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Bioinformatics for Biologists:
A Series of Three Minicourses
Spring 2006
Whitehead
Institute
9 Cambridge
Center
Cambridge,
Massachusetts
Unix, Perl
& BioPerl
February 27,
March 1, 2 1:30pm - 3:30pm
These sessions introduce
the concept of customizing and automating data analysis with bioinformatics
programming tools.
| Day 1: |
Unix
Basics about the Unix operating system and how to download genomic
data and run programs locally for analysis. Includes examples
of blast and emboss.
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| Day 2: |
Perl
You will learn the basics of programming with Perl, a scripting
language used extensively in Bioinformatics. Examples of basic
bioinformatics scripts will be presented.
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| Day 3: |
Perl and BioPerl
This session will continue the discussion of Perl and will demonstrate
how to write web scripts, draw graphics and use BioPerl (a package
that lets you manipulate sequence information easily).
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Relational
Databases for Biologists
March 27, 29, 30 1:30pm - 3:30pm
Databases provide a
powerful method to organize, efficiently search, and relate data
sets. In this series of three lessons, learn to effectively manage
your experimental results by developing, implementing, and querying
custom relational database systems.
| Day 1: |
Conceptualization and Database
Design
Designing an appropriate and extensible database is the foundation
of database methodology. We will explore different techniques
to conceptualize and develop custom databases. A series of design
exercises will not only demonstrate data organization, but also
emphasize the relationship of data structures to each other.
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| Day 2: |
Mining a Database
The primary advantage of storing data within a database is the
ability to search across all of the data with a high level of
specificity and efficiency. We will learn how to use the common
database language, SQL, to query and data mine specific database
information.
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| Day 3: |
Building and Modifying a Database
With the skills of designing and traversing a database established,
we will examine how to insert, modify, and delete data held
within a database. To further illustrate the utility of databases,
we will learn to automate repetitive tasks, like loading data,
with a few easy steps.
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Analysis of Microarray
Data
April 24, 26, 27
1:30pm - 3:30pm
The massive amount of data generated from microarray experiments requires knowledge
of analytical and statistical methods in order to make sense of the data. Here
we explore some of these methods to make biological discovery.
| Day 1: |
Experimental Design and Data
Normalization
Effective design is crucial for any large-scale experiment,
so we'll look briefly at some issues to consider before performing
a microarray experiment. After data collection, effectively
analyzing expression data requires some initial data normalization
and transformation. We will discuss methods to remove unwanted
variation within and between chips.
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| Day 2: |
Differential Expression, Filtering and Clustering
We will discuss methods
to identify genes exhibiting differential expression and ways to filter all
the genes on a chip to some manageable number for further analysis. We will
also review the specifics of some common clustering and segmentation methods
used to find genes with similar expression patterns.
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| Day 3: |
Functional Analysis and Visualization
Once you have a list of genes
with "interesting" expression patterns, what do you do next? We will
discuss ways to take advantage of gene annotation to further analyze expression
data. We will also survey some ways of visualizing large amounts of expression
data.
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Updated
May 10, 2006 11:08 AM
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