Bit Brochure.Pdf
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Bioimage Analysis Tools
Bioimage Analysis Tools Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, Ulrike Schulze, Simon Norrelykke, Christian Tischer, Thomas Pengo To cite this version: Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, et al.. Bioimage Analysis Tools. Kota Miura. Bioimage Data Analysis, Wiley-VCH, 2016, 978-3-527-80092-6. hal- 02910986 HAL Id: hal-02910986 https://hal.archives-ouvertes.fr/hal-02910986 Submitted on 3 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 2 Bioimage Analysis Tools 1 2 3 4 5 6 Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Pau/-Gilloteaux, - Ulrike Schulze,7 Simon F. Nerrelykke,8 Christian Tischer,9 and Thomas Penqo'" 1 European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany National Institute of Basic Biology, Okazaki, 444-8585, Japan 2/nstitute for Research in Biomedicine ORB Barcelona), Advanced Digital Microscopy, Parc Científic de Barcelona, dBaldiri Reixac 1 O, 08028 Barcelona, Spain 3German Center of Neurodegenerative -
Other Departments and Institutes Courses 1
Other Departments and Institutes Courses 1 Other Departments and Institutes Courses About Course Numbers: 02-250 Introduction to Computational Biology Each Carnegie Mellon course number begins with a two-digit prefix that Spring: 12 units designates the department offering the course (i.e., 76-xxx courses are This class provides a general introduction to computational tools for biology. offered by the Department of English). Although each department maintains The course is divided into two halves. The first half covers computational its own course numbering practices, typically, the first digit after the prefix molecular biology and genomics. It examines important sources of biological indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses data, how they are archived and made available to researchers, and what are sophomore level, etc. Depending on the department, xx-6xx courses computational tools are available to use them effectively in research. may be either undergraduate senior-level or graduate-level, and xx-7xx In the process, it covers basic concepts in statistics, mathematics, and courses and higher are graduate-level. Consult the Schedule of Classes computer science needed to effectively use these resources and understand (https://enr-apps.as.cmu.edu/open/SOC/SOCServlet/) each semester for their results. Specific topics covered include sequence data, searching course offerings and for any necessary pre-requisites or co-requisites. and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. The second half covers computational cell biology, including biological modeling and image analysis. It includes homework requiring Computational Biology Courses modification of scripts to perform computational analyses. -
The Design and Realisation of the Myexperiment Virtual Research Environment for Social Sharing of Workflows
The Design and Realisation of the myExperiment Virtual Research Environment for Social Sharing of Workflows David De Roure a Carole Goble b Robert Stevens b aUniversity of Southampton, UK bThe University of Manchester, UK Abstract In this paper we suggest that the full scientific potential of workflows will be achieved through mechanisms for sharing and collaboration, empowering scientists to spread their experimental protocols and to benefit from those of others. To facilitate this process we have designed and built the myExperiment Virtual Research Environ- ment for collaboration and sharing of workflows and experiments. In contrast to systems which simply make workflows available, myExperiment provides mecha- nisms to support the sharing of workflows within and across multiple communities. It achieves this by adopting a social web approach which is tailored to the par- ticular needs of the scientist. We present the motivation, design and realisation of myExperiment. Key words: Scientific Workflow, Workflow Management, Virtual Research Environment, Collaborative Computing, Taverna Workflow Workbench 1 Introduction Scientific workflows are attracting considerable attention in the community. Increasingly they support scientists in advancing research through in silico ex- perimentation, while the workflow systems themselves are the subject of ongo- ing research and development (1). The National Science Foundation Workshop on the Challenges of Scientific Workflows identified the potential for scientific advance as workflow systems address more sophisticated requirements and as workflows are created through collaborative design processes involving many scientists across disciplines (2). Rather than looking at the application or ma- chinery of workflow systems, it is this dimension of collaboration and sharing that is the focus of this paper. -
Data Curation+Process Curation^Data Integration+Science
