Showing posts with label TREE OF LIFE. Show all posts
Showing posts with label TREE OF LIFE. Show all posts

Thursday, February 14, 2013

MAMMAL DIVERSITY AFTER THE DINOSAURS

Newborn Boston Terrior.  Credit:  Wikimedia Commons.
FROM: NATIONAL SCIENCE FOUNDATION
Placental Mammal Diversity Blossomed After Age of Dinosaurs
Scientists build new 'tree of life' for placentals, visualize common ancestor


Scientists have reconstructed the common ancestor of placental mammals--an extremely diverse group including animals ranging from rodents to whales to humans--using the world's largest dataset of both genetic and physical traits.

In research results published today in the journal Science, the scientists reveal that, contrary to a commonly held theory, placental mammals did not diversify into their present-day lineages until after the extinction event that eliminated non-avian dinosaurs and about 70 percent of all species on Earth, some 65 million years ago.

This finding and the visualization of the placental ancestor, a small, insect-eating animal, was made with the help of a powerful cloud-based and publicly accessible database called MorphoBank.

The Science paper is the result of a multi-year collaborative project funded by the National Science Foundation's (NSF) Assembling the Tree of Life initiative.

"Molecular clock estimates and the fossil record do not agree on the time of origin and diversification of many modern and extinct biotic groups," said H. Richard Lane, program director in NSF's Division of Earth Sciences, which co-funded the research with NSF's Division of Environmental Biology. "Data from the NSF-supported Assembling the Tree of Life initiative have been the key to these conclusions."

Analysis of this massive dataset shows that placental mammals didn't originate during the Mesozoic Era, according to the paper's lead author, Maureen O'Leary of Stony Brook University and the American Museum of Natural History (AMNH).

"Species like rodents and primates did not share the Earth with non-avian dinosaurs but arose from a common ancestor--a small, insect-eating, scampering animal--shortly after the dinosaurs' demise."

There are two major types of data for building evolutionary trees of life: phenomic data, which includes observational traits such as anatomy and behavior, and genomic data encoded by DNA.

Some researchers have argued that integration of both is necessary for robust tree-building because examining only one type of data leaves out significant information.

The evolutionary history of placental mammals, for example, has been interpreted in very different ways depending on the data analyzed.

"This discovery about the diversification of placental mammals is remarkable, highlighting that resolution of the complete tree of life requires data from both molecules and morphology," said Robb Brumfield, program director in NSF's Division of Environmental Biology. "In this case, the inclusion of fossils was a key to understanding timing and branching history deep in the tree."

One leading analysis based on genomic data alone predicted that a number of placental mammal lineages existed in the Late Cretaceous and survived the Cretaceous-Paleogene (KPg) extinction that occurred about 66 million years ago.

Other analyses place the start of placental mammals near this boundary, and still others set their origin after this event.

"There are more than 5,100 living placental species and they exhibit enormous diversity, varying greatly in size, locomotor ability and brain size," said Nancy Simmons of the AMNH and a paper co-author.

"Given this diversity, it's of great interest to know when and how this clade first began evolving and diversifying."

The new study combines genomic and phenomic data in a simultaneous analysis for a more complete picture of the tree of life.

"Despite the considerable contributions of DNA sequence data to the study of species relationships, phenomic data have a major role in the direct reconstruction of trees," said Michael Novacek, a paleontologist at the AMNH and paper co-author.

"Such data include features preserved in fossils where DNA recovery may be impossible. The mammalian record is notably enriched with well-preserved fossils, and we don't want to build trees without using the direct evidence these fossils contribute."

"Discovering the tree of life is like piecing together a crime scene," said O'Leary.

"It's a story that happened in the past that you can't repeat. Just like with a crime scene, the new tools of DNA add important information, but so do other physical clues like a body or, in the scientific realm, fossils and anatomy. Combining all the evidence produces the most informed reconstruction of a past event."

The tree of life produced in this study shows that placental mammals arose rapidly after the KPg extinction, with the original ancestor speciating 200,000-400,000 years after the event.

