Identifying and describing the mechanisms that are responsible for the regulation of the gene expression is a major challenge in biology. Besides all other types of regulatory elements, an essential task in this challenge is to detect the binding sites in deoxyribonucleic acid (DNA) for transcription factors, called motifs. A large number of motif finding computational methods were developed as a result of the recent advances in genome sequencing techniques. This paper reviews a few greedy and random types of algorithms. Many of the motif finding algorithms which can be easily implemented have several disadvantages. Despite the fact that many of them are guaranteeing the correct result, their runtime is huge, which is the biggest obstacle why scientists avoid using them. On the other hand, greedy and random algorithms give us the speed that we need, but in exchange for the accuracy. To test their accuracy, we use benchmarks and tools which are relying on different statistics methods already proven as a reliable mechanism in this field. We make a comparative analysis of some greedy and random algorithms so we can identify their advantages and disadvantages in the process of detecting the binding sites of the transcription factors in DNA sequences and define the directions for their improvement in order to get better performances. Results, presented as various types of charts and tables and compared to other motif finding algorithms, are giving us one global picture of the overall performances of the greedy and random algorithms.
DNA · Gene Expression Motif Motif Finding Algorithms Greedy Algorithms Random Algorithms.