Recently in Evolution Category

At Jerry Coyne’s bl*g Why Evolution Is True he has a new post calling attention to a web site on The Third Way of Evolution. It was apparently put up last year by James Shapiro, Denis Noble, and Raju Pookottil. It presents statements by 43 people expressing their view that a new Way of Evolution is needed. It has apparently been up for over 8 months, but only recently was mentioned by Denyse O’Leary at Uncommon Descent.

None of these people are, as far as I can tell, creationists. Many are working, or retired scientists or engineers. Jerry gives telling analyses of the views of some of the more prominent critics among them, citing his own past demolitions of their views. An interesting point is that all of these people are said to have agreed to being listed on the TWOE website.

A unified statement by 43 people, mostly scientists of some reputation, laying out a new evolutionary synthesis, should attract a lot of attention. However, the Third Way site does not do that. The difficulty is that each of these people seems to march to a different drummer, and in a different direction. They go off over the horizon in different directions, each convinced that theirs is the promising new direction. The common theme is that “The Modern Synthesis is dead, and I have a replacement for it!” But there is no agreement on what the replacement should be.

It is fun reading. Let’s have a thread there. Calling these folks creationists is not helpful; overwhelmingly they simply aren’t creationists. (The Second Way is, Shapiro et al. point out, creationism. To me it is a bit strange to hear creationism cited as a Way of Evolution, when what it actually says is “no way”.)

A very useful activity would be to characterize the views of some of the 43. Are they:

  • Lamarckians?
  • Mutational teleologists?
  • Saltationists?
  • (etc.)

Let’s discuss. I will, as usual, try to vigorously pa-troll the comments and send off-topic comments to the Bathroom Wall. Interventions by our usual creationist trolls and replies to those will go to the BW.

Delving into the History of Insects

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What comes to mind when you think about insects? For a lot of people, the word sends a shiver up their spine as they imagine the tiny, creeping legs, buzzing wings, stinging tails, and biting fangs. But what those people may not know is that insects comprise one of the most important classes of animal; there are more species of insect than any other animal group, and they can claim being the first animals to achieve many things, including flight and social societies.

Insect evolution is historically poorly understood, and the lack of a well-resolved and supported tree of insects has left researchers with many questions about their evolutionary relationships. For example, how are grasshoppers, crickets, cockroaches, and termites related? Which species are the closest living relatives to Holometabola, the group containing beetles, moths, butterflies, wasps, bees, and ants? What is the timeline of insect evolution? Answering these questions could help us understand how different insect traits evolved, which could reveal insights into the mechanism of evolution itself.

Silverfish (left) evolved to lose their wings and other appendages independently from other insects like jumping bristletails (right), and they make up their own branch on the new phylogenetic tree of insects. Images: Wikipedia

Scientists with the international 1KITE project set out to answer these questions and more by using phylogenomics to compare 1478 genes among 103 species of insect. First, they sequenced the DNA to find genes that were present in all the species, most of which coded for proteins involved in translation, protein transport, neurogenesis, and other basic cellular functions. Similar to the study of birds that we talked about last time, Misof et al. used improved methods of analysis to reduce errors from such a large dataset. Before analyzing the data, the researchers accounted for possible sources of bias by removing confounding factors; for example, they removed any data that violated the assumption that evolution is a time-reversible process. They then discarded any sequences that were misaligned and generated their tree with maximum likelihood models as well as a partitioning scheme, to improve the accuracy of the assumed model of evolution. Using data from two sources, nucleotides and amino acid sequences, the researchers generated two matching phylogenetic trees.

The new phylogenetic tree was able to answer many questions about insects with a higher statistical confidence than previous studies:

  • Earwigs, ground lice, stoneflies, crickets, gladiators, ice crawlers, webspinners, stick and leaf insects, praying mantids, and termites comprise a branch on the tree (a monophyly) called Polyneoptera, a hypothesis proposed in previous studies.
  • The study proposed the new conclusion that lice are the closest living relatives to beetles, moths, butterflies, wasps, bees, and ants.
  • Insects originated around 479 million years ago, a finding that contradicts previous estimates of about 400 million years ago.
  • Insects inhabited land at about the same time as plants (around 450 million years ago) and developed flight after they had established colonies, corroborating a 2013 study.
  • Remipedia, a class of blind crustaceans found in caves, is the closest living species to insects, confirming prior studies.
  • Silverfish comprise their own branch on the tree, as other recent studies have proposed, implying that they evolved to lose their head endoskeleton, leg-like structures called styli, and the sacs on their legs (coxal vesicles) in parallel to but separately from winged insects.

