Overlap-Based Similarity Metrics for Motif Search in DNA Sequences

Thanh, Hai; Wang, Dianhui

doi:10.1007/978-3-642-10684-2_52

Cited by 2 publications

(5 citation statements)

References 21 publications

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“…6 serves as an example. It shows that the similarity measure proposed by [18] gives the highest average ranks for most of the TF's but is the best one among the three measures for TF lrp when the 2-centroid method is used. It also shows that Sim2 yields lower average ranks than Sim except for a few TF's such as cytR and fur when used along with the 2-centroid method.…”

Section: No Best Methods For All Tf'smentioning

confidence: 98%

“…Do and Wang [18] proposed a novel distance measure by first transforming a sequence of length l into an (l − 1)element vector. To measure the distance between two sequences s and t, t can be shifted to the left or to the right (with penalty) to find the best alignment between s and t. Since shifting is implicitly done in scoring a nonbinding site in our CV experiments, we use the distance measure without considering shifting:…”

Section: The 2-centroid Methods With a Novel Similarity Measurementioning

confidence: 99%

“…Fig. 6 compares the performance of the similarity measures Sim in (1) (w i = 1, ∀i) and Sim2 in (3) (w i = 1, ∀i and w i,j = 1, ∀i, j) to the one proposed in [18]. The TF's are ordered by their median information content across the l nucleotides, i.e., the median of {IC i |i = 1, 2, .…”

Section: The 2-centroid Methods With a Novel Similarity Measurementioning

confidence: 99%

“…However, no methods relying on only positive examples were compared. Recently, Do and Wang [18] formulated the TFBS search problem as a classification problem, proposed a novel similarity measure, and investigated three classification techniques. Five-fold CV results showed that learning vector quantization performed better than P-Match [19], which requires only positive examples.…”

Section: Introductionmentioning

confidence: 99%

“…We propose and characterize two novel extensions of the centroid method introduced in [15]. Besides the sequence similarity measures employed in [15], we also incorporate the novel similarity measure in [18] into an extension of the centroid method. We compare our proposed methods to methods that do not rely upon negative examples, that is, the centroid method, the ULPB method [20] and the well-known position-specific scoring matrix method.…”

Section: Introductionmentioning

confidence: 99%

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Negative Example Aided Transcription Factor Binding Site Search

Lee,

Huang

2011

Preprint

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Computational approaches to transcription factor binding site identification have been actively researched for the past decade. Negative examples have long been utilized in de novo motif discovery and have been shown useful in transcription factor binding site search as well. However, understanding of the roles of negative examples in binding site search is still very limited. We propose the 2-centroid and optimal discriminating vector methods, taking into account negative examples. Cross-validation results on E. coli transcription factors show that the proposed methods benefit from negative examples, outperforming the centroid and position-specific scoring matrix methods. We further show that our proposed methods perform better than a state-of-the-art method. We characterize the proposed methods in the context of the other compared methods and show that, coupled with motif subtype identification, the proposed methods can be effectively applied to a wide range of transcription factors. Finally, we argue that the proposed methods are well-suited for eukaryotic transcription factors as well. Software tools are available at: http://biogrid.engr.uconn.edu/tfbs search/.

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Section: No Best Methods For All Tf'smentioning

confidence: 98%

Section: The 2-centroid Methods With a Novel Similarity Measurementioning

confidence: 99%

Section: The 2-centroid Methods With a Novel Similarity Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%