Tentative definition of bioinformatics Bioinformatics, often also called genomics, computational genomics, or computational biology, is a new interdisciplinary field at the intersection of biology, computer science, statistics, and mathematics. Its subject matter is the extraction of biologically useful information from large sets of molecular data, such as DNA or protein sequence data or gene expression data. The term “bioinformatics” is currently used mainly to refer to the extraction of information from sequence data, while the creation and analysis of gene expression data is called functional genomics.

Biology’s dilemma: There is too much to know about living things Roughly 1.5 million species of organisms have been described and given scientific names to date. Some biologists estimate that the total number of all living species may be several times higher. It is impossible to learn everything about all these organisms. Biologists solve the dilemma by focusing on some species, so-called model organisms, and trying to find out as much as they can about these model organisms.

Some important model organisms Mammals: Human, chimpanzee, mouse, rat Fish: Zebrafish, Pufferfish Insects: Fruitfly (Drosophila melanogaster) Roundworms: Ceanorhabditis elegans Protista: Malaria parasite (Plasmodium falciparum) Fungi: Baker’s yeast (Saccharomyces cerevisiae) Plants: Thale cress (Arabidopsis thaliana), corn, rice Bacteria: Escherichia coli, Mycoplasma genitalis Archea: Methanococcus janaschii

Let’s find out everything about some species What would it mean to learn everything about a given species? All available evidence indicates that the complete blueprint for making an organism is encoded in the organism’s genome. Chemically, the genome consists of one or several DNA molecules. These are long strings composed of pairs of nucleotides. There are only four different nucleotides, denoted by A, C, G, T. The information about how to make the organism is encoded by the order in which the nucleotides appear.

Some genome sizes zHIV2 virus 9671 bp zMycoplasma genitalis 5.8 · 10 5 bp zHaemophilus influenzae 1.83 · 10 6 bp zSaccharomyces cerevisiae 1.21 · 10 7 bp zCaenorhabditis elegans 10 8 bp zDrosophila melanogaster 1.65 · 10 8 bp zHomo sapiens 3.14 · 10 9 bp zSome amphibians 8 · bp zAmoeba dubia 6.7 · bp

Sequencing Genomes Contemporary technology makes it possible to completely sequence entire genomes, that is, determine the sequence of A’s, C’s, G’s, and T’s in the organism’s genome. The first virus was sequenced in the 1980’s, the first bacterium (Haemophilus influenzae) in 1995, the first multicellular organism (Caenorhabditis elegans) in A draft of the human genome was announced in 2000.

Where to store all these data? In databases of course. Some of the sequence data are stored in proprietary data bases, but most of them are stored in the public data base Genbank and an be accessed via the World Wide Web. In fact, most relevant journals require proof of submission to Genbank before an article discussing sequence data will be published. The URL for Genbank is:

What’s in the databases? In 1981, Genbank contained less than 500,000 bp of info. In 1986, Genbank contained 9,615,371 bp of info. In 1991, Genbank contained 71,947,426 bp of info. In 1996, Genbank contained 651,972,984 bp of info. In 2001, Genbank contained 15,849,921,438 bp of info. In 2004, Genbank contained 37,893,844,733 bp of info. In 2009, Genbank contained 106,533,156,756 bp of info.

What’s in the databases? On March 18, 2005 there were 1791 completely sequenced viruses, 204 completely sequenced bacteria, 21 completely sequenced archaea, and 9 complete genomes of Eukaryotes, among them two yeasts, the roundworm C. elegans, the fruitfly Drosophila melanogaster, the mosquito A. gambiae, the malaria parasite P. falciparum, and the plant Arabidopsis thaliana (thale cress). There are also drafts of 11 other genomes of eukaryotes, most notably of the human genome.

What’s in the databases? On December 17, 2010 there were 3518 completely sequenced viruses, 952 completely sequenced bacteria, 68 completely sequenced archaea, and 73 complete genomes of Eukaryotes, among them cow, wolf, horse, human, a monkey, pig, chimpanzee.

First challenge: Sequencing large genomes Currently, much of the sequencing process is automated. However, contemporary sequencing machines can only sequence stretches of DNA that are a few hundred base pairs long at a time. The process of assembling these stretches of sequence into a whole genome poses some interesting mathematical problems.

First challenge: Sequencing large genomes For example, the publicly financed Human Genome Project uses an approach called genome mapping to facilitate sequence assembly. Celera Genomics, a private enterprise, announced that they will be able to complete the sequencing of the entire human genome much faster by using an approach called shotgun sequencing. There was much debate over the feasibility of the latter approach, but it apparently worked. At its core, this was a debate over the mathematics of sequence assembly.

