[BioC] Course: Learning R / Bioconductor for Sequence Analysis, Seattle, WA Oct 27-29
N.T.Do at leeds.ac.uk
Mon Sep 15 18:53:40 CEST 2014
I totally agree!
Dr Thuy Do
Lecturer in Microbiology
Dpt of Oral Biology
School of Dentistry, University of Leeds,
Level 6 Worsley Building, Room 6.106.
Clarendon way, Leeds, LS2 9LU, U.K.
Tel: +44 113 3438936
From: bioconductor-bounces at r-project.org [bioconductor-bounces at r-project.org] On Behalf Of Dale N. Richardson [drichardson at igc.gulbenkian.pt]
Sent: 15 September 2014 17:34
To: Son Pham
Cc: bioc-devel at r-project.org; bioconductor at r-project.org
Subject: Re: [BioC] Course: Learning R / Bioconductor for Sequence Analysis, Seattle, WA Oct 27-29
Seconded! An online version of the course would be indispensable.
Dale Richardson, Ph.D.
Laboratory of Plant Molecular Biology
Instituto Gulbenkian de Ci�ncia
Rua da Quinta Grande, 6
Tel: +351 967 992 816
Email: drichardson at igc.gulbenkian.pt
On 15/09/2014, at 17:27, Son Pham <spham at salk.edu> wrote:
> Thanks Martin for offering the course. It's fantastics -- and if it would
> be an online course, like coursera, it will also be great for a lot of
> distant people.
> Son Pham, Ph.D
> On Mon, Sep 15, 2014 at 7:13 AM, Martin Morgan <mtmorgan at fhcrc.org> wrote:
>> Course: Learning R / Bioconductor for Sequence Analysis
>> Dates: October 27-29, Seattle, WA.
>> Registration: https://register.bioconductor.org/Seattle-Oct-2014/
>> This course is directed at beginning and intermediate users who would like
>> an introduction to the analysis and comprehension of high-throughput
>> sequence data using R and Bioconductor. Day 1 focuses on learning essential
>> background: an introduction to the R programming language; central concepts
>> for effective use of Bioconductor software; and an overview of
>> high-throughput sequence analysis work flows. Day 2 emphasizes use of
>> Bioconductor for specific tasks: an RNA-seq differential expression work
>> flow; exploratory, machine learning, and other statistical tasks; gene set
>> enrichment; and annotation. Day 3 transitions to understanding effective
>> approaches for managing larger challenges: strategies for working with
>> large data, writing re-usable functions, developing reproducible reports
>> and work flows, and visualizing results. The course combines lectures with
>> extensive hands-on practicals; students are required to bring a laptop with
>> wireless internet access and a modern version of the Chrome or Safari web
>> Computational Biology / Fred Hutchinson Cancer Research Center
>> 1100 Fairview Ave. N.
>> PO Box 19024 Seattle, WA 98109
>> Location: Arnold Building M1 B861
>> Phone: (206) 667-2793
>> Bioconductor mailing list
>> Bioconductor at r-project.org
>> Search the archives: http://news.gmane.org/gmane.
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