Choosing panels of genomics assays using submodular optimization

Genome Biology

Volumn 17, Issue 1, 2016, Pages

  Wei, Kai ^a   Libbrecht, Maxwell W ^a   Bilmes, Jeffrey A ^a   Noble, William Stafford ^b  

^a University of Washington   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Discrete optimization; Genomics assays; Submodularity

Indexed keywords

BIOLOGY; GENOMICS; MODEL; BINDING SITE; CHROMATIN IMMUNOPRECIPITATION; EPIGENETICS; GENOME; HIGH THROUGHPUT SEQUENCING; HUMAN; METABOLISM; NUCLEIC ACID DATABASE; PROCEDURES; STANDARDS;

HISTONE; PROTEIN BINDING; TRANSCRIPTION FACTOR;

BINDING SITES; CHROMATIN IMMUNOPRECIPITATION; DATABASES, NUCLEIC ACID; EPIGENOMICS; GENOME; GENOMICS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HISTONES; HUMANS; PROTEIN BINDING; TRANSCRIPTION FACTORS;

EID: 84998590108     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-016-1089-7     Document Type: Article

References (51)

1. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol. 2010; 28(10):1045-8. http://dx.doi.org/10.1038/nbt1010-1045.
2. ENCODE Project Consortium. An Integrated Encyclopedia of DNA Elements in the Human Genome. Nature. 2012; 489:57-74.
3. Ernst J, Kellis M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat Biotechnol. 2015; 33(4):364-76.
4. Andrés ME, Burger C, Peral-Rubio MJ, Battaglioli E, Anderson ME, Grimes J, et al.CoREST: a functional corepressor required for regulation of neural-specific gene expression. Proc Nat Acad Sci. 1999; 96(17):9873-8.
5. Lin H, Bilmes J. Learning mixtures of submodular shells with application to document summarization. In: Uncertainty in Artificial Intelligence (UAI). Catalina Island, USA: AUAI: 2012. p. 479-90.
6. Liu Y, Wei K, Kirchhoff K, Song Y, Bilmes J. Submodular feature selection for high-dimensional acoustic score spaces. Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2013;:7184-8.
7. Mirzasoleiman B, Karbasi A, Sarkar R, Krause A. Distributed submodular maximization: identifying representative elements in massive data. In: Advances in neural information processing systems. MIT Press: 2013. p. 2049-57.
8. Gomes R, Krause A, Perona P. Discriminative clustering by regularized information maximization. In: Advances in neural information processing systems. MIT Press: 2010. p. 775-83.
9. Vives X. Oligopoly pricing: old ideas and new tools. MIT Press; 2001.
10. Carter M. Foundations of mathematical economics. MIT Press; 2001.
11. Topkis DM. Supermodularity and complementarity. Princeton University Press; 1998.
12. Shapley LS. Cores of convex games. Int J Game Theory. 1971; 1(1):11-26.
13. Edmonds J. Submodular functions, matroids and certain polyhedra. Combinatorial structures and their applications. 1970;:69-87.
14. Lovász L. Submodular functions and convexity In: A Bachem MG, Korte B, editors. Mathematical programming - the state of the art. Springer-Verlag: 1983. p. 235-57.
15. Schrijver A. Combinatorial optimization.Springer; 2004.
16. Narayanan H. Submodular functions and electrical networks. Ann Discrete Math. 1997:54.
17. Cornunéjols G, Nemhauser GL, Wolsey LA. The uncapacitated facility location problem In: Mirchandani PB, Franci RL, editors. Discrete location theory. New York: Wiley/Interscience: 1990. p. 119-71.
18. Narasimhan M, Bilmes J. A submodular-supermodular procedure with applications to discriminative structure learning. In: Uncertainty in, Artificial Intelligence (UAI). Edinburgh: Morgan Kaufmann Publishers: 2005. p. 404-12.
19. Krause A, Singh A, Guestrin C. Near-optimal sensor placements in Gaussian processes: theory, efficient algorithms and empirical studies. J Mach Learn Res. 2008; 9:235-84.
20. Wei K, Liu Y, Kirchhoff K, Bartels C, Bilmes J. Submodular subset selection for large-scale speech training data. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE: 2014. p. 3311-15.
21. Nemhauser GL, Wolsey LA, Fisher ML. An analysis of approximations for maximizing submodular set functions. Math Program. 1978; 14(1):265-94.
22. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2014; 43:gku989.
23. Li B, Carey M, Workman JLW. The role of chromatin during transcription. Cell. 2007; 128(4):707-19.
