2010/12/22 by Amin Zia, Alan M. Moses, Zia, Amin +2
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithms and Data Compression #FOS: Biological sciences #Genomics (q-bio.GN) #Genomics and Chromatin Dynamics #Genomics and Phylogenetic Studies #q-bio.GN
paper · pdf · doi:10.48550/arxiv.1012.5011
Submitted to PLOS Computational Biology
arxiv created 2010/12/22 · openalex publication_date 2010/12/22 · arxiv updated 2010/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Detection of false-positive motifs is one of the main causes of low performance in motif finding methods. It is generally assumed that false-positives are mostly due to algorithmic weakness of motif-finders. Here, however, we derive the theoretical dependence of false positives on dataset size and find that false positives can arise as a result of large dataset size, irrespective of the algorithm used. Interestingly, the false-positive strength depends more on the number of sequences in the dataset than it does on the sequence length. As expected, false-positives can be reduced by decreasing the sequence length or by adding more sequences to the dataset. The dependence on number of sequences, however, diminishes and reaches a plateau after which adding more sequences to the dataset does not reduce the false-positive rate significantly. Based on the theoretical results presented here, we provide a number of intuitive rules of thumb that may be used to enhance motif-finding results in practice.