"This is about 36 million years later than the prediction based on purely genetic data," said Marcelo Weksler, also a co-author and a researcher at the National Museum of Brazil.

The finding also contradicts a genomics-based model called the "Cretaceous-Terrestrial Revolution" that argues that the impetus for placental mammal speciation was the fragmentation of supercontinent Gondwana during the Jurassic and Cretaceous, millions of years earlier than the KPg event.

"The new tree indicates that the fragmentation of Gondwana came well before the origin of placental mammals and is an unrelated event," said John Wible of the Carnegie Museum of Natural History and paper co-author.

As part of the study, researchers used MorphoBank, an initiative funded primarily by NSF, with additional support from Stony Brook University, the American Museum of Natural History and the National Oceanic and Atmospheric Administration to record phenomic traits for 86 placental mammal species, of which 40 were fossil species.

The resulting dataset has more than 4,500 traits detailing characteristics such as the presence or absence of wings, teeth, certain bones, type of hair cover and structures found in the brain, as well as more than 12,000 supporting images, all publicly available online.

The dataset is 10 times larger than what has previously been used for studies of mammal relationships.

Because phenomic datasets are built on physical objects like fossils that are limited in number and take time to excavate, prepare and analyze, evolutionary trees based on anatomy usually don't exceed several hundred traits.

Large-scale collection of such data for tree-building is now being called "phylophenomics."

"Cyberinfrastructure for organizing molecular biology has historically outstripped infrastructure for phenomic data, but new technologies like MorphoBank allow scientists working with phenomic data to produce larger and more complex projects, and to enrich these databases with images, references and comments," said Andrea Cirranello, paper co-author and researcher at Stony Brook University and the AMNH.

The team reconstructed the anatomy of the placental common ancestor by mapping traits onto the tree most strongly supported by the combined phenomic and genomic data and comparing the features in placental mammals with those seen in their closest relatives.

This method, known as optimization, allowed the researchers to determine what features first appeared in the common ancestor of placental mammals, and also what traits were retained unchanged from more distant ancestors.

The researchers conclude that the common ancestor had features such as a two-horned uterus, a brain with a convoluted cerebral cortex and a placenta in which the maternal blood came in close contact with the membranes surrounding the fetus, as in humans.

In addition, the study reveals that a branch of the placental mammal tree called Afrotheria, whose living members include animals -- ranging from elephants to aardvarks-- that live in Africa today, did not originate on that continent but rather in the Americas.

"Determining how these animals first made it to Africa is now an important research question, along with many others that can be addressed using MorphoBank and the phylophenomic tree produced in this study," said co-author Fernando Perini of Minas Gerais Federal University in Brazil.

Added co-author Mary Silcox, an anthropologist at the University of Toronto Scarborough, "this project exposes a way forward to collect data on other phenomic systems and other species."

-NSF-

Tuesday, October 16, 2012

EVOLUTIONARY THEORY AND DNA ANALYSIS


Photo caption: From left, Los Alamos scientists Joel Berendzen, Ben McMahon, Mira Dimitrijevic, Nick Hengartner and Judith Cohn.
FROM: LOS ALAMOS NATIONAL LABORATORY
Evolutionary Theory, Web-Search Technology Combine for DNA Analysis

Bioinformatics breakthrough has clinical & environmental applications
LOS ALAMOS, NEW MEXICO, October 4, 2012—New software from Los Alamos National Laboratory called Sequedex uses evolutionary theory to swiftly identify short "reads" of DNA, calling out the specific organisms from which the DNA came and their likely activity.

"Sequedex makes it possible for a researcher to analyze data hot off a DNA sequencer using a laptop," said Joel Berendzen, a scientist on the project. "The tool characterizes whole communities of microorganisms such as those in the mouth in a matter of minutes."

Sequedex works like a web search engine, making exact matches between DNA sequences and a list of "keywords" called phylogenetic signatures, then placing any hits on the appropriate branch of the Tree of Life. Advantages over current methods include a factor of 250,000 in speed and the ability to work with pieces of DNA as short as 30 bases long.