While many of the conclusions drawn by the new study are not completely new findings, the history of insect evolution is controversial and relationships previously proposed lacked certainty. The ability of the 1KITE researchers to confirm and deny these relationships with such high confidence shows the power of genomic analysis. But as with the recent bird phylogeny paper, the methods of analysis had to change to accommodate a larger dataset; specifically, confounding factors that could lead to biased conclusions were a larger concern than for previous studies. Jarvis et al. chose with their bird analysis to modify their programs to create a better phylogenetic tree, while Misof et al. removed data with these confounding factors during analysis. It remains to be seen which genomic data analyses produce the best results, but what we do know is that genome sequencing will play a major role in future phylogenetic studies of all species.

This series is supported by NSF Grant #DBI-1356548 to RA Cartwright.

Phylogenomics Produces New and Improved Tree of Birds

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What do flamingoes and pigeons have in common? You might say very little—after all, flamingoes are long–legged, vibrantly–colored water–dwellers and the pigeons we often see inhabiting our cities appear to be completely the opposite. But according to a study published last month in Science magazine, flamingoes and pigeons are more closely related than previously thought.

The groundbreaking new study used phylogenomics to compare the genes of 48 bird species. It is the first study of its kind to use whole genomes to construct the tree of birds, thousands of genes altogether. Prior studies attempting to resolve some of the more controversial bird relationships only examined 10–20 genes, meaning that the researchers in the new study had much more data to analyze and could be more confident in their results.

Flamingoes and pigeons are more closely related than you might think, according to a new study. Images: Wikipedia

Scientists have been revising our understanding of the tree of birds using phylogenetics over the past decade. In 2006, when the cost to sequence a single genome was $10 million, Ericson et. al. published one of the earliest phylogenetic bird papers, using 5 genes from 87 species for their analysis. Hackett et. al. conducted another phylogenetic study of birds in 2008, when sequencing a genome had fallen to $1 million, this time using 19 genes from 169 species for comparison. While these studies were able to divide modern birds into their larger classifications, some of the deeper relationships remained unresolved and the researchers were still unable to establish with certainty the timing of the bird “big bang”—the rapid and successive divergence of birds into many species. Scientists agree that this divergence occurred around the time of the mass extinction of non-avian dinosaurs about 65 million years ago, but they debate whether birds diversified before or after the mass extinction.

Jarvis et. al. (2014) found that the bird big bang happened immediately after the extinction, taking a relatively short 10–15 million years. Using thousands of genes, they could draw this and other conclusions with more certainty. But with so much data, the researchers could not use standard phylogenetic analysis tools; they needed to develop new ones.

First of all, the team developed a custom algorithm for filtering out gene sequences that were unaligned or incorrectly aligned. Once the data from the aligned genes were gathered, the researchers used a new and more efficient program (implementing a maximum likelihood model) to construct the phylogenetic relationships from the raw data. Finally, the researchers used a method called data binning to reduce errors that arise from the mathematical assumption that species divergence occurred instantaneously (when it more likely occurred gradually). Using these new methods and the added information from so many genes, the researchers were able to confirm and reject with more conviction some of the branches proposed by the previous studies, like the flamingo-pigeon relationship.

The red-billed tropicbird is a member of the Tropicbird family, which is excluded from Pelecaniformes in the new phylogenetic tree of birds. Image: Wikipedia

Along with this relationship and resolving the timing of the bird divergence, the researchers discovered several other important findings about birds. From some of the traits of the bird tree, they could conclude that the common ancestor of land birds was an apex predator, or a predator at the top of the food chain with no predators of its own. Also, the new tree of birds contradicts previous trees by excluding eagles and New World vultures from Falconiformes, the group containing falcons, kestrels, and other birds of prey. Similarly, the group Pelecaniformes excluded tropicbirds, a family of seabirds. Finally, the study revealed some characteristics about the way songbirds gained their vocal abilities with a gene that is similar to the one giving humans the ability to learn speech. This finding has gained a lot of recognition because of its potential application to the study of human speech.

