May 2016 - BBClist - maillist.psb.ugent.be

[Gtpb] Fwd: URGENT: Expressions of interest - Training on 3C-based analysis and modelling...
by Pedro Fernandes 11 May '16

11 May '16

Dear friends and colleagues, Apologies for this mass posting. Some of you have previously expressed interest in our courses for hands-on training on TADbit and TADkit (http://www.3DGenomes.org http://www.3dgenomes.org ). We are about to open a new course in the GTPB programme from the 6th to 9th of June. Additional info on the course can be found at the end of the e-mail. 3DAROC16 3C-based data analysis and 3D reconstruction of chromatin folding May we ask you to *quickly* inform us if you would be interested in attending, or recommending that your collaborators would attend? We will run the course on those dates but we need to find-out whether or not we should offer it in a later date, November 2016 for example. Best, Pedro -- Pedro Fernandes Instituto Gulbenkian de Ciência Apartado 14 2781-901 OEIRAS PORTUGAL Tel +351 21 4407912 http://gtpb.igc.gulbenkian.pt >>>>>>>>>>>> Course description 3C-based methods, such as Hi-C, produce a huge amount of raw data as pairs of DNA reads that are in close spatial proximity in the cell nucleus. Overall, those interaction matrices have been used to study how the genome folds within the nucleus, which is one of the most fascinating problems in modern biology. The rigorous analysis of those paired-reads using computational tools has been essential to fully exploit the experimental technique, and to study how the genome is folded in the space. Currently, there is a clear expansion on the wealth of data on genome structure with the availability of many datasets of Hi-C experiments down to 1Kb resolution (see for example: http://hic.umassmed.edu/welcome/welcome.php ; http://promoter.bx.psu.edu/hi-c/view.php or http://www.aidenlab.org/data.html ). In this course, participants will learn to use TADbit, a software designed and developed to manage all dimensionalities of the Hi-C data: ? 1D - Map paired-end sequences to generate Hi-C interaction matrices ? 2D - Normalize matrices and identify constitutive domains (TADs, compartments) ? 3D - Generate populations of structures which satisfy the Hi-C interaction matrices ? 4D - Analyze and compare different samples. Participants can bring- specific biological questions and/or their own 3C-based data to analyze during the course. At the end of the course, participants will be familiar with the TADbit software and will be able to fully analyze Hi-C data. Note: Although the TADbit software is central in this course, alternative software will be discussed for each part of the analysis. Target Audience The course design is oriented towards experimental researchers and bioinformaticians at the graduate and post-graduate levels. The last edition of this course was attended by people with different backgrounds and interested in the genome organization. Moreover, Hi-C data have recently been used in metagenomics studies to accurately cluster metagenome assembly contigs into groups that contain nearly complete genomes of each species. It is likely that the participants to this course aim at getting involved in generating Hi-C data for chromosome structure determination or that they just want to be able to correctly interpret and analyse publicly available data. Course Pre-requisites Recommended Linux and basic Python programming skills, graduate level in Life Sciences. Course fee Eur 360.00, lunches and coffee-breaks included. _______________________________________________ GTPB mailing list GTPB(a)igc.gulbenkian.pt https://lists.igc.gulbenkian.pt/mailman/listinfo/gtpb

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Two variant analysis courses at CSC in Finland 13- 17 June 2016
by Celia.vanGelder＠radboudumc.nl 10 May '16