The software, developed by Los Alamos scientists Joel Berendzen, Nicolas Hengartner, Judith Cohn, Mira Dimitrijevic and Benjamin McMahon, recognizes proteins from short DNA sequences, analyzing them both individually for phylogeny and function and collectively for biodiversity and environmental similarities.

"Sequedex is bioinformatics redesigned from the ground up," said Berendzen, "making use of the wealth of genomic data that has become available in the 20 years since the most commonly used algorithms were written."

Data analysis is widely perceived as a bottleneck preventing broader use of DNA sequencing for problems such as rapid clinical diagnoses of viral and bacterial diseases, genetic matchmaking between individual tumors and chemotherapy agents, and improved production methods for algal biofuels. A number of ways around this bottleneck have been proposed, including special computer hardware and farming out analysis to large numbers of computers on computing clouds.

The Sequedex team was originally tasked with investigating DNA analysis on the Laboratory’s Roadrunner supercomputer, but quickly realized that improvements in the algorithm made having so much hardware unnecessary. "They asked us to build a rocket ship," Berendzen said, "but instead we built a 10,000 mph motorcycle."

Sequedex software running on a single CPU core can analyze sequences at a rate of 6 billion DNA bases per hour. This rate is more than twice the speed of data generated by today’s fastest sequencing instruments, and it is also more than twice the rate of typical upload speeds to a cloud-computing site.

A journal article on the project, "Rapid Phylogenetic and Functional Classification of Short Genomic Fragments with Signature Peptides," was published in the open-access, peer-reviewed journal BMC Research Notes.

Sequedex was recently announced as one of this years' winners of R&D Magazine’s "R&D 100" awards (
http://www.rdmag.com/), one of four from Los Alamos National Laboratory and its partners. The project was funded with Laboratory Directed Research and Development dollars. A free demo version is available online at http://sequedex.lanl.gov/. The laboratory’s technology transfer office is actively seeking strategic partnership opportunities.

DNA sequencing came to prominence as a result of the Human Genome Project, which was completed in 2003 and found some 25,000 genes in the 3 billion chemical bases that make up the sequence of human DNA. The Human Genome Project arose out of research at Los Alamos and elsewhere in the U.S. Department of Energy into the effects of energy use on human health.

DNA sequencing technology is evolving at a dramatic rate. Costs have dropped by a factor of roughly 300,000 in the past 10 years and the resulting increased flows of sequence data have placed more stress on an already overburdened analysis process. The current most-widely-used piece of DNA analysis software, a package called the Basic Local Alignment Search Tool (BLAST), was a refinement of software written by Los Alamos scientists Temple Smith and Michael Waterman in 1981.

Wednesday, June 6, 2012

NSF AND THE TREE OF LIFE BRANCHES AND EVOLUTION

FROM:  NATIONAL SCIENCE FOUNDATION
June 4, 2012
A new initiative aims to build a comprehensive tree of life that brings together everything scientists know about how all species are related, from the tiniest bacteria to the tallest tree. Researchers are working to provide the infrastructure and computational tools to enable automatic updating of the tree of life, as well as develop the analytical and visualization tools to study it.

Scientists have been building evolutionary trees for more than 150 years, since Charles Darwin drew the first sketches in his notebook.

Darwin's theory of evolution explained that millions of species are related and gave biologists and paleontologists the enormous challenge of discovering the branching pattern of the tree of life.

But despite significant progress in fleshing out the major branches of the tree of life, today there is still no central place where researchers can go to visualize and analyze the entire tree.

Now, thanks to grants totaling $13 million from the National Science Foundation's (NSF) Assembling, Visualizing, and Analyzing the Tree of Life (AVAToL) program, three teams of scientists plan to make that a reality.

"The AVAToL awards are an exciting new direction for an area that's a foundation of much of biology," says Alan Townsend, director of NSF's Division of Environmental Biology. "That's critical to understanding a changing relationship between human society and Earth's biodiversity."

Figuring out how the millions of species on Earth are related to one another isn't just important for pinpointing an antelope's closest kin, or determining if tuna are more closely related to starfish or hagfish.

