RefSelect: a reference sequence selection algorithm for planted (l, d) motif search

Abstract

BackgroundThe planted (l, d) motif search (PMS) is an important yet challenging problem in computational biology. Pattern-driven PMS algorithms usually use k out of t input sequences as reference sequences to generate candidate motifs, and they can find all the (l, d) motifs in the input sequences. However, most of them simply take the first k sequences in the input as reference sequences without elaborate selection processes, and thus they may exhibit sharp fluctuations in running time, especially for large alphabets.ResultsIn this paper, we build the reference sequence selection problem and propose a method named RefSelect to quickly solve it by evaluating the number of candidate motifs for the reference sequences. RefSelect can bring a practical time improvement of the state-of-the-art pattern-driven PMS algorithms. Experimental results show that RefSelect (1) makes the tested algorithms solve the PMS problem steadily in an efficient way, (2) particularly, makes them achieve a speedup of up to about 100× on the protein data, and (3) is also suitable for large data sets which contain hundreds or more sequences.ConclusionsThe proposed algorithm RefSelect can be used to solve the problem that many pattern-driven PMS algorithms present execution time instability. RefSelect requires a small amount of storage space and is capable of selecting reference sequences efficiently and effectively. Also, the parallel version of RefSelect is provided for handling large data sets.

Quorum Planted Motif Discovery and Motif Finding Using S2f and Fff Algorithms

“…The current PMP problem algorithms are classed into two groups such as the exact and approximation algorithms. Exact PMP algorithms, which are widely used include qPMS series [4], RefSelect [5], PairMOtif series [6], RecMotif [7], and iTriplet [8]. All kinds of exact algorithms require substantial computational effort if ℓ and d are quite high.…”

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes

“…x B i is a binary descriptor vector for the ith training molecule, w B and w N B are weight vectors corresponding to x B and x N B , respectively, L is the number of a training molecules, and k B (·) is a binary kernel function.Rather than using a single kernel or linear regression, MultiDK utilizes multiple kernels such as a nonlinear binary kernel for binary descriptors and linear processing for non-binary descriptors separately. To optimize a kernel function[70][71][72] , multiple combinatorial kernels have been used in various applications including biomedical data73 and YouTube video data74,75 . Here, we use a multiple kernel approach to apply appropriate kernels for different features instead of training the kernel.…”