10 May '16

Please note that registration is now open for the following two variant analysis courses at CSC in Finland: Variant analysis workshop 13.-15.6.2016 (www.csc.fi/web/training/-/variant-analysis<http://www.csc.fi/web/training/-/variant-analysis>) This ELIXIR EXCELERATE -funded course covers several aspects of variant analysis including variant annotation and prioritization. It consists of lectures and hands-on exercises which are suitable for everybody, as analysis is performed with easy-to-use graphical tools (the VEP hands-on tutorial on day 2 contains also an optional session for participants with coding experience). Participants can select and combine the following course days according to their needs: 13.6.2016 Introduction to variant analysis from sequencing data -Variant analysis with Chipster 14.6.2016 Variant annotation and effect prediction -Analysing variation data with the Variant Effect Predictor (VEP) -Genome variant annotation and interpretation in a clinical context using omnomicsQ -TBA 15.6.2016 Variant analysis and prioritization -European Genome-phenome Archive (EGA) -RD-Connect genomics analysis platform -Variant prioritization with UMD-Predictor, VarAFT and Human Splicing Finder Variant analysis with GATK 16.-17.6.2016 (www.csc.fi/web/training/-/gatk<http://www.csc.fi/web/training/-/gatk>) This course covers the core steps involved in calling variants with the Broad's Genome Analysis Toolkit (GATK), using the Best Practices developed by the GATK team. It highlights key functionalities such as the GVCF workflow for joint variant discovery in cohorts, RNAseq specific processing, and somatic variant discovery using MuTect2. The trainers also preview capabilities of the upcoming GATK version 4, including a new workflow for CNV discovery. The course consists of a lecture day which is suitable for everybody, and an optional hands-on day for which you need to have basic familiarity with the command line environment. Best regards, Eija Eija Korpelainen, Ph.D Product Manager Application services, Bioinformatics CSC - IT Centre for Science P.O.Box 405 02101 Espoo, Finland Phone +358 50 381 9726 Het Radboudumc staat geregistreerd bij de Kamer van Koophandel in het handelsregister onder nummer 41055629. The Radboud university medical center is listed in the Commercial Register of the Chamber of Commerce under file number 41055629.

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Postdoc position at University of Pretoria
by Ridder, Dick de 10 May '16

10 May '16

FYI: The Fruit Tree Biotechnology Programme (FTBP) at FABI, University of Pretoria has a research position available for a bioinformatician. Our established research programme is funded by the Hans Merensky Foundation and focuses on the understanding of tolerance and pathogenicity in the avocado-Phytophthora cinnamomi system. Description: the successful candidate will be responsible to manage the AvoGenome project by initiating and leading projects in the field of genomics of both avocado and P. cinnamomi. Duties include (but are not limited to): - Perform quality bioinformatics analysis of high throughput genomic data and assist in the interpretation of analyzed data - Conceive and develop bioinformatics tools and processes in order to analyze large scale datasets - Initiate new projects and work independently on a variety of bioinformatics analysis projects. - Manage databases, conduct statistical and genomic analysis, and participate in the preparation of manuscripts and presentations. - Collaborate with wet-bench biologists to analyze data derived from projects using high-throughput sequencing and microarrays. - 3-5 year contract. Requirements: - PhD in bioinformatics, biostatistics, genomics (or similar) - Experience with scripting in R, Python or Perl (etc.), biological datasets and statistics - Excellent communication skills, strengths in problem-solving and creative thinking - Extensive knowledge in bioinformatics, proven by a record of scientific publications in bioinformatics and/or genomics journals - Candidate with dual background in both computation and biology will be advantageous HOW TO APPLY: - Send a cover letter and CV to: Prof. Noëlani van den Berg (noelani.vdberg(a)fabi.up.ac.za)

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'STATS COURSE - Genetic data analysis/exploration using R'
by Oliver Hooker 10 May '16