BRIEFINGS IN BIOINFORMATICS. VOL 9. NO 6. 506^517 doi:10.1093/bib/bbn034 Advance Access publication December 6, 2008 Data curation 1 process curation^data integration 1 science Carole Goble, Robert Stevens, Duncan Hull, Katy Wolstencroft and Rodrigo Lopez Submitted: 16th May 2008; Received (in revised form): 25th July 2008 Abstract In bioinformatics, we are familiar with the idea of curated data as a prerequisite for data integration. We neglect, often to our cost, the curation and cataloguing of the processes that we use to integrate and analyse our data. Downloaded from https://academic.oup.com/bib/article/9/6/506/223646 by guest on 27 September 2021 Programmatic access to services, for data and processes, means that compositions of services can be made that represent the in silico experiments or processes that bioinformaticians perform. Data integration through workflows depends on being able to know what services exist and where to find those services. The large number of services and the operations they perform, their arbitrary naming and lack of documentation, however, mean that they can be difficult to use. The workflows themselves are composite processes that could be pooled and reused but only if they too can be found and understood. Thus appropriate curation, including semantic mark-up, would enable processes to be found, maintained and consequently used more easily.This broader view on semantic annotation is vital for full data integration that is necessary for the modern scientific analyses in biology.This article will brief the community on the current state of the art and the current challenges for process curation, both within and without the Life Sciences. -
Data Management in Systems Biology I
Data management in systems biology I – Overview and bibliography Gerhard Mayer, University of Stuttgart, Institute of Biochemical Engineering (IBVT), Allmandring 31, D-70569 Stuttgart Abstract Large systems biology projects can encompass several workgroups often located in different countries. An overview about existing data standards in systems biology and the management, storage, exchange and integration of the generated data in large distributed research projects is given, the pros and cons of the different approaches are illustrated from a practical point of view, the existing software – open source as well as commercial - and the relevant literature is extensively overviewed, so that the reader should be enabled to decide which data management approach is the best suited for his special needs. An emphasis is laid on the use of workflow systems and of TAB-based formats. The data in this format can be viewed and edited easily using spreadsheet programs which are familiar to the working experimental biologists. The use of workflows for the standardized access to data in either own or publicly available databanks and the standardization of operation procedures is presented. The use of ontologies and semantic web technologies for data management will be discussed in a further paper. Keywords: MIBBI; data standards; data management; data integration; databases; TAB-based formats; workflows; Open Data INTRODUCTION the foundation of a new journal about biological The large amount of data produced by biological databases [24], the foundation of the ISB research projects grows at a fast rate. The 2009 (International Society for Biocuration) and special edition of the annual Nucleic Acids Research conferences like DILS (Data Integration in the Life database issue mentions 1170 databases [1]; alone Sciences) [25]. -
Distributed Computing Environments and Workflows for Systems
Going with the Flow Distributed Computing for Systems Biology Using Taverna Prof Carole Goble The University of Manchester, UK http://www.mygrid.org.uk http://www.omii.ac.uk Data pipelines in bioinformatics Genscan Resources /Services EMBL BLAST Clustal-W Multiple Query Protein sequences Clustal-W sequence alignment 2 Example in silico experiment: Investigate© the evolutionary relationships between proteins [Peter Li] z Manual creation z Semi-automation using bespoke software 12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct tttttagctt z Issues: 12301 gaccatccta atagatacac agtggtgtct cactgtgatt ttaatttgca ttttcctgct 12361 gactaattat gttgagcttg ttaccattta gacaacttca ttagagaagt gtctaatatt 12421 taggtgactt gcctgttttt ttttaattgg gatcttaatt tttttaaatt attgatttgt 12481 aggagctatt tatatattct ggatacaagt tctttatcag atacacagtt tgtgactatt z Volatility of data12541 ttcttataag in tctgtggtttlife ttatattaatsciences gtttttattg atgactgttt tttacaattg 12601 tggttaagta tacatgacat aaaacggatt atcttaacca ttttaaaatg taaaattcga 12661 tggcattaag tacatccaca atattgtgca actatcacca ctatcatact ccaaaagggc 12721 atccaatacc cattaagctg tcactcccca atctcccatt ttcccacccc tgacaatcaa z Data and metadata12781 taacccattt tctgtctcta storage tggatttgcc tgttctggat attcatatta atagaatcaa z Integration of heterogeneous biological data z Visualisation of models z Brittleness 3 © 12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct -
Mygrid: a Collection of Web Pages
