If we have access to the genome sequence of an individual there are a very large number of questions to be posed regarding that genome. For instance, what genetic markers are present that are of a predictive value in medical terms? What trait and disease alleles are present? What genetic properties are present that make the individual sensitive or resistant to a certain drug treatment? We may also want to know about the relationship of the individual to other individuals and whether there are markers characteristic of a certain human population. These are all questions that may be addressed using bioinformatics tools. In the previous chapter we examined SNPs and used them to get an idea about the genetic differences between individuals in general. Here, we will again use SNP data to analyse genomes, but we will see how we may identify SNPs that are shared between a group of individuals. We will also illustrate how SNP data may be mapped to information regarding exons, thus identifying SNPs that are likely to be in coding regions. In this way we are able to learn about the consequences of different SNPs at the level of protein products. The genomes that we are to examine are those of a few South African individuals. These genomes also highlight interesting questions regarding the early history of man.

Although the material in this appendix is not used directly in the rest of the book, it has a profound importance in understanding the very essence of randomness and complexity, which are fundamental to probabilities and statistics. The algorithmic information or complexity theory is founded on the theory of recursive functions, the roots of which go back to the logicians Godel, Kleen, Church, and above all to Turing, who described a universal computer, the Turing machine, whose computing capability is no less than that of the latest supercomputers. The field of recursiveness has grown into an extensive branch of mathematics, of which we give just the very basics, which are relevant to statistics. For a comprehensive treatment we refer the reader to [26].

The field is somewhat peculiar in that the derivations and proofs of the basic results can be performed in two very different manners. First, since the partial recursive functions can be axiomatized as is common in other branches of mathematics the proofs are similar. But since the same set is also defined in terms of the computer, the Turing machine, a proof has to be a program. Now, the programs as binary strings of that machine are very primitive, like the machine language programs in modern computers, and they tend to be long and both hard to read and hard to understand. To shorten them and make them more comprehensible an appeal is often made to intuition, and the details are left to the reader to fill in.

We currently see a vast amount of information being generated as a result of experimental work in biomedicine. Particularly impressive is the development in DNA sequencing. As a result, we are now facing a new era of genomics where a lot of different species, as well as many different human individuals, are being analysed. There are many important biological questions being addressed in such genome-sequencing projects, including questions of medical relevance. A critical technical part of all these projects is computational analysis. With the large amount of sequence information generated, computational analysis is often a bottleneck in the pipeline of a genomics project. Therefore, there is great demand for individuals with the appropriate computational competence. Ideally, such individuals should not only be proficient in the relevant mathematical and computer scientific tools, but should also be able to fully understand the different biological problems that are posed. This book was partly motivated by the urgent need for bioinformatics competence due to recent developments in genomics.

A student or scientist may enter into bioinformatics from different disciplines. This book is written mainly for the biologist that wants to be introduced to computational and programming tools. There are certainly books out there already for that type of audience. However, I was attracted by the idea of assembling a book that would cover a large number of relevant biological topics and, at the same time, illustrate how these topics may be studied using relatively simple programming tools. Therefore, an important principle of the book is that it will attempt to convince the reader that relatively simple programming is sufficient for many bioinformatics tasks and that you need not be a programming expert to be effective. Another important principle of the book is that I wanted the bioinformatics examples to be very practical and explicit. Thus, the reader should be able to follow all the details in a procedure all the way from a biological problem to the results obtained through a technical approach. As one demonstration of this principle, all files and scripts mentioned in this book are available for download at www.cambridge.org/samuelsson. This means the reader is able to try it all out on his/her own computer. I also wanted this book to illustrate the interdisciplinary nature of bioinformatics.

This chapter will introduce molecular phylogeny, a science where DNA, RNA or protein sequences are used to deduce relationships between organisms. Such relationships are typically shown in the form of a tree. The entities under study are often species, but phylogenetic methods could be used to examine other types of evolutionary relationships, such as how individuals in a population are related or how different members of a protein family are related by orthology and paralogy. For the example in this chapter a phylogenetic tree will show the relationship between different HIV isolates. But we will start out with a somewhat ghastly criminal story.

A fatal injection

This story takes place in Lafayette in Louisiana. Maria Jones, a married nurse of age 20, met a gastroenterologist named Robert White. Robert, aged 34, was also married and he had three children. As Robert and Maria entered a relationship, Maria divorced her husband. In return Robert promised to divorce his wife, but never followed through with it. Still, the relationship between Maria and Robert continued. After five years Maria had become pregnant three times, but Robert convinced her every time to have an abortion. Maria did give birth to a child; Robert was the father. Robert eventually became exceedingly jealous and controlling. When Maria saw other men, Robert would sometimes threaten to kill them. He also threatened Maria. After ten turbulent years, Maria finally decided to leave Robert in July 1994.

Now that we know how to estimate real-valued parameters optimally the question arises of how confident we can be in the estimated result, which requires infinite precision numbers. It is clear that if we repeat the estimation on a new set of data, generated by the same physical machinery, the result will not be the same. It seems that the model class is too rich for the amount of data we have. After all, if we fit Bernoulli models to a binary string of length n, there cannot be more than 2n properties in the data that we can learn, even if no two strings have a common property. And yet the model class has a continuum of parameter values, each representing a property.

