The National Resource for Network Biology (NRNB) held its External Advisory Council meeting on December 12, 2012. The NRNB is focused on developing network biology tools and collaborating with investigators. It oversees various technology research and development projects, software releases including Cytoscape 3.0, collaboration projects, and outreach/training events. The meeting agenda covered progress updates and sought advice on future plans.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource
for interactively exploring multidimensional cancer genomics data sets. It provides simple and intuitive integrated access to cancer genomics data, including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data, on more than 5,000 tumor samples from 20 cancer studies (including 16 TCGA cancer types).
During the past year, we have added network visualization and analysis features to
the cBio Portal. These new features enable researchers to analyze genomic alterations in the context of known biological pathways and interaction networks, and to more easily mine data generated by the TCGA. A network of interest is derived from the Pathway Commons project, based on the query genes specified by the user. Multidimensional genomic data are overlaid onto each node of the network, highlighting the frequency of somatic mutation and copy number alteration (and optionally mRNA up/down-regulation). Users can manage the complexity of the network by filtering by total alteration frequency of genes or by type and source of the interactions. This provides an effective means of managing network complexity, while automatically highlighting those genes most directly relevant to the cancer type in question. In addition, drugs and drug target data can optionally be shown in relation to the network of interest. In this talk, we would like to illustrate the main network analysis features using data from the TCGA project. We will also discuss our future plans for the network view.
A collaborative model for bioinformatics education: combining biologically i...Elia Brodsky
Presented at the 6th Annual LA Conference on Computational Biology & Bioinformatics
Authors:
Kimberlee Mix*, Patricia Dorn*, Donald Hauber*, Scott McDermott**, Ryan Harvey** , Jack LeBien***, Sahil Sethi***, Julia Panov***, Avi Titievsky****, Elia Brodsky***
Departments of Biological Sciences*, Mathematics and Computer Science**, Loyola University New Orleans, 6363 St Charles Avenue, New Orleans, LA 70118
Pine Biotech, Inc***, 1441 Canal St. New Orleans, LA 70112
Tauber Bioinformatics Research Center****, University of Haifa Multi Purpose Building Room 225A Mount Carmel, Haifa 3498838 ISRAEL
Despite the growing impact of bioinformatics in the biological science community, integration of an on-site bioinformatics curriculum is cost prohibitive for many universities due to the necessary infrastructure and computational resources. Furthermore, many programs prioritize the technical aspects of bioinformatics over the biological concepts and logic of analyses, thus limiting the emphasis on critical thinking, problem solving, and in-depth inquiry. To address the gap in bioinformatics education and train students to approach complex biomedical problems, we present a new model for curriculum development that combines our unique online learning environment with traditional pedagogical approaches delivered through academic partnerships. The T-BioInfo platform (https://t-bio.info) allows users to combine computational analysis modules into pipelines to develop solutions for ‘omics data and machine learning problems. State-of-the-art tools for analysis, integration, and visualization of data are offered through a user-friendly interface. In parallel, online educational modules provide a theoretical framework for the analysis methods and experimental techniques. This model for bioinformatics training was implemented at Loyola University New Orleans, a liberal arts institution, for the first time in January 2018. Twelve undergraduate students and five faculty members participated in a new one-semester bioinformatics course. After completing a core set of online modules and pipelines, students conducted team research projects on topics such as patient derived xenograft (PDX) models, immune responses in cancer, and precision medicine. Gains in critical thinking and problem-solving skills were observed and participants were enthusiastic about engaging in bioinformatics research. In conclusion, our collaborative model for bioinformatics education combines best-practices in online and in-class learning with a powerful computational platform. This model could be implemented in undergraduate and graduate curricula to enhance research, build partnerships with industry, and strengthen the scientific workforce.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Visualization and Analysis of Dynamic Networks Alexander Pico
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugin’s functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource
for interactively exploring multidimensional cancer genomics data sets. It provides simple and intuitive integrated access to cancer genomics data, including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data, on more than 5,000 tumor samples from 20 cancer studies (including 16 TCGA cancer types).
