Sentence examples for depth of redundancy from inspiring English sources

To evaluate the 9LOCI method, we compared the depth of redundancy derived from the sequence read numbers between HLA alleles and among HLA loci.

The resulting (circular) genome of KZN-V4207 has 4,397,375 nucleotides and has an overall depth of coverage (average redundancy) of 134.3-fold 134.3-foldandard deviation of 41.4 (excluding the top 5% of most-highly covered sites as outliers due to redundancy).

Each of the 4 genomes was sequenced to a high level of redundancy (sequencing depth was 722 to 1627 fold).

The coverage depth track shows the extent of redundancy in coverage at any nucleotide location; for this gene, peak coverage is at approximately 12 reads in any single location.

Based on a binomial distribution, the expected coverage of a target sequence given a certain depth of coverage or level of redundancy, R, can be approximated by the equation: E(Coverage) = 1 - e-R Based on this relationship, for a 15 kb mt genome and a mean sequence read length of 200 bp, approximately 500 reads of mitochondrial sequence are needed to obtain full coverage.

Table 2 compares the expected coverage and redundancy (depth of coverage) as predicted by the Lander-Waterman model with the observed genome coverage and redundance.

The redundancy and depth of coverage of 454 pyrosequencing also provides and unbiased representation of transcript abundance, which is useful for relative gene expression analysis especially in non-model plants that lack complete genome sequence information [ 33, 34].

Conventional dual-end depth-of-interaction (DOI) PET detector readout requires two 2D SiPM arrays; with top and bottom SiPM reading the same pixel, there is information redundancy.

Would it be redundant to introduce the concept of redundancy at this point?

In the Lander and Waterman analysis of genome coverage [ 17] G = Size (bp) of Reference Genome, L = Sequence Length (bp) and N = # sequences; Redundancy represents the depth of sequence coverage and Coverage represents the fraction of genome covered by sequence data.

Both residual prediction methods try to reduce the amount of redundancy in residuals of the low and the high bit-depth layers.