A new analysis of the Martian rock that gave hints of water on the Red Planet — and, therefore, optimism about the prospect of life — now suggests the water was more likely a thick brine, far too salty to support life as we know it.The finding, by scientists at Harvard University and Stony Brook University, is detailed this week in the journal Science.

“Liquid water is required by all species on Earth and we’ve assumed that water is the very least that would be necessary for life on Mars,” says Nicholas J. Tosca, a postdoctoral researcher in Harvard’s Department of Organismic and Evