Difference between revisions of "Applied Bioinformatics Main Page"

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Continue [[BCB410_2012|'''here''']] for the current BCB410 course page ...
 
Continue [[BCB410_2012|'''here''']] for the current BCB410 course page ...
 
</div>
 
</div>
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==Topics==
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<table width="33%">
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<tr class="sh"><td>Hardware</td></tr>
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<tr class="s1"><td>High performance computing <!-- (... at the bench: GPUs, FPGAs, Clusters) --></td></tr>
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<tr class="s2"><td>Cloud computing</td></tr>
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</table>
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 +
<table width="33%">
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<tr class="sh"><td>Systems and Tools</td></tr>
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<tr class="s1"><td>[[Unix]]</td></tr>
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<tr class="s2"><td>[[Network Configuration]]</td></tr>
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<tr class="s1"><td>[[Apache]]</td></tr>
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<tr class="s2"><td>[[MySQL]]</td></tr>
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<tr class="s1"><td>[[Tools for the bioinformatics lab]]</td></tr>
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<tr class="s2"><td>[[GBrowse|GBrowse and LDAS]]</td></tr>
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</table>
 +
 +
 +
<table width="33%">
 +
<tr class="sh"><td>Programming</td></tr>
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<tr class="s1"><td>[[IDE|IDE (Integrated Development Environment)]]</td></tr>
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<tr class="s2"><td>[[Regular Expressions]]</td></tr>
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<tr class="s1"><td>[[Screenscraping]]</td></tr>
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<tr class="s2"><td>[[Perl]]</td></tr>
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<tr class="s1"><td>[[BioPerl]]</td></tr>
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<tr class="s2"><td>[[PHP]]</td></tr>
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<tr class="s1"><td>[[Relational database principles]]</td></tr>
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<tr class="s2"><td>BioPython <!-- (scope, highlights, installation, use, support) --></td></tr>
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<tr class="s1"><td>Graphical output <!-- (PNG and SVG) --></td></tr>
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<tr class="s2"><td>[[Autonomous agents]]</td></tr>
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</table>
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 +
<table width="33%">
 +
<tr class="sh"><td>Algorithms on Sequences</td></tr>
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<tr class="s1"><td>[[Dynamic Programming]]</td></tr>
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<tr class="s2"><td>[[Multiple Sequence Alignment]]</td></tr>
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<tr class="s1"><td>[[Genome Assembly]]</td></tr>
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<tr class="sh"><td>Algorithms on Structures</td></tr>
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<tr class="s1"><td>[[Docking]]</td></tr>
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<tr class="s2"><td>Protein Structure Prediction <!-- ''ab initio'' --></td></tr>
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<tr class="sh"><td>Algorithms on Trees</td></tr>
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<tr class="s1"><td>Computing with trees <!-- Bayesian approaches for phylogenetic trees, tree comparison) --></td></tr>
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<tr class="sh"><td>Algorithms on Networks</td></tr>
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<tr class="s1"><td>Network metrics <!-- (Degree distributions, Centrality metrics, other metrics on topology, small-world- vs. random-geometric controversy) --></td></tr>
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<tr class="s2"><td>[[Dijkstras Algorithm]]</td></tr>
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<tr class="s1"><td>[[Floyd Warshall Algorithm]]</td></tr>
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</table>
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 +
 +
<table width="33%">
 +
<tr class="sh"><td>Communication and collaboration</td></tr>
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<tr class="s1"><td>[[MediaWiki]]</td></tr>
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<tr class="s2"><td>[[HTML essentials]]</td></tr>
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<tr class="s1"><td>[[HTML 5]]</td></tr>
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<tr class="s2"><td>[[SADI|SADI Semantic Automated Discovery and Integration]]</td></tr>
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<tr class="s1"><td>[[CGI]]</td></tr>
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</table>
 +
 +
 +
<table width="33%">
 +
<tr class="sh"><td>Statistics
 +
<tr class="s1"><td>[[Pattern discovery]]</td></tr>
 +
<tr class="s1"><td>Correlation <!-- (Covariance matrices and their interpretation, application to large problems, collaborative filtering, MIC and MINE) --></td></tr>
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<tr class="s1"><td>Clustering methods <!-- (Algorithms and choice (including: hierarchical, model-based and partition clustering, graphical methods (MCL), flow based methods (RRW) and spectral methods). Implementation in R if possible) --></td></tr>
 +
<tr class="s1"><td>Cluster metrics <!-- (Cluster quality metrics (Akaike, BIC)–when and how) --></td></tr>
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<tr class="s2"><td>[[Map equation|The Map Equation]] </td></tr>
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<tr class="s1"><td>Machine learning <!-- (Classification problems: Neural Networks, HMMs, SVM..) --></td></tr>
 +
<tr class="s2"><td>[[R]]</td></tr>
 +
<tr class="s1"><td>- R plotting</td></tr>
 +
<tr class="s2"><td>- [[R programming]]</td></tr>
 +
<tr class="s1"><td>- R EDA</td></tr>
 +
<tr class="s2"><td>- R regression</td></tr>
 +
<tr class="s1"><td>- R PCA</td></tr>
 +
<tr class="s2"><td>- R Clustering</td></tr>
 +
<tr class="s1"><td>- R Classification <!-- Phrasing inquiry as a classification problem, dealing with noisy data, machine learning approaches to classification, implementation in R) --></td></tr>
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<tr class="s2"><td>- R hypothesis testing</td></tr>
 +
<tr class="s1"><td>- [[Bioconductor]]</td></tr>
 +
</table>
 +
 +
 +
<table width="33%">
 +
<tr class="sh"><td>Applications</td></tr>
 +
<tr class="s1"><td>[[Data integration]] <!-- Add BioMart: Biodata integration, and data-mining of complex, related, descriptive data --></td></tr>
 +
<tr class="s2"><td>Text mining <!-- (Use cases, tasks and metrics, taggers, vocabulary mapping, Practicals: R-support, Python/Perl support, others...) --></td></tr>
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<tr class="s1"><td>[[HMMER]]</td></tr>
 +
<tr class="s2"><td>High-throughput sequencing</td></tr>
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<tr class="s1"><td>Functional annotation <!-- GFF --></td></tr>
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<tr class="s2"><td>Microarray analysis <!-- (... in R: differential expression and multiple testing; Loading and normalizing data, calculating differential expression, LOWESS, the question of significance, FWERs: Bonferroni and FDR; SAM and LIMMA) --></td></tr>
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</table>
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== Topics ==
 
