Sentence examples for massive sequence datasets from inspiring English sources

The accompanying article [ 12] introduced a highly efficient tool, Tallymer, to analyze k-mers within the massive sequence datasets available from maize.

The advent of ultra-high-throughput sequencing has made it possible to obtain massive sequence datasets from experimentally challenging organisms - and even collections of intimately associated organisms - on a scale unimaginable even a few years ago.

These massive sequencing datasets demand high-performance computational resources, rapid data transfer, large-scale data storage, and competent data analysts.

On the bioinformatics end, assembling massive sequencing datasets is extremely demanding on computing cycles, memory usage, storage requirements, and for parallel programming implementations on communication traffic.

Such demand will soon result in large-scale clinical sequence datasets requiring massive data analysis and interpretation at reimbursable cost points thereby producing a technological barrier and price-limiting step of clinical genome usage [ 3, 4].

Next generation sequencing (NGS) allows fast and massive production of both genome and transcriptome sequence datasets.

Sequence datasets were quality filtered, normalized and clustered into species-level operational taxonomic units (OTUs).

All sequence datasets are provided in both raw and processed fashion.

Comparative results for detection rate and accurary for the long sequence datasets.

The raw sequence datasets generated and analyzed during the current study are available through the Sequence Read Archive as follows: Viral Mock Community, BioProject ID PRJNA431646; NetoVir mock community, BioProject: PRJNA319556; Influenza dataset, BioProject ID PRJEB7888; Brazil dataset, BioProject ID PRJNA395784.

Transcriptome sequencing can provide massive sequence data for analysis of gene expression.