Sentence examples for these datasets pose from inspiring English sources

Compared to the heavily processed and scored E-MAP interaction matrices, the original double-mutant fitness measurements provided by the SGA and GIM analyses may encode more in-depth and elemental information on the complex genotype-phenotype relationships, but these datasets pose also modeling challenges beyond the reach of the traditional modeling strategies.

However, these datasets pose considerable technical challenges, which are only partially solved.

These datasets pose significant challenges to assembly algorithms and can, depending on the repeat complexity of the genome, lead to fragmented or incorrect assemblies.

Analysis of these datasets poses an unprecedented informatics challenge due to the sheer number of reads that a single run of an HTS machine can produce, the shortness of the reads, and the various technologies' different sequencing biases and error rates.

The high-dimensionality of the datasets poses also challenging computational problems; for instance, the current version of the SGA genetic interaction database comprises millions of quantitative fitness measurements [32].

The increasing size and complexity of high-throughput datasets pose a growing challenge for researchers.

Viewing, understanding, and effectively using such datasets pose many challenges to analysts.

With standard amplicon libraries, chance is relied upon to produce reads with sufficient overlap for assembly, and thus short-read datasets pose particular challenges by limiting the amount of overlap obtainable between any two reads.

All administrative datasets pose challenges, including the structure and quality of the data and generalizability [ 21].

Modern reference datasets pose a couple of challenges to this paradigm.

From the computational perspective, the ever increasing sizes of the raw NGS and imputed GWA datasets pose great challenges to the computational algorithms.