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The gap between sequencing throughput and computer capabilities in dealing with such big data is growing [ 1].
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In the past several years, there have been major advances in sequencing technologies, resulting in the development of ultra high-throughput sequencing (UHTS) platforms that have allowed significant increases in sequencing throughput and decreases in sequencing cost [33], [34].
These technologies have significantly improved sequencing throughput and decreased cost, making mammalian genome sequencing feasible [ 20].
With the development of next-generation sequencing (NGS) technologies the sequencing throughput has dramatically increased and the costs have decreased.
Technological advances have increased sequencing throughput, with decreased sequencing times and costs [ 21].
Many datasets now in the public domain were generated when sequencing throughput was lower than it is now and costs were higher (between 2007 and 2013, sequencing throughput has increased by approximately two orders of magnitude).
One was sequencing throughput.
There existed some consistency in expression levels of GA3-responsive Vvi-miRNAs between high throughput sequencing and qRT-PCR essays.
As shown previously, the results of miRNA concentrations estimates tend to vary substantially between high-throughput sequencing and microarrays [ 35], as well as miRNA between different high-throughput sequencing methodologies [ 36, 37].
Recent advances in next-generation sequencing technologies have drastically increased throughput and significantly reduced sequencing costs.
The tools include high-throughput sequencing and hardware and software to process the resulting data.
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