During the past year, we have added network visualization and analysis features to
the cBio Portal. These new features enable researchers to analyze genomic alterations in the context of known biological pathways and interaction networks, and to more easily mine data generated by the TCGA. A network of interest is derived from the Pathway Commons project, based on the query genes specified by the user. Multidimensional genomic data are overlaid onto each node of the network, highlighting the frequency of somatic mutation and copy number alteration (and optionally mRNA up/down-regulation). Users can manage the complexity of the network by filtering by total alteration frequency of genes or by type and source of the interactions. This provides an effective means of managing network complexity, while automatically highlighting those genes most directly relevant to the cancer type in question. In addition, drugs and drug target data can optionally be shown in relation to the network of interest. In this talk, we would like to illustrate the main network analysis features using data from the TCGA project. We will also discuss our future plans for the network view.
A collaborative model for bioinformatics education: combining biologically i...Elia Brodsky
Presented at the 6th Annual LA Conference on Computational Biology & Bioinformatics
Authors:
Kimberlee Mix*, Patricia Dorn*, Donald Hauber*, Scott McDermott**, Ryan Harvey** , Jack LeBien***, Sahil Sethi***, Julia Panov***, Avi Titievsky****, Elia Brodsky***
Departments of Biological Sciences*, Mathematics and Computer Science**, Loyola University New Orleans, 6363 St Charles Avenue, New Orleans, LA 70118
Pine Biotech, Inc***, 1441 Canal St. New Orleans, LA 70112
Tauber Bioinformatics Research Center****, University of Haifa Multi Purpose Building Room 225A Mount Carmel, Haifa 3498838 ISRAEL
Despite the growing impact of bioinformatics in the biological science community, integration of an on-site bioinformatics curriculum is cost prohibitive for many universities due to the necessary infrastructure and computational resources. Furthermore, many programs prioritize the technical aspects of bioinformatics over the biological concepts and logic of analyses, thus limiting the emphasis on critical thinking, problem solving, and in-depth inquiry. To address the gap in bioinformatics education and train students to approach complex biomedical problems, we present a new model for curriculum development that combines our unique online learning environment with traditional pedagogical approaches delivered through academic partnerships. The T-BioInfo platform (https://t-bio.info) allows users to combine computational analysis modules into pipelines to develop solutions for ‘omics data and machine learning problems. State-of-the-art tools for analysis, integration, and visualization of data are offered through a user-friendly interface. In parallel, online educational modules provide a theoretical framework for the analysis methods and experimental techniques. This model for bioinformatics training was implemented at Loyola University New Orleans, a liberal arts institution, for the first time in January 2018. Twelve undergraduate students and five faculty members participated in a new one-semester bioinformatics course. After completing a core set of online modules and pipelines, students conducted team research projects on topics such as patient derived xenograft (PDX) models, immune responses in cancer, and precision medicine. Gains in critical thinking and problem-solving skills were observed and participants were enthusiastic about engaging in bioinformatics research. In conclusion, our collaborative model for bioinformatics education combines best-practices in online and in-class learning with a powerful computational platform. This model could be implemented in undergraduate and graduate curricula to enhance research, build partnerships with industry, and strengthen the scientific workforce.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Visualization and Analysis of Dynamic Networks Alexander Pico
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugin’s functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.
Receta gazpacho andaluz olmeda origenes gourmet hecho con aceite de oliva virgen extra y vinagre de Jerez Olmeda origenes, una de las rectas más famosas de la gastronomía española gazpacho andaluz.
We have world-class expertise building world-class businesses. We are leading global experts in qualified, professional and skilled recruitment and employ 9,214 people in 33 countries across 20 specialisms.
Visit https://www.haysplc.com/investors/annual-report-2016 to view and download the full Annual Report 2016.
El cuerpo humano requiere alimentos, así como ejercicio y una buena actitud mental, para mantenerse sano y desarrollar su potencial al máximo, para lograr esto la base fundamental es la buena alimentación, la cual se basa en comer productos variado aportándole al cuerpo diferentes vitaminas y minerales para su correcto funcionamiento.
Para tener clara que es una alimentación balanceada se divide los alimentos, clasificándolos por grupos para ser mas prudente en la orientación de consumo, lo cual es de gran importa para mantener los niveles de energías altos ya que el cuerpo funciona tal cual un automóvil cuya gasolina la aportan los alimentos.
