Essential Computing Skills for Biologists

The following sections are included:

• Preface
• Contents

In recent years, with the rapid development of computer and network technology, a large number of biological information resources can be retrieved through the Internet. Such a large number of biological databases, software resources and Internet connection are making life science research more convenient and efficient.

The following sections are included:

• Pairwise Sequence Alignment
• Multiple Sequence Alignment
• Basic Local Alignment Search Tool
• References

The following sections are included:

• Introduction to Molecular Evolution
• Models of DNA and Amino Acid Substitution
• Tree-Building Method
• Evaluating Tree
• Perspectives
• References

With the rapid accumulation of genomic sequence information, there is a growing need to use computational approaches to accurately predict gene structure. Computational gene prediction is a prerequisite for detailed functional annotation of genes and genomes. The process includes a detection of the location of open reading frames (ORFs) and a delineation of the structures of introns as well as exons if the genes of interest are of eukaryotic origin. The ultimate goal is to describe all the genes computationally with high accuracy, which can significantly reduce the amount of experimental verification work required. However, this may still be a distant goal, particularly for eukaryotes, because many problems in computational gene prediction are still largely unsolved. Gene prediction, in fact, represents one of the most difficult problems in the field of pattern recognition. This is because coding regions normally do not have conserved motifs. Detecting coding potential of a genomic region has to rely on subtle features associated with genes that may be very difficult to detect.

The following sections are included:

• Overview of Protein Structure
• Principles of Protein Structure
• Protein Structure Prediction
• Protein Structure Determining and Analysis
• References

The following sections are included:

• Human Genetic Variation
• Human Disease
• References

Gene expression profiling is a technique used in molecular biology to measure the expression of thousands of genes simultaneously and create a whole picture of cellular function. In cancer, gene expression profiling has been used to more accurately classify tumors and help in predicting the patient’s clinical outcome…

Proteomics is the large-scale study of entire proteins. As a complementary approach to genomics and transcriptomics, it can provide comprehensive and direct information in exploring biological process. Mass Spectrometry (MS)- based technologies are the main approaches in modern proteomics studies. Protein identification and quantification are two major tasks in proteomics study, which could advance our understanding of the complex and dynamic nature of proteins. Proteomics studies have been widely applied in biological and clinical fields in identifying biomarkers, monitoring disease status, and assessing treatment effect. However, researchers have to face a pressing problem that how to deal with the high-throughput data generated form MS-based proteomics platform. In this chapter, we will introduce some bioinformatics databases and tools which are widely used in protein identification and quantification, proteomics data analysis, storage and sharing.

The following sections are included:

• Metabonomics and Metabolomics
• Basic Approaches to Study Metabonomics
• Data Analysis Methods
• Metabonomics Databases
• Summary
• References

The large-scale datasets generated by gene sequencing, proteomics, and other high-throughput experimental technologies are the bases for understanding life as a molecular system and for developing medical, industrial, and other practical applications. In order to facilitate bioinformatics analysis of such large-scale datasets, it is essential to organize our knowledge on higher levels of systemic functions in a computable form, so that it can be used as a reference for inferring molecular systems from the information contained in the building blocks. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are the most used commonly and effectively databases.

Algorithm is regarded as the key to open the door of bioinformatics. We will see how popular bioinformatics algorithms work and we will see what principles are behind their design. It is important to understand how an algorithm works in order to be confident in its results; it is even more important to understand an algorithm’s design methodology in order to find its weaknesses and improve them. In this chapter, principles of basic algorithms such as Graph Theory, Dynamic Programming, Hidden Markov Model, Neural Network and Clustering Analysis will be introduced together with some applications to show their power that solve biological problems.

R has becoming the most widely used software in bioinformatics. The purpose of this chapter is to provide basic guidance for biologists on how to use R. For readers, no prior knowledge of programming is required or assumed. We hope this chapter can motivate and help more biologists to use R in their scientific career.

The following section is included:

• Index