Difference between revisions of "User:Boris/Temp/APB"
< User:Boris | Temp
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| − | ; | + | ;Hardware |
| − | + | * [[High Performance Computing]] (... at the bench: GPUs, FPGAs, Clusters) | |
| − | |||
| − | * [[High Performance Computing]] | ||
* [[Cloud computing]] | * [[Cloud computing]] | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
; Tools | ; Tools | ||
| Line 19: | Line 11: | ||
* [[Unix automation]] shellscripts, cron | * [[Unix automation]] shellscripts, cron | ||
* [[Unix wget]] | * [[Unix wget]] | ||
| − | * [[ | + | * [[Network Configuration]] |
* [[Apache]] (serving a local file) | * [[Apache]] (serving a local file) | ||
* [[MySQL]] (introduction, installation, MariaDB) | * [[MySQL]] (introduction, installation, MariaDB) | ||
| Line 27: | Line 19: | ||
** [[Database example-PHP form interface]] | ** [[Database example-PHP form interface]] | ||
** [[DBI Mac OS X installation notes]] | ** [[DBI Mac OS X installation notes]] | ||
| + | * [[Tools for the bioinformatics lab]] (APB, CLUSTAL, EMBOSS, PHYLIP, T-Coffee, HMMER) | ||
* [[GBrowse]] (installation notes, viewing annotations, LDAS installation notes, LDAS usage) | * [[GBrowse]] (installation notes, viewing annotations, LDAS installation notes, LDAS usage) | ||
| − | + | ;Programming | |
| − | ; | ||
* [[IDE|IDE (Integrated Development Environment)]] (Komodo) | * [[IDE|IDE (Integrated Development Environment)]] (Komodo) | ||
* [[Regular Expressions]] | * [[Regular Expressions]] | ||
* [[Screenscraping]] | * [[Screenscraping]] | ||
* [[Perl]] (installation notes, CPAN, principles, syntax, one liners) | * [[Perl]] (installation notes, CPAN, principles, syntax, one liners) | ||
| − | ** [[Perl basic | + | ** [[Perl basic programming]] |
** [[Perl hash example]] | ** [[Perl hash example]] | ||
** [[Perl LWP example]] | ** [[Perl LWP example]] | ||
| Line 57: | Line 49: | ||
** [[BioPerl exercise signal cleavage]] | ** [[BioPerl exercise signal cleavage]] | ||
* [[PHP]] | * [[PHP]] | ||
| + | * [[BioPython]] (scope, highlights, installation, use, support) | ||
| + | * [[Graphical output]] (PNG and SVG) | ||
| + | * [[Autonomous agents]] | ||
| − | ;Network, | + | ; Algorithms |
| + | :; Algorithms on Sequences | ||
| + | * [[Dynamic Programming]] (Dynamic Programming and Optimal Pairwise Sequence Alignment, appropriate problems for DP, procedural and recursive formulation of solutions) | ||
| + | * [[Multiple Sequence Alignment]] | ||
| + | * [[Genome Assembly]] (long and short reads) | ||
| + | :; Algorithms on Structures | ||
| + | * [[Docking]] | ||
| + | * [[Protein Structure Prediction]] (''ab initio'') | ||
| + | :; Algorithms on Trees | ||
| + | * [[Computing with trees]] (Bayesian approaches for phylogenetic trees, tree comparison) | ||
| + | :; Algorithms on Networks | ||
| + | * [[Network metrics]] (Degree distributions, Centrality metrics, other metrics on topology, small-world- vs. random-geometric controversy) | ||
| + | ** [[Dijkstras Algorithm]] | ||
| + | ** [[Floyd Warshall Algorithm]] | ||
| + | |||
| + | ;Communication and collaboration | ||
* [[MediaWiki]] | * [[MediaWiki]] | ||
* [[Web Communication]] | * [[Web Communication]] | ||
* [[HTML essentials]] | * [[HTML essentials]] | ||
| + | * [[HTML 5]] | ||
| + | * [[SADI|SADI Semantic Automated Discovery and Integration]] | ||
* [[CGI]] (introduction, configuring apache, executing a script, query string input) | * [[CGI]] (introduction, configuring apache, executing a script, query string input) | ||
| − | |||
| − | |||
| − | |||
| + | ; Statistics | ||
| + | * [[Pattern discovery]] | ||
| + | * [[Correlation]] (Covariance matrices and their interpretation, application to large problems, collaborative filtering, MIC and MINE) | ||
| + | * [[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) | ||
| + | * [[Cluster metrics]] (Cluster quality metrics (Akaike, BIC)–when and how) | ||
| + | * [[Map equation|The Map Equation]] (A network flow approach to hierarchical partitioning of large datasets) | ||
| + | * [[Machine learning]] (Classification problems: Neural Networks, HMMs, SVM..) | ||
| + | * [[R]] | ||
| + | ** [[R plotting]] | ||
| + | ** [[R programming]] (Scope, passing parameters, scalars, matrices, lists, functions) | ||
| + | ** [[R EDA]] | ||
| + | ** [[R regression]] (linear and non-linear, Calculation, confidence limits and interpretation) | ||
| + | ** [[R PCA]] | ||