Research Data Alliance (RDA) Webinar: What do you really know about that anti...dkNET
What do you really know about that antibody? Ask dkNET
Research resources-defined here as the tools researchers use in their scientific studies-are a foundation of the biomedical enterprise. It is critical for researchers to be able to select the proper tools for their research, but also be aware of any issues that may arise in their application. Software tools and datasets may have bugs, cell lines get contaminated, knock outs may be incomplete and antibodies may have specificity problems. Such problematic resources can continue to be used in scientific studies, even after problems are detected. Many factors, including the inability to easily retrieve alerts about problematic resources, results in their continued use, wasting both time and money. To make it easy to find information about research resources and how they perform, dkNET (NIDDK Information Network, https://dknet.org), an on-line portal supported by the US National Institute of Diabetes, Digestive and Kidney diseases (NIDDK), has developed a resource information network that utilize Research Resource Identifiers (RRIDs) and natural language processes to aggregate information about individual antibodies, cell lines, organisms, digital tools, plasmids and biosamples. This information is presented in a Resource Report that provides information such as which papers have been published using these resources, who is using them and whether issues have been reported. Using this information, dkNET also provides tools to create authentication reports in support of the NIH rigor and reproducibility guidelines. The dkNET portal includes additional information to enable researchers to easily use and navigate large amounts of data and information about research resources in support of reproducible science.
By the end of this webinar, participants will be familiar with the services and tools provided at dkNET and will be able to create a detailed research resource report and produce an authentication report in support of NIH mandates and policies.
Presenter: Maryann Martone, PhD, FAIR Data Informatics Lab (FDI Lab), University of California, San Diego
MseqDR consortium: a grass-roots effort to establish a global resource aimed ...Human Variome Project
The success of whole exome sequencing (WES) for highly heterogeneous disorders, such as mitochondrial disease, is limited by substantial technical and bioinformatics challenges to correctly identify and prioritize the extensive number of sequence variants present in each patient. The likelihood of success can be greatly improved if a large cohort of patient data is assembled in which sequence variants can be systematically analysed, annotated, and interpreted relative to known phenotype. This effort has engaged and united more than 100 international mitochondrial clinicians, researchers, and bioinformaticians in the Mitochondrial Disease Sequence Data Resource (MSeqDR) consortium that formed in June 2012 to identify and prioritize the specific WES data analysis needs of the global mitochondrial disease community. Through regular web-based meetings, we have familiarized ourselves with existing strengths and gaps facing integration of MSeqDR with public resources, as well as the major practical, technical, and ethical challenges that must be overcome to create a sustainable data resource. We have now moved forward toward our common goal by establishing a central data resource (http://mseqdr.org/) that has both public access and secure web-based features that allow the coherent compilation, organization, annotation, and analysis of WES and mtDNA genome data sets generated in both clinical- and research-based settings of suspected mitochondrial disease patients. The most important aims of the MSeqDR consortium are summarized in the MSeqDR portal within the Consortium overview sections. Consortium participants are organized in 3 working groups that include (1) Technology and Bioinformatics; (2) Phenotyping, databasing, IRB concerns and access; and (3) Mitochondrial DNA specific concerns. The online MSeqDR resource is organized into discrete sections to facilitate data deposition and common reannotation, data visualization, data set mining, and access management. With the support of the United Mitochondrial Disease Foundation (UMDF) and the NINDS/NICHD U54 supported North American Mitochondrial Disease Consortium (NAMDC), the MSeqDR prototype has been built. Current major components include common data upload and reannotation using a novel HBCR based annotation tool that has also been made publicly available through the website, MSeqDR GBrowse that allows ready visualization of all public and MSeqDR specific data including labspecific aggregate data visualization tracks, MSeqDR-LSDB instance of nearly 1250 mitochondrial disease and mitochodnrial localized genes that is based on the Locus Specific Database model, exome data set mining in individuals or families using the GEM.app tool, and Account & Access Management. Within MSeqDR GBrowse it is now possible to explore data derived from MitoMap, HmtDB, ClinVar, UCSC-NumtS, ENCODE, 1000 genomes, and many other resources that bioinformaticians recruited to the project are organizing.
Introduction to Jackson Labs, JMCRS, Clinical Services and Scientific Services at the Jackson Labs. Differences between long and short read sequencing. FAIR Data Action Plan. Metadata needs. Data Commons and the need to capture sample specific gene models discovered.