10 May '16

"Genetic data analysis/exploration using R" Delivered by Dr. Thibaut Jombart http://prstatistics.com/course/genetic-data-analysis-using-r-gdar/ This course will run from 16th – 20th August, Millport Field Station, Ilse of Cumbrae, Scotland This course will provide an extensive overview of exploratory methods for the analysis of genetic data using the R software and aim to equip participants with powerful resources for tackling increasingly common challenges in genetic data analysis. It is aimed at PhD students, research postgraduates, and practicing academics as well as persons in industry working with genetic data in fields such as molecular ecology, evolutionary biology, and phylogenetics. This course will provide a comprehensive introduction to various statistical approaches for the analysis of genetic data. Course content is as follows Day 1 Introduction to phylogenetic reconstruction • Lecture 1a: Reconstructing phylogenies from genetic sequence data. Three main approaches covered: distance-based phylogenies; maximum parsimony; and likelihood-based approaches. • Lecture 1b: Short R refresher. • Practical 1: Phylogenetic reconstruction using R. Three main approaches plus rooting a tree; assessing/testing for a molecular clock; and bootstrapping. Main packages: ape, phangorn. Day 2 Introduction to multivariate analysis of genetic data • Lecture 2: Key concepts in multivariate analysis. Focus on using factorial methods for genetic data analysis. • Practical 2: Basics of multivariate analysis of genetic data in R. Topics include: data handling, population genetic tests of population structure (PCA, PCoA). Main packages: adegenet, ade4, ape. Day 3 Exploring group diversity • Lecture 3: Approaches to identifying and describing genetic clusters. Topics include: hierarchical clustering, K-means, population-level multivariate analysis (between-group-PCA, DA, DAPC). • Practical 3: Applying the approaches covered in morning lecture and emphasising their strengths and weaknesses. Main packages: adegenet, ade4. Day 4 Spatial genetic structure • Lecture 4: Discussing the origin and significance of spatial genetic patterns, and how to test or them. • Practical 4: Visualising and analysing spatial genetic data. Topics: spatial density estimates, Moran/Mantel tests, mapping principal components in PCA, spatial PCA. • Main packages: adegenet, glmnet. Main packages: adegenet, glmnet. Day 5 Using R for reproducible science • Lecture 5: Using R for reproducible science. • Practical 5: Practical session based on morning lecture • Main packages: knitr, Sweave, rmarkdown • Option to discuss own data (time permitting) Please email any inquiries to oliverhooker(a)prstatistics.com or visit our website www.prstatistics.com Please feel free to distribute this material anywhere you feel is suitable Upcoming courses - email for details oliverhooker(a)prstatistics.com ADVANCING IN STATISTICAL MODELLING USING R (May) TIMES SERIES DATA ANALYSIS FOR ECOLOGISTS AND CLIMATOLOGISTS (May) INTRODUCTION TO PYTHON FOR BIOLOGISTS (May) ADVANCES IN SPATIAL ANALYSIS OF MULTIVARIATE ECOLOGICAL DATA (July) ADVANCES IN DNA TAXONOMY USING R (August) INTRODUCTION TO BIOINFORMAITCS USING LINUX (August) INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (August) MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (October) LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (October) APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (October) PHYLOGENETIC DATA ANALYSIS USING R (November) SPATIAL ANALYSIS OF ECOLOGIC AL DATA USING R (November) Dates still to be confirmed - email for details oliverhooker(a)prstatistics.com STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R INTRODUCTION TO R AND STATISTICS FOR BIOLOGISTS BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS Oliver Hooker PR statistics 3/1 128 Brunswick Street Glasgow G1 1TF +44 (0) 7966500340 www.prstatistics.com www.prstatistics.com/organiser/oliver-hooker/

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Still seats available for the BioSB course: Algorithms for Biological Networks (4th edition), May 23-27 2016, Delft
by Femke Francissen 10 May '16