| + | ** [[R Clustering]] | ||
| + | ** [[R Classification]] (Phrasing inquiry as a classification problem, dealing with noisy data, machine learning approaches to classification, implementation in R) | ||
| + | ** [[R hypothesis testing]] (Major approaches and when to apply them (including simulation methods for arbitrary PDFs)) | ||
| + | ** [[Bioconductor]] (Scope, contents, highlights use) | ||
| + | ;Applications | ||
| + | * [[Data integration]] (BioMart: Biodata integration, and data-mining of complex, related, descriptive data) | ||
| + | * [[Text mining]] (Use cases, tasks and metrics, taggers, vocabulary mapping, Practicals: R-support, Python/Perl support, others...) | ||
| + | * [[High-throughput sequencing]] | ||
| + | * [[Functional annotation]] (GFF) | ||
| + | * [[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) | ||
</div> | </div> | ||
Revision as of 02:35, 15 September 2012
- Hardware
- High Performance Computing (... at the bench: GPUs, FPGAs, Clusters)
- Cloud computing
- Tools
- Unix (installation notes, commands, principles, pipe)
- Unix system administration (and security)
- Unix automation shellscripts, cron
- Unix wget
- Network Configuration
- Apache (serving a local file)
- MySQL (introduction, installation, MariaDB)
- Tools for the bioinformatics lab (APB, CLUSTAL, EMBOSS, PHYLIP, T-Coffee, HMMER)
- GBrowse (installation notes, viewing annotations, LDAS installation notes, LDAS usage)
- Programming
- IDE (Integrated Development Environment) (Komodo)
- Regular Expressions
- Screenscraping
- Perl (installation notes, CPAN, principles, syntax, one liners)
- Perl basic programming
- Perl hash example
- Perl LWP example
- Perl MySQL example
- Perl MySQL introduction (DBI Mac OSX installation notes)
- Perl OBO parser
- Perl programming
- Perl programming exercises 1
- Perl programming exercises 2
- Perl programming Data Structures
- Perl references
- Perl simulation
- Perl retrieve fasta
- Perl/MySQL example
- Perl: Object oriented programming
- Perl: Ugly programming
- Perl: Using forms
- BioPerl (installation notes, programming, BP run, BP ext, tutorials)
- PHP
- BioPython (scope, highlights, installation, use, support)
- Graphical output (PNG and SVG)
- Autonomous agents
- Algorithms
-
- Algorithms on Sequences
- Dynamic Programming (Dynamic Programming and Optimal Pairwise Sequence Alignment, appropriate problems for DP, procedural and recursive formulation of solutions)
- Multiple Sequence Alignment
- Genome Assembly (long and short reads)
- Algorithms on Structures
- Docking
- Protein Structure Prediction (ab initio)
- Algorithms on Trees
- Computing with trees (Bayesian approaches for phylogenetic trees, tree comparison)
- Algorithms on Networks
- Network metrics (Degree distributions, Centrality metrics, other metrics on topology, small-world- vs. random-geometric controversy)
- Communication and collaboration
- MediaWiki
- Web Communication
- HTML essentials
- HTML 5
- SADI Semantic Automated Discovery and Integration
- CGI (introduction, configuring apache, executing a script, query string input)
- Statistics
- Pattern discovery
- Correlation (Covariance matrices and their interpretation, application to large problems, collaborative filtering, MIC and MINE)
- 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)
- Cluster metrics (Cluster quality metrics (Akaike, BIC)–when and how)
- The Map Equation (A network flow approach to hierarchical partitioning of large datasets)
- Machine learning (Classification problems: Neural Networks, HMMs, SVM..)
- R
- R plotting
- R programming (Scope, passing parameters, scalars, matrices, lists, functions)
- R EDA
- R regression (linear and non-linear, Calculation, confidence limits and interpretation)
- R PCA
- R Clustering
- R Classification (Phrasing inquiry as a classification problem, dealing with noisy data, machine learning approaches to classification, implementation in R)
- R hypothesis testing (Major approaches and when to apply them (including simulation methods for arbitrary PDFs))
- Bioconductor (Scope, contents, highlights use)
- Applications
- Data integration (BioMart: Biodata integration, and data-mining of complex, related, descriptive data)
- Text mining (Use cases, tasks and metrics, taggers, vocabulary mapping, Practicals: R-support, Python/Perl support, others...)
- High-throughput sequencing
- Functional annotation (GFF)
- 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)
