The idea that compensation is unnecessary would also be true with respect to data obtained in sequencing-based methodologies, such as RNA-seq [ 30], when a transcript is measured with a sufficient number of reads.
This difference emerges from what has already been noted by Baertsch et al. (2008): mRNA-based methodologies (such as we used) are more efficient for identifying retrocopies that are involved in non-CDSs, such as 3′ untranslated regions (3′ UTRs) and noncoding RNAs.
Formal model based safe design methodologies such as Discrete Event Systems (DESs) are often desirable in the construction of sound and complete fault diagnosis mechanisms.
Given a set of documents, clustering uses a distance based approach to separate documents into groups, by using general methodologies such as partitioning and hierarchical clustering [17].
Based on the results, we will determine whether the application of other systematic review methodologies, such as meta-analysis or meta-synthesis, will be appropriate for any of the subgroups that we identify for future research.
Novel data analysis methodologies, such as machine learning, offer large potential for identifying relevant data for specific biological outcomes based on complex multidimensional datasets.
Based on this principle, we reduced the proportion of lectures, to as low as 30% and tried to maximize learner-centered methodologies, such as small- and large-group discussions, role-playing, and participatory workshops.
