@article { ISI:000300490500018,
	title = {Clare: cracking the language of regulatory elements},
	journal = {Bioinformatics},
	volume = {28},
	number = {4},
	year = {2012},
	month = {FEB},
	pages = {581-583},
	publisher = {OXFORD UNIV PRESS},
	address = {GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND},
	abstract = {<p>CLARE is a computational method designed to reveal sequence encryption of tissue- specific regulatory elements. Starting with a set of regulatory elements known to be active in a particular tissue/process, it learns the sequence code of the input set and builds a predictive model from features specific to those elements. The resulting model can then be applied to user-supplied genomic regions to identify novel candidate regulatory elements. CLARE{\textquoteright}s model also provides a detailed analysis of transcription factors that most likely bind to the elements, making it an invaluable tool for understanding mechanisms of tissue- specific gene regulation.</p>},
	issn = {1367-4803},
	doi = {10.1093/bioinformatics/btr704},
	author = {Taher, Leila and Narlikar, Leelavati and Ovcharenko, Ivan}
}
@article {43026,
	title = {Identification and computational analysis of gene regulatory elements},
	journal = {Cold Spring Harbor Protocols},
	volume = {1},
	year = {2015},
	month = {JAN},
	abstract = {<p>Over the last two decades, advances in experimental and computational technologies have greatly facilitated genomic research. Next-generation sequencing technologies have made de novo sequencing of large genomes affordable, and powerful computational approaches have enabled accurate annotations of genomic DNA sequences. Charting functional regions in genomes must account for not only the coding sequences, but also noncoding RNAs, repetitive elements, chromatin states, epigenetic modifications, and gene regulatory elements. A mix of comparative genomics, high-throughput biological experiments, and machine learning approaches has played a major role in this truly global effort. Here we describe some of these approaches and provide an account of our current understanding of the complex landscape of the human genome. We also present overviews of different publicly available, large-scale experimental data sets and computational tools, which we hope will prove beneficial for researchers working with large and complex genomes. {\textcopyright} 2015 Cold Spring Harbor Laboratory Press.</p>},
	doi = {10.1101/pdb.top083642},
	author = {Taher, Leila and Narlikar, Leelavati and Ovcharenko, Ivan}
}
