<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparison of mechanistic signaling pathway activity analysis methods.</style></title><secondary-title><style face="normal" font="default" size="100%">Brief Bioinform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brief Bioinform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Postmortem Changes</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">1655-1668</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29868818?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ewing, Adam D</style></author><author><style face="normal" font="default" size="100%">Houlahan, Kathleen E</style></author><author><style face="normal" font="default" size="100%">Hu, Yin</style></author><author><style face="normal" font="default" size="100%">Ellrott, Kyle</style></author><author><style face="normal" font="default" size="100%">Caloian, Cristian</style></author><author><style face="normal" font="default" size="100%">Yamaguchi, Takafumi N</style></author><author><style face="normal" font="default" size="100%">Bare, J Christopher</style></author><author><style face="normal" font="default" size="100%">P’ng, Christine</style></author><author><style face="normal" font="default" size="100%">Waggott, Daryl</style></author><author><style face="normal" font="default" size="100%">Sabelnykova, Veronica Y</style></author><author><style face="normal" font="default" size="100%">Kellen, Michael R</style></author><author><style face="normal" font="default" size="100%">Norman, Thea C</style></author><author><style face="normal" font="default" size="100%">Haussler, David</style></author><author><style face="normal" font="default" size="100%">Friend, Stephen H</style></author><author><style face="normal" font="default" size="100%">Stolovitzky, Gustavo</style></author><author><style face="normal" font="default" size="100%">Margolin, Adam A</style></author><author><style face="normal" font="default" size="100%">Stuart, Joshua M</style></author><author><style face="normal" font="default" size="100%">Boutros, Paul C</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants</style></author><author><style face="normal" font="default" size="100%">Liu Xi</style></author><author><style face="normal" font="default" size="100%">Ninad Dewal</style></author><author><style face="normal" font="default" size="100%">Yu Fan</style></author><author><style face="normal" font="default" size="100%">Wenyi Wang</style></author><author><style face="normal" font="default" size="100%">David Wheeler</style></author><author><style face="normal" font="default" size="100%">Andreas Wilm</style></author><author><style face="normal" font="default" size="100%">Grace Hui Ting</style></author><author><style face="normal" font="default" size="100%">Chenhao Li</style></author><author><style face="normal" font="default" size="100%">Denis Bertrand</style></author><author><style face="normal" font="default" size="100%">Niranjan Nagarajan</style></author><author><style face="normal" font="default" size="100%">Qing-Rong Chen</style></author><author><style face="normal" font="default" size="100%">Chih-Hao Hsu</style></author><author><style face="normal" font="default" size="100%">Ying Hu</style></author><author><style face="normal" font="default" size="100%">Chunhua Yan</style></author><author><style face="normal" font="default" size="100%">Warren Kibbe</style></author><author><style face="normal" font="default" size="100%">Daoud Meerzaman</style></author><author><style face="normal" font="default" size="100%">Kristian Cibulskis</style></author><author><style face="normal" font="default" size="100%">Mara Rosenberg</style></author><author><style face="normal" font="default" size="100%">Louis Bergelson</style></author><author><style face="normal" font="default" size="100%">Adam Kiezun</style></author><author><style face="normal" font="default" size="100%">Amie Radenbaugh</style></author><author><style face="normal" font="default" size="100%">Anne-Sophie Sertier</style></author><author><style face="normal" font="default" size="100%">Anthony Ferrari</style></author><author><style face="normal" font="default" size="100%">Laurie Tonton</style></author><author><style face="normal" font="default" size="100%">Kunal Bhutani</style></author><author><style face="normal" font="default" size="100%">Nancy F Hansen</style></author><author><style face="normal" font="default" size="100%">Difei Wang</style></author><author><style face="normal" font="default" size="100%">Lei Song</style></author><author><style face="normal" font="default" size="100%">Zhongwu Lai</style></author><author><style face="normal" font="default" size="100%">Liao, Yang</style></author><author><style face="normal" font="default" size="100%">Shi, Wei</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Cheryl C K Lau</style></author><author><style face="normal" font="default" size="100%">Justin Guinney</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature methods</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cancer</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">variant calling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 May 18</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.</style></abstract></record></records></xml>