10 May '16

*Still seats available for BioSB Course: Algorithms for Biological Networks (4th edition)!* *General course information* *Date: * 23-27 May 2016 *Course coordinators:* Dick de Ridder (WUR), Jeroen de Ridder (TUD) *Location:* Delft University of Technology (TUD), Delft, the Netherlands Website <http://biosb.nl/education/course-portfolio/course-algorithms-for-biological…> Registration <http://biosb.nl/education/course-portfolio/course-algorithms-for-biological…> *Target audience* PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. *Course description* In the fourth edition of this course, we will first give a brief overview of molecular biology, the advent of high-throughput measurement techniques and large databases containing biological knowledge, and the importance of networks to model all this. We will highlight a number of peculiar features of biological networks. Next, a number of basic network models (linear, Boolean, Bayesian) will be discussed, as well as methods of inferring networks from observed measurement data and of integrating various data sources and databases to refine networks. Once networks are derived they often serve as the cornerstone in the visualization, analysis and interpretation of high-throughput data; we will discuss a number of methods in this area. As an alternative to static networks, a number of alternative dynamic network models more suited for high-level simulation of cellular behaviour for will be introduced. Finally, we will give some examples of algorithms exploiting the networks found to learn about biology, specifically for inspecting protein interaction networks and for finding active sub networks. *Course programme* The course website, including full course programme, can be found at http://helix.ewi.tudelft.nl/nbic/abn/. *BioSB Education Programme* This course is part of the Education Programme of BioSB, the Netherlands Bioinformatics and Systems Biology research school, which offers training and education for in bioinformatics and systems biology. More information about BioSB can be found at www.biosb.nl.

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[Gtpb] GTDA16 Genomic and Transcriptomic Data Analysis - Announcement/Reminder
by Pedro Fernandes 09 May '16

09 May '16

ANNOUNCEMENT / REMINDER (Last days to decide to apply) Applications for GTDA16 - Genomic and Transcriptomic Data Analysis with Francisco García and Alejandro Alemán are open. Candidates with adequate profile will be accepted in the next 72 hours after the application until we reach 20 participants. This is a relatively short but very effective way of providing skills in NGS data analysis using online tools. Target audience The course is oriented to experimentalists, end-users and graduate students who want to learn about state-of-the-art data analysis methodologies in this fast developing area. Students will acquire the necessary skills for analysing their own data using known software packages. Description Course Description This is a 3 day long theoretical and practical course on genomic data analysis. Questions such as How can I find the causative mutation of the disease in this family? What pathways are activated in my RNA-seq experiment? are becoming more frequent as Next Generation Sequencing (NGS) technologies are increasingly used in the laboratory. This activity has been designed to provide the researchers with the skills and tools to address these questions and many other ones related. The whole course is composed of two group of sessions that focus on the analysis of Genomic variation and Transcriptomics of Next Generation Sequencing Data. The genomics sessions review the different NGS technologies and their applications as well as the computational requirements of NGS-based projects. Following an introduction to NGS of genomes, exomes and panels, attendants will learn about NGS data quality issues, data pre-processing, how to compare sequenced reads with a reference genome and how to visualize the results in a genomic context. Then, the main aspects of genomic variant annotation, detection and prioritization of candidate variants will be covered. State-of-the-art software to assist in gene prioritization process will be used. Finally, NGS in the clinic will be reviewed with the use of panels of genes for precision diagnostic and the management of variants of uncertain effect. The transcriptomics sessions is devoted to transcriptomic studies (RNA-seq and miRNA-seq). After an introduction to NGS of transcriptomes, the attendants will learn to perform all the preprocessing steps to convert NGS reads into gene expression measurements. Then, gene expression data will be used to determine differential expression, to cluster expression patterns, or for building predictors. Functional profiling methodologies will be used to interpret the results according to gene ontology enrichment, network analysis, etc. Finally, sophisticated methods for pathway analysis of transcriptome profiles will be demonstrated. The Babelomics suite, a well-known package of gene expression analysis, will be used to illustrate. By the end of the course, participants will have acquired skills to interpret NGS data and to use multiple software tools for genetic variant detection and for transcriptomic studies. More details at the GTPB website http://gtpb.igc.gulbenkian.pt/bicourses/GTDA16/ Best wishes Pedro Fernandes -- Pedro Fernandes GTPB Coordinator Instituto Gulbenkian de Ciência Apartado 14 2781-901 OEIRAS PORTUGAL Tel +351 21 4407912 http://gtpb.igc.gulbenkian.pt _______________________________________________ GTPB mailing list GTPB(a)igc.gulbenkian.pt https://lists.igc.gulbenkian.pt/mailman/listinfo/gtpb

