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Population genetic inference from genomic sequence variation.
Pool, J.E., Hellmann, I., Jensen, J.D. & Nielsen, R. Population genetic inference from genomic sequence variation.
Genomic Sequence Variation Markup Language (GSVML) will focus on genomic sequence variation data and human health applications, such as gene based medicine or pharmacogenomics.
With the aim of making good use of internationally accumulated genomic sequence variation data, which is increasing rapidly due to the explosive amount of genomic research at present, the development of an interoperable data exchange format and its international standardization are necessary.
These results demonstrate a link between genomic sequence variation, transcript abundance, protein level, and enzymatic activity.
Fragments of each gene were sequenced from the founder lines of the resource population to identify genomic sequence variation.
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Inclusion of the 2 most recently reported sequences of EBV genomes, Akata and Mutu, in our analysis of genomic sequence variations reveals the significant geographical distribution factor in addition to the factors of disease or tissue association.
In other species studied, genomic sequence variations prevent expression of the EGF1 domain and only the EGF2 domain is present (Fülöp et al. [1996]).
Recently, high-throughput DNA sequencing technology has begun to have a major impact on this field and is shedding light on genomic sequence variations between human individuals [7] [10].
A total of 203 distinct genomic sequence variations were identified in the type I collagen genes among the 63 subjects with OI type II, 98 in COL1A1 and 105 in COL1A2 (Table 1).
Our results show that under specific conditions, using CGH to quantify inter-genomic sequence variation can yield data that support the input topology.
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