The large heterogeneous nature of genomes and both current and future availability of increasing amounts of sequences merit the consideration of methodologies that can appropriately handle the amount of diversity present in large scale biological data.
In some countries, legislation limiting the amount of time that embryos can be stored has been established, resulting in large scale disposal of abandoned embryos [ 2– 9].
Although this syntenic relationship was restricted to these few species, they have been demonstrated to retain a large amount of microsynteny in larger scale analyses (Irimia et al. 2012; Simakov et al. 2013).
In large-scale power system, the amount of calculation for power flow calculation mainly comes from solving correction equations and updating the Jacobian matrix.
In large-scale power systems the amount of energy storage to reach ultra-high levels of VRE while large, can be a relatively small fraction of the overall system size.
We conclude that both methods are adequate options in large-scale projects where the amount of a common reference RNA pool is usually restrictive.
These methods have major drawbacks that render them non-applicable in large-scale PPI prediction, namely the amount of time, associated cost and minimal protein interaction network coverage per run.
Compared to bacteria, fungi have an advantage in large-scale production as it produces large amounts of enzymes involved in silver nanoparticle synthesis.
However this theory relies heavily on the learners individual mindset and focus, and so is difficult to incorporate in large-scale feedback as it requires similar amounts of personal interaction as other widely accepted feedback models (Pendleton's rules [ 3], ALOBA [ 4], the Chicago model [ 5], SET-GO [ 6], the SCOPME model [ 7], and the six-step problem-solving model [ 8]).
With the recent progress made in large-scale genome sequencing projects a vast amount of novel data is becoming available.