  
 
== Resources ==
 
== Resources ==
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[[Category:Applied Bioinformatics]]
 
[[Category:Applied Bioinformatics]]
  
 
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Revision as of 18:24, 18 September 2012

Applied Bioinformatics

Welcome to the Applied Bioinformatics Course Wiki.

These wiki pages are provided to coordinate information, activities and projects in the applied bioinformatics courses taught by Boris Steipe at the University of Toronto. If you are not one of my students, you can still browse this site, however only users with a login account can edit or contribute or edit material. If you are here because you are interested in general aspects of bioinformatics or computational biology, you may want to review the Wikipedia article on bioinformatics, or visit Wikiomics. Contact boris.steipe(at)utoronto.ca with any questions you may have.



The Courses

BCB410H1F is the undergraduate course code and JTB2020H1S is the course code for graduate students. However the delivery and scope of the courses is very different:

  • BCB410 is intended for students in the Bioinformatics and Computational Biology Specialist Program. Therefore I assume that all students are very familiar with a wide variety of computer science related topics and their practical application.
  • JTB2020 is designed for students in the Collaborative PhD Program in Bioinformatics and Genome Biology. These students have a wide variety of backgrounds and prior experience. They participate in the Computational Systems Biology Course and go through a number of targeted exercises in applied bioinformatics to add as much material to their knowledge- and skill set as can reasonably be acquired in a single term.

Continue here for the current BCB410 course page ...


Topics

Hardware
High performance computing
Cloud computing


Systems and Tools
Unix
Network Configuration
Apache
MySQL
Tools for the bioinformatics lab
GBrowse and LDAS


Programming
IDE (Integrated Development Environment)
Regular Expressions
Screenscraping
Perl
BioPerl
PHP
Relational database principles
BioPython
Graphical output
Autonomous agents


Algorithms on Sequences
Dynamic Programming
Multiple Sequence Alignment
Genome Assembly
Algorithms on Structures
Docking
Protein Structure Prediction
Algorithms on Trees
Computing with trees
Algorithms on Networks
Network metrics
Dijkstras Algorithm
Floyd Warshall Algorithm


Communication and collaboration
MediaWiki
HTML essentials
HTML 5
SADI Semantic Automated Discovery and Integration
CGI


Statistics
Pattern discovery
Correlation
Clustering methods
Cluster metrics
The Map Equation
Machine learning
R
- R plotting
- R programming
- R EDA
- R regression
- R PCA
- R Clustering
- R Classification
- R hypothesis testing
- Bioconductor


Applications
Data integration
Text mining
HMMER
High-throughput sequencing
Functional annotation
Microarray analysis
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