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NEGATIVE INFORMATION FOR MOTIF DISCOVERY

K. T. TAKUSAGAWA, D. K. GIFFORD
2003 Biocomputing 2004  
Within the word-counting algorithmic approach to motif discovery, we present a method of incorporating information from negative intergenic regions where a transcription factor is thought not to bind,  ...  We discuss a method of combining genome-wide transcription factor binding data, gene expression data, and genome sequence data for the purpose of motif discovery in S. cerevisiae.  ...  Benjamin Gordon, and Ziv Bar-Joseph for help with the data sources used in this project. K.T.T. was supported by a NDSEG/ASEE Graduate Fellowship.  ... 
doi:10.1142/9789812704856_0034 fatcat:bpol56mrbzgf7ahreuysdrperi

Negative information for motif discovery

K T Takusagawa, D K Gifford
2004 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
Within the word-counting algorithmic approach to motif discovery, we present a method of incorporating information from negative intergenic regions where a transcription factor is thought not to bind,  ...  We discuss a method of combining genome-wide transcription factor binding data, gene expression data, and genome sequence data for the purpose of motif discovery in S. cerevisiae.  ...  Benjamin Gordon, and Ziv Bar-Joseph for help with the data sources used in this project. K.T.T. was supported by a NDSEG/ASEE Graduate Fellowship.  ... 
pmid:14992517 fatcat:77rpmqerdveotdqhbmvvg6dglu

BayesMotif: de novo protein sorting motif discovery from impure datasets

Jianjun Hu, Fan Zhang
2010 BMC Bioinformatics  
Conclusion: We proposed BayesMotif, a novel Bayesian classification based algorithm for de novo discovery of a special category of anchored protein sorting motifs from impure datasets.  ...  Methods: We formulated the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based algorithm (BayesMotif) for de novo identification of a common type  ...  Acknowledgment is also made to the University of South Carolina's High Performance Computing Group for the computing time used in this research.  ... 
doi:10.1186/1471-2105-11-s1-s66 pmid:20122242 pmcid:PMC3009540 fatcat:rqcxhxpl65dztl4jxfkmgep2te

Bayesian Classifier for Anchored Protein Sorting Discovery

Fan Zhang, Jianjun Hu
2009 2009 IEEE International Conference on Bioinformatics and Biomedicine  
We formulate the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based motif discovery algorithm (BayesMotif) to find a common type of sorting motifs  ...  Experiments showed that our algorithm has the advantage of finding long lowly conserved sorting signals compared to other protein motif discovery algorithms such as MEME.  ...  The web server for this program will be made available at http://mleg.cse.sc.edu/sortmotif.  ... 
doi:10.1109/bibm.2009.43 dblp:conf/bibm/ZhangH09 fatcat:ebijbygsybfchmt2ywnmrdq37a

Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

Emma Redhead, Timothy L Bailey
2007 BMC Bioinformatics  
Results: We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set.  ...  We also show that DEME can find highly informative thermalstability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at  ...  We thank David La for providing the sequence sets for orthologous proteins in mesophiles and thermophiles. ER and TLB were supported by NIH Grant NIH R0-1 RR021692-01.  ... 
doi:10.1186/1471-2105-8-385 pmid:17937785 pmcid:PMC2194741 fatcat:ufcthyqi4rg5bjz4vlvuukflr4

Discriminative Motif Discovery via Simulated Evolution and Random Under-Sampling

Tao Song, Hong Gu, Enrique Hernandez-Lemus
2014 PLoS ONE  
Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention.  ...  the imbalance between the positive and negative datasets.  ...  Acknowledgments The authors wish to thank the editor and anonymous reviewers for their helpful comments and suggestions. Author Contributions  ... 
doi:10.1371/journal.pone.0087670 pmid:24551063 pmcid:PMC3923751 fatcat:fbmscys5gngqzbzgdvti6zpxwi

TRAINING SET DESIGN FOR PATTERN DISCOVERY WITH APPLICATIONS TO PROTEIN MOTIF DETECTION

YANLI SUN, ZHENGYUE DENG, GIRI NARASIMHAN, KALAI MATHEE
2005 Advances in Bioinformatics and Its Applications  
Supervised pattern discovery techniques have been successfully used for motif detection. However, this requires the use of an efficient training set.  ...  We present a new strategy for designing good training sets that uses phylogenetic trees to automatically reduce the bias in training sets.  ...  Consequently, the resulting detector may have many false negatives for that motif subtype. 3.  ... 
doi:10.1142/9789812702098_0022 fatcat:dpoewwszijaexhsb6g43t2rc3a

