User:Boris/Temp/APB

Hardware

• High performance computing (... at the bench: GPUs, FPGAs, Clusters)
• Cloud computing

Systems and Tools

• Unix
• Network Configuration
• Apache
• MySQL
• 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
• BioPerl
• PHP
• Relational database principles
• BioPython (scope, highlights, installation, use, support)
• Graphical output (PNG and SVG)
• Autonomous agents

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
• 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)