Sentence examples for sequence in parallel from inspiring English sources
Recently, two new droplet-based RNA-seq technologies, named as Drop-seq and inDrop (indexing droplets), has been exploited to sequence in parallel thousands of single cells from a tissue [62, 63].
It is noteworthy that, in MapReduce framework, each node computes its own consensus sequence in parallel.
A similar study by Sande et al. on hairpin deoxyoligonucleotides having oligonucleotides sequence in parallel polarities (PS-hairpin) also confirmed the existence of parallel-stranded conformation.
In our study, we followed a time- and cost-efficient approach to sequence in parallel complete mitochondrial genomes of closely related non-model organisms performing multiplexed Illumina HiSeq sequencing.
Unlike methods that depend on pre-calculated whole-genome alignments to assess intra- or intergenic regions across species [ 13], our software can be applied to any valid sequence, in parallel to revision of existing sequence records and newly deposited proteins.
These next-generation sequencing platforms, such as the Solexa/Illumina Genome Analyzer and ABI/SOLiD Gene Sequencer, can sequence in parallel massive amounts of DNA molecules derived directly from mRNA, producing millions or even billions of high-quality short reads [ 13, 14].
Given a 250 bp amplicon and assuming 12,000 reads of ~250 bp length per 1/16th segment of a GS FLX sequencer picotiterplate (GS FLX amplicon protocol), 16 PCR products can be sequenced in parallel with 12,000× coverage.
Finally, the algorithm can run these sequences in parallel m number of times obtaining m imputed data sets.
In addition, transactional memory has the ability to execute atomic code sequences in parallel as long as no data conflicts occur.
The read length of these NGS platforms is shorter than the original Sanger method (~250 bp for 454 and 35 to 50 bp for Solexa and SOLiD) but ideal for small RNA sequencing: 454 can produce >400,000 reads in one run; Solexa and SOLiD are capable of generating even tens of millions of sequences in parallel [66].
It has been recommended in [3] and reinforced in [4] that in order to prevent possible correlations between output sequences in parallel implementation of the same algorithm using different initial seeds, it is necessary to use a random number generator that has a period greater than.
