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Target audience
- This course is aimed at individuals working across life sciences who have little or no experience in bioinformatics. Applicants are expected to be at an early stage of using bioinformatics in their research with the need to develop their knowledge and skills further. No previous knowledge of programming is required for this course; group projects may give you the opportunity to learn basic programming, but participants will be supported in this by their mentors. Depending on your chosen project, an introductory programming tutorial may be given as homework prior to attending the course.2
- Applicants should be researchers who are using large multi-omics datasets to infer systems biology models. This is an advanced-level course, and so we will select applicants who already have some experience (ideally 1-2 years) of working with systems biology modelling or related large-scale multi-omics data analysis. Additionally, applicants will be expected to have a working knowledge of using Linux commands, and experience of using a programming language (e.g. Python or Perl).1
- Computational biologists1
- Computer science1
- Life Science Researchers1
- PhD students1
- PhD students, postDoc and PIs in Life Sciences, Biomedicine and Bioinformatics with little or no knowledge of network biology tools, who are interested in learning how to apply such tools in their research.1
- Postdoctoral students1
- Researchers1
- This course is aimed at advanced PhD students and post-doctoral researchers who are currently working with large-scale omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses. Applicants are expected to have a working knowledge of the Linux operating system and the ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Python turorial: https://www.w3schools.com/python/ R tutorial: https://www.datacamp.com/courses/free-introduction-to-r Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
- This course is aimed at experimental biologists, bioinformaticians and mathematicians who have just started in systems biology, are familiar with the basic terminology in this field and who are now keen on gaining a better knowledge of systems biology modelling approaches to understand biological and biomedical problems. A working knowledge of the Linux operating system and ability to use the command line or experience of using a programming language (e.g Python) would be a benefit but is not mandatory. An undergraduate knowledge of molecular and cellular biology or some background in mathematics is highly beneficial. 1
- This course is aimed at experimental biologists, bioinformaticians and mathematicians who have just started in systems biology, are familiar with the basic terminology in this field and who are now keen on gaining a better knowledge of systems biology modelling approaches to understand biological and biomedical problems. An experience of using a programming language (e.g Python, R, Matlab) would be a benefit but is not mandatory. An undergraduate knowledge of molecular and cellular biology or some background in mathematics is highly beneficial. 1
- This course is aimed at researchers from Masters-level upwards within Latin America who are working with and/or generating their own plant genomic and transcriptomic datasets. Prerequisites: Some basic computational or previous bioinformatics experience is required for this workshop, particularly using the UNIX operating system (basic command line skills) and R. You may find the resources below useful: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Basic R concept tutorials: www.r-tutor.com/r-introduction Important: All participants must bring a laptop for the course. We will use a virtual machine (VM) provided by instructors for the course practical sessions. All laptops must be of 64-bit architecture with any Operating System and have at least 60 GB free space. Please also note: this course will be taught in Spanish, however the trainers are fluent in English and can offer language support where feasible. A number of travel fellowships are available for this course - early-stage researchers and researchers from underrepresented groups are especially encouraged to apply for CABANA travel fellowships. You can apply for travel fellowships via the course application form.1
- This introductory course is aimed at bench-based biologists, who are involved in, or embarking on projects that will use network and pathway analysis or protein interaction data. For example, you may be using these tools in the interpretation of biological datasets or as part of a systems biology approach. The course requires no prior knowledge of pathway analysis or computer programming skills. Preference will be given to those actively involved in or commencing interaction/pathway-based projects.1
- bioinformaticians1
- data librarians1
- data managers1
- life science industry1
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Eligibility
- First come first served2
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- English1
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Instructor
- Alexandra Holinski4
- Rahuman Sheriff4
- Birgit Meldal3
- Evangelia Petsalaki3
- Pablo Porras Millan3
- Alex Bateman2
- Anna Swan2
- Dmytro Fishman2
- Girolamo Giudice2
- Hema Bye-A-Jee2
- Javier De Las Rivas2
- Kausthubh Ramachandran2
- Konrad Förstner2
- Lee Larcombe2
- Lutz Brusch2
- Miguel Ponce de León2
- Nikiforos Karamanis2
- Peter McQuilton2
- Sarah Morgan2
- Thawfeek Mohamed Varusai2
- Tomas Helikar2
- Vincent Noël2
- Alfonso Martinez Arias1
- Alfonso Martinez-Arias1
- Altuna Akalin1
- Andreas Dräger1
- Andres Rabinovich1
- Anjali Shrivastava1
- Anne-Laure Boulesteix1
- Ariel Chernomoretz1
- Arnau Montagud1
- Aurelien Dugourd1
- Baron Koylass1
- Boris Adryan1
- Carmen Pin1
- Christine Orengo1
- Daniel Seaton1
- David Armstrong1
- Davide Cirillo1
- Dayane Rodrigues Araujo1
- Diego Jahn1
- Dénes Türei1
- Eliot Rageneau1
- Elisa Passini1
- Emanuel Goncalves1
- Eva Geissen1
- Eva-Maria Geissen1
- Fabio Madeira1
- Federica Eduati1
- Gaurhari Dass1
- Glyn Bradley1
- Hedi Peterson1
- Henrik Nortamo1
- James Yates1
- Jan Hasenauer1
- Jasmin Fisher1
- Javier Conejero1
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- Julio Saez Rodriguez1
- Julio Saez-Rodriguez1
- Jörn Starruß1
- Kalpana Paneerselvam1
- Kalpana Panneerselvam1
- Krishna Kumar Tiwari1
- Krishna Tiwari1
- Leo Parts1
- Leopold Parts1
- Livia Perfetto1
- Manik Garg1
- Marco Ruscone1
- Maria Zimmermann1
- Marton Olbei1
- Matt Rogon1
- Maximiliano Beckel1
- Maximo Rivarola1
- Melissa Burke1
- Michal Szpak1
- Miroslav Kratochvil1
- Mohamed Alibi1
- Monica Abrudan1
- Nandana Madhusoodanan1
- Nataša Pržulj1
- Nicolas Rodriguez1
- Noemi Del Toro Ayllon1
- Pablo Rodriguez Mier1
- Pablo Rodríguez Mier1
- Patricia Carvajal Lopez1
- Priit Adler1
- Rea Antoniou-Kourounioti1
- Ricard Argelaguet1
- Rob Ewing1
- Robert Mueller1
- Sander Wuyts1
- Sandra Orchard1
- Sara-Jane Dunn1
- Selene L. Fernandez-Valverde1
- Sergio Gonzalez - INTA Castelar1
- Tamas Korcsmáros1
- Thalia Diniaco1
- Thawfeek Varusai1
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