Education Collection

Education

A Primer on Regression Methods for Decoding cis-Regulatory Logic

Debopriya Das, Matteo Pellegrini & Joe W. Gray describe the basic aspects of regression methods and their application to evaluating the activity of regulatory elements.

Das D, Pellegrini M, Gray JW

doi:10.1371/journal.pcbi.1000269

Analyzing ChIP-chip data using Bioconductor

Joern Toedling and Wolfgang Huber cover basic ChIP-chip data analysis with Bioconductor; an open source and open development software project for the analysis and comprehension of genomic data.

Toedling J, Huber W

doi:10.1371/journal.pcbi.1000227

Support Vector Machines and Kernels for Computational Biology

Ben-Hur et al. discuss the use of support vector machines and related kernel methods in realizing the full potential of large datasets. They employ a running example of splice site recognition to illustrate different properties of SVMs using different kernels.

Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G

doi:10.1371/journal.pcbi.1000173

The Rough Guide to In Silico Function Prediction, or How To Use Sequence and Structure Information To Predict Protein Function

This Tutorial provides an essential guide for biologists and computational biologists considering which type of computational tool is appropriate for the analysis of their protein of interest, and what kind of insights into its function these tools can provide.

Punta M, Ofran Y

doi:10.1371/journal.pcbi.1000160

Advanced Genomic Data Mining

Data mining allows us to make sense of the information found in data banks. Fernández-Suárez and Birney focus on advanced ways of interacting with BioMart using other applications to retrieve information through different platforms.

Fernández-Suárez X, Birney E

doi:10.1371/journal.pcbi.1000121

Structure-Guided Comparative Analysis of Proteins: Principles, Tools, and Applications for Predicting Function

How can we make meaningful use of genomic and proteomic data from genome sequencing projects and structural genomic initiatives? Mazumder and Vasudevan define a ten-step procedure, that can be followed as a general rule for functional inference of an uncharacterized protein.

Vasudevan S, Mazumder R

doi:10.1371/journal.pcbi.1000151

An Introduction to Bioinformatics for Glycomics Research

Kiyoko F. Aoki-Kinoshita from Soka University, Tokyo, gives an overview Tutorial of the current status of carbohydrate databases and the newest analytical techniques, as well as the informatics needed for rapid progress in glycomics research.

Aoki-Kinoshita K

doi:10.1371/journal.pcbi.1000075

A Quick Guide for Computer-Assisted Instruction in Computational Biology and Bioinformatics

Costa, Galembeck, Marson and Torres provide a guide to the effective use of CAI in the training of life scientists.

Costa MJ, Galembeck E, Marson GA, Torres BB

doi:10.1371/journal.pcbi.1000035

Comprehensive Analysis of Affymetrix Exon Arrays Using BioConductor

Okoniewski and Miller introduce BioConductor, a collection of open source software packages designed to support the analysis of biological data, in this Tutorial for PLoS Computational Biology.

Okoniewski MJ, Miller CJ

doi:10.1371/journal.pcbi.0040006

Strategies for Identifying RNA Splicing Regulatory Motifs and Predicting Alternative Splicing Events

Uwe Ohler and Dirk Holste contribute a Tutorial on the regulation of gene expression to the PLoS Computational Biology Education section.

Holste D, Ohler U

doi:10.1371/journal.pcbi.0040021

A Primer on Python for Life Science Researchers

The Python computer language is introduced here by Sebastian Bassi from the Universidad Nacional de Quilmes, Argentina, providing a comprehensive overview of the language and its capabilities, to be used as a teaching tool.

Bassi, S

doi:10.1371/journal.pcbi.0030199

From Pathways Databases to Network Models of Switching Behavior

Aguda and Goryachev address the use of pathways databases as sources of dynamical models for biological phenomena from the point of view of a non-biologist. They use models based on molecular interactions to explain observed cellular behavior.

Aguda BD, Goryachev AB

doi:10.1371/journal.pcbi.0030152

Recent Evolutions of Multiple Sequence Alignment Algorithms

This review by Cédric Notredame from CNRS focuses on the recent developments in the assembly of MSA techniques, the accuracy of which is becoming ever more vital to biological modeling methods.

Notredame C

doi:10.1371/journal.pcbi.0030123

A Primer on Learning in Bayesian Networks for Computational Biology

Chris Needham and colleagues from the University of Leeds aim to introduce Bayesian networks to the computational biologist, focusing on the concepts behind methods for learning the parameters and structure of models, at a time when they are becoming the machine learning method of choice.

Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR

doi:10.1371/journal.pcbi.0030129

Introduction to Computational Proteomics

This tutorial introduces computational proteomics to researchers in bioinformatics who may have no in-depth knowledge of the field; concentrating on computational aspects of protein identification.

Colinge J, Bennett KL

doi:10.1371/journal.pcbi.0030114

Machine Learning and Its Applications to Biology

Supervised and unsupervised machine learning can address biological problems not easily solved with statistical methods. Using the data analysis language R, the authors provide practical demonstrations of machine learning methods applied to biological data.

Tarca AL, Carey VJ, Chen Xw, Romero R, Drăghici S

doi:10.1371/journal.pcbi.0030116

Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases

In the first part of this two-part-analysis, the authors examine different techniques of protein interaction identification and the available databases classifying experimental data. They discuss the success of a variety of methods for validating and verifying such data.

Shoemaker BA, Panchenko AR

doi:10.1371/journal.pcbi.0030042

Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners

In the second part of this two-part-analysis, Panchenko and Shoemaker examine different approaches to predict protein interaction partners and highlight recent achievements in the prediction of specific domains mediating protein-protein interactions.

Shoemaker BA, Panchenko AR

doi:10.1371/journal.pcbi.0030043

Automated Querying of Genome Databases

In this tutorial, the first of several presented at the Intelligent Systems for Molecular Biology meeting in Fortaleza, Brazil, in August 2006, tools that have been developed for facilitating automated, genome-database–querying are described, and some applications for which they are well-suited are presented.

Schattner P

doi:10.1371/journal.pcbi.0030001

Modularity and Dynamics of Cellular Networks

Qi and Ge investigate how computational analyses of cellular networks, particularly the evolutionary conservation of motifs and modules, can reveal their importance to specific biological processes. For this study, the authors use mammalian cell signaling as case studies, and further explain how computational modeling yields insight about cell signaling pathways.

Qi Y, Ge H

doi:10.1371/journal.pcbi.0020174

Functional Classification Using Phylogenomic Inference

"What function does this protein perform?" In this paper, authors Duncan Brown and Kimmen Sjölander explore the use of phylogenomic inference to infer the function of a protein. Brown and Sjölander explain how this non-traditional multi-step process, using annotated subfamily groupings, can ultimately answer this question.

Brown D, Sjölander K

doi:10.1371/journal.pcbi.0020077

Practical Strategies for Discovering Regulatory DNA Sequence Motifs

This article presents an overview of the basic workflow necessary for discovery and analysis of human genome motifs. The authors offer some practical strategies for what they call "motif discovery," and show how to successfully mine sequence data for biologically important regulatory motifs.

MacIsaac KD, Fraenkel E

doi:10.1371/journal.pcbi.0020036