Subtraction of background damage in PFGE experiments on DNA fragment-size distributions.
The non-random distribution of DNA breakage in pulsed-field gel electrophoresis (PFGE) experiments poses a problem of proper subtraction of the background damage to obtain a fragment-size distribution due to radiation only. As been pointed out by various authors, a naive bin-to-bin subtraction of the background signal will not result in the right DNA mass distribution histogram, and may even result in negative values. Previous more systematic subtraction methods have been based mainly on random breakage, appropriate for low-LET radiation but problematic for high LET. Moreover, an investigation is needed whether the background breakage itself is random or non-random. Previously a new generalized formalism based on stochastic processes for the subtraction of the background damage in PFGE experiments for any LET and any background was proposed, and as now applied it to a set of PFGE data for Fe ions. We developed a Monte Carlo algorithm to compare the naïve subtraction procedure in artificial data sets to the result produced by the new formalism. The simulated data corresponded to various cases, involving non-random (high-LET) or random radiation breakage and random or non-random background breakage. The formalism systematically gives better results than naïve bin-by-bin subtraction in all these artificial data sets.
Ponomarev
Artem L
AL
NASA Johnson Space Center, Mail Code SK, build. 37, room 119, Houston, TX 77058, USA. aponomar@ems.jsc.nasa.gov
Belli
Mauro
M
Hahnfeldt
Philip J
PJ
Hlatky
Lynn
L
Sachs
Rainer K
RK
Cucinotta
Francis A
FA
D000465:Algorithms; D001417:Background Radiation; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry / Q000235:genetics* / Q000528:radiation effects*; D053938:DNA Fragmentation / Q000528:radiation effects*; D003627:Data Interpretation, Statistical; D016521:Electrophoresis, Gel, Pulsed-Field / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016008:Statistical Distributions
D004247:DNA