The Meaning of Life

In a football field-size room that genetics researchers have dubbed “the factory,” row upon row of sequencing machines churn through strands of DNA and record long strings of As, Ts, Gs, and Cs. Each symbol represents the stuff of life—the chemical bases adenine, thymine, guanine, and cytosine that make up the genetic code of every living organism on Earth.

The 300 sequencing machines at the factory in Celera Genomics’ headquarters in Rockville, Md., run day and night in the race to decipher the genetic blueprints of dozens of organisms including people, mice, flies, and flowers. Celera isn’t alone in its efforts. Other scientists from around the world, many affiliated with a competing, massive public effort to map genomes, dump more than 100 million bases each week into a public data repository.

Raw genetic sequences, however, tell little. It’s the messages among all those letters that the scientists are after. Only by finding patterns in the long strings of DNA will scientists understand “how the genome is wired” and ultimately how life is structured, says mathematician Pavel A. Pevzner of the University of Southern California in Los Angeles.

The task of making sense of the raw information is formidable. “The speed of acquiring data is now exceeding our ability to comprehend it and put it into the proper biological context,” said Michigan State University biologist George M. Garrity at a conference on microbial genomes in Chantilly, Va., last February.

Gone are the days when biologists could analyze most of their data with a pencil and a sheet of paper, says Steven L. Salzberg of the Institute for Genomic Research, also in Rockville. Today’s biologists need computing power to find even the most obvious needles in molecular haystacks of information, he says.

That’s where the field of bioinformatics comes in, says Sean Eddy, a computational biologist at the Washington University School of Medicine in St. Louis. The burgeoning field, also called biological computing, straddles the lines dividing biology, computer science, and mathematics.

The process of making sense out of a DNA sequence by finding genes and other interesting patterns in the strings of letters is called annotation, and it’s often the most difficult aspect of a sequencing project, says computer scientist Peter D. Karp of SRI Internationale in Menlo Park, Calif.

“Genome annotation is a lot like passing a piano through a nine-inch hole,” he told attendees of the Chantilly conference. It’s very difficult, and it isn’t immediately obvious how such a task can be accomplished. It’s also essential to understanding biology, he says.

Processing all the data is going to take a lot of time and resources, says Celera’s president, J. Craig Venter. His company has already identified all the bases of one person’s DNA sequence and is planning to decode the sequences of four or five more people. Celera’s announcement on April 6 came a week after the Human Genome Project, a publicly funded consortium of researchers, reported that it had finished determining 2 billion of the 3 billion bases of the human genome.

Venter predicts, however, that it will take most of this century to analyze the data.

“It’s only through having phenomenal computers and computer tools that we will be able to try and understand how biology works,” Venter says. “It doesn’t matter what analysis [of the human genome] we do this year, it will be only the most cursory analysis.” Scientists will have to invent ever-more-powerful computer algorithms to deal with and understand the data, he says. “Scientists will be making major discoveries from the human genetic code a hundred years from now,” he says.

Scientists generally start by searching for obvious patterns in the DNA with a computer program. The trouble is, Pevzner says, that scientists don’t know how a cell processes all the information contained in its DNA. But one thing is certain: “The way we do annotation today is very different from the way nature does it,” Pevzner says.

Ultimately, researchers want to use computers to take raw DNA-sequence information and construct an entire biochemical model of an organism, says Karp. That’s still a long way off, but some patterns are beginning to take shape on computer screens.

Making sense of data

Without annotation, the billions of bases of DNA sequenced are essentially useless, says bioinformatician Sylvia J. Spengler of Lawrence Berkeley (Calif.) National Laboratory. All those As, Cs, Gs, and Ts might as well be alphabetized, she quips. “If we can’t make sense of it, we don’t have any information,” she says. “All we have is data.”

Right now, biologists are most interested in finding the genes. “That’s where all the action is,” says Salzberg.

Most genes lay the plan for strings of amino acids, which make up proteins. Some genes, however, encode various forms of RNA that interact with proteins and other molecules to run the machinery of cells. And long segments of DNA between genes—and even within them—appear to code for nothing. These strings of bases, which are nevertheless being sequenced in the factory and other genome laboratories, are called junk DNA.

