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Both mammalian (human and mouse) p53 proteins, which had the lowest bit score (Table 1), showed the lowest specificity.
It is also interesting to note that they exhibited the lowest absolute specificity, as reflected by the lowest bit score of the derived motif.
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The usage of phylogenetic trees to discriminate between PKS I and non-PKS I sequences seems to be a feasible approach which also partially overcomes the problem of low bit score values and fragmentation of environmental proteins using traditional sequence similarity searches.
To this end, AnABlast uses non-restrictive parameters including a low bit score threshold for considering hits.
Because bit score thresholds are not allowed in BLAST tools, low bit score values (from 25) were established by using high E-values (up to 2,00,000).
Especially the many outliers with low bit scores in the type I PKS group coming from metagenomic proteins fall in the inter-quartile range of the non-PKS I group.
However, the low bit scores suggest that these may have other origins.
In real datasets however, there are sometimes sporadic close hits with low bit scores (Additional file 1 : Figure S1D).
Unusually high (or low) bit scores (and therefore classification of tRNAs in core or peripheral set) may have also been caused by a biased nucleotide composition.
Meanwhile, (4) sequence similarity due to randomness instead of homology may also bring in some distal hits with low bit scores (Additional file 1 : Figure S1G, lower panel).
A covariance model with high specificity assigns low bit scores to all sequences that do not belong to the model family.
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CEO of Professional Science Editing for Scientists @ prosciediting.com