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An earlier publication [ 47] claims that the mapping of nucleosome positioning using MNase1 generates a sequence-dependent bias in the resulting fragments, which may affect sequence-dependent models for nucleosome positioning.
Selection on the PIP1.1 gene would overcome the baseline demographic signal, which would provide negative statistics which are detected by SSRs (n.b. SSRs can only detect recent bottlenecks, while older ones may still affect sequence diversity).
Selection targeting a microsatellite may affect linked sequence diversity in a fundamentally different manner than posited by the canonical model of selective sweeps (Maynard Smith 1976), meaning that standard genomic scans will fail to detect these targets of selection.
For different factors that may affect protein sequence evolution, a significant negative correlation between ω values and gene position was observed (ρ = −0.370 [−0.114] −0.114], P = 0.020 after FDR correction; Figure 2 and Additional file 1: Table S4), indicating that the downstream genes were more conserved and therefore underwent stronger purifying selection.
Some differences may affect coding sequences, while others may occur in upstream regulatory regions and influence gene expression.
What is clear is that sensible changes in structure are correlated with changes in function; these may affect coding sequences or regulatory modules, and the precise outcome of duplication likely reflects both selective pressures (adaptation) and historical constraints, that is, the function, indispensability and connectivity of the original gene [ 43].
Alternative splicing may affect the protein sequence in two ways: (i) by deleting or inserting a sequence and creating long and short isoforms, or (ii) by substituting one segment of the amino acid sequence for another [ 11].
Here, we assess the performance of these six well-established tests and explore what characteristics specific to human mtDNA sequence may affect their efficacy by simulating sequence under various parameters with levels of recombination that vary around an empirically derived estimate for human mtDNA (population parameter ρ = 5.492).
The number of sequences in each sample, termed sequencing depth, may affect the accuracy of the sequence signature methods.
To shed light on how co-infection may affect evolution, Sentandreu sequences parts of the genome of both viruses and analyses genetic variability and adaptive evolution in co-infected patients.
It should be noted, however, that addition of novel HEV sequences may affect estimates for TMRCA, if these sequences would affect genetic heterogeneity of HEV genotypes.
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