In the last 30 years we have seen a dramatic development in molecular biology research. Genetic information has been mapped in great detail for many different living organisms. We are able to examine gene expression, biochemical reactions and molecular interactions within the cell in a manner that was quite impossible 50 years ago. This basic research has had a great impact on many areas, including medicine and biotechnology. For instance, molecular details of many diseases such as cancer have been worked out, making new methods of diagnosis and therapy possible. In addition, pharmaceutically important proteins such as insulin may be produced in high yield. In the world of plants, crops have been genetically modified to achieve increased crop yields and resistance to insects, or to make them produce specific substances in large quantities.

Extensive sugar decoration

In the two previous chapters we dealt with problems of assigning function to proteins or protein domains. We concerned ourselves with methods relying on sequence similarity, i.e. methods based on local alignment or profiles. We will now see an example of a functional domain which is not easily identified on the basis of sequence similarity or with profiles. It has specific properties as it has a characteristic amino acid composition and functional properties based on that composition. However, the properties of the domain may not be captured on the basis of sequence alignment or by position-specific information. The domain in question is characteristic of a group of proteins referred to as mucins (Perez-Vilar and Hill, 1999; Hollingsworth and Swanson, 2004).

A characteristic property of all mucins is the ability to form gels. Mucins are a major component of the mucous layer that is present on the surface of epithelial cells of the lung and intestine. The proteins act as a diffusion barrier to prevent harmful microorganisms and substances having more intimate contact with the cell. Mucins also function as lubricants to protect epithelial cells from dehydration and physical and chemical injury. Because mucins protect against pathogens, they play an important role in immune defence. In addition, certain mucins are associated with colon cancer (Hollingsworth and Swanson, 2004) and there is a strong association between the mucin Muc2 and the inflammatory bowel disease ulcerative colitis. For example, inflammation of the large intestine similar to ulcerative colitis is observed in mice that are deficient in the mucin Muc2 (Heazlewood et al., 2008).

Restriction enzymes, one of many tools of the molecular biology toolbox, were introduced in the previous chapter. The present chapter is devoted to another important method designed to examine the function of specific genes.

Interfering with gene expression

In order to study the biological function of a gene, the molecular biologist needs methods that allow alteration of that gene – for example, an alteration that reduces the production of the protein specified by the gene. The behaviour of a normal cell may then be compared to a cell where the gene of interest has been manipulated, thereby allowing a conclusion regarding the function of the gene of interest. In the yeast Saccharomyces cerevisiae, for instance, there are methods that allow the complete removal of genes (such a removal is often referred to as a gene knock-out ). If, for instance, the yeast gene named PSY3 is removed or inactivated, it gives rise to an increased level of mutations. This observation suggests to us that this particular gene is related to the repair of DNA damage.

For studies of human genes, you may want to examine a species more closely related to man than is yeast. You can delete or inactivate genes in mammals such as mice, but this is more technically involved than in yeast. On the other hand, there are other methods that make possible a reduction in gene expression in animals. One such important method involves RNA interference (RNAi) (also known as RNA silencing). In one common type of experiment, an mRNA is inactivated by the introduction of a small synthetic RNA which is complementary to the mRNA. In the bioinformatics example below we will see how we can design a small RNA for such an experiment. RNAi is an example of a gene knock-down method, as the expression of the target gene is reduced (compared to the gene knock-out, where a specific gene is completely removed or inactivated).

Elucidation of the human genome sequence was a significant milestone in the life sciences (Lander et al., 2001; Venter et al., 2001; International Human Genome Sequencing Consortium, 2004). However, with access to this information an obvious but entirely non-trivial problem was encountered. What does all the genetic information in the form of some three billion bases represent in biological terms? One important category of information is the sequences that specify genes, i.e. regions that give rise to mRNAs that in turn encode specific protein molecules. Not only proteins are specified by the genome; also a large number of RNAs are transcribed from DNA that do not give rise to mRNA, but have other functions. (These are non-coding RNAs and will be discussed in Chapter 17.) In the next three chapters we will deal with the computational problems of finding proteins and non-coding RNA genes, starting out with a genomic sequence.

When it comes to the protein-coding genes of a mammalian genome, only a very small fraction, about 1.5–2%, of the genome codes for protein. For these genomes we are faced with the problem of identifying relatively small and scattered coding regions in a vast sea of non-coding material. There is a striking difference in this respect between mammals and a bacterium like Escherichia coli, whose genome contains as much as 83% of coding sequence. In the next chapter the focus will be on prediction of exon regions of protein-coding genes. Here we will address another sub-problem of finding protein-coding genes. We will see how a simple prediction of regions known as CpG islands will help us to locate sites in the genome that are close to the transcription start sites of genes.

Basic principles of genetic inheritance

In this chapter we will discuss inheritance within a family and address questions about the parental origin of specific sites in a genome. First we need to understand a few basic elements of genetics. All human cells contain two copies of each chromosome, one of parental and one of maternal origin. Exceptions are the reproductive cells or gamete s, i.e. eggs in females and sperms in males, which have only one copy of each chromosome. The gametes are formed in the process of meiosis (Figs. 20.1 and 20.2). Meiosis starts out with a cell having two copies of each chromosome (one from the father and one from the mother; two such chromosomes are said to be homologous). The DNA is first copied in order to generate two copies each of the paternal and maternal chromosomes. Through crossing-over, which occurs by homologous recombination, segments are occasionally interchanged between parental and maternal chromosomes (Fig. 20.2). Such recombination occurs about 50–100 times during meiosis, and there is evidence that in humans the recombination in maternal meiosis is about 1.7 times more frequent than in paternal meiosis (Petkov et al., 2007; Roach et al., 2010).

