Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models

BMC Bioinformatics

Volumn 12, Issue SUPPL. 1, 2011, Pages

  Costa, Ivan G ^a   Roider, Helge G ^b   do Rego, Thais G ^a   de Carvalho, Francisco de A T ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

^b MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CELL DIFFERENTIATION; CHROMATIN MODIFICATION; DIFFERENTIATION PROCESS; GENE EXPRESSION PATTERNS; LINEAR MIXTURE MODELS; LINEAR REGRESSION MODELS; TRANSCRIPTION FACTOR BINDING; TRANSCRIPTIONAL REPRESSION;

BINDING ENERGY; CHEMICAL ACTIVATION; MIXTURES; REGRESSION ANALYSIS; STEM CELLS; T-CELLS; BIOINFORMATICS;

TRANSCRIPTION FACTORS;

DNA; HISTONE; TRANSCRIPTION FACTOR;

ARTICLE; BAYES THEOREM; CD4+ T LYMPHOCYTE; CELL DIFFERENTIATION; CYTOLOGY; GENE EXPRESSION REGULATION; GENETICS; METABOLISM; PROTEIN BINDING; STATISTICAL MODEL;

BAYES THEOREM; CD4-POSITIVE T-LYMPHOCYTES; CELL DIFFERENTIATION; DNA; GENE EXPRESSION REGULATION; HISTONES; LINEAR MODELS; PROTEIN BINDING; TRANSCRIPTION FACTORS;

EID: 79951531339     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-S1-S29     Document Type: Article

References (33)