Keynote presentation for Network Biology SIG 2013 by Esti Yeger-Lotem, Senior Lecturer in Clinical Biochemistry at The National Institute for Biotechnology in the Negev, Israel
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
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This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
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Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
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Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
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Hemodialysis: Chapter 3, Dialysis Water Unit - Dr.Gawad
NRNB EAC Meeting 2012
1. National Resource for
Network Biology
External Advisory Council (EAC) Meeting
Wednesday December 12, 2012
2. Overview Technology Software Collabs Outreach Plans
NRNB Overview
• An NIGMS Biomedical Technology Resource Center focused
on Network Biology
• Research and develop new bioinformatic technologies to
enable the use of biological networks (molecular, genetic, and
social) among NIH-funded investigators
• Collaborate directly with biological and clinical users to push
the envelope of network analysis capabilities
• Distribute open-source network technologies to the greater
scientific community
• Stimulate a portfolio of educational and outreach
opportunities surrounding network biology
3. Overview Technology Software Collabs Outreach Plans
NRNB Investigators
Trey Ideker, PhD
Principal Investigator, NRNB Gary Bader, PhD
Departments of Medicine and Bioengineering Assistant Professor, Terrence Donnelly Centre
University of California, San Diego for Cellular & Biomolecular Research
Dr. Ideker uses genome-scale measurements to University of Toronto
construct network models of DNA damage Dr. Bader works on biological network analysis
response and cancer. He was the 2009 recipient and pathway information resources.
of the Overton Prize from the International
Society for Computational Biology.
James Fowler, PhD
Alex Pico, PhD Associate Professor, CalIT2 Center for Wireless &
Executive Director, NRNB Population Health Systems and Political Science
Gladstone Institute of Cardiovascular Disease University of California, San Diego
Staff Research Scientist
Dr. Fowler’s research concerns social networks,
University of California, San Francisco
behavioral economics, evolutionary game theory,
Dr. Pico develops software tools and resources and genopolitics (the study of the genetic basis of
that help analyze, visualize and explore political behavior). His research on social networks
biomedical data in the context of these networks has been featured in Time’s Year in Medicine.
Chris Sander, PhD
Chair, Computational Biology Center, Benno Schwikowski, PhD
Tri-Institutional Professor Chef du Laboratoire/Group Leader
Memorial Sloan-Kettering Cancer Center Pasteur Institute
Dr. Sander’s research focuses on Computational Dr. Schwikowski’s expertise lies in
and Systems Biology of molecules, pathways, and combinatorial algorithms for Computational
processes. and Systems Biology.
4. The National Resource for Network Biology:
Integrating genomes & networks to understand health & disease
NIH NCRR / NIGMS P41 GM103504
Draft Network Assembly
Patient genotype
Genome sequencing
Phenotype
Disease diagnosis
Response to therapy/drug
Side effects
Developmental outcome
1) How to assemble and visualize
Rate of aging, etc.
Gene expression & network models of the cell?
other large scale
molecular state
measurements 2) How to use networks in healthcare?
5. Overview Technology Software Collabs Outreach Plans
NRNB statistics ‘at-a-glance’
Year 3 of 5 funded years, $700,000 per year direct + $175,000 supplement
4 technology projects with matched driving biology
20 NRNB-supported publications since start of funding
Cytoscape used in ~250 pubs / year and has supported ~1400 NIH grants
96 collaborations with NIH-funded investigators (60 new this year)
27 training events over 7 countries, 32 academic courses by NRNB staff
16 Google Summer of Code Students = $80,000 invested by Google
Representative Publications:
Dutkowski et al. A gene ontology inferred from molecular networks. Nature Biotech. (2013) [cover]
Rügheimer et al. Applications of Intelligent Data Analysis for the Discovery of Gene Regulatory
Networks. Computational Intelligence in Intelligent Data Analysis (2013)
R Saito et al. A travel guide to Cytoscape plugins. Nature Methods (2012)
Cerami et al. The cBio Cancer Genomics Portal. Cancer Discovery (2012)
Morris, S. et al. ClusterMaker: multi-algorithm clustering for Cytoscape. BMC Bioinformatics (2011)
Ideker, T. et al. Boosting signal-to-noise in complex biology: prior knowledge is power. Cell (2011)
Dutkowski et al. Protein networks as logic functions in development and cancer. PLoS Comp Bio (2011).
