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Although sequences in all datasets were assigned to similar bacterial taxa, it is not clear how similar the sequences are across datasets.
The sequence reads from 454 and SOLiD datasets were assigned to corresponding samples (libraries) based on specific barcode sequences added to the small RNAs during sample preparation.
Three other samples that fell in lineage 1, according to the all-gene and neutral gene datasets, were assigned to lineage 3 based on the PDV genes.
Using centroid prediction [ 12] as described previously, the tumours from both datasets were assigned to the five Norway/Stanford subtypes (basal, luminal A, luminal B, ERBB2 and normal-like [ 6- 8]), one of the tumours in this third cluster was assigned to the luminal B subtype, thirteen to the ERBB2 subtype and 9 could not clearly be assigned to a subtype (Fig 2B v).
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Each sample from the microarray datasets was assigned to one of the five subtypes according to the status of the five markers.
The peaks identified in our C24 H3K27me3 dataset were assigned to 3976 genes, 77% of which were also present in the list of H3K27me3 genes from Col. To verify this result and quantitatively compare the H3K27me3 abundance between the two ecotypes, 10 genes were randomly selected from each of three groups; present in C24+Col, C24-only and Col-only.
In total, 4,223 ASVs from the LGC dataset were assigned to the genus Ostreococcus, among which 10 had more than 200 reads (Table 1).
In the Table 3, we illustrate the ratio of how many points of each country dataset were assigned to each type of road, how many roads there are in OpenStreetMap in that country.
Only one to two reads per metagenome dataset were assigned to other exotoxin genes.
All sequences found in our dataset were assigned to specific miRNA transcript and ambiguous sequences were underlined.
Six of the CRISPR-like arrays identified from DSS dataset were assigned to Metallosphaera sedula based on their repeat sequence.
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