Information about evolutionary relationships is fundamental to comparative biology research. It helps scientists identify promising new medicines; develop hardier, higher-yielding crops; and fight infectious diseases such as HIV, anthrax and influenza.
If evolutionary trees are so widely used, why has assembling them across all life been so hard to achieve?

It's not for lack of research, or data. Advances in DNA sequencing and evolutionary analysis, discovery of pivotal early fossils, and novel methods and tools have enabled thousands of new evolutionary trees to be published in scientific journals each year.

However, most of these focus on specific, disconnected branches of the tree of life.
Part of the difficulty lies in the sheer enormity of the task. The largest evolutionary trees to date contain roughly 100,000 groups of organisms.

Assembling the branches for all species of animals, plants, fungi and microbes--and the countless more still being named or discovered--will require new computational tools for analyzing large data sets, for combining diverse kinds of data, and for connecting vast numbers of published trees into a synthetic whole.

Another difficulty lies in how scientists typically disseminate their results. A tiny fraction of all evolutionary trees have been published.  Researchers estimate a mere four percent end up in a database in a digital form.

Most of the knowledge is locked up in figures in static journal articles in file formats that may be difficult for other researchers to download, reanalyze or merge with new information.

AVAToL aims to change that.
What makes this program different from previous efforts, scientists say, is its scope: its focus on creating an open, dynamic, evolutionary framework that can be continually refined as new biodiversity data is collected, and its development of computational and visualization tools to scale up tree-based evolutionary analyses.

Researchers will be able to go online and compare their trees to others that have already been published, or download trees for further study.

They'll also be able to expand the tree, filling in the missing branches and placing newly named or discovered species among their relatives.

The goal is to incorporate new trees automatically, so the complete tree can be continuously updated.

In addition to the creation of an updatable tree of life, AVAToL scientists will create new tools for the kinds of research that rely on evolutionary trees and for the collection and analysis of important evolutionary data, including from fossils critical to the placement of many branches in the tree of life.

The three NSF-funded AVAToL projects are:
Automated and Community-Driven Synthesis of the Tree of Life
Principal Investigator: Karen Cranston, Duke University and the National Evolutionary Synthesis Center
This project will produce the first online, comprehensive first-draft tree of all 1.8 million named species, accessible to both the public and scientists.  Assembly of the tree will incorporate previously published results and efforts to develop, test and improve methods of data synthesis. This initial tree of life, called the Open Tree of Life, will not be static. Scientists will develop tools for researchers to update and revise the tree as new data come in.

Arbor: Comparative Analysis Workflows for the Tree of Life
Principal Investigator: Luke Harmon, University of Idaho
Scientists deal with daunting volumes of data.  One of the most basic challenges facing researchers is how to organize that information into a usable format that can inspire new scientific insights. This project team is working to develop a way to visually portray evolutionary data so scientists can see, at a glance, how organisms are related. The team will create software tools that will enable researchers to visualize and analyze data across the tree of life, enabling research in all areas of comparative biology at multiple evolutionary, space and time scales. The results have the potential to transform the way biologists test evolutionary and ecological hypotheses, enabling new research in fields from medicine to public health, from agriculture to ecology to genetics.

Next Generation Phenomics for the Tree of Life
Principal Investigator: Maureen O'Leary, SUNY-Stony Brook
This team of biologists, computer scientists and paleontologists will extend and adapt methods from computer vision, machine learning and natural language processing to enable rapid and automated study of species' phenotypes on a vast scale across the tree of life. The team's goal is to develop large phenomic datasets using new methods, and to provide the scientific community and the public with tools for future such work. Phenomics is an area of biology that measures the physical and biochemical traits of organisms as they change in response to genetic mutations and environmental influences.

Enormous phenomic datasets, many with images, will foster public interest in biodiversity and the fossil record. Phenotypic data allow scientists to reconstruct the evolutionary history of fossil species, in turn crucial for an understanding of the history of life. This project will leverage recent advances in image analysis and natural language processing to develop novel approaches to rapidly advance the collection and analysis of phenotypic data for the tree of life.

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