As we’ve talked about in previous posts, using a complete set of genomic data can give us a more accurate phylogenetic tree and more confidence in results like the ones we just mentioned, as long as the analytical methods are appropriate for big data sets. Because the researchers in this new study improved their methods to reduce the error and noise that can be found in big data sets, their tree is probably the most accurate tree of birds produced so far. But all mathematical models of natural phenomena are at least somewhat incorrect, so it is likely that researchers will make further improvements to the methods and the tree.

Regardless, the field of phylogenetics is changing to realize the full potential of genome sequencing. As the tools to analyze these data improve, we’ll continue to gain new insights into species relationships and evolution with greater confidence than ever before. Who knows what other surprising relationships we’ll discover?

See the complete tree of birds here.

This series is supported by NSF Grant #DBI-1356548 to RA Cartwright.

Equus quagga burchellii

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Photograph by Alice Levine.

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Equus quagga burchellii – Burchell’s zebra, Namibia.

Proterozoic schists

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Photograph by Jim Kocher.

Photography contest, Honorable Mention.

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Painted Wall – Black Canyon of the Gunnison River, Montrose, Colorado, May, 1999; Kodachrome 64. Proterozoic schists intruded by pegmatite dikes (~1.25 Ga). Vertical relief is ~2,200 ft.

Color vision may be 300 Ma old

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According to a blurb in Science yesterday, researchers have discovered a fossilized fish whose eyes show traces of pigment and also fossilized rods and cones. The existence of the cones suggests that color vision developed at least 300 Ma ago. You may read the full article, which appears in Nature Communications, by following the link from the Science article; you can read it only on screen – a pdf will cost you $32.

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P.S. Yes, I learned about Nature‘s sharing policy by tracing the link from Science. If you follow the link to the Nature article itself, you get only the abstract.

Are men idiots?

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That is, are male members of the species Homo sapiens idiots? No, but according to a recent article, they are more likely to be idiots than women are.

The only thing surprising about this conclusion is that it is so unsurprising. For years now, whenever my daughter or I see a bicyclist dash madly across 4 lanes of traffic, we announce to each other, “Another male trying to improve the gene pool.” We are uncertain who said it first, but my daughter somewhat sheepishly thinks it was she. Which, of course, makes me think that we brought her up right.

The study that drew the unsurprising conclusion looked at the recipients of the Darwin Awards over the past 20 years. To qualify for a Darwin Award, you have to remove yourself from the gene pool, generally by killing yourself, but I suppose that castration would do about as well.

After the usual mutterings about selection bias and noting that the study was retrospective (double-blind would have been kind of tricky), the authors conclude that ~90 % of Darwin Award winners were male. They propose a Male Idiot Theory, which to my mind is at least as good as Molière’s diagnosis, she is mute because she has lost her speech.

NPR reported on the article here. Some of the comments are interesting, and some suggest a sociobiological explanation, which I will leave to your imagination – suffice it to say that among our early ancestors, only the men had to take the risk of hunting elephants. Or whatever.

The authors of the original article assure us that they plan an observational study and even now are scheduling holiday parties, both with and without alcohol.

Phylogenomic Fallacies

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This is the fourth in a series of articles for the general public focused on understanding how species are related and how genomic data is used in research. Today, we talk about some common fallacies in phylogenomics.

Where do humans fit on the evolutionary tree of life? This is an important topic in evolutionary biology. A lot of people believe humans are the most important and highly-evolved organisms, but in reality, all modern species are equally evolved. Our natural tendency to assume that humans are evolutionarily superior has led to a few misconceptions about phylogenetic trees.

