Sentence examples for average sequence data from inspiring English sources

Areas of the genome that were not previously annotated and showed >0.5 coverage (average sequence data coverage depth) were reported when reads were unambiguously mapped to the A. marginale genome [ 42].

While we are not undoubtedly de-prioritizing genes with inconsistent measures of statistical significance with this nominal choice of average sequence data dispersion, it should allow us to focus on changes that could be consistent between replicates were the observed BCV is typical of what is reported in mRNA-Seq studies with next-generation sequencing data [ 20].

BLSOM learning for oligonucleotide composition was conducted as described previously [ 7, 8]; the average number of sequence data per neuron was chosen as four.

We concluded that the φ6 populations evolved under low multiplicity showed greater genetic variability than the high MOI populations, on average, based on sequence data representing ~10% of the viral genome.

Weights in the first dimension (I) were arranged into lattices corresponding to a width of five times the standard deviation (5 σ1) of the first principal component: the second dimension (J) was defined by the nearest integer greater than σ2/ σ1 × I; and I was set in the present study as the average number of sequence data per neuron which becomes approximately four.

The average amount of mappable sequence data per sample was 31 Mb, resulting in an evenly distributed average coverage of 26× per exon per sample (evenness score = 97.3%; Figs. 1B 1D).

A single 454 pyrosequencing run generated 361,671 and 274,269 reads totaling 164.49 and 124.60 Mb of sequence data (average read length of 454 nucleotides), which assembled into 23,821 and 17,813 isotigs and 18,147 and 15,129 singletons for both varieties, respectively.

Approximately 2.95 million reads were obtained generating, on average, 1.05 Gb of sequence data per pool.

On average, 138.9 million raw sequence data were collected per lane; 51.4% of reads were mapped uniquely, whereas 20.9% of reads were mapped to multiple positions in the chickpea scaffold assemblies.

For each SV calling tool, the required computational resources for SV detection from the simulation data with an average depth of 30× and real data (NA12878 whole genome sequence data with average depth of 45×) are summarized in Table 1.

The software package Mr. Bayes [ 81] was used to construct Bayesian inference trees, with two independent runs of 4,000,000 generations among four chains with model averaging for amino acid sequence data.