One way to balance the learnable information in the data and the model class is to restrict the precision in the estimated parameters. However, we should not take any fixed precision, for example each parameter quantized to two decimals, because that does not take into account the fact that the sensitivity of models with respect to changes in the parameters depends on the parameters. The problem is related to statistical robustness, which while perfectly meaningful is based on practical considerations such as a model's sensitivity to outliers rather than on any reasonably comprehensive theory. If we stick to the model classes of interest in this book the parameter precision amounts to interval estimation.

This chapter describes approaches used for video processing, in particular, for face and gesture detection and recognition. The role of video processing, as described in this chapter, is to extract all the information necessary for higher-level algorithms from the raw video data. The target high-level algorithms include tasks such as video indexing, knowledge extraction, and human activity detection. The main focus of video processing in the context of meetings is to extract information about presence, location, motion, and activities of humans along with gaze and facial expressions to enable higher-level processing to understand the semantics of the meetings.

Object and face detection

The object and face detection methods used in this chapter include pre-processing through skin color detection, object detection through visual similarity using machine learning and classification, gaze detection, and face expression detection.

Skin color detection

For skin color detection, color segmentation is usually used to detect pixels with a color similar to the color of the skin (Hradiš and Juranek, 2006). The segmentation is done in several steps. First, an image is converted from color into gray scale using a skin color model – each pixel value corresponds to a skin color likelihood. The gray scale image is binarized by thresholding. The binary image is then filtered by a sequence of morphological operations so as to avoid noise. Finally, the components of the binary image can be labeled and processed in order to recognize the type of the object.

It was a somewhat historic event when President Bill Clinton announced, on 26 June 2000, the completion of the first survey of the entire human genome. We were able for the first time to read all three billion letters of the human genetic make-up. This information was the ground-breaking result of the Human Genome Project. The success of this project relied on advanced technology, such as a number of experimental molecular biology methods. However, it also required a significant contribution from more theoretical disciplines such as computer science. Thus, in the final phase of the project, numerous pieces of information like those in a giant jigsaw puzzle needed to be appropriately combined. This step was critically dependent on programming efforts. Adding further tension to the programming exercises was the fact that a private company, Celera, was competing with the academic Human Genome Project. This competition was sometimes referred to as ‘the Genome War’ (Shreeve, 2004). While computationally talented people like Jim Kent ‘churned out computer code’, other gifted bioinformaticians, such as Gene Myers at Celera, worked on related jigsaw-puzzle problems. Ideally, scientists should not war against each other; however, there was an important conclusion from these projects in which important genetic information was generated: computing is an essential part of biological research.

After a discussion of gene technology methods in the preceding chapters, we now turn to a few topics of more immediate medical interest. We will be dealing with different kinds of human diseases that have a genetic component and see how some aspects of these diseases may be examined using bioinformatics tools such as Perl.

Inherited disease and changes in DNA

In Chapter 1 we saw how genetic information flows from DNA to proteins. The sequence of bases in DNA determines the sequence of amino acids in protein and that sequence in turn determines the biological function of the protein. The relationship between genetic information in the form of DNA sequences and biological function in proteins has been demonstrated by numerous experiments carried out in molecular biology laboratories. At the same time it is intriguing to note that this relationship is also elegantly demonstrated in nature. Thus, we know of numerous examples of naturally occurring changes in DNA that have marked effects on the function of the corresponding protein. Such changes often give rise to disease. In discussing this further we need to clarify what types of alterations are observed in DNA. We may distinguish two major categories. First, there are highly local changes, such as point mutations (single nucleotide changes) and addition or deletion of a smaller number of nucleotides. Second, there are large-scale rearrangements of DNA sequences that result from DNA recombination events.

Operating systems

Whenever you are using a computer you interact with it with the help of an operating system (OS), a vital interface between the hardware and the user. The operating system does a number of different things. For example, multiple programs are often run at the same time and in this situation the operating system allocates resources to the different programs or may be able to appropriately interrupt programs. Another common feature of an operating system is a graphical user interface (GUI), originally developed for personal computers. Examples of popular operating systems are Microsoft Windows, Mac OS X and Linux.

Linux is an example of a Unix (or ‘Unix-like’) operating system. Unix was originally developed in 1969 at Bell Laboratories in the United States. Many different flavours of the Unix OS have been developed, such as Solaris, HP-UX and AIX, and there are a number of freely available Unix or Unix-like systems such as GNU/Linux in different distributions such as Red Hat Enterprise Linux, Fedora, SUSE Linux Enterprise, openSUSE and Ubuntu.

Introduction

Analyzing the behaviors of people in smart environment using multimodal sensors requires to answer a set of typical questions: who are the people? where are they? what activities are they doing? when? with whom are they interacting? and how are they interacting? In this view, locating people or their faces and characterizing them (e.g. extracting their body or head orientation) allows us to address the first two questions (who and where), and is usually one of the first steps before applying higher-level multimodal scene analysis algorithms that address the other questions. In the last ten years, tracking algorithms have experienced considerable progress, particularly in indoor environment or for specific applications, where they have reached a maturity allowing their deployment in real systems and applications. Nevertheless, there are still several issues that can make tracking difficult: background clutter and potentially small object size; complex shape, appearance, and motion, and their changes over time or across camera views; inaccurate/rough scene calibration or inconsistent camera calibration between views for 3D tracking; real-time processing requirements. In what follows, we discuss some important aspects of tracking algorithms, and introduce the remaining chapter content.

Scenarios and Set-ups. Scenarios and application needs strongly influence the considered physical environment, and therefore the set-up (where, how many, and what type of sensors are used) and choice of tracking method. A first set of scenarios commonly involves the tracking of people in the so-called smart spaces (Singh et al., 2006).