Genes that code for proteins have some easily recognized patterns. A string of three-letter words, called codons, spells out the code for the 20 amino acids used to build proteins. For example, GCC spells alanine in the cell’s language, while ACC spells threonine. Each protein gene also has a starting codon—the letters ATG—and one of three different three-letter stop signs—TGA, TAG, or TAA.

Even though protein-coding genes obligingly follow these rules, it’s not easy to recognize a gene, says Salzberg. The genes are big, some ATG sequences don’t indicate the beginning of a gene, and it’s difficult to decipher exactly how to group the letters to form the codons, he says. For instance, the letters GCCCGAAGAC could be read as GCC (alanine) CGA (arginine) AGA (arginine) C, but the pattern might also read G CCC (proline) GAA (glutamic acid) GAC (aspartic acid).

A complex statistical analysis can tell scientists the likelihood that a base fits with the bases that come before or after it to form a codon. That’s something people can’t do very quickly and efficiently.

But computers can. Bioinformaticians have developed mathematical and statistical formulas, or algorithms, for sorting through large chunks of raw data to locate genes. Most gene-finding programs use a statistical method to test sequences by determining their “coding potential,” the likelihood that a string of bases codes for a protein.

For bacterial genes, the process is relatively straightforward because each gene is a continuous unit. In plants, animals, and some other organisms, however, the genes are often interrupted by chunks of junk DNA called introns.

Cells make RNA copies of genes, then slice out the introns and splice the protein-coding stretches—called exons—back into a single molecule that’s the template for making a protein. Although cells identify protein-coding regions and junk DNA with aplomb, computer programs can have difficulty searching over long stretches of junk—sometimes several thousand bases—to find the next exon, says Salzberg.

Luckily, the boundaries between introns and exons are marked. These borders aren’t as pronounced as the start and stop codons, Salzberg says, but there is a pattern to them that cells and computer programs can pick up.

Gene-finding computer programs mark the stretches of DNA that are likely to contain a gene. Some programs perform the task better than others do, and programmers train their algorithms to recognize subtle differences in the way genes are flagged in different organisms. A person still has to check to make sure the computer program hasn’t made an obvious mistake.

Earlier this year, Celera researchers called in 40 fruit fly scientists to help the company analyze the data encoded in the 120 million bases of the Drosophila melanogaster genome—the largest genome yet sequenced (SN: 2/26/00, p. 132: Available to subscribers at Shotgun approach bags the fruit fly genome.). During a 2-week “annotation jamboree,” the researchers put two different gene-finding programs to work on the fruit fly genome and got two very different results.

The gene-hunting program named Genie found 13,189 genes for the fruit fly, but another program, Genscan, identified 17,464 genes, the scientists reported in the March 24 Science. After checking the gene predictions against the 2,500 genes known from nearly a hundred years of genetic experiments on fruit flies, the researchers decided that the lower number of genes is closer to correct.

The Genie program came up with the more accurate number because the researchers primed it with examples of previously sequenced fruit fly genes. Genscan made mistakes because it didn’t have a bank of Drosophila-specific information to work with, the researchers say.

Gene-hunting programs generally work best if they have learned the rules for each organism they analyze. For instance, a bacterial-gene finder expects 90 percent of the DNA to contain genes. These great expectations would lead the program to identify far too many genes in human DNA, Salzberg says. Conversely, a gene-finding program trained to recognize human genes would probably fail to find most of the genes in a bacterium, he says, because the program only expects 3 percent of the DNA to be part of a gene.

Finding proteins

Programs that find protein-forming genes aren’t good at looking for other features of DNA, says bioinformatician Gustavo Glusman of the Weizmann Institute of Science in Rehovot, Israel. These include genes that code for RNA but not proteins.

Eddy and his colleagues at Washington University study a class of RNA molecules called small nucleolar RNAs, or snoRNAs. Each of about 60 snoRNAs directs an enzyme to a certain spot on ribosomal RNA, which is a component of the cell’s protein-building machinery. Traditional biology had been able to uncover only about a dozen of the snoRNAs when Eddy and his colleagues joined the search.

