Abstract
The identification of regulatory elements as over-represented motifs in the promoters of potentially co-regulated genes is an important and challenging problem in computational biology. Although many motif detection programs have been developed so far, they still seem to be immature practically. In particular the choice of tunable parameters is often critical to success. Thus knowledge regarding which parameter settings are most appropriate for various types of target motifs is invaluable, but unfortunately has been scarce. In this paper, we report our parameter landscape analysis of two widely-used programs (the Gibbs Sampler (GS) and MEME). Our results show that GS is relatively sensitive to the changes of some parameter values while MEME is more stable. We present recommended parameter settings for GS optimized for four different motif lengths. Thus, running GS four times with these settings should significantly decrease the risk of overlooking subtle motifs.
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n - An initial “guesstimate” of the total number of motifs in all of the sequences
c - The Plateau period is the number of iterations between successive local maxima. For each seed, the program samples until Plateau period iterations are performed without an increase in the MAP value (MAP value (maximum a posteriori) is a measure of the statistical significance of the motifs alignment comparing to a “null” alignment.)
t - The number of times the sampler restarts with different seeds
m - Maximum number of iterations per seed
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Polouliakh, N., Konno, M., Horton, P., Nakai, K. (2005). Parameter Landscape Analysis for Common Motif Discovery Programs. In: Eskin, E., Workman, C. (eds) Regulatory Genomics. RRG 2004. Lecture Notes in Computer Science(), vol 3318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32280-1_8
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DOI: https://doi.org/10.1007/978-3-540-32280-1_8
Publisher Name: Springer, Berlin, Heidelberg
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