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  • University of Cambridge Bioinformatics Training10
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  • HDRUK10
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• • Scientific topic
  • Data visualisation
  • Bioinformatics15
  • Data rendering10
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  • Comparative transcriptomics7
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  • Python5
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  • Aerobiology3
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  • Coding RNA1
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• • Event type
  • Workshops and courses10
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  • Craik-Marshall Building10
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  • Cambridge10
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  • United Kingdom10
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  • University of Cambridge10
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• • Target audience
  • Everyone is welcome to attend the courses
  • Graduate students115
  • Postdocs and Staff members from the University of Cambridge115
  • Institutions and other external Institutions or individuals114
  • please review the policies.10
  • Existing R users who are not familiar with dplyr and ggplot28
  • Those with programming experience in other languages that want to know what R can offer them8
  • The course is aimed at biologists interested in microbiology7
  • The course is aimed primarily at mid-career scientists – especially those whose formal education likely included statistics6
  • but who have not perhaps put this into practice since.6
  • <span style="color:#FF0000">After you have booked a place5
  • Note that we will not cover specific topics in phylogenomics (whole-genome phylogenies) or bacterial genomics.5
  • This course is aimed at researchers with no prior experience in phylogenetic analysis who would like an introduction to the foundations of building phylogenies from relatively small sequences (viral genomes and/or targeted regions of eukaryotic genomes).5
  • but not essential as we will walk through a MS typical experiment and data as part of learning about the tools.5
  • if you are unable to attend any of the live sessions and would like to work in your own time5
  • including for registered university students.<span style="color:#FF0000">5
  • please email the Team as Attendance will be taken on all courses. A charge is applied for non-attendance5
  • Anyone who is using sequencing as part of their work and/or research.4
  • Beginner4
  • Familiarity with mass spectrometry or proteomics in general is desirable4
  • Researchers who are applying or planning to apply image analysis in their research4
  • Researchers who want to extract quantitative information from microscopy images4
  • The course is targeted to either proteomics practitioners or data analysts/bioinformaticians that would like to learn how to use R to analyse proteomics data.4
  • This course is aimed at researchers with an interest in metabolomics and its applications4
  • prokaryotic genomics and antimicrobial resistance.4
  • Institutions and other external institutions or individuals.3
  • No prior experience in the analysis of these types of data is required.3
  • Postdocs and other Research Staff from the University of Cambridge3
  • Researchers3
  • The course is open to Graduate students3
  • This course is aimed at researchers with an interest in metabolomics and its applications.3
  • analysis of complex microbiomes and antimicrobial resistance.3
  • prokaryotic genomics3
  • <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £575 </span style>2
  • <span style="color:#0000FF">All Members of the University of Cambridge to pay £250 </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>2
  • <span style="color:#FF0000">There is no fee charged for this event''<span style="color:#FF0000">2
  • Students2
  • This course is aimed at researchers with no prior experience in the analysis of ChIP-seq data2
  • pathways and diseases.2
  • <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee.1
  • <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Members of Industry to pay 575.00 GBP. </span style> <span style="color:#0000FF">All Members of the University of Cambridge1
  • <span style="color:#FF0000">There is no fee charged for this event''<span style="color:#FF0000">.1
  • Affiliated Institutions and other academic participants from External Institutions and Charitable Organizations to pay 250.00 GBP. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
  • Applicants are expected to have an interest in learning about bioinformatics and/or are in the beginning stages of using bioinformatics in their research with the need to develop their skills and knowledge further.1
  • BioImage Analysts with some experience of basic microscopy image analysis1
  • Bioinformaticians1
  • Bioinformaticians and Biologists who want to learn how to manipulate, process data, and make plots using R1
  • Biologists1
  • Biophysicists1
  • Cell Biologists1
  • Day1 is intended for biologists and computer scientists interested in using LithoGraphX. Some experience in imaging is desirable but not required.1
  • Day2 is intended for computer scientists wanting either to write their own algorithm or automate complex protocols. Basic python knowledge and familiar with C++ are required.1
  • Experimental Researchers1
  • Institutions and other external Institutions or individuals.1
  • Life Science Researchers1
  • Master students1
  • No previous knowledge of programming is required for this course.1
  • People from any area with little or no prior computational experience1
  • PhD students1
  • Research presentation1
  • Researcher1
  • The course is targeted to either proteomics practitioners or data analysts/bioinformaticians that would like to learn how to use R to analyse proteomics data. Familiarity with mass spectrometry or proteomics in general is desirable1
  • The hands­on component is aimed at novice to intermediate users who are seeking detailed guidance with GATK and related tools.1
  • The lecture­ based component of the workshop is aimed at a mixed audience of people who are new to the topic of variant discovery or to GATK1
  • This course is aimed at individuals working across biological and biomedical sciences who have little or no experience in bioinformatics.1
  • This course is aimed at individuals working across biological and biomedical sciences who have little to no experience in bioinformatics.1
  • 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.1
  • This course is appropriate for researchers who are relatively proficient with computers but maybe not had the time or resources available to become programmers.1
  • This course is suitable for all users who have an interest in biomedical research and therapeutics. A special emphasis will be given on drug discovery and target validation. It will also be useful to those who seek for practical examples on how large-scale genomic experiments and computational techniques are integrated and visualised in a web platform.1
  • This course is suitable for anyone who has an interest in biomedical and therapeutic research with a special emphasis on target identification and prioritisation1
  • This course is suitable for anyone who has an interest in biomedical research and therapeutics with a special emphasis on drug discovery and target validation. It is also useful to those who wish to find out how large-scale genomic experiments1
  • This course is targeted at biologists who want to explore, and gain further insight, into their own data through the use of visualisation and design approaches.&nbsp;Some prior experience in programming would be beneficial, but pre-reading and exercises will be sent out to all successful applicants prior to the start of the course. Example datasets will be provided which include: gene-gene interaction data gene-disorder links phylogeny of transcription factors ChIP-Seq and RNA-Seq1
  • This course may be of interest to physical scientists looking to develop their knowledge of Python coding in the context of bioimage analysis1
  • This hands-on event is suitable for anyone who has an interest in building data science workflows with different kinds of life science data.1
  • This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain a better understanding of the biological challenges when working with integrated datasets. No programming or command line experience is required to attend this course. Please note this course does not cover statistical approaches for data integration. For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on&nbsp;Systems Biology: From large datasets to biological insight.1
  • This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists/bioinformaticians who wish to gain a better knowledge of the biological challenges presented when working with integrated datasets. Some practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction1
  • This webinar is suitable for students and early career researchers in the Life Sciences.1
  • cellular models of disease and computational techniques are used to identify and validate the causal links between targets1
  • computational and statistical techniques are used to identify and validate the causal links between targets1
  • early stages of drug discovery. It is also useful to those who wish to find out how large-scale genomic experiments1
  • or who are already GATK users seeking to improve their understanding of and proficiency with the tools.1
  • seeking an introductory course into the tools1
  • who would like to get started in processing their data and perform downstream analysis and visualisation of their results.1
  • who would like to get started in processing their data using a standardised pipeline and perform downstream analysis and visualisation of their results.1
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