We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

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Model-based Analysis of ChIP-Seq (MACS)

Ferroptosis: An Iron-Dependent Form of Nonapoptotic Cell Death

March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter.

Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter. 

Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …

Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

/pdf/identification-and-analysis-of-functional-elements-in-1-of-2o640l8ikl.pdf

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

Une correlation existe entre l'expression du polypeptide JMJD6 et des metastases du cancer du sein. En consequence, l'invention concerne un biomarqueur de diagnostic, de pronostic et therapeutique permettant de faire la distinction entre un cancer precoce et un cancer avance/metastasique, en particulier pour le cancer du sein, y compris des composes et des procedes de traitement correspondants.

Biomarqueur et traitement pour le cancer

A method of obtaining cut-off expression values should be selected so as to maximise the separation of the respective survival curves of the two groups of patients. Pairs of genes are statistically significant genes are generated by generating a plurality of models, each of which represents a way of partitioning a set of subjects based on the optimal cut-off expression values of the pair of genes. Those gene pairs are identified for which one of the models has a high prognostic significance. Novel survival significant gene sets forming functional modules which could be used to develop specific prognostic and predictive tests are derived.

A method, system and computer program product for the systematic evaluation of the prognostic properties of gene pairs for medical conditions.

We investigate the differences in molecular signature and clinical outcomes between multiple lesion glioblastoma (GBM) and single focus GBM in the modern treatment era. Between August 2000 and May 2010, 161 patients with GBM were treated with modern radiotherapy techniques. Of this group, 33 were considered to have multiple lesion GBM (25 multifocal and 8 multicentric). Patterns of failure, time to progression and overall survival were compared based on whether the tumor was considered a single focus or multiple lesion GBM. Genomic groupings and methylation status were also investigated as a possible predictor of multifocality in a cohort of 41 patients with available tissue for analysis. There was no statistically significant difference in overall survival (p \ 0.3) between

Comparison of clinical outcomes and genomic characteristics of single focus and multifocal glioblastoma Anna K. PaulssonJordan A. HolmesAnn M. PeifferLance D. Miller • Wennuan LiuJianfeng XuWilliam H. HinsonGlenn J. Lesser • Adrian W. LaxtonStephen B. TatterWaldemar DebinskiMichael D. Chan

A13 Gene expression signatures of tumor biomarkers, generally involving 10 or more genes, are often used to elucidate the underlying molecular mechanisms of tumor progression. Many microarray studies have demonstrated that changes in gene expression associated with tumor progression are predictive of tumor progression and can therefore be used to direct treatment strategy and improve patient outcome. Although high-density microarray analysis is a powerful tool for discovering biomarker candidates associated with a particular disease, it is not practical for detailed biomarker validation with increased sample throughput. In this study, we generated a multiplex assay that targets 20 genes selected from a prior breast cancer microarray study. This multiplex contains primers for 11 genes associated with tumorigenesis, apoptosis, cell-cycle regulation and cell proliferation along with 9 reference genes. Gene expression profiles of 50 breast tumors of varying histological grades (G1, G2 and G3) were characterized with this 20-gene multiplex. Distinctive patterns of gene expression were identified and these patterns correlated with tumor histological grade. The multiplex assay results were highly concordant to a larger gene probe set previously established to differentiate G1 and G3 tumors by microarray analysis. This new approach not only offers savings in sample, time and expense with increased throughput; but also provides high precision in quantitation with R2 greater than 0.99. This multiplex, quantitative assay transfers biomarker detection from the large-scale microarray discovery platform into a more sensitive, quantitative and high-throughput method; which better fits a routine testing environment.  Note: For research use only; not for use in diagnostic procedures.

Lance D. Miller

Papers

Biomarqueur et traitement pour le cancer

A method, system and computer program product for the systematic evaluation of the prognostic properties of gene pairs for medical conditions.

Comparison of clinical outcomes and genomic characteristics of single focus and multifocal glioblastoma Anna K. PaulssonJordan A. HolmesAnn M. PeifferLance D. Miller • Wennuan LiuJianfeng XuWilliam H. HinsonGlenn J. Lesser • Adrian W. LaxtonStephen B. TatterWaldemar DebinskiMichael D. Chan

High throughput, quantitative multiplex gene expression assay accelerates the validation of breast cancer biomarkers

ASO Visual Abstract: Plasma Exosome Gene Signature Differentiates Colon Cancer from Healthy Controls