The value of position-specific priors in motif discovery using MEME

Timothy L Bailey, Mikael Bodén, Tom Whitington, Philip Machanick
2010 BMC Bioinformatics  
Information of many types-including sequence conservation, nucleosome positioning, and negative examples-can be converted into a prior over the location of motif sites, which then guides the sequence motif  ...  This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been  ...  The authors thank Alexander Hartemink and Raluca Gôrdan for providing the scripts for computing the prior.  ... 
doi:10.1186/1471-2105-11-179 pmid:20380693 pmcid:PMC2868008 fatcat:vk3a6ibjlffn7b6rlcpwvb3any

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction

Yuchun Guo, Kevin Tian, Haoyang Zeng, Xiaoyun Guo, David Kenneth Gifford
2018 Genome Research  
KMAC also identifies correct motifs in more experiments than four state-of-the-art motif discovery methods.  ...  The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of diseaseassociated non-coding  ...  ACKNOWLEDGMENTS We thank Jens Keilwagen for providing suggestions and codes for training and using Slim model.  ... 
doi:10.1101/gr.226852.117 pmid:29654070 fatcat:fhnnhu432raafiotqlji6qsr2u

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction [article]

Yuchun Guo, Kevin Tian, Haoyang Zeng, Xiaoyun Guo, David K. Gifford
2017 bioRxiv   pre-print
KMAC also identifies correct motifs in more experiments than four state-of-the-art motif discovery methods.  ...  The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated non-coding  ...  ACKNOWLEDGMENTS We thank Jens Keilwagen for providing suggestions and codes for training and using Slim model.  ... 
doi:10.1101/130815 fatcat:hkrehkj7sfc27lctijg5gzgn3y

Locating transcription factor binding sites by fully convolutional neural network

Qinhu Zhang, Siguo Wang, Zhanheng Chen, Ying He, Qi Liu, De-Shuang Huang
2021 Briefings in Bioinformatics  
Besides, we find that the regions located by FCNA can be used by motif discovery tools to further refine the prediction performance.  ...  In recent years, an increasing number of deep learning (DL) based methods have been proposed for predicting TF binding sites (TFBSs) and achieved impressive prediction performance.  ...  Similarly, the global context of DNA sequences is also important for motif discovery, e.g. the information of flanking regions can influence the binding activity of TF-DNA [20] .  ... 
doi:10.1093/bib/bbaa435 pmid:33498086 pmcid:PMC8425303 fatcat:n6khq5iz2jestocmm44ho2dvwm

DLocalMotif: a discriminative approach for discovering local motifs in protein sequences

A. M. Mehdi, M. S. B. Sehgal, B. Kobe, T. L. Bailey, M. Boden
2012 Bioinformatics  
Results: This article introduces the method DLocalMotif that makes use of positional information and negative data for local motif discovery in protein sequences.  ...  Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery.  ...  To our knowledge, none of the available protein motif discovery methods make use of both types of information.  ... 
doi:10.1093/bioinformatics/bts654 pmid:23142965 fatcat:uftkq4xizjhqlh4ncsemvkshky

Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

Ehsaneddin Asgari, Alice C. McHardy, Mohammad R. K. Mofrad
2019 Scientific Reports  
two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins,  ...  (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with  ...  We use 10% of both the positive and negative sequences as the reserved set for evaluation and 90% for motif discovery and training purposes.  ... 
doi:10.1038/s41598-019-38746-w pmid:30837494 pmcid:PMC6401088 fatcat:mfyecf7z5zb4vftssko5n7tojq

Assessing the Effects of Symmetry on Motif Discovery and Modeling

Lala M. Motlhabi, Gary D. Stormo, Peter Csermely
2011 PLoS ONE  
Citation: Motlhabi LM, Stormo GD (2011) Assessing the Effects of Symmetry on Motif Discovery and Modeling. PLoS ONE 6(9): e24908.  ...  Predicting DNA binding sites computationally suffers from high false positives and false negatives due to various contributing factors, including the inaccurate models for transcription factor specificity  ...  Acknowledgments We thank all members of the Stormo lab for helpful discussions and suggestions regarding this work. We especially thank Mohammed Khan for help with the site sampling program.  ... 
doi:10.1371/journal.pone.0024908 pmid:21949783 pmcid:PMC3176789 fatcat:gjnadjvhyjh3zh7coons7vjgbq

Probabilistic variable-length segmentation of protein sequences for discriminative motif mining (DiMotif) and sequence embedding (ProtVecX) [article]

Ehsaneddin Asgari, Alice McHardy, Mohammad R. K. Mofrad
2018 bioRxiv   pre-print
(i) DiMotif: we present DiMotif as an alignment-free discriminative motif miner and evaluate the method for finding protein motifs in different settings.  ...  We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence.  ...  Additional Information Competing interests The authors declare no competing interests. Corresponding author Correspondence to Mohammad R.K. Mofrad.  ... 
doi:10.1101/345843 fatcat:sqwdgdx3yzdarlkzrcaqviqbpy
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