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An improved method for comparing genomes

Researchers take a hint from text-comparison methods used to detect plagiarism in term papers

| 05 February 2009

With nearly a thousand genomes partly or fully sequenced, scientists are jumping on comparative genomics as a way to construct evolutionary trees, trace disease susceptibility in populations, and even track down people's ancestry.

To date, the most common techniques have relied on comparing a limited number of highly conserved genes in organisms that have all these genes in common. But a team of Berkeley researchers has developed an improved method for comparing whole-genome sequences one that can be used to compare even distantly related organisms, or organisms with genomes of vastly different sizes and diversity, and that can compare the entire genome, not just a selected small fraction of the gene-containing portion known to code for proteins.

The technique produces groupings of organisms largely consistent with current groupings, but with some interesting discrepancies, according to Sung-Hou Kim, a Berkeley professor of chemistry and a faculty researcher at Lawrence Berkeley National Laboratory. However, the relative positions of the groups in the family tree that is, how recently these groups evolved are quite different from those based on conventional gene-alignment methods.

Current methods for comparing the genomes of different organisms focus on a small set of genes that the organisms being compared have in common. The genomes are then lined up in order to count the sequence similarities and differences, from which a computer program constructs a family tree, with near relatives assumed to have more similar sequences than distant relatives.

When genes tell you different things

This technique assumes organisms have genes in common, however, or that these "homologous" genes can be identified. When comparing distantly related species such as bacteria that live in vastly different environments this gene-centric method may not work, Kim says.

"What do you do when one gene tells you the organisms are closely related, and another gene tells you they're distantly related?" he asks. "It happens."

Kim wanted a technique that could be used to compare genomes of all sizes, and even genomes only partially sequenced. He also wanted a method that would compare all regions of the genome, not just the exons that is, the DNA transcribed into mRNA, the blueprint for proteins. Exons make up only 1 percent of the human genome, with the remainder being non-coding "introns" regulatory DNA, duplicate or redundant DNA, and transposons (genes that have jumped from other places in the genome).

A different vocabulary

Kim thought that traditional text comparison used, for example, to assess the authorship of a work of literature or to identify plagiarized text might provide a model for whole-genome comparison and a way to test comparison methods. But while text comparison involves looking at word frequency, genomes cannot be broken down into words.

"I can compare two books in two different ways. I can pick a few sentences, say a hundred that I subjectively decide are important, and compare them, but some are very similar and some very different in the two books," he explained. "So, how can I decide? I need a second method to compare some features representing one whole book to those of the other whole book."

Teaming up with biophysicist Gregory Sims, statistical mathematician Se-Ran Jun, and theoretical physicist Guohong Wu, Kim decided to try a simple variant of the word-frequency technique. In a test of free online books obtained through Project Gutenberg, the researchers eliminated all punctuation and spaces from each text, created a dictionary of all the two-letter, three-letter, and other word combinations in the books, and counted the variety of each fixed-length "word" or feature. The features were not consecutive letter combinations but overlapping sequences obtained by sliding a two-, three- or more-letter window along the text, advancing one letter at a time.

This method, which they called the feature-frequency-profile (FFP) method, proved more successful at identifying related books books by the same author, books of the same genre, books from the same historical era than did word-frequency profile analysis.

"I was just stunned when I saw this," Kim says. One of the reasons this method works better, he says, may be that, while word-frequency analysis treats each word independently, feature-frequency analysis picks up syntax.

"Here, if I take a nine-letter window and slide it along the text," he says, "I am actually picking up the relationship between the first and second words the local syntax which was impossible to pick up from the word-frequency method. Apparently, that is very important."

Mammalian and bacterial genomes

Buoyed by this success, the researchers applied the technique to whole genomes of mammals, where there is the least controversy in evolutionary relationship. "We treat the genome like a book without spaces," Kim says.

Since these genomes are very large, the researchers translated the genome sequences using a reduced, two-letter alphabet R for the purine nucleic acids, adenine and guanine, and Y for the pyrimidine nucleic acids, thymine and cytosine to reduce the complexity of calculation. Using an optimal feature length of 18 base pairs, this test created a family tree identical to the phylogenetic trees constructed by scientists using genetic, morphological, anatomical, fossil and behavioral information. This was surprising, especially since the overwhelming majority of the mammalian genomes do not code for genes, Kim says.

Next, they tried the FFP method on 518 genomes, the bulk of them bacteria and Archaea, but also six eukaryotes of varying complexity, and two random sequences. The researchers found that the FFP method clearly segregates whole proteomes of all bacteria, archaea, eukaryotes, and random sequences into separate groups or domains. Most of the phylum groups within each domain and class groups in each phylum also were well segregated, with some interesting discrepancies compared to the currently accepted groupings.

In most of the cases where the FFP grouping disagreed with an accepted phylogenetic grouping, the problem organism had been the subject of debate among biologists because of conflicting conclusions from genetics, behavior, and morphology, Kim says. The new method did classify several so-far unclassified bacteria, however.

The major differences are found not in how the organisms are grouped, he says, but in the relative position of these groups in the organism trees.

The 'genes are us' fallacy

Kim stresses the major difference between FFP and gene-centric comparisons of genomes: FFP takes into account all or most of the DNA or protein sequences in the genome, while gene-alignment analysis chooses a small set of genes out of the large number of genes in each organism, and uses that to represent the organism.

"The fallacy of the view that organisms can be represented by a small set of their genes is really due to our prejudice that genes are us," Kim says. "We know now, more and more, that this is oversimplification.

"It is likely that some of the observations we come up with will turn out to be wrong, but the method will evolve and get better and better as experts come in and tell us where we have gone wrong. The math is there, now we have to remove the human bias as much as possible."

In addition to applying the method to comparative genomics, Kim expects it will help in grouping and finding relationships among sets of other information, such as electronic information encoding text, sounds, and images. It may also help in tracing human ancestry and disease demography using whole genome sequences, and in grouping of metagenomic data the sequences of genome fragments from many organisms, most of which are unknown species, found in a given environmental niche or body organ.

In addition, Kim hopes someday to apply the method to a long-running mystery in the humanities, sorting out the provenance of Shakesperean texts, once and for all.