Parameter Name | Variable | Default Value | Parameter Range | Description |
---|---|---|---|---|
FRAG_UR_D0 | 1 | >0 | minimum length of fragments produced by hydrolysis | |
FRAG_UR_DELTA | NaN1 | geometry of the fragmentation process (1=linear, 2=surface-diameter, 3=volume-diameter, etc.); if not explicitly specified (NaN), the geometry of breakage depends logarithmically on the molecule length | ||
FRAG_UR_ETA | NaN1 | intensity of fragmentation, determining the number of breaks per unit length; if not explicitly specified (NaN), is determined by the corresponding corresponding value and an expectation of 200nt (or the mean filtered fragment size, if size selection is used) long fragments |
1 NaN stands for "Not a Number" and marks the uninitialized state of a parameter
Frequencies of fragment sizes d sizes produced by a uniform random fragmentation process have demonstrated to fall along Weibull distributions , if the fragmentation thermodynamics depends on the molecule size:
f(d)= d/h (d/ h)d-1 exp—(d/h)d (2)
Scale parameter h Scale parameter represents the intensity of fragmentation (i.e., breaks per unit length), and—as a determinant of the mean expected fragment size—is assumbed assumed to be constant across molecules of different lengths for fragmentation protocols where the number of produced fragments depends on the molecule length. Shape parameter d parameter reflects the geometric relation in which random fragmentation is breaking a molecule (e.g., d=1 corresponds to uniform fragmentation on the linear chain of nucleotides, d=2 splits uniformly the surface, and d=3 and the volume, etc.).
Employing empirical data from spike-in sequences, we evaluated the fitting obtained by weighted subsampling from Weibull distributions with varying shape parameters. Weights for the subsampling (Fig. 2B, solid line) were derived by separating the characteristics of the combined Weibull distributions before filtering (dashed line in Fig. 2B and 2C) from the observed insert size distribution (Fig. 2B, dashed-dotted line). The quality of fit was measured as the p-value computed by a Kolgomorov-Smirnov test, comparing the in silico produced insert size distribution (Fig.2A, dashed lines) for each of the spike-in sequences under investigation with its experimental couterpart (Fig.2A, solid lines) under the null hypothesis that both samples were drawn from the same distribution. By this, we empirically found that the observed differences can be qualitatively reproduced under a constant decay rate (h=200nt), when shape parameter d depends logarithmically on the molecule length (Supplementary Fig.4).
In our uniform random fragmentation model, we adopt The Flux Simulator uses a 3-step algorithm to tokenize a respective molecule; first, geometry d geometry and the number n number of fragments that are obtained from the molecule are determined. We found empirically that parameter d depends logarithmically on lenon , the length of the molecule that is fragmented d=log(len) . The number of fragments produced from a specific RNA molecule is determined by n=len/E(dmax), where E(dmax) , where is the expectancy of the most abundant fragment size, computed from h and the gamma-function G function of d :
Second, E(dmax)= hG(1/d + 1) (3)breakpoints are sampled uniformly from the interval [0;1[, resulting in relative length fractions for all fragments. Third, relative fragment sizes are transformed from unit space to sizes that follow a Weibull distribution of shape by:
where is a constant of the transformation to ensure that the sizes of the fragments sum up exactly to the given molecule length. Latter transformation produces a slightly distorted Weibull distribution for the sizes , however the deviation is sufficiently small to be neglected in our applications.