Sentence examples for both sequencing data and from inspiring English sources

Whilst the majority of new miRNAs exhibited discrete lengths, as determined from both sequencing data and primer extension analysis, we observed variations in the 5'ends of both Ae. albopictus miR-2951 and Cx.

B cell epitopes were predicted using both sequence data and 3D modeling of the FMDV Asia1 serotype.

Here I review progress in the collection of both sequence data and physical clone reagents for protein coding genes.

In the case of transcriptome sequencing it has become possible to obtain both sequence data and estimates of gene expression levels in a single experiment [ 12].

Therefore, the effective management and compression of both sequence data and corresponding annotation data are indispensable considerations for biological database management systems.

Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low.

As individual specimens, social Hymenoptera were among the most abundant members of the collection, but also among the least diverse, and this was verified by both sequence data and morphological examination.

Our own work argues that the Anbu proteasome (or peptidase according to [ 23]) is more likely than HslV to be the 20S proteasome's direct ancestor based on both sequence data and structure predictions [ 24].

Since microsatellite markers allow for simultaneously sampling different genome regions, we opted to use a novel package that can deal with both sequence data and microsatellites jointly through the use of an approximate Bayesian computation (ABC) method [ 31].

Subsequent iterations of Stitch will make use of any in silico maps that join growing super scaffolds, effectively using both sequence data and genome maps to stitch together the final super scaffolds (Fig. 3 g– h).

30 Most recent binding site classification methods now use a combination of both sequence data and structural properties and as such require more advanced classification methodologies such as Support Vector Machines, Bayesian Networks, Neural Networks, Hidden Markov Models or Conditional Random Fields.