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Extended deadline META'2016
by El-ghazali Talbi 09 May '16

09 May '16

Apologies for cross-posting. Appreciate if you can distribute this CFP to your network. ********************************************************************** META'2016 International Conference on Metaheuristics and Nature Inspired Computing 27-31 Oct 2016 Marrakech, Morocco http://meta2016.sciencesconf.org/ ********************************************************************** META is one of the main event focusing on the progress of the area of metaheuristics and their applications. As in previous editions, META’2016 will provide an opportunity to the international research community in metaheuristics to discuss recent research results, to develop new ideas and collaborations, and to meet old friends and make new ones in a friendly and relaxed atmosphere. META'2016 welcomes presentations that cover any aspects of metaheuristic research such as new algorithmic developments, high-impact applications, new research challenges, theoretical developments, implementation issues, and in-depth experimental studies. META'2016 strives for a high-quality program that will be completed by a number of invited talks, tutorials, workshops and special sessions. The scope of the META’2016 conference includes, but is not limited to: * Local search, tabu search, simulated annealing, VNS, ILS, … * Evolutionary algorithms, swarm optimization, scatter search, … * Emergent nature inspired algorithms: quantum computing, artificial immune systems, bee colony, DNA computing, … * Parallel algorithms * Hybrid methods with machine learning, game theory, mathematical programming, constraint programming, co-evolutionary, … * Application to: logistics and transportation, networks, scheduling, data mining, engineering design, energy, cloud, bio-medical, … * Theory of metaheuristics, landscape analysis, convergence, problem difficulty, very large neighbourhoods, … * Multi-objective optimization, bi-level optimization * Dynamic optimization, problems with uncertainty, … * Parameter tuning (static, dynamic, adaptive, self-adaptive) * Hyper-heuristics, cross-domain metaheuristics * Software frameworks for metaheuristics and nature inspired computing Submission of papers: ____________________ - Submission of papers via the website S1) Short papers: Extended abstracts of work-in-progress and position papers of a maximum of 3 pages. S2) Long papers: Original research contributions of a maximum of 10 pages. S3) Journal papers: High-quality manuscripts that have recently, within the last year, been submitted or accepted for journal publication. For this special case, the submission should have the same title that the accepted work, the abstract AND the complete reference of the work. - Proceedings of the conference will be provided. Selected papers of type S1 and S2 will be published in a Springer book and international journals (ISI journals). - Predefined styles are available on the website http://meta2016.sciencesconf.org/ - Proposals for invited sessions, workshops and tutorials: Deadline 15th April 2016. contact el-ghazali.talbi(a)univ-lille1.fr Important dates _______________ - Extended Submission deadline: May 27, 2016 - Notification of acceptance: June 3, 2016 E-G. Talbi (Conference Chair) -- ********************************** META'2016 Marrakech, 27-31 Oct 2016 http://meta2016.sciencesconf.org ********************************** Prof. E-G. Talbi Univ. Lille, CNRS, INRIA -- ********************************** META'2016 Marrakech, 27-31 Oct 2016 http://meta2016.sciencesconf.org ********************************** Prof. E-G. Talbi Univ. Lille, CNRS, INRIA

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'STATS COURSE - Spatial Analysis of Multivariate Ecological Data Using R - PIERRE LEGENDRE'
by Oliver Hooker 05 May '16

