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NDARC16 NGS Data Analysis, RNAseq, ChIPseqDownloadable poster in PDF |
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IMPORTANT DATES for this Course
Candidates with adequate profile will be accepted in the next 72 hours after the application, until we reach 20 participants. |
Instructors: |
Mark Dunning obtained his PhD in the Statistics and Computational Biology group of Simon Tavare at
the University of Cambridge. During this time, he developed Bioconductor packages for the analysis of
Illumina microarray data. He then joined the Bioinformatics Core at Cancer Research Uk Cambridge Institute
and has several years of experience working in analysing data from various high-throughput technologies and
developing computational pipelines. In his current role, Mark organises and develops Bioinformatics training
courses.
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Tom Carroll After completing his masters in Bioinformatics, Tom Carroll studied for his PhD at
Kings College London investigating the transcriptomic effects of environmental toxicants. Following this,
Tom joined Cancer Research Uk Cambridge Institute as a Senior Bioinformatics Analyst working on a variety
of high throughput sequencing projects before joining the MRC Clinical Sciences Centre as the Head Of
Bioinformatics in 2014. While working as the Head of Bioinformatics, Tom directs training activities, data
capture and processing from research cores and manages the Core Bioinformatics Team. Tom has gained
substantial experience in ChIP-seq analysis working both at the MRC and CRUK and has created and maintains
several ChIP-seq related Bioconductor packages (ChIPQC, tracktables, soGGi).
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Nuno Barbosa-Morais graduated in Technologic Physics Engineering from Instituto Superior Técnico
(Lisboa, Portugal) and did his PhD in Biomedical Sciences at the University of Lisbon Medical School with
Prof. Carmo Fonseca, although most of the PhD research actually took place at the University of Cambridge
(UK) with Dr. Samuel Aparicio. He then became a Research Associate in the Computational Biology Group
(led by Prof. Simon Tavaré) at the University of Cambridge, based at the CRUK Cambridge Institute, where he
developed a general pipeline for microarray probe reannotation and his work in determining the molecular
mechanisms responsible for the stability of transcriptional programs has involved the analysis, annotation
and integration of different sorts of array and sequence information. Nuno then moved to the University of
Toronto (Canada) to become a Senior Postdoctoral Fellow in Prof. Ben Blencowe's lab, where his survey of the
evolution of tissue-specific alternative splicing in vertebrates involved analysing transcriptomic
high-throughput sequencing data. Nuno now leads the Computational Biology research group at Instituto de
Medicina Molecular in Lisboa, with his research focused on disease transcriptomics.
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Course description |
OverviewHigh-throughput technologies such as next generation sequencing (NGS) can routinely produce massive amounts of data. These technologies allow us to describe all variants in a genome or to detect the whole set of transcripts that are present in a cell or tissue. However, such datasets pose new challenges in the way the data have to be analyzed, annotated and interpreted which are not trivial and are daunting to the wet-lab biologist. This course covers state-of-the-art and best-practice tools for NGS RNA-seq and ChIP-seq data analysis, which are of major relevance in today's genomic and gene expression studies.MethodsThe course is comprised of practical exercises preceded by short lectures. Exercises will be conducted primarily in the R programming language.Target AudiencesEnthusiastic and motivated wet-lab biologists who want to gain more of an understanding of NGS data and eventually progress to analysing their own data.Pre-requisitesThere is a lot of material to cover in the course, so we will assume that you are familiar with a few basics before you come. The tool that will we do most of the analysis in is R. There will be a short recap of the key concepts at the beginning of the course; however it will be beneficial if you are already familiar with how to read data into R, perform basic subset operations and produce simple plots.Several online videos are available that cover this material. For example: Some introductory statistics, such as summary statistics for continuous data (mean, variance etc) and interpreting the results of a t-test, will be also be assumed. See "Statistics at Square One"" Chapters 1, 2, 3 and 7 (Statistics at Square One - BMJ) for a good overview. Basic unix skills, such as being able to list the contents of a directory and copy files, would also be an advantage. See "Session 1" of the Software Carpentry training for a Unix introduction (Shell-novice material from the Software Carpentry Foundation). |
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Detailed Program |
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Instituto Gulbenkian de Ciência, Apartado 14, 2781-901 Oeiras, Portugal Last updated: February 18th 2015 |