1. Zhu J, Paul WE. CD4 T cells: fates, functions, and faults. Blood 2008, 112(5):1557-1569. 10.1182/blood-2008-05-078154, 2518872, 18725574.
2. Goldberg AD, Allis CD, Bernstein E. Epigenetics: a landscape takes shape. Cell 2007, 128(4):635-638. 10.1016/j.cell.2007.02.006, 17320500.
3. Kouzarides T. Chromatin modifications and their function. Cell 2007, 128(4):693-705. 10.1016/j.cell.2007.02.005, 17320507.
4. Turner BM. Defining an epigenetic code. Nat Cell Biol 2007, 9:2-6. 10.1038/ncb0107-2, 17199124.
5. Bibikova M, Laurent LC, Ren B, Loring JF, Fan JB. Unraveling epigenetic regulation in embryonic stem cells. Cell Stem Cell 2008, 2(2):123-134. 10.1016/j.stem.2008.01.005, 18371433.
6. Schoenborn JR, Dorschner MO, Sekimata M, Santer DM, Shnyreva M, Fitzpatrick DR, Stamatoyannopoulos JA, Stamatoyonnapoulos JA, Wilson CB. Comprehensive epigenetic profiling identifies multiple distal regulatory elements directing transcription of the gene encoding interferon-gamma. Nat Immunol 2007, 8(7):732-742. 10.1038/ni1474, 2144744, 17546033.
7. Costa IG, Roepcke S, Schliep A. Gene expression trees in lymphoid development. BMC Immunol 2007, 8:25. 10.1186/1471-2172-8-25, 2244641, 17925013.
8. Costa IG, Roepcke S, Hafemeister C, Schliep A. Inferring differentiation pathways from gene expression. Bioinformatics 2008, 24(13):i156-i164. 10.1093/bioinformatics/btn153, 2718631, 18586709.
9. Bussemaker HJ, Foat BC, Ward LD. Predictive modeling of genome-wide mRNA expression: from modules to molecules. Annu Rev Biophys Biomol Struct 2007, 36:329-347. 10.1146/annurev.biophys.36.040306.132725, 17311525.
10. Bussemaker HJ, Li H, Siggia ED. Regulatory element detection using correlation with expression. Nat Genet 2001, 27(2):167-171. 10.1038/84792, 11175784.
11. Keles S, van der Laan M, Eisen MB. Identification of regulatory elements using a feature selection method. Bioinformatics 2002, 18(9):1167-1175. 10.1093/bioinformatics/18.9.1167, 12217908.
12. Karlic R, Chung HR, Lasserre J, Vlahovicek K, Vingron M. Histone modification levels are predictive for gene expression. Proc Natl Acad Sci U S A 2010, 107(7):2926-2931. 10.1073/pnas.0909344107, 2814872, 20133639.
13. Woolf E, Xiao C, Fainaru O, Lotem J, Rosen D, Negreanu V, Bernstein Y, Goldenberg D, Brenner O, Berke G, Levanon D, Groner Y. Runx3 and Runx1 are required for CD8 T cell development during thymopoiesis. Proc Natl Acad Sci U S A 2003, 100(13):7731-7736. 10.1073/pnas.1232420100, 164656, 12796513.
14. DeSarbo W, Cron W. A maximum likelihood methodology for clusterwise linear regression. Journal of Classification 1988, 5(2):249-282.
15. Hinton GE, Revow M, Dayan P. Recognizing Handwritten Digits Using Mixtures of Linear Models. NIPS 1994, 1015-1022. MIT Press, Tesauro G, Touretzky DS, Leen TK.
16. Wei G, Wei L, Zhu J, Zang C, Hu-Li J, Yao Z, Cui K, Kanno Y, Roh TY, Watford WT, Schones DE, Peng W, Sun HW, Paul WE, O'Shea JJ, Zhao K. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 2009, 30:155-167. 10.1016/j.immuni.2008.12.009, 2722509, 19144320.
17. Roider HG, Kanhere A, Manke T, Vingron M. Predicting transcription factor affinities to DNA from a biophysical model. Bioinformatics 2007, 23(2):134-141. 10.1093/bioinformatics/btl565, 17098775.
18. Barreda DR, Belosevic M. Transcriptional regulation of hemopoiesis. Dev Comp Immunol 2001, 25(8-9):763-789. 10.1016/S0145-305X(01)00035-0, 11602195.
19. Matthias P, Rolink AG. Transcriptional networks in developing and mature B cells. Nat Rev Immunol 2005, 5(6):497-508. 10.1038/nri1633, 15928681.
20. Rothenberg EV, Moore JE, Yui MA. Launching the T-cell-lineage developmental programme. Nat Rev Immunol 2008, 8:9-21. 10.1038/nri2232, 18097446.
21. Roider HG, Lenhard B, Kanhere A, Haas SA, Vingron M. CpG-depleted promoters harbor tissue-specific transcription factor binding signals-implications for motif overrepresentation analyses. Nucleic Acids Res 2009, 37(19):6305-6315. 10.1093/nar/gkp682, 2770660, 19736212.
22. Mo X, Kowenz-Leutz E, Laumonnier Y, Xu H, Leutz A. Histone H3 tail positioning and acetylation by the c-Myb but not the v-Myb DNA-binding SANT domain. Genes Dev 2005, 19(20):2447-2457. 10.1101/gad.355405, 1257399, 16195416.
23. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B 2005, 67(2):301-320.
24. Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 1977, 39:1-38.
25. McLachlan GJ, Peel D. Finite Mixture Models 2000, Wiley Series in Probability and Statistics., Wiley, New York.
26. Breiman L. Bagging Predictors. Machine Learning 1996, 123-140.
27. MacKay DJC. Bayesian Interpolation. Neural Computation 1992, 4(3):415-447.
28. Matys V, Fricke E, Geffers R, Gössling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV, Kloos DUU, Land S, Lewicki-Potapov B, Michael H, Münch R, Reuter I, Rotert S, Saxel H, Scheer M, Thiele S, Wingender E. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic acids research 2003, 31:374-378. 10.1093/nar/gkg108, 165555, 12520026.
29. Ji H, Jiang H, Ma W, Johnson DS, Myers RM, Wong WH. An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat Biotechnol 2008, 26(11):1293-1300. 10.1038/nbt.1505, 2596672, 18978777.
30. Huber W, von Heydebreck A, Sültmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002, 18(Suppl 1):S96-104.
31. Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, Kasprzyk A. BioMart-biological queries made easy. BMC Genomics 2009, 10:22. 10.1186/1471-2164-10-22, 2649164, 19144180.
32. Brusco MJ, Cradit JD, Steinley D, Fox GL. Cautionary Remarks on the Use of Clusterwise Regression. Multivariate Behavioral Research 2008, 43:29-49.
33. Georgi B, Costa IG, Schliep A. PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data. BMC Bioinformatics 2010, 11:9. 10.1186/1471-2105-11-9, 2823712, 20053276.