It’s been difficult to pick up the scent of snoRNA genes, Eddy says. These genes don’t have three-letter codons or obvious start and stop signals. Also, the genes don’t seem much alike outside of two short sequences, known as C and D boxes. However, scientists can see a familial resemblance of RNA molecules if they look beyond the sequence of the bases.

Despite great differences in their base sequence, snoRNAs all fold up into similarly shaped, compact structures. RNA’s four bases (the same A, C, and G as DNA, but uracil, or U, instead of T) pair in predictable ways to form the RNA structure. Eddy’s group trained its computer bloodhounds to follow the twists and turns of the snoRNA molecule.

The researchers use algorithms called stochastic context-free grammars. Originally designed to analyze languages, they can calculate whether a sequence would fold into the snoRNA structure. This method requires the computer programmer to know in advance the structure that the algorithm should seek. Currently, there’s no good statistical way to identify novel structures, Eddy says.

Important patterns

One way to find important patterns in an organism’s genome is to look at another organism, Eddy says. Over evolutionary time, DNA sequences can change dramatically, but organisms tend to hang onto the sequences that are most important to their function. “Let evolution tell you [what’s important], because statistics takes us a long way, but not far enough,” Eddy says.

“In the absence of good predictive models, comparing sequences is the last resort. A very powerful resort,” says Glusman. This last, best hope for finding patterns and making sense of them is known as comparative genomics.

“Comparative genomics is going to be the big win,” says Eddy. The biggest wins of all will result from the comparison of mice and people, he predicts. “When [the] mouse [genome] comes along, you can say, ‘Now I understand the human genome,'” Eddy says. Celera researchers plan to begin sequencing the mouse genome this summer.

Researchers aren’t waiting for the completion of whole genomes to begin finding biologically important patterns, though. The GenBank DNA database, the public data repository for DNA sequences managed by the National Center for Biotechnology Information in Bethesda, Md., already contains sequences from 62,000 species of animals, plants, bacteria, and viruses, and more are added every day, says the center’s Dennis A. Benson. Scientists from around the world compare newly identified, short sequences of DNA to the sequences in GenBank, hoping to find a matching pattern that will give them clues about a gene’s functions.

With comparative genomics, scientists match up the complete genetic code of one organism to the code of a second organism, rather than doing a piecemeal comparison of snippets. This large-scale comparison will find regions of the genome that are important biologically, says Spengler.

“It’s as if there’s a giant ‘Watch This Space’ sign over the DNA. It lets you know something important is going on there, even if you don’t know what that might be,” she says.

One of the most important things that could be going on in gene-free stretches of the DNA is the regulation of genes. The comparative approach has already identified one such region.

Each gene has DNA sequences associated with it, often located in the junk DNA, that turn the gene on and off at the proper time and place during an organism’s development and adult life. These often short but complex regulatory regions are difficult for a computer program to pick out from the surrounding jumble of bases, says Spengler.

Since people, mice, flies, and even worms need to turn on many of their genes in similar ways, the sequence of the regulatory regions may have been preserved during evolution, she says.

Comparative genomics doesn’t make distinctions between genes and junk, says Spengler. Right now, whole-genome comparisons are the only good way to look for regulatory regions of genes, she says.

By comparing 1 million bases of human DNA with a similar stretch of mouse DNA, a research team from several universities found a regulatory region that governs three genes for proteins that influence the immune response. The results were published in the April 7 Science.

The great hope of whole-genome comparison can’t be realized unless scientists are actually able to match up the sequences of entire organisms. That’s a difficult task, says Salzberg. For one thing, it takes enormous amounts of computer memory to keep track of all the bases and their matches with the bases of another genome. Another problem is that it takes an astounding amount of time to compare very long DNA sequences with each other.

Computer programs could take days to match up a single human chromosome with a single mouse chromosome, Salzberg says. Scientists are now devising programs to handle whole genomes more quickly. “Those programs didn’t exist before because no one needed them,” says Salzberg. The completion of more genome sequences will certainly change that.

The field of bioinformatics is growing rapidly, and new algorithms are being developed to deal with the avalanches of data. The more genomes that are sequenced, the richer the biological databases, and the better the annotation, says Spengler.

There’s still a long road ahead. “We’re still defining the questions we want to ask,” Salzberg says. “We certainly haven’t developed all the solutions yet.”