05 May '16

“Advances in Spatial Analysis of Multivariate Ecological Data: Theory and Practice” http://prstatistics.com/course/advances-in-spatial-analysis-of-multivariate… This course is being delivered by Prof. Pierre Legendre who is a leading expert in numerical ecology and author of the book titled ‘Numerical ecology’ This course will run from 11th – 15th July 2016 at SCENE Field Station, Loch Lomond national park, Scotland. The course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology. The umbrella concept of the course is beta diversity, which is the spatial variation of communities. These methods are applicable to all types of communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The new methods, collectively referred to as spatial eigen-function analysis, are grounded into techniques commonly used by community ecologists, which will be described first: simple ordination (PCA, CA, PCoA), multivariate regression and canonical analysis, permutation tests. The choice of dissimilarities that are appropriate for community composition data will also be discussed. The focal question is to determine how much of the community variation (beta diversity) is due to environmental sorting and to community-based processes, including neutral processes. Recently developed methods to partition beta diversity in different ways will be presented. Extensions will be made to temporal and space-time data. Course content is as follows Day 1 • Introduction to data analysis. • Ordination in reduced space: principal component analysis (PCA), correspondence analysis (CA), principal coordinate analysis (PCoA). • Transformation of species abundance data tables prior to linear analyses. Day 2 • Measures of similarity and distance, especially for community composition data. • Multiple linear regression. R-square, adjusted R-square, AIC, tests of significance. • Polynomial regression. • Partial regression and variation partitioning. Day 3 • Statistical testing by permutation. • Canonical redundancy analysis (RDA) and canonical correspondence analysis (CCA). Multivariate analysis of variance by canonical analysis. • Forward selection of environmental variables in RDA. Day 4 • Origin of spatial structures. • Beta diversity partitioning and LCBD indices • Replacement and richness difference components of beta diversity. Day 5 • Spatial modelling: Multi-scale modelling of the spatial structure of ecological communities: dbMEM, generalized MEM, and AEM methods. • Community surveys through space and time: testing the space-time interaction in repeated surveys. • Additional module depending on time – Is the Mantel test useful for spatial analysis in ecology and genetics? Please email any inquiries to oliverhooker(a)prstatistics.com or visit our website www.prstatistics.com or to book online http://prstatistics.com/course/advances-in-spatial-analysis-of-multivariate… Please feel free to distribute this material anywhere you feel is suitable Upcoming courses - email for details oliverhooker(a)prstatistics.com SPATIAL ANALYSIS OF ECOLOGIC AL DATA USING R (April) ADVACNING IN STATISTICAL MODELLING USING R (May) TIMES SERIES DATA ANALYSIS FOR ECOLOGISTS AND CLIMATOLOGISTS (May) INTRODUCTION TO PYTHON FOR BIOLOGISTS (May) ADVANCES IN DNA TAXONOMY USING R (August) GENETIC DATA ANALYSIS USING R (August) INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (August) MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (October) LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (October) APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (October) Dates still to be confirmed - email for details oliverhooker(a)prstatistics.com STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R INTRODUCTION TO R AND STATISTICS FOR BIOLOGISTS PHYLOGENETIC DATA ANALYSIS USING R BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS Oliver Hooker PR Statistics 3/1 128 Brunswick Street Glasgow G1 1TF +44 (0) 7966500340 www.prstatistics.com www.prstatistics.com/organiser/oliver-hooker/

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Edinburgh Genomics - MinION and Python workshops
by Bert Overduin 04 May '16