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To understand the first misconception, let’s look at a phylogenetic tree of plants (from “The Amborella Genome and the Evolution of Flowering Plants”). Eudicots and monocots are two classes of flowering plants, or angiosperms, and the plants in black are non-flowering plants. The term “basal” refers to the base of a phylogenetic tree, and a basal group is a species that branches closer to that base. The authors chose to label the angiosperms that are not eudicots or monocots as “basal angiosperms.” But this label is arbitrary; all the angiosperms are equidistant from the common ancestor and thus equally evolved. We sometimes tend to give more weight to branches that contain the species of interest and call other branches basal, almost assigning them a lesser importance. In this case, the species of interest is plants that consist of many foods that humans eat; a species is often deemed more important as it relates to humans. But modern species are equally evolved from a root common ancestor regardless of when their branch diverged from the common ancestor. To avoid confusion, it might be best to eliminate the “basal” term altogether.

This type of thinking also leads us to place humans at the end of phylogenetic trees. However, this placement is arbitrary and trees can be drawn in many equivalent ways. For example, compare a tree of primates with the branches rotated. The tree on the left, with humans at the top of the tree, is one you might see more often. But both of these trees are actually identical, and the relationships between species that can be inferred from the tree on the right is the same as the relationships in the tree on the left. Species at the tip of a tree are equidistant from the root common ancestor, so they can be considered evolutionarily equivalent.

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primate tree 2.png

Similarly, a common misconception is that humans evolved directly from monkeys. Monkeys, though, are modern species just like we are and have been evolving and changing over time. The common ancestor we share with monkeys may have looked much different than monkeys do now. This assumption that modern species represent an ancestral state of human evolution is what T. Ryan Gregory calls the platypus fallacy. Gregory uses the example that we can’t examine the traits of platypuses and think that humans at one point in their evolution possessed these same traits. We can no more infer the traits of human ancestor species from platypuses than platypuses can infer the traits of their ancestors from us.

Human-centered thinking is very prevalent in our society, affecting our laws, religions, and customs. While it probably influences all of us on a personal level, it can lead to false conclusions and misconceptions in science, like thinking that humans are the most highly evolved species. But all modern species are evolutionarily equivalent because they have been evolving for the same amount of time. Eliminating this fallacy will enable us to better understand the evolutionary process.

For more information on basal groups, check out: “Which side of the tree is more basal?, Krell, Frank et al. Systematic Entomology (2004).

This series is supported by NSF Grant #DBI-1356548 to RA Cartwright.

Libellula pulchella forensis

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Libellula pulchellaeight twelve-spotted skimmer, Elmer’s Two-Mile Creek, Boulder, Colorado. See here for eight-spotted skimmer, L. forensis.

The Hebrew Bible says that God made humans from dust,* but maybe it was a slurry of clay and water. That is a tentative conclusion you might draw from an experiment that used a (very) high-powered laser beam to zap a suspension of clay in an aqueous solution of formamide, a very simple organic compound. The result has been reported in the press, but there is a somewhat more-precise article in Science magazine. (You may find the abstract of the original article here and the supporting information here. I did not get access to the full article.)

In a nutshell, a team at the J. Heyrovský Institute of Physical Chemistry in Prague used a laser that can produce up to 1 kJ in a 300 ps pulse,** irradiated the suspension, and produced adenine, cytosine, guanine, and uracil, which are the bases of the RNA molecule. And apparently not a drop of thymine, one of the bases of DNA. The experiment is supposed to simulate the bombardment of the early Earth by comets and presumably supports the hypothesis that an RNA world came first.

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* Actually, Job, Isaiah, Psalms, and I imagine elsewhere say clay, as in, “We are the clay, and you are our potter.” (Don’t get excited; I consider the fact to have no significance whatsoever.)

** I am a laser physicist and wrote my thesis on laser-produced plasmas, so you must forgive me for somewhat stressing the laser, which to this day gives me a certain amount of pulse envy.

Coyote Buttes

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Photograph by Vivian Dullien.

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Coyote Buttes, Arizona.

Analyzing the Genome with Statistics

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This is the third in a series of articles for the general public focused on understanding how species are related and how genomic data is used in research. Today, we talk about the challenges of using statistics to analyze phylogenomic data.

