You probably recall this entry on The Steamrollers, technologies that are improving much faster than Moore's law if not more. It turns out that at the Paris Machine Learning Meetup #5, Jean-Philippe Vert  provided some insight as to what happened in 2007-2008 on this curve:
The answer is yes.
Why ? from 
However, nearly all of these new techniques concomitantly decrease genome quality, primarily due to the inability of their relatively short read lengths to bridge certain genomic regions, e.g., those containing repeats. Fragmentation of predicted open reading frames (ORFs) is one possible consequence of this decreased quality.
it is one thing to go fast it is another to produce good results. Both of these issues of speed and accuracy due to short reads may well answered with nanopore technology and specifically the fact that entities like Quantum Biosystems Provides Raw Data Access to New Sequencing Technology and I am hearing through the grapevine that their data release program is successful. What are the consequences of the current capabilities ? Eric Schadt calls it the The Stephanie Event (see also ). How much faster are we going to have those events in the future if we have very accurate long read lengths and attendant algorithms [2, 5] ? I am betting more.
 Jean-Philippe Vert, Machine Learning for personalized medicine / Apprentissage statistique pour la médecine personnalisée.
 Gene fragmentation in bacterial draft genomes: extent, consequences and mitigation, Jonathan L Klassen and Cameron R Currie
 Stephen Hsu 's blog entry on Cancer genomics
 Application of compressed sensing to genome wide association studies and genomic selection and version 2 of that paper.
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