Home The myGrid team produce and use a suite of tools designed to “help e-Scientists get on with science and get on with scientists”. The tools support the creation of e-laboratories and have been used in domains as diverse as systems biology, social science, music, astronomy, multimedia and chemistry. The tools have been adopted by a large number of projects and institutions. The team has developed tools and infrastructure to allow: • the design, editing and execution of workflows in Taverna • the sharing of workflows and related data by myExperiment • the cataloguing and annotation of services in BioCatalogue and Feta • the creation of user-friendly rich clients such as UTOPIA These tools help to form the basis for the team’s work on e-Labs. Taverna Workbench 2.1.2 available for download The myGrid team have released Taverna Workbench 2.1.2. It supports secure access to data on the web, secure web services and secured private workflows. Taverna 2.1.2 includes • Copy/paste, shortcuts, undo/redo, drag and drop • Animated workflow diagram • Remembers added/removed services • Secure Web services support • Secure access to resources on the web • Up-to-date R support • Intermediate values during workflow runs • myExperiment integration • Excel and csv spreadsheet support Hosted at the School of Computer Science, University of Manchester, UK. Copyright 2008 - All Rights Reserved Sponsored by Microsoft, EPSRC, BBSRC, ESRC and JISC From www.mygrid.org.uk/ 1 27 May 2010 Outreach >Outreach The myGrid consortium put a lot of effort into reaching out to users. The team do not just “preach to the converted” but seek new users by offering Tutorials and giving talks, papers and posters in a very wide variety of places. -
Bioimage Informatics
Bioimage Informatics Lecture 22, Spring 2012 Empirical Performance Evaluation of Bioimage Informatics Algorithms Image Database; Content Based Image Retrieval Emerging Applications: Molecular Imaging Lecture 22 April 23, 2012 1 Outline • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 2 • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 3 Overview of Algorithm Evaluation (I) • Two sets of data are required - Input data - Corresponding correct output (ground truth) • Sources of input data - Actual/experimental data, often from experiments - Synthetic data, often from computer simulation • Actual/experimental data - Essential for performance evaluation - May be costly to acquire - May not be representative - Ground truth often is unknown Overview of Algorithm Evaluation (II) • What exactly is ground truth? Ground truth is a term used in cartography, meteorology, analysis of aerial photographs, satellite imagery and a range of other remote sensing techniques in which data are gathered at a distance. Ground truth refers to information that is collected "on location." - http://en.wikipedia.org/wiki/Ground_truth • Synthetic (simulated) data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data 5 Overview of Algorithm Evaluation (III) • Simulated data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data • Realistic synthetic data - To use as much information from real experimental data as possible • Quality control of manual analysis Test Protocol Development (I) • Implementation - Source codes are not always available and often are on different platforms. • Parameter setting - This is one of the most challenging issues. -
Hosts: Monash Eresearch Centre and Messagelab Seminar :The Long Tail Scientist Presenter: Prof Carole Goble, Computer Science
Hosts: Monash eResearch Centre and MessageLab Seminar :The Long tail Scientist Presenter: Prof Carole Goble, Computer Science, University of Manchester Venue: Seminar Room 135, Building 26 Clayton Time and Date: Wed 3 August 2011, 5-6pm Abstract Big science with big, coordinated and collaborative programmes – the Large Hadron Collider, the Sloan Sky Survey, the Human Genome and its successor the 1000 Genomes project – hogs headlines and fascinates funders. But this big science makes up a small fraction of research being done. Whilst the big journals – Nature, Science - are often the first to publish breakthrough research, work in a vast array of smaller journals still contributes to scientific knowledge. Every day, PhD students and post-docs are slaving away in small labs building up the bulk of scientific data. In disciplines like chemistry, biology and astronomy they are taking advantage of the multitude of public datasets and analytical tools to make their own investigations. Jim Downing at the Unilever Centre for Molecular Informatics was one of the first to coin the term of “Long Tail Science” – that large numbers of small researcher units is an important concept. We are not just standing on the shoulders of a few giants but standing on the shoulders of a multitude of the average sized. Ten years ago I started up the myGrid e- Science project (http://www.mygrid.org.uk ) specifically to help the long tail bioinformatician, and later other long tail scientists from other disciplines. And it turns out that the software, services and methods we develop and deploy (the Taverna workflow system, myExperiment, BioCatalogue, SysMO-SEEK, MethodBox) apply just as well to the big science projects. -
Bioimage Informatics 2019