Suppose you were a door manufacturer trying to figure out the average height of a population living in a certain country. You might conduct an experiment where you ask a group of people to report their height. You would then assemble those measurements in a data set. But in order to study this data set and draw conclusions you would need to analyze it using statistics. For example, how tall should your door be in order to fit 95% of people in the country? How many people do you need to survey to accurately represent the total population? These questions can be answered with statistical analysis.

Because acquiring data from experiments can be costly and time-consuming, we often use small data sets to represent a larger population of interest. In our height experiment, we would not be able to ask every single person in the country his or her height. We would choose a group of people under the assumption that they accurately reflect the population as a whole. However, when we are trying to map out the evolutionary history of organisms using data from sequenced genomes (phylogenomics, which we talked about last time), we need to change our method of analysis.

Let’s look at the treeshrew, for instance. It looks like a rodent but actually shares some internal similarities with primates (studied by Sir Wilfrid Le Gros Clark in the 1920s), like brain anatomy and reproductive traits. To figure out if the treeshrew is more similar to rodents or primates, we could sequence its genome and, using statistics, compare its genes to those of rodents and primates. But typical statistical models are based on subsets of populations, while by definition, genomic sequencing gives us a complete data set - all of the treeshrew’s genes. These typical models may not be suitable for interpreting genomic data.

The treeshrew. Source: Wikipedia

Before reaching a conclusion about the tree shrew, or any set of data, scientists must consider precision and accuracy. Multiple measurements of the same quantity are precise if they are similar to each other. Another way of saying this is that their variance is small. On the other hand, measurements are accurate if they are close to the true value of what they are trying to measure. For genomic data, we need better statistical tools to ensure that the accuracy of our conclusions matches the precision characteristic of these huge data sets.

Larger data sets provide more precise conclusions than smaller ones. For example, when we ask more people to report their height, we are more confident that our sample represents the variability of the actual population. Similarly, we analyze more genes in the treeshrew’s genome to increase our confidence that our conclusion is precise. However, our results might not necessarily be accurate; big data sets may lead us to draw incorrect conclusions with high confidence. The treeshrew’s genome contains some genes that are more similar to rodents’ genes and some that are more similar to primates’ genes (Fan et al., Nie et al., and Xu et al.), and with so much data we could find that the treeshrew is most similar to either group with high confidence. We need analysis tools that will tell us which genes give the correct answer.

Why are conclusions from data sometimes inaccurate? Statistical biases are external factors that produce consistent error in our measurements. Biases have many sources, including faulty experimental design, violation of assumptions made in analyzing the data, and errors in the data collection process. Bias in our height experiment might arise if we unintentionally ask the height of more women than men, causing our estimate of the average height to be lower. But in the case of phylogenomics, we are likely to have biases because of our relative lack of knowledge about the genome: we don’t always know which genes to analyze or the correct way to model the data. For example, some models assume that evolution followed the same pattern throughout all time, but this most likely was not the case.

Furthermore, the process of genome sequencing and analysis itself may create error, especially in the reconstruction of the genome and the alignment of genes for comparison. If we are comparing the genome of the treeshrew to the genomes of primates and rodents, it is difficult for us to know which genes are correlated between species when we are looking at a data set of billions of points. We might use a probability model to determine correlated genes, but all models are at least somewhat incorrect and introduce bias. In smaller data sets, biases are offset by a low precision and relatively small confidence in reaching conclusions. However, in genomic-size data sets, even small biases can be amplified and lead to high confidence in the wrong answer and incorrect phylogenetic trees.

When analyzing phylogenomic datasets, we need to use analyses that are appropriate for large data sets. This will unlock the potential of phylogenomic research to draw unbiased conclusions, like figuring out the correct phylogenetic classification of the treeshrew (still a topic of controversy among evolutionary biologists). However, phylogenomics is such a young field that these tools do not yet exist. When they are developed, we can increase our chances of correctly classifying species’ relationships and discovering the true history of evolution.

For more detail, check out: “Statistics and Truth in Phylogenomics”, Kumar, Sudhir et al. Molecular Biology and Evolution (2011).

