@article {1093,
	title = {Understanding disease mechanisms with models of signaling pathway activities.},
	journal = {BMC systems biology},
	volume = {8},
	year = {2014},
	month = {2014 Oct 25},
	pages = {121},
	abstract = {BackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.},
	keywords = {Disease mechanism, pathway, signalling, Systems biology},
	issn = {1752-0509},
	doi = {10.1186/s12918-014-0121-3},
	url = {http://www.biomedcentral.com/1752-0509/8/121/abstract},
	author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Ana Conesa and Sonia Tarazona and Amadoz, Alicia and Armero, Carmen and Salavert, Francisco and Vidal-Puig, Antonio and Montaner, David and Joaqu{\'\i}n Dopazo}
}
@article {938,
	title = {Qualimap: evaluating next-generation sequencing alignment data.},
	journal = {Bioinformatics (Oxford, England)},
	volume = {28},
	year = {2012},
	month = {2012 Oct 15},
	pages = {2678-9},
	abstract = {MOTIVATION: The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol and/or the selected mapping algorithm introduce some unwanted biases in these data. The systematic detection of such biases is a non-trivial task that is crucial to drive appropriate downstream analyses. RESULTS: We have developed Qualimap, a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties. Qualimap takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data. Such quality-control data are vital for highlighting problems in the sequencing and/or mapping processes, which must be addressed prior to further analyses. AVAILABILITY: Qualimap is freely available from http://www.qualimap.org. CONTACT: aconesa@cipf.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
	keywords = {NGS},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/bts503},
	url = {http://bioinformatics.oxfordjournals.org/content/28/20/2678.long},
	author = {Garc{\'\i}a-Alcalde, Fernando and Okonechnikov, Konstantin and Carbonell, Jos{\'e} and Cruz, Luis M and G{\"o}tz, Stefan and Sonia Tarazona and Joaqu{\'\i}n Dopazo and Meyer, Thomas F and Ana Conesa}
}