BioImage Informatics 2019 Co-hosted by: October 2-4, 2019 Allen Institute 615 Westlake Avenue N Seattle, WA 98109 alleninstitute.org/bioimage-informatics twitter #bioimage19 THANKS TO OUR SPONSORS BioImage Informatics 2019 Wednesday, October 2 8:00-9:00am Registration and continental breakfast 9:00-9:15am Welcome and introductory remarks, BioImage Informatics Local Planning Committee • Michael Hawrylycz, Ph.D., Allen Institute for Brain Science (co-chair) • Winfried Wiegraebe, Ph.D., Allen Institute for Cell Science (co-chair) • Forrest Collman, Ph.D., Allen Institute for Brain Science • Greg Johnson, Ph.D., Allen Institute for Cell Science • Lydia Ng, Ph.D., Allen Institute for Brain Science • Kaitlyn Casimo, Ph.D., Allen Institute 9:15-10:15am Opening keynote Gaudenz Danuser, UT Southwestern Medical Center “Inference of causality in molecular pathways from live cell images” Defining cause-effect relations among molecular components is the holy grail of every cell biological study. It turns out that for many of the molecular pathways this task is far from trivial. Many of the pathways are characterized by functional overlap and nonlinear interactions between components. In this configuration, perturbation of one component may result in a measureable shift of the pathway output – commonly referred to as a phenotype – but it is strictly impossible to interpret the phenotype in terms of the roles the targeted component plays in the unperturbed system. This caveat of perturbation approaches applies equally to genetic perturbations, which lead to long-term adaptation, and more acute pharmacological and optogenetic approaches, which often induce short-term adaptation. For nearly two decades my lab has been devoted to circumventing this problem by exploiting basal fluctuations of unperturbed systems to establish cause-and-effect cascades between pathway components. -
A Bioimage Informatics Platform for High-Throughput Embryo Phenotyping James M
Briefings in Bioinformatics, 19(1), 2018, 41–51 doi: 10.1093/bib/bbw101 Advance Access Publication Date: 14 October 2016 Paper A bioimage informatics platform for high-throughput embryo phenotyping James M. Brown, Neil R. Horner, Thomas N. Lawson, Tanja Fiegel, Simon Greenaway, Hugh Morgan, Natalie Ring, Luis Santos, Duncan Sneddon, Lydia Teboul, Jennifer Vibert, Gagarine Yaikhom, Henrik Westerberg and Ann-Marie Mallon Corresponding author: James Brown, MRC Harwell Institute, Harwell Campus, Oxfordshire, OX11 0RD. Tel. þ44-0-1235-841237; Fax: +44-0-1235-841172; E-mail: [email protected] Abstract High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene–phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encom- pass a range of modalities, proprietary software and metadata. To facilitate robust downstream analysis, images and metadata must be standardized to account for these differences. As an open scientific enterprise, making the data readily accessible is essential so that members of biomedical and clinical research communities can study the images for them- selves without the need for highly specialized software or technical expertise. In this article, we present a platform of soft- ware tools that facilitate the upload, analysis and dissemination of 3D images for the IMPC. -
![View of Above Observations and Arguments, the Fol- Scopy Environment) [9]](https://docslib.b-cdn.net/cover/1595/view-of-above-observations-and-arguments-the-fol-scopy-environment-9-2681595.webp)
View of Above Observations and Arguments, the Fol- Scopy Environment) [9]
Loyek et al. BMC Bioinformatics 2011, 12:297 http://www.biomedcentral.com/1471-2105/12/297 SOFTWARE Open Access BioIMAX: A Web 2.0 approach for easy exploratory and collaborative access to multivariate bioimage data Christian Loyek1*, Nasir M Rajpoot2, Michael Khan3 and Tim W Nattkemper1 Abstract Background: Innovations in biological and biomedical imaging produce complex high-content and multivariate image data. For decision-making and generation of hypotheses, scientists need novel information technology tools that enable them to visually explore and analyze the data and to discuss and communicate results or findings with collaborating experts from various places. Results: In this paper, we present a novel Web2.0 approach, BioIMAX, for the collaborative exploration and analysis of multivariate image data by combining the webs collaboration and distribution architecture with the interface interactivity and computation power of desktop applications, recently called rich internet application. Conclusions: BioIMAX allows scientists to discuss and share data or results with collaborating experts and to visualize, annotate, and explore multivariate image data within one web-based platform from any location via a standard web browser requiring only a username and a password. BioIMAX can be accessed at http://ani.cebitec. uni-bielefeld.de/BioIMAX with the username “test” and the password “test1” for testing purposes. Background key challenge of bioimage informatics is to present the In the last two decades, innovative biological and biome- large amount of image data in a way that enables them dical imaging technologies greatly improved our under- to be browsed, analyzed, queried or compared with standing of structures of molecules, cells and tissue and other resources [2].