04 May '16

Dear all, We still have a few places available on our workshop "Oxford Nanopore MinION Sequencing - Data Handling, Analysis and Applications" on Monday 13 June 2016. This 1-day workshop will focus on the handling, analysis and applications of MinION sequence data. The workshop will be a combination of presentations, demos and hands-on tutorials. In the hands-on tutorials a wide range of software programs will be used, some of which were especially developed for the MinION. The workshop is aimed at anyone who is working with, or is planning to work with, Oxford Nanopore MinION sequence data. Note that we are also offering three(!) different weeklong Python courses, an introductory one, an advanced one, as well as one focused on data manipulation and visualisation. All three are taught by dr Martin Jones, founder of Python for Biologists (http://pythonforbiologists.com) and author of several books on Python ( http://pythonforbiologists.com/index.php/books/) Registration for all workshops can be done through the workshop pages indicated below, on which you can also find much more detailed information about each workshop. Please don't hesitate to contact me if you have a question with regard to any of the workshops. I hope to see you at one (or more) of our workshops! With kind regards, Bert Overduin ************************* *1-day Oxford Nanopore MinION Sequencing - Data Handling, Analysis and Applications* Date: Mon 13 June 2016 Price: £155 Registration: participant selection Deadline: Mon 16 May 2016 noon Details: https://genomics.ed.ac.uk/oxford-nanopore-minion-sequencing-data-handling-a… *"I liked this course - it's a good, well-rounded primer for this subject area." (Feb 2016)* ************************* *5-day Introduction to Python for Biologists* Date: Mon 11- Fri 15 July 2016 Price: £500 Registration: first come first serve Deadline: Mon 13 June 2016 noon Details: https://genomics.ed.ac.uk/introduction-python-biologists-july-2016 *"Great learning from someone with a biology background rather than computer science - meant we were on the same wavelength. The course really was excellent!" (Feb 2016)* *"This is the best course I have ever taken - well paced, excellent teaching, and the feeling at the end that I had learnt skills I could apply to my research." (Apr 2016)* ************************* *5-day Advanced Python for Biologists* Date: Mon 25 - Fri 29 July 2016 Price: £500 Registration: first come first serve Deadline: Mon 27 June 2016 noon Details: https://genomics.ed.ac.uk/advanced-python-biologists-july-2016 *"It was just what I needed to start writing more complex code." (Aug 2015)* ************************* *5-day Data Manipulation and Visualisation with Python* Date: Mon 5 - Fri 9 September 2016 Price: £500 Registration: first come first serve Deadline: Mon 8 August 2016 noon Details: https://genomics.ed.ac.uk/data-manipulation-and-visualization-python ************************* -- Bert Overduin, PhD TRAINING AND OUTREACH BIOINFORMATICIAN Bert.Overduin(a)ed.ac.uk orcid.org/0000-0002-5281-8838 EDINBURGH GENOMICS The University of Edinburgh Ashworth Laboratories The King's Buildings Charlotte Auerbach Road Edinburgh EH9 3FL Scotland, United Kingdom tel. +44(0)1316507403 http://genomics.ed.ac.uk The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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Network Inference workshop at ECCB2016: abstract submission deadline extended on May 22nd
by Eric Bonnet 04 May '16

04 May '16

We are glad to announce the ECCB 2016 workshop: Network Inference : New Methods and Data EXTENDED ABSTRACT SUBMISSION DEADLINE: May 22nd http://www.eccb2016.org/programme/workshops/w2/ Scope This one day session (SIG) will bring together experts from computational biology and machine learning to present recent advances in the development and application of gene regulatory network inference methods, as well as novel emerging data types (such as single-cell, epigenomics ...) suitable for network inference. The SIG will be split into two half day sessions. The first half will focus entirely on novel network inference methods, while the second half will focus on opportunities and challenges arising from new data types. Each session will feature an invited speaker and three short talks. Registration https://easychair.org/conferences/?conf=ninf2016 Venue The workshop will take place in the World Forum Convention Centre of Hague, The Netherlands on September 4th 2016 as part of the 15th European Conference on Computational Biology (ECCB 2016). Topics Network Inference Machine learning Biological networks Biological data types NGS Applications of biological network inference Audience The workshop is suitable for wide audience as computational biologists interested in networks inference, computational and experimental biologists, clinicians, plant biologists and anyone who is interested to be exposed to the latest trends and applications of network biology inference from various types of biological data. Organizing Committee: Dr. Anagha Joshi, Roslin Institute, University of Edinburgh, UK. Dr. Tom Michoel, Roslin Institute, University of Edinburgh, UK. Dr. Eric Bonnet, Centre National de Génotypage, CEA, Paris, France.

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