References:

Fan, Yu, et al. “Genome of the Chinese tree shrew.” Nature communications 4 (2013): 1426.

Nie, Wenhui, et al. “Flying lemurs-The’flying tree shrews’? Molecular cytogenetic evidence for a Scandentia-Dermoptera sister clade.” BMC biology 6.1 (2008): 18.

Xu, Ling, et al. “Evaluating the Phylogenetic Position of Chinese Tree Shrew ( Tupaia belangeri chinensis) Based on Complete Mitochondrial Genome: Implication for Using Tree Shrew as an Alternative Experimental Animal to Primates in Biomedical Research.” Journal of Genetics and Genomics 39.3 (2012): 131-137.

Our next installment will cover some misused terminology in phylogenomics. This series is supported by NSF Grant #DBI-1356548 to RA Cartwright.

Lenticular clouds

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Interesting cloud formation, Boulder, Colorado. The camera is facing south, and the wind is coming from the west, or right.

One hour later, in Golden,

Philae craft lands on comet

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Rosetta headquarters announced a few moments ago that the Philae lander is now sitting on the surface of the comet and transmitting data. Unfortunately, the European Space Agency is not exactly releasing a trove of pictures. I know this is not biology, but where did you think those hydrocarbons came from in the first place?

Phylogenomics: Deciphering a Billion-Piece Puzzle

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This is the second in a series of articles for the general public focused on understanding how species are related and how genomic data is used in research. Today, we talk about phylogenomics, the application of whole genome sequencing to understand evolutionary relationships among species.

DNA Chemical Structure. Source: Madeleine Price Ball

The haploid human genome is 3.2 billion DNA bases long, and each base can be one of four nucleotides: A, T, C, and G. Uncoiled, the DNA in a single human cell would be 2 meters long, and the DNA in a human body would stretch from the sun to Pluto multiple times. With 3.2 billion bases, each person’s genome is unique, and this plays an essential role in shaping our physical and mental individuality. However, despite being unique, each human genome is very very similar, due to our shared ancestral heritage. Similarly, species that share a recent ancestral heritage also have similar genomes. Species that are distantly related are likely to demonstrate significant differences in their genomes. This is why, as we discussed last week, evolutionary biologists compare traits and genes to determine the relationships of different species.

Unfortunately, some genes give us the wrong answer about how species are related. A section of a gene can be identical for two species due to independent mutations. After all, any given base can only mutate into one of three other bases. Chances are the same mutation could happen twice, or multiple mutations can produce the same sequence. Consider two species that are distantly related; one contains an AGA fragment, while the corresponding fragment in the other species is TGT, i.e. they differ in 2 out of 3 positions. As these species evolve, by chance the first species may experience a change in the first position such that AGATGA, and the second species may experience a change in the third position such that TGTTGA. Now, these two sequences look the same so you might think the species share a recent common ancestor; however, it is only an accident of biology that they appear closely related. Because some fragments may be identical due to independent mutations and not shared ancestry, estimating species relationships with using whole genomes is better than just a few genes. The more information we have, the more likely we are to figure out species’ relationships correctly.

The cost to sequence whole genomes has fallen from $100 million to $1000 in just the past twelve years. It now takes days to sequence a genome compared to the 13 years it took for the first human genome. The challenge now is not to obtain the data but to compare all the billions of base pairs in one genome to those in another. Current sequencing methods, while fast, can only read the genome by dividing it into millions of short fragments, which must be reassembled like an enormous puzzle. Researchers then have to figure out which genes correspond to one another in different species’ genomes. These comparisons are challenging because genes in one genome might be in a different order, on different chromosomes, or missing completely in another species’ genome.

Biologists are beginning to use genomic information to understand how species are related and measure how fast or slowly different genes evolve. Then in turn allows us to understand how evolution happens. For example, using genomic information we can figure out how genes mutate, characterize and diagnose genetic diseases, and track harmful pathogens. But before that can happen, we need to address the difficulties of analyzing these large genomic datasets. You might think that more data is always better, but having a lot of data can lead us to have very high confidence in the wrong answer. In a pool of thousands of genes, we need to find the ones that tell us the right answer.

