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Just as the vast sequence data available for the human [ 1, 2] and additional organisms [ 3- 5] require sophisticated genomic browsing tools [ 6- 8], computational methods are required to thoroughly explore the corpus of biomedical literature.
In the near future, large-scale international projects (Hudson et al., 2010; McLendon et al., 2008) generating vast sequence data repositories from hundreds of individual tumours will be complete.
As vast sequence data become available from whole-genome sequencing projects, the findings of this work should enable the detailed analysis of how Sireviruses interact with their plant hosts and shape their genomes, towards which the fact that Sireviruses inhabit only plants may have important implications.
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Despite the importance of this process, few methods exist to distinguish, in the vast sequencing data available, genes that are inherited versus those genes which have been mobilized and transferred between organisms.
With the availability of vast genome sequence data from so many species of bacteria it has become possible to make comparative sequence analyses, to add further value to the work carried out with model bacteria such as E. coli and N. meningitidis.
The vast number of teleost species should provide a vast wealth of sequence data to more precisely define how these unique amplification of gene families occur.
However, with its inherent difficulty and expense, experimental characterization of function cannot scale up to accommodate the vast amount of sequence data already available1.
This approach searches through vast troves of sequence data to identify evolutionarily conserved amino acid interactions, then uses those correlations to predict a novel sequence's folding patterns.
Deep sequencing enables the immediate generation of a vast number of sequence data.
High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data.
Over the last decades, our exponentially increasing capacity for genome sequencing has generated vast amounts of sequence data of prokaryotic and eukaryotic providence.
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