You have sequenced your genome - what do you do with it? This is known as genome analysis or sequence analysis. At present, most of bioinformatics is concerned with sequence analysis. Here are some of the questions studied in sequence analysis: zgene finding zprotein 3D structure prediction zgene function prediction zprediction of important sites in proteins zreconstruction of phylogenies

Genes and proteins The genome controls the making and workings of an organism by telling the cell which proteins to manufacture under which conditions. Proteins are the workhorses of biochemistry and play a variety of roles. A gene is a stretch of DNA that codes a given protein.

Where are the genes? The objective of gene finding is to identify the regions of DNA that are genes. Ideally, we want to make statements like: “Positions 28,354 through 29,536 of this genome code a protein.” The mathematical challenge here is to identify patterns in DNA that reliably indicate where a gene starts and ends, especially in eukaryotes.

Protein structure prediction When a protein is manufactured in the cell, it assumes a characteristic 3D structure or fold. It is very costly to determine the 3D structure of a protein experimentally (by NMR or X-ray crystallography). It would be much cheaper if we could predict the 3D structure of a protein directly from its primary structure, i.e., from the sequence of its amino acids. This is known as the protein folding problem. Many approaches have been proposed to develop algorithms for solving this problem; so far results are mixed.

Prediction of protein function Suppose you have identified a gene. What is its role in the biochemistry of its organism? Sequence databases can help us in formulating reasonable hypotheses. zSearch the database for proteins with similar amino acid sequences in other organisms. zIf the functions of the most similar proteins are known and if they tend to be the same function (e.g., “enzyme involved in glucose metabolism”), then it is reasonable to conjecture that your gene also codes an enzyme involved in glucose metabolism.

Prediction of protein function: homology searches Given a nucleotide or DNA sequence, searching the data base(s) for similar sequences is known as “homology searches”. The most popular software tool for performing these searches is called BLAST; therefore biologists often speak of “BLAST searches”. There are two interesting problems here: zHow to measure “similarity” of two sequences. zHow much similarity constitutes evidence of biologically meaningful homology as opposed to random chance?

Prediction of important sites in proteins Not all parts of a protein are equally important; the function of most of its amino acids is often just to maintain an appropriate 3D structure, and mutations of those less crucial amino acids often don't have much effect. However, most proteins have crucial parts such as binding sites. Mutations occurring at binding sites tend to be lethal and will be weeded out by evolution.

How to predict binding sites from sequence data: zGet a collection of proteins of similar amino acid sequences and analogous biochemical function from your database. zAlign these sequences amino acid by amino acid. zCheck which regions of the protein are highly conserved in the course of evolution. zThe binding site should be in one of the highly conserved regions.

The importance of being aligned DNA and protein molecules evolve mostly by three processes: point mutations (exchange of a single letter for another), insertions, and deletions. If a group of homologuous proteins from different organisms has been identified, it is assumed that these proteins have evolved from a common ancestor. The process of multiple sequence alignment aims at identifying loci in the individual molecules that are derived from a common ancestral locus. These form the columns of the alignment.

Example of a multiple alignment A T G - - T T C G G A C T | | | A C G A A T C C A G - C T | | | - C G A A T C C T A A C C | | | - T G A G C A C T A A C C

Reconstruction of phylogenetic trees A phylogenetic tree depicts the evolutionary history of a group of species. By observing similarities and differences between species, we may be able to reconstruct their phylogeny. Classically, the degree of similarity between two species has been assessed from morphological characters. By comparing genomic sequence data, we actually can quantify the degree of similarity between any two species, and use these degrees of similarity as a basis for reconstructing phylogenetic trees.

Reconstruction of phylogenetic trees The most common approach to using genomic data for reconstruction of phylogenetic trees is to look at genes with analogous function and thus supposedly common ancestry and see how far the genes taken from the extant organisms have diverged. The observed differences in the amino acid composition are then used to reconstruct the phylogeny. The current partition of organisms into eubacteria, archaea and eukaria was discovered in this way by analyzing rRNA.

The new frontier: Functional genomics It is fashionable nowadays to talk about functional genomics. Many people use this term as if it were a new discipline separate from bioinformatics, but I think it is more appropriate to consider it a new subfield of bioinformatics. The ultimate aim of functional genomics is to understand what genes do, when they do it, and how they do it. Ideally, we would like to understand the cell, or organism, as a giant network of chemical pathways that regulate each other.

Microarrays (gene chips) Microarrays or Gene Chips allow to monitor the level of activity of all the gene represented on the chip simultaneously under a variety of environmental conditions, in various organs, and at various stages of development. There are two types of challenges here: To determine when a change in activity level detected by the chip is statistically significant, and to use the data so obtained to make inferences about gene regulation.

What do we do with all these data? The bread and butter method of microarray data analysis is clustering. This allows to identify, for a sequence of experiments on the same set of genes under various conditions, groups of genes that are up- or down-regulated simultaneously. It is believed that genes acting in the same chemical pathway would normally belong to the same cluster. Some algorithms for clustering will be discussed in this course.