The HSPH Center for Health Bioinformatics, through the Harvard Clinical and Translational Science Center will be offering a one-day introductory workshop on RNA-sequencing this November 28th, 9am-5pm at the Countway Library, Room 403. The workshop will cover best practices for quality control, read alignment, and expression analysis using RNA-seq data. No prior programming experience or command line training is required.
Space is limited, register now if you would like to attend.
Who should attend
• are generating, or hope to generate RNAseq data
• want to use a simple, graphical interface to analyse their results
• want to analyse their results in a reproducible fashion
The workshop will introduce basic concepts and illustrate an end-to-end
data analysis workflow through live tutorials using the Galaxy
analytical framework. At the end of this activity, participants will
be able to start their own Galaxy servers using Amazon’s EC2 environment
to analyze their data, without using the command line.
9 am – 11am: Introduction to Galaxy
11am – 5pm: RNA-seq (includes a break for lunch1 and a short break in
While biological and medical knowledge are helpful, students do not need to have prior programming or “command line” experience to participate in the course. For those unfamiliar with the Galaxy platform, we will start the the workshop with an optional introductory Galaxy tutorial, covering such topics as data import, filtering, combining, conversion and how to perform actions on genomic regions. After a short break we will spend the rest of our time applying this knowledge to RNA-seq. Those with prior Galaxy experience may choose to skip the first session and join us at 11am, though all are welcome to attend.
Time and location
9am-5pm, Wednesday November 28th, 2012
10 Shattuck Street
Boston, MA 02115
1 Lunch will not be provided, but there are a number of cafeterias and
restaurants in the immediate vicinity.
The 1000G project just published it’s main manuscript, and MassGenomics provides an excellent summary:
The 1,000 Genomes project has provided a sort of “null expectation” for the number of rare, low-frequency, and common variants of different functional consequences found in randomly-chosen [healthy] individuals from various populations. […] It also tells you that if you sequence an individual’s whole genomes and don’t find about 3 million SNPs, something is probably wrong.
And Luke Jostin’s publication in Nature reports on a record number of genetic associations for Inflammatory Bowel Disease, along with the struggle to turn these associations into actionable predictions:
We could imagine genotyping healthy people and use our new IBD variants to find a “high risk” group that we can monitor more closely. How well would this work? Given the variants reported in the paper, the answer is “not very well”. Suppose we take people in the top 0.05% of IBD risk. Even in this high risk group only 1 in 10 people will get IBD. Even worse, 99% of real IBD patients WON’T be in this group, and so would be missed by the test!
As part of its new Center for Stem Cell Bioinformatics, the Harvard Stem Cell Institute (HSCI) has partnered with the Harvard School of Public Health Bioinformatics Core (HBC) to improve Bioinformatics assessment of stem cell regulation and function. Integral to the HSCI, our multi-disciplinary team works with HSCI researchers across the Harvard, MIT and Broad biomedical community, providing a comprehensive portfolio of infrastructure, data analysis, training and development to HSCI researchers. As we expand the team we seek an experienced computational biologist to become an integral component of the partnership and provide dedicated bioinformatics expertise to the Massachusetts General Hospital Center for Regenerative Medicine (CRM).
This is a unique opportunity to participate in world-class stem cell research that aims to make an impact on human health. You will be working directly with researchers on projects ranging from understanding hematopoietic stem cell biology, to muscle regeneration or the role of pluripotency genes in stem-cell self renewal or cancer. You will act as the liaison between HBC and CRM to ensure developed solutions can be standardized and made available to the broader scientific community.
You have a background in biomedical or quantitative science and a strong interest in working with researchers in stem cell biology and regenerative medicine. You thrive on scientific challenges, enjoy collaborating with an interdisciplinary team and excel at communicating and coordinating between programmers and wet-lab scientists alike. Learning and applying new methods is natural for you, and you are motivated to continuously expand your skills. You have a good handle on your data and project management skills. Working on different projects and deadlines, frequently in collaboration with other Cores and research groups, doesn’t intimidate you. You enjoy teaching and organizing workshops to train other researchers in newly developed methods and workflows.
You will support researchers at CRM with their data management, quality control, analysis of data and presentation of results. Projects are usually a mix of short-term support tasks and long-term collaborations; most support tasks and methods should eventually transition into standardized workflows. You will need broad experience in several bioinformatics domains such as microarray analysis (mRNA, miRNA), sequencing (RNA-seq, ChIP-seq, Exome-seq) or functional analysis. You will be the initial point of contact for CRM researchers, work with the HBC director to prioritize projects and expand the HSCI Core Bioinformatics Program.
- PhD in a biomedical or quantitative science (or equivalent experience), 2+ years of work experience
- Expertise in at least one listed bioinformatics domain
- Evidence of working directly with wet laboratories to perform analyses
- Proven ability to interpret and analyze large data sets and present results synthetically
- Working knowledge of biology, genetics and cell biology
- Excellent written English and a familiarity with presenting biological results
- Strong interpersonal skills
- Computational experience
- Scripting abilities (Perl, Python)
- Experience with R/BioC, GenePattern, Galaxy or similar environments
- Competency in statistics
- Demonstrated experience in teaching and outreach
How to apply
Send us an email with your CV, cover letter and any questions you might have.