Sentence examples for dataset read from inspiring English sources

Below we present analysis of the insert size distributions for the following data: Brachybacterium faecium isolate dataset (read length 150 bp) and S taphylococcus aureus single-cell dataset (read length 101 bp).

In order to investigate the low coverage of CpG sites in the WGS dataset, read depth analysis was performed on both the targeted control and WGS datasets.

For Figure 5(a), we use artificially low numbers of repetitions on the low diversity dataset; read length was held at 400 bp and the error rate was held at 0.01, and we again show haplotype assemblies produced over 5 trials of sequencing.

Bacterial communities in S. phoenix plants were dominated by OTUs assigned to the phyla Bacteroidetes (five most abundant OTUs in the family Chitinophagaceae) and Proteobacteria (two OTUs in the family Pseudomonadaceae), which collectively comprised 37% and 34% of the total dataset reads, respectively.

As a pre-analysis step, dataset reads are aligned to the reference genome using Bowtie [5].

To reduce the size for each dataset, reads with identical sequences were pooled into unique reads, coded based on their dataset source, and analyzed using the developed four steps bioinformatics pipeline.

Briefly, the first cleaning step allowed removal of most imperfect reads from the dataset (reads with incomplete primers or barcodes, reads corresponding to sequences that were observed only once in the entire dataset and reads exhibiting indels of sizes that were not multiples of three base pairs).

Models were constructed for each of the top 13 OTUs (95% of the dataset reads) to determine whether clinical factors shown in Table 1 and/or case/control status of the samples correlate with read numbers.

For the two chimeras predicted in both datasets, read support is presented as A08823 support / A08878 support.

Chromosomal sequences were indexed using Bowtie2 software and wallaby datasets read mapping was performed against Mondom5 genome assembly using Bowtie2 [ 93].

Since only uniquely mappable reads are typically retained in ChIP-seq datasets, reads falling in low complexity regions are predisposed to being discarded, resulting in the bias.