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We then used the MAQGene variant output file to find the homozygous Bristol variants that fall in the defined mapping interval (see Materials and Methods for variant filtering).
The available tools implement different methods for variant annotation.
Both single and multi sample methods for variant detection were applied.
AA-ALIGNER was designed to be modular allowing for allowing for the incorporation of alternative methods for variant identification and tests for significance of imbalances.
Another disadvantage of using these indirect methods for variant characterization is the difficulties encountered to compare datasets generated in different laboratories, or in the same laboratory but at different times.
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Investigation of genetic diversity of this group and development of effective screening methods for variants of HBoV is required for studies of human disease.
Our goal was to choose the optimal enrichment method for variant discovery in continuous custom-selected target regions.
At present, WES is a useful and powerful method for variant discovery within coding regions offering most of the benefits of WGS while allowing for easier analysis and storage of the data produced.
Further consideration for the choice of the visualization tool is the possibility to handle the data format used in previous analysis methods for reporting variants and variant annotations.
With the development and publication of several methods that can selectively isolate sequences of interest from a genomic DNA sample, there are now high-throughput methods available for variant discovery within genomic regions identified by GWAS or by candidate gene approaches.
Machine learning algorithms and methods used for variant design are detailed in Additional file 1. Variant set 1 (active variants in Additional file 2) was the training data set used for the design of set 2. Multiple measurements of the same variant were treated as separate pairs.
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