The concept of evolution is of fundamental importance in genomics and bioinformatics, as already emphasized in Chapter 1. Over the next three chapters we will be looking at questions related to the evolution of species, as well as the evolution of individual genes and proteins. First, we will specifically address the question of how humans are genetically different to other animals, such as our close relative the chimpanzee. At the level of bioinformatics and computational tools we will introduce multiple sequence alignment s and show how to analyse such alignments with programming tools.

Genetic differences between humans and chimpanzees

Now that the complete genome sequences of humans and many other animals are known, we are in a position to address interesting questions about similarities and differences between animals in terms of genetic information. Humans clearly have functions that other animals seem to be lacking, such as advanced spoken and written languages and abstract-level thinking. Many cultural activities such as music and painting also seem to be specific to humans. We are definitely distinct from other animals in these respects, even when considering our close primate relatives. With the genome sequences now available, there is an obvious question to answer: what are the significant genetic differences between us and other primates such as the chimpanzee? It would be particularly interesting to identify the genetic elements that are likely to affect brain function. First of all, it should be kept in mind that the identity between the human and chimpanzee genomes is quite extensive. Thus, if we consider the portions of the human and chimpanzee genomes that may be aligned to each other, there are approximately 35 million single nucleotide differences (Chimpanzee Sequencing and Analysis Consortium, 2005). This is to say that in this respect the genomes are in fact 99% identical. In addition, there are about five million insertion/deletion events, accounting for another 3% difference between humans and chimpanzees.

These words are from a letter written by Yevgeny Botkin, the family doctor of the Russian Tsar Nicholas II and his wife Alexandra (Steinberg and Khrustalëv, 1995). The suffering boy is Alexei, the son of the couple, born in 1904 and the successor to the throne of all Russians. He suffered from a congenital bleeding disease known as haemophilia, in which the defect is a missing or malfunctioning blood-clotting factor. The particular haemophilia that affected Alexei is a genetic disease linked to the X chromosome, and for this reason it is more prevalent in males. Females rarely show symptoms of the disease but may carry the gene on to the next generation.

Royal disease

Alexei inherited his disease gene from his great grandmother, Queen Victoria of England (Fig. 12.1). In fact, the queen transmitted her gene to a number of members of European royalty and thus decimated the thrones of Britain, Germany, Russia and Spain. Typically the affected males are exceptionally sensitive to injuries – for example, both Infante Gonzalo of Spain and Prince Rupert of Teck died young as a result of bleeding following car accidents. The last carrier of the disease in the royal family was Prince Waldemar of Prussia, who died in 1945.

The Perl programming language was invented by Larry Wall; his version 1.000 was presented in 1987. Perl is said not to be an acronym, but still you occasionally see it said to represent ‘Practical Extraction and Report Language’ or ‘Pathologically Eclectic Rubbish Lister’. The word ‘Perl’ (with a capital P) refers to the programming language as such, whereas ‘perl’ refers to the interpreter (implementation). Larry Wall originally invented the language to help out in system administration and in the analysis of huge text files, but through the years Perl has been continuously developed and has for many years been widely used in areas such as web programming and bioinformatics.

The information found in this appendix is very far from a complete description of Perl. The focus here is on the features of Perl that are mentioned in this book. There are certain important elements that are left out, such as variable references and object-oriented approaches. For more extensive information the reader is referred to other sources as listed towards the end of this appendix – for example, Perl Programming, for which Larry Wall is one of the coauthors.

In the two preceding chapters a number of diseases with a genetic background have been discussed. Many cancer diseases also have a genetic component; this will be the topic of this chapter. We will focus on a chromosomal aberration that gives rise to a specific type of cancer. From a bioinformatics perspective we will approach the problem of sequence alignments and make use of BLAST to study the effect of the chromosomal aberration.

Cancer as a genetic disease

Characteristic of cancer cells is that they divide in an uncontrolled manner (Hanahan and Weinberg, 2000, 2011). This behaviour may be achieved by an overproduction of proteins that stimulate cell growth, or by the inactivation of functions that normally restrict growth. Cancer is mostly a disease developed as a result of environmental and lifestyle factors. For instance, tobacco and obesity are two significant factors. However, there are also important genetic factors. There are changes present in germline (reproductive) cells which may result in an inherited predisposition to cancer; for example, there are certain inherited mutations in the BRCA1 and BRCA2 genes that are associated with an elevated risk of breast cancer. However, tumours are more commonly the result of mutations in somatic cells, i.e. cells that are not germline cells.

In order to work with DNA in the laboratory we often need to produce that DNA in a larger quantity. In addition, a longer DNA molecule like a whole human chromosome is difficult to work with, and we rather want to focus on a smaller region of DNA. In a classic cloning experiment a smaller DNA fragment is introduced into a circular plasmid DNA, which is allowed to replicate within bacterial cells. Once the bacterial cells have grown to a certain density, these cells may be isolated by centrifugation and the plasmid DNA purified from the bacterial cells. This is a somewhat time-consuming procedure, but there is an alternative method in gene technology that is more convenient. Thus, our third example of gene technology is the polymerase chain reaction (PCR), invented by Kary Mullis in 1984 (Mullis et al., 1986).

What is PCR?

PCR is one of the most widely used techniques in DNA technology. As in traditional cloning it is a means to amplify a shorter region of DNA, i.e. to produce a distinct region of DNA in a large quantity. However, PCR is carried out in a simple test tube reaction where DNA is amplified with the help of the enzyme DNA polymerase. Another technical advantage of PCR compared to conventional cloning in some bacterial host is that the DNA product is relatively pure.

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.

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.

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.