24. Day N, Hemmaplardh A, Thurman RE, Stamatoyannopoulos JA, Noble WS. Unsupervised segmentation of continuous genomic data. Bioinformatics. 2007; 23(11):1424-6.
25. Ernst J, Kellis M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat Biotechnol. 2010; 28(8):817-25.
26. Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods. 2012; 9(5):473-6.
27. Thurman RE, Day N, Noble WS, Stamatoyannopoulos JA. Identification of higher-order functional domains in the human ENCODE regions. Genome Res. 2007; 17:917-27.
28. Lian H, Thompson W, Thurman RE, Stamatoyannopoulos JA, Noble WS, Lawrence C. Automated mapping of large-scale chromatin structure in ENCODE. Bioinformatics. 2008; 24(17):1911-16.
29. Filion GJ, van Bemmel JG, Braunschweig U, Talhout W, Kind J, Ward LD, et al. Systematic protein location mapping reveals five principal chromatin types in Drosophila cells. Cell. 2010; 143(2):212-24.
30. Kharchenko PV, Alekseyenko AA, Schwartz YB, Minoda A, Riddle NC, Ernst J, et al. Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature. 2010; 471:480-5.
31. Jaschek R, Tanay A. Spatial clustering of multivariate genomic and epigenomic information. In: Proceedings of the 13th Annual International Conference on Computational Molecular Biology. vol. 5541. Springer: 2009. p. 170-83.
32. Larson JL, Huttenhower C, Quackenbush J, Yuan GC. A tiered hidden Markov model characterizes multi-scale chromatin states. Genomics. 2013; 102(1):1-7.
33. Biesinger J, Wang Y, Xie X. Discovering and mapping chromatin states using a tree hidden Markov model. BMC Bioinform. 2013; 14(Suppl 5):S4.
34. Buchbinder N, Feldman M, Naor J, Schwartz R. A tight linear time (1/2)-approximation for unconstrained submodular maximization. In: Proceedings of Annual Symposium on. IEEE, Foundations of Computer Science (FOCS): 2012. p. 649-58.
35. Buchbinder N, Feldman M, Naor JS, Schwartz R. Submodular maximization with cardinality constraints. Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM. 2014; 25:1433-52.
36. http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/.
37. https://sites.google.com/site/anshulkundaje/projects/epigenomeroadmap.
38. Hoffman MM, Ernst J, Wilder SP, Kundaje A, Harris RS, Libbrecht M, et al. Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res. 2013; 41(2):827-41.
39. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015; 518(7539):317-30.
40. Johnson NL. Systems of frequency curves generated by methods of translation. Biometrika. 1949; 36(1/2):149-76.
41. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al.Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008; 9(9):R137. http://dx.doi.org/10.1186/gb-2008-9-9-r137.
42. Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, et al.ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012 Sep; 22(9):1813-31. http://dx.doi.org/10.1101/gr.136184.111.
43. Fujishige S. Submodular functions and optimization. vol. 58: Elsevier Science; 2005.
44. Feige U. A threshold of lnn for approximating set cover. J ACM. 1998; 45(4):634-52.
45. Minoux M. Accelerated greedy algorithms for maximizing submodular set functions. Optimization Tech. 1978;:234-43.
46. Visel A, Blow MJ, Li Z, Zhang T, Akiyama JA, Holt A, et al.ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature. 2009; 457(7231):854-8. http://dx.doi.org/10.1038/nature07730.
47. Burgess-Beusse B, Farrell C, Gaszner M, Litt M, Mutskov V, Recillas-Targa F, et al. The insulation of genes from external enhancers and silencing chromatin. Proc Nat Acad Sci USA. 2002; 99(Suppl 4):16433.
48. Craven M, Bockhorst J. Markov networks for detecting overlapping elements in sequence data. Adv Neural Inform Process Syst. 2005; 17:193.
49. Davis J, Goadrich M. The relationship between precision-recall and ROC curves. In: Proceedings of the International Conference on Machine Learning. IMLS: 2006. p. 233-40.
50. Libbrecht M, Ay F, Hoffman MM, Gilbert DM, Bilmes JA, Noble WS. Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression. Genome Res. 2015; 25(4):544-57.
51. Bilmes J, Rogers R. The Graphical Models Toolkit GMTK Source Distribution; 2015. https://melodi.ee.washington.edu/gmtk.