Fowler et al. Correlated genotypes in friendship networks. PNAS (2011)
Bandyopadhyay and Ideker. Integrated Systems Biology [Poster], Nature Genetics (2010)
6. Overview Technology Software Collabs Outreach Plans
Canadian Institutes
of Health
Research, 23 NICHD, 22
Others, 127
NIDA, 25
Wellcome Trust, 29
NINDS, 39
NIGMS, 354
NIMH, 47
NLM, 54
NHGRI, 58
NIEHS, 62 NCI, 205
NIDDK, 88
NHLBI, 88 NCRR, 110
NIAID, 103
Use of Cytoscape by grantees. We have identified >1,400
grants acknowledged in publications citing a Cytoscape
paper (Shannon P et al. 2003; Cline MS et al. 2007; Smoot
ME et al. 2011). One pie slice represents the number of
grants from a particular agency. NIH institutes are
represented by standard abbreviations.
8. Overview Technology Software Collabs Outreach Plans
Response to previous EAC critiques
• Clearly articulate the link between funding and progress
See previous chart; all described work is directly funded by NRNB
• Focus not only on network visualization but on network-based
analysis and decision making
Exciting progress has been made for using networks as
diagnostic biomarkers under TRD A
• Encourage increased citation of Cytoscape
Refactored Cytoscape.org to encourage citation; perform
systematic literature tracking
• Better track Cytoscape plugins and leverage developers
Developed new Appstore (apps.cytoscape.org)
• Track use of Cytoscape in the classroom
Identified 32 courses using Cytoscape in 2011 - 2012
9. Overview Technology Software Collabs Outreach Plans
Agenda
• Progress on Technology Research & Dev.
• Release of Cytoscape 3.0
• Review of Collaboration and Service Projects
• Progress on Training and Outreach
• Plans for NRNB moving forward
• Generate discussion and seek advice on all
aspects
11. Overview Technology Software Collabs Outreach Plans
Networks (in various states of curation) Circuits Coupled w/ Decision Tree Logic
(or somatic mutation)
Patient Expression
TRD A:
Protein networks as
biomarkers of disease
Ideker group
Janusz Dutkowski et al
PLoS Comp Bio 2011
13. Overview Technology Software Collabs Outreach Plans
TRD A: cBio Cancer Genomics Portal (Chris Sander / MSKCC)
http://www.cbioportal.org
• Make Cancer Genomics data available to a
broad audience:
– “Wet lab” biologists involved in functional
studies.
– Computational biologists
• Allow quick access to small data slices (few
genes, subset of samples)
• Facilitate explorative data analysis / hypothesis
generation and testing
Cerami et al. 2012, Cancer Discovery
15. Overview Technology Software Collabs Outreach Plans
Features for the network view
Targeted-drugs Suggesting new targeted-drugs
Direct targets (Released) indirect targets (work in progress)
Targeted-drug
Pathway data
data
Tumor data
Infer and suggest novel,
drug-based therapy options
Sources: DrugBank, KEGG Drugs, Cancer.gov
e.g. AKT inhibitor for PTEN deletion
16. Overview Technology Software Collabs Outreach Plans
TRD B: Using Cytoscape for Social Network Research
Social network of the Hadza hunter-gatherers of Tanzania. This analysis in Cytoscape
reproduces the results published earlier this year in Nature by Fowler et al., which
show a strong social network-dependence on the donation of public goods across and
within groups. The histogram plot is based on the correlation values calculated by
CyNetworkSignificance on the original and randomized networks.
17. Overview Technology Software Collabs Outreach Plans
TRD C: Network Visualization and Representation (Pico / Bader)
Visualizing Complex Networks as Ontology-Partitioned Mosaics
Mosaic control panel, context menu and tiled result windows. The
control panel shows both the color mapping legend and subnetwork
display. Context menus for listed subnetworks allow the user to partition
deeper within a given ontology branch.
