UEA Computational Biology Laboratory

Introduction

The identification and understanding of transcriptional regulatory networks and their interactions is a major challenge in biology. We use a Relevance Vector Machine (RVM) [2] to classify gene expression according to the composition of promoter sequences. The RVM is used with a Bayesian Automatic Relevance Determination (ARD) prior to select a small subset of promoter motifs for its discriminatory rule to optimally distinguish between regulated genes. The RVM also has the useful property that no parameters are set or statistical assumptions made, such as the threshold of significance of a feature, since the entire model is generated automatically from the data. It also considers the significance of a feature in the context of the features already selected. This makes the application especially suitable for biological problems with many variables of unknown significance that may influence each other.

Sparse Bayesian Learning and The Relevance Vector Machine

In a statistical pattern recognition setting, the Relevance Vector Machine (RVM) [2] is essentially a Bayesian training algorithm for logistic regression models with input feature selection via Bayesian Automatic Relevance Determination. The RVM has a number of advantages over more conventional feature selection algorithms: Firstly there are no hyper-parameters that must be tuned in order to maximised generalisation performance, for instance an arbitrary input feature salience threshold. Secondly the algorithm is not greedy in the sense that a feature picked early in the training procedure may be dropped later if a more compact, more informative combination of features is discovered. Lastly the algorithm is based on sound Bayesian principles (it is a type-II maximum-likelihood procedure), rather than being merely a heuristic. The RVM implementation used here is based on the greedy marginal likelihood maximisation algorithm of Tipping and Faul [4]. An older implementation of the RVM, based on the methods presented in [2] is available from www.relevancevector.com.

Online Bayesian Regulatory Element Detection (BRED) Tool

To perform an analysis of the promoters of a set of co-regulated genes in Arabidopsis, simply enter the gene IDs (e.g. At1g01020, At2g269930 or At5g67620) of the up- and down-regulated genes, title of the experiment, your name and email address in the boxes below and press "submit". A report on the results of the promoter analysis will be emailed to you in due course. A full list of permissible gene IDs is available here genes.txt. At the moment, the tool is limited to only around 100 genes of each category, it seems due to a limitation in the MATLAB Webserver, for which we are currently working on a solution.

Title:

Name:	Email:

up-regulated genes	down-regulated genes
AT5G66985 AT1G61800 AT5G64510 AT1G17180 AT3G15650 AT4G33070 AT1G73600 AT2G37580 AT2G22470 AT2G15490 AT5G17450 AT5G26220 AT5G24660 AT5G09840 AT1G24280 AT1G59860 AT5G51440 AT3G14200 AT5G10830 AT5G39840 AT3G28345 AT5G12110 AT1G08430 AT5G01790 AT1G62560 AT4G34950 AT4G29520 AT2G26150 AT4G36830 AT3G24290 AT1G64780 AT5G48570 AT1G74310 AT4G35320 AT2G32990 AT2G34650 AT1G71000 AT3G12580 AT1G30960 AT4G21990 AT4G04610 AT5G41670 AT1G06000 AT1G03020 AT1G78050 AT1G57590 AT1G60750 AT5G52020 AT1G17170 AT1G70985 AT5G28540 AT5G53420 AT1G78340 AT5G52640 AT5G03760 AT1G73010 AT2G44940 AT1G18320 AT4G05020 AT2G35860 AT1G66080 AT3G15030 AT1G04220 AT2G20560 AT4G39210 AT1G32900 AT1G30510 AT1G30530 AT1G78090 AT4G39190 AT5G09590 AT5G23480 AT1G05100 AT1G51080 AT5G37950 AT4G37410 AT1G11700 AT5G60890 AT5G49330 AT4G23200 AT3G12860 AT2G40435 AT3G44990 AT1G78000 AT2G45900 AT1G65840 AT3G10520 AT3G58990 AT1G54540 AT4G05390 AT5G45070 AT1G67360 AT1G65060 AT5G45670 AT5G61790 AT3G16050	AT1G15045 AT2G17880 AT5G44260 AT3G62950 AT1G70290 AT5G22920 AT3G48360 AT4G15690 AT3G15450 AT4G37610 AT2G47270 AT2G18700 AT1G23390 AT3G23880 AT5G56870 AT2G20670 AT4G15700 AT4G26260 AT1G02640 AT5G56100 AT1G11260 AT2G25900 AT5G14470 AT5G49450 AT5G28770 AT2G19800 AT1G23870 AT1G80920 AT3G07350 AT1G77210 AT3G49790 AT2G45170 AT1G80440 AT2G28120 AT3G15630 AT3G30775 AT2G15890 AT1G54740 AT1G52250 AT3G15500 AT2G33830 AT5G61440 AT5G19120 AT3G61060 AT5G21170 AT5G18670 AT5G41080 AT1G68190 AT3G57520 AT5G20250 AT4G38470 AT2G32150 AT5G52250 AT5G04770 AT2G40000 AT3G15440 AT1G74670 AT5G49360 AT1G28330 AT1G71030 AT4G03510 AT4G27450 AT1G10070 AT2G24550 AT1G33055 AT1G08630 AT1G03850 AT4G10910 AT5G23350 AT5G24890 AT5G61590 AT3G62550 AT3G06070 AT5G06690 AT4G17245 AT1G80160 AT4G25580 AT5G60680 AT2G39570 AT4G39675 AT2G15080 AT5G48430 AT3G13750 AT1G56220 AT1G25230 AT1G60140 AT2G41250 AT2G02710 AT5G14120 AT1G08570 AT3G46690 AT5G17300 AT5G16340 AT5G57240 AT2G38820 AT4G01330 AT5G19530 AT1G78450 AT4G36670 AT3G26510 AT5G49700 AT1G30820 AT3G62930 AT2G25200 AT5G39580 AT1G76410 AT5G47390 AT5G48110

Performing the analysis is likely to take 30-40 minutes or so, largely depending on the total number of up- and down-regulated genes. If I were you, I'd go off and make myself a nice cup of tea and then have a look at cricinfo, rather than just sit at my computer patiently waiting for it.

Acknowledgements

This work was supported by grants from the U.K. Biotechnology and Biological Sciences Research Council (BBSRC) under the Exploiting Genomics initiative (grant numbers and 83/EGM16126 and 83/EGM16128 "Computational approaches to identifying gene regulatory systems in Arabidopsis").

References

N.B. DOI represents a link to on-line material via the Digital Object Identifier DOI system, where available. WWW represents a link to on-line materials available via the publisher's web-site.

[1] Bussemaker, H. J., Li, H. and Siggia, E.D., "Regulatory element detection using correlation with expression", Nature Genetics, vol. 27, pp. 167-171, 2001. (DOI)

[2] Tipping, M. E., "Sparse Bayesian learning and the Relevance Vector Machine", Journal of Machine Learning Research, vol. 1., pp. 211-244, June 2001. (WWW)

[3] Li, Y., Lee, K. K., Smith, C., Walsh, C., Sorefan, K., Cawley, G. and Bevan, M., "Establishing Glucose- and ABA-regulated Transcription Networks in Arabidopsis by Microarray Analysis and Promoter Classification using a Relevance Vector Machine", Genome Research (submitted), 2005.

[4] Tipping, M. E. and Faul, A. C., "Fast marginal likelihood maximisation for sparse Bayesian models", In C. M. Bishop and B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6 2003. (WWW)

Research Team: Kamel Saadi, Gary Lee, Gavin Cawley, Yunhai Li (JIC), Mike Bevan (JIC).