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Data Normalization is a hugely important step, correcting for technical variations between samples or unintentional differences between microarray batches, and there is plenty of choice from Linear Regression, Division by Central Tendency to Logarithm, Divide by Maximum, and Scaling Between 0 and 1.
Reference Sequences are more than a collection of A, C, G, and T, they set the base numbering and directionality of the contig.
If you are comparing sequences to a reference sequence, Assemble to Reference is a ligntening-fast algorithm that leverages the power of Sequencher’s Reference Sequence feature.
This means that not only can you share your results with colleagues but you can produce outstanding graphics for presentations and publications.
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