Abstract
Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called “hotspots”, while with lower probability in so-called “coldspots”. With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called “iRSpot-TNCPseAAC”, in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew’s correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.
Highlights
Meiosis and recombination are two indispensible aspects for cell reproduction and growth (Figure 1).The former is a special type of cell division by which the genome is divided in half to generate daughter cells for participating in sexual reproduction, while the latter is to produce single-strand ends that can invade the homologous chromosome [1].system
The benchmark dataset S used in this study was taken from Liu et al [14], which contains 490 recombination hotspots and 591 recombination coldspots, as can be formulated by: S S
As we can clearly see from the table, the iRSpot-TNCPseAAC predictor is superior to iRSpot-PseDNC [25] in three of the four metrics as defined by Equation (16); i.e., it can yield higher accuracy Acc, higher Mathew’s correlation coefficient MCC, and higher sensitivity Sn
Summary
Meiosis and recombination are two indispensible aspects for cell reproduction and growth (Figure 1). One of the most important, and most difficult, problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. Encouraged by the success of introducing PseAAC for proteins, recently, Chen et al [25] proposed the pseudo dinucleotide composition or PseDNC to represent DNA sequences for identifying the recombination spots by counting some sequence effects, remarkably improving the prediction results in comparison with those by Liu et al [14], without including any sequence information. Let us elaborate how to deal with these procedures one-by-one
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