24. Overview Technology Software Collabs Outreach Plans
TRD D: Inference of Transcriptional Networks
(Schwikowski group)
• Create „fill-in-the-algorithm‟ infrastructure for inference of gene regulatory networks
• Make methods accessible to biologists within the Cytoscape framework
Approach: Create a software infrastructure (CYNI) for network inference algorithms
25. Overview Technology Software Collabs Outreach Plans
Since the last EAC meeting – Summary
-C design prototype
-Hired engineer Oriol Guitart-Pla (02/15/12)
-Working CYNI App
-Documented API
-User and CYNI App writer documentation
-Additional CYNI functionality: Discretization and Imputation
-Implemented and documented downloadable “Hello World
examples”
-CYNI App presented at two French network biology meetings
-CYNI App and Comprehensive documentation publicly
available
28. Overview Technology Software Collabs Outreach Plans
Cytoscape 3.0 Progress
(Core NRNB Software)
Major update of technology behind Cytoscape
Creates a fully modular architecture and API
Cytoscape 2.x would not support the features
needed for the current vision of network
analysis, e.g. scripting, multiple and 3D
renderers, crowd-sourcing, interoperability with
web and other tools
29. Overview Technology Software Collabs Outreach Plans
- Cytoscape 2.x: Maintenance Releases
- 2.8.2: Bug fixes and Improved memory allocation
- 2.8.3: Bug fixes and better trackpad support for Mac
platforms
- Cytoscape 3
- 3.0.0 M5: June 2012: For developers
- 3.0.0-Beta1: 10/9/2012: First Release for Users
- 3.0.0 Final: This Week
31. Overview Technology Software Collabs Outreach Plans
Cytoscape 3.0 Advantages:
Feature Beneficiary
Welcome screen New users (for solicitousness),
existing users (for convenience)
Import network Experienced users (for ease of use)
Edge bend visual property Paper and presentation writers
Edge bundling Users of high degree networks
Network annotations Users of hierarchical networks
Enhanced search Users of highly populated networks
Show All in Table Browser Users with highly annotated networks
Multiple network management All users
Disadvantages:
• Unproven stability (i.e., likely numerous bugs)
• Slow startup (4 times longer than v2.8)
• Fewer plugins (though the missing plugins are the less popular ones)
• Larger memory footprint leaves less room (especially on WinXP) for networks 31
Ultimately, the cell is not a flat list of subnetworks or gene clusters, it is a multi-scale hierarchical structure. This structure is well described by the Gene Ontology, the essential gold standard reference of gene function nearly everyone in the field uses. The Gene Ontology is manually-curated by experts which has not scaled well.
Welcome screen: easy access to data for users
From a user perspective, the benefits of v3.0 include…And the current disadvantages we have yet to overcome…
Rintaro’s Nature Methods paper is the first of its kind survey of Cytoscape plugins. Defines transition point to 3.0 and App Store.
Specific collaboration projects from Sander group
The NRNB NetworkPeople = blue circlesProjects = gold diamondsPublications = green trianglesNRNB funded = red borderThere are 315 nodes and 404 connections in the network. NRNB funds 41 (13%) of these nodes, which make 217 (54%) of the connections.
If you remove NRNB from this network… chaos…and wasted potential
A prominent feature of our collaboration network is GSoC: this summer we mentored 16 GSoC students (largest class yet). Leveraging $80,000 from Google!NRNB.org traffic increases 7-fold during GSoC application week. Averaged over the entire year, it is 4x more popular than any other page on our site.
The next most popular page is our Training Page, which lists upcoming Training events, Courses and TutorialsNext is the annual NetBio SIG meeting we organize in conjunction with ISMB each year. We just got accepted by ISMB to host for a 3rd year in a row. We are getting great speakers and impressive attendance for this meeting.Also high on the list is our Tools Gallery, which now includes screenshots and citation info. Over the last year, we added Cytoscape Web, WikiPathways and cBio Cancer Portal to the list of NRNB-supported tools.5,000 pageviews/week (cytoscape.org: 15,000 pv/wk)New paradigm for finding functionality in CytoscapeTagged navigation; user ranking and reviewsOne-click installAssisted app submission, including support for apps that export their own APIs for other apps to develop off of!First Cytoscape App Competition – helped get new apps in before 3.0 release – Winner will be announced on Friday during the Cytoscape App Expo