Main description:

This book offers a comprehensive introduction to using Mathematica and the Wolfram Language for Bioinformatics. The chapters build gradually from basic concepts and the introduction of the Wolfram Language and coding paradigms in Mathematica, to detailed worked examples derived from typical research applications using Wolfram Language code. The coding examples range from basic sequence analysis, accessing genomic databases, differential gene expression, and machine learning implementations to time series analysis of longitudinal omics experiments, multi-omics integration and building dynamic interactive bioinformatics tools using the Wolfram Language. The topics address the daily bioinformatics needs of a broad audience: experimental users looking to understand and visualize their data, beginner bioinformaticians acquiring coding expertise in providing biological research solutions, and practicing expert bioinformaticians working on omics who wish to expand their toolset to include the Wolfram Language.

Contents:

1 Introduction to Bioinformatics

1.1 Principles of Genomics

1.2 Beyond the Genomes: Multiple Omics

1.3 Systems and Networks

1.4 Bioinformatics in modern genetics

2. A Mathematica Primer for Bioinformaticians

2.1 Getting Started

2.2 Variables

2.3 Lists

2.4 Functions

2.4 Importing and Exporting Data

2.5 Graphics

2.6 Associations and Query

2.7 Wolfram Alpha

2.8 Wolfram Language Resources

3. Statistics for Genomic Analysis

3.1 Probability

3.2 Distributions

3.3 Hypotheses

3.4 Parametric Testing

3.5 Non-parametric Testing

3.6 Markov Chains

3.7 Exploratory Data Analysis

4. Genomic Sequences

4.1 Sequence Alignments

4.2 Pairwise Alignment

4.3 Dynamic Programming

4.4 Processing Sequencing Files

<4.5 Processing BLAST Output

5. Databases

5.1 Connecting to Databases

5.2 UCSC Browser and MySQL

5.3 Gene Ontology Annotations

5.4 Biological Pathways

5.5 Genomic Medicine Data Sources

6. Transcriptomics

6.1 Transcriptomic Data

6.2 Quality Control

6.3 Normalization

6.4 Differential Gene Expression

6.5 Visualization

6.6 Classification and Biological Significance

7. Proteomics

7.1 Proteomics Data

7.2 Spectral Visualization

7.3 Quality Control

7.4 Normalization

7.5 Differential Protein Expression

7.6 Visualization

8. Metabolomics

8.1 Metabolomics Data

8.2 Metabolomics Databases

8.3 Metabolomics Data Analysis

8.4 Putative Identification of Compounds

8.5 Clustering and Principal Co

mponents Analysis

9. Systems Biology

9.1 Introduction to Systems Biology

9.2 Basic Models

9.3 Visualization of Data

9.4 Beyond Basic Models

10. Networks

10.1 Introduction to Network Analysis

10.2 Basic Network Construction

10.3 Network Properties

10.4 Network Comparison

10.5 Scale Free Networks

11. Time Series Analysis

11.1 Introduction to Time Series

11.2 Regularly Sampled Time Series

11.3 Frequency Representation of Longitudinal Data

11.4 Uneven Sampling

12. Omics Integration and Systems Medicine

12.1 Introduction to Multiple Omics Integration

12.2 Systems Medicine Approaches

12.3 Example Integration of Omics Datasets

13. Bioinformatics Development with Mathematica

13.1 Introduction to Pa

ckages<

13.2 Creating a Simple Bioinformatics Package

13.3 Dynamics, Manipulate and Interactive Interfaces