Systems Biology: The Next Frontier for Bioinformatics

Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful we…
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Bioinformatics: Methods Express

Bioinformatics: Methods Express

Bioinformatics: Methods Express is unique . It is a book on bioinformatics that makes sense to non-bioinformaticians.
Bioinformatics: Methods Express helps you answer common questions such as:
*what else is similar to my gene?
*does this protein have any transmembrane regions?
*how do I visualize an alignment between these DNAs?
*where can I find specific transcription factor sequences?

The book provides the clear advice and explicit protocols that non-bioinformaticians need in order to understand what to do – and how to avoid common pitfalls. Each chapter guides you through the major databases and tools with worked examples – all accompanied by sample data files available online.

Topics covered include:
*data access
*sequence searches and alignments
*identification and annotation of features
*the transcriptome
*protein structure and function
*comparisons and phylogeny

Bioinformatics: Methods Express is a comprehensive manual for all wet-bench scientists who need to use bioinformatics – from postgraduate student to principal investigator.

www.scionpublishing.com/bioinformatics contains comprehensive datasets which allow the reader to practise the techniques described in the book.

List Price: $ 125.00

Price: $ 120.77

Analysis of Phylogenetics and Evolution with R (Use R)

The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification.

This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists’ data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments.

Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R’s powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author’s favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.

List Price: $ 64.95

Price: $ 47.25

Modelling Nonstationary Gene Regulatory Processes

An important objective in systems biology is to infer gene regulatory networks from postgenomic data, and dynamic Bayesian networks have been widely applied as a popular tool to this end. The standard approach for nondiscretised data is restricted to a linear model and a homogeneous Markov chain. Recently, various generalisations based on changepoint processes and free allocation mixture models have been proposed. The former aim to relax the homogeneity assumption, whereas the latter are more flexible and, in principle, more adequate for modelling nonlinear processes. In our paper, we compare both paradigms and discuss theoretical shortcomings of the latter approach. We show that a model based on the changepoint process yields systematically better results than the free allocation model wh…
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Probabilistic analysis of gene expression measurements from heterogeneous tissues

Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content.

Results: We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches.

Availability and Software: All codes are written in Matlab, and are freely available upon request as well as at the project web page http://www.cs.tut.fi/~erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection.

Bioinformatics – recent issues

A computational genomics pipeline for prokaryotic sequencing projects.

Publication Date: 2010 Jun 2 PMID: 20519285
Authors: Kislyuk, A. O. – Katz, L. S. – Agrawal, S. – Hagen, M. S. – Conley, A. B. – Jayaraman, P. – Nelakuditi, V. – Humphrey, J. C. – Sammons, S. A. – Govil, D. – Mair, R. D. – Tatti, K. M. – Tondella, M. L. – Harcourt, B. H. – Mayer, L. W. – Jordan, I. K.
Journal: Bioinformatics

MOTIVATION: New sequencing technologies have accelerated research on prokaryotic genomes and have made genome sequencing operations outside major genome sequencing centers routine. However, no off-the-shelf solution exists for the combined assembly, gene prediction, genome annotation, and data presentation necessary to interpret sequencing data. The resulting requirement to invest significant resources into custom informatics support for genome sequencing projects remains a major impediment to the accessibility of high-throughput sequence data. RESULTS: We present a self-contained, automated high-throughput open source genome sequencing and computational genomics pipeline suitable for prokaryotic sequencing projects. The pipeline has been used at the Georgia Institute of Technology and the Centers for Disease Control and Prevention for the analysis of Neisseria meningitidis and Bordetella bronchiseptica genomes. The pipeline is capable of enhanced or manually assisted reference-based assembly using multiple assemblers and modes; gene predictor combining; and functional annotation of genes and gene products. Because every component of the pipeline is executed on a local machine with no need to access resources over the Internet, the pipeline is suitable for projects of a sensitive nature. Annotation of virulence-related features makes the pipeline particularly useful for projects working with pathogenic prokaryotes. Availability and Implementation: The pipeline is licensed under the open-source GNU General Public License and available at the Georgia Tech Neisseria Base (http://nbase.biology.gatech.edu/). The pipeline is implemented with a combination of Perl, Bourne Shell, and MySQL and is compatible with Linux and other Unix systems. CONTACT: king.jordan@biology.gatech.edu SUPPLEMENTARY INFORMATION: See http://nbase.biology.gatech.edu.

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