Next week, we’ll discuss statistical challenges associated with big data analysis, especially as it relates to phylogenomics. This series is supported by NSF Grant #DBI-1356548 to RA Cartwright.

I started this post thinking I’d write a review of Andreas Wagner’s recent book “Arrival of the Fittest: Solving Evolution’s Greatest Puzzle” (links below), an engrossing book about how biological innovation arises from the structure of metabolic, genotype, and protein networks, and how robustness–the stability of phenotypes in the face of underlying genetic variability–is critical in evolutionary innovations. But there are several excellent reviews already out there, so another would be redundant. I’ll mention only a couple of points I think worth emphasizing below the fold.

Phelsuma laticauda

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Photograph by Tony Gamble.

Photography contest, Honorable Mention.

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Phelsuma laticauda – gold dust day gecko.

The Family Tree of Life

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In the next few weeks, we’ll be posting a series of articles for the general public focused on understanding how species are related and how genomic data is used in research. We start with a background on phylogenetic trees.

Imagine you could go back in time and meet your great grandmother or even your great-great-great-great-great grandmother, when they were your age. Would they look like you? Or would they look more like your siblings or cousins? Maybe you would all look a little different. Scientists try to figure out how the distant ancestors of apes, other animals, plants, and all organisms living today looked and behaved, much in the same way that people use a family tree to trace their ancestry.

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The common ancestor of great apes lived about 18 million years ago. Source: Smithsonian National Museum of Natural History http://humanorigins.si.edu/evidence/genetics

In evolutionary biology scientists use a type of tree called a “phylogenetic tree” to organize the history of how species descended from common ancestors. The closer two species are to a common ancestor on the phylogenetic tree, the more closely the two are related.

Take the phylogenetic tree of primates, for example. The common ancestor of apes lived about 18 million years ago. But over time, this one group branched off to form many different species, including humans, which have their own separate branch on this tree.

How did so many unique species develop from one ancestor? New branches formed by a process known as divergence. When groups of ancient organisms became geographically isolated from one another, either through migration or geologic events like earthquakes, each group began to develop its own unique set of physical attributes. Sometimes, by chance, a change in a characteristic enabled an individual to survive better in its environment and produce more offspring.

Perhaps individuals in one group with larger arms were better able to break open the hard-shelled fruits that were common in one region, while some individuals in another group had the ability to travel more easily through tall trees that offered protection from predators. Whatever the reason may have been, selection favored genetic differences that improved survival. Over time, this gradual process of isolation and selection produced distinct species, which in turn branched into more species.

The end result of divergence is many species, related in a tree-like fashion, and we display these relationships using phylogenetic trees. Scientists now use increasingly sophisticated methods to determine how species were related and build phylogenetic trees. In the past, scientists built these trees simply by comparing physical traits, like how many limbs an organism has or whether it has a tail. But with the recent surge in fast and affordable gene sequencing technologies, researchers today can directly compare species’ DNA to determine how they are related.

But analyzing entire genomes, with billions of DNA base pairs, presents its own unique set of challenges, and researchers often struggle to determine if the DNA differences they find between species are truly significant or are simply due to common variability. As computer software and statistical analysis become more adept at handling these challenges, our understanding of species’ relationships could change — providing exciting new insights into our family tree of life.

Check back next week when we discuss the differences between studying small and large datasets, and the challenges associated with big data analysis. This series is supported by NSF Grant #DBI-1356548 to RA Cartwright.

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Pinhole-camera images of solar eclipse formed by spaces between leaves in canopy. According to Jon Grepstad, this phenomenon was explained by Aristotle. The eclipse is just ending; the picture was as close to total as it got here (Boulder, Colorado).

Aeshna cyanea

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Photograph by Marilyn Susek.

Photography contest, Honorable Mention.

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Aeshna cyanea – southern hawker.

Beginning this week, we will run photographs every other Monday, so no picture next week; we no longer have enough honorable mentions and other miscellaneous photographs to continue posting a photograph every week. But polish your lenses (very carefully) and keep an eye out for the contest in the summer.

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