Sentence examples for tags were generated from inspiring English sources

In order to test the new tag generation algorithm, 24 bases long tags were generated.

Melanocyte targeted fluorescent tags were generated using the constitutive Tyr Cre transgene8.

100 bp paired-end read RNA-seq tags were generated using the Hiseq 2500 (Illumina) following the methods in the User Guide.

RAD tags were generated from the genomic DNA of two E. phyllopogon plants, and sequenced to produce 5197.7 Mb and 5242.9 Mb high quality sequences, respectively.

Based on this original vector, a series of transient expression vectors with N-/C-terminal tags of HA, cMyc, EGFP or mVenus or without any tags were generated.

They demonstrated also that most of the tags were generated by a relatively small group of users and a significant set of tags was not reused, whereas few tags were reused a significant number of times.

Among the 843,898,432 reads generated from the three lanes of HiSeq, 12,937,086 tags were generated from the TASSEL-GBS Discovery pipeline described by Glaubitz et al. (2014).

The tags are generated automatically based on a machine learning algorithm called Random Forests, which recognizes combinations of words that belong to a given topic and groups them by subject.

The tags are generated automatically based on a machine learning algorithm called Random Forests, which recognizes combinations of words that belong to a given topic and groups them by subject.

DGE is a tag-based transcriptome sequencing approach where short raw tags are generated by endonuclease.

Briefly, small sequence tags are generated from mRNA samples and sequenced in large numbers.