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A read of size ℓ from a transcript is a random segment of size ℓ taken from the transcript sequence generated using the reference DNA.
Because p(Y) is the observed distribution of insert sizes for inserts of size s(X i ), our formula computes the likelihood that a given read in the cluster was generated from a donor genome A that has an extra DNA segment of size r.
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To prevent excessive fracturing of the original tumor segmentation profile, we added a breakpoint at the hotspot site only for segments of size greater than the median size of all segments by disease type.
As described in Section 3, we assume the elements x∈X 0 to be bit strings of length N and divide each of them into n segments of size (m=frac {N}{n}) (assuming without loss of generality that N is a multiple of n).
In this section, we construct a stochastic process in input space with the properties we need for the main fuzzing algorithm presented in Section 5. First, we divide the input into n segments of size (m=frac {N}{n}) (assuming without loss of generality that N is a multiple of n).
Small segments (of size less than 1 Mb, say) compared to the complete chromosome (∼150 Mb), were omitted.
Next, the integrated curve is divided into time segments of size n.
For example, a cousin-pair (d=4) will share segments of size 25 cM on average.
IBD segments of size 1 cM are the expected size for common ancestry 50 generations ago (100 meioses linking the pair of individuals) while segments of size 2 cM correspond to 25 generations.
For several of the methods, depending on parameter settings, power is high to detect segments of size ≥1 cM, particularly when the larger sample size is used.
Moreover we had high power to detect IBD segments of size 2 cM or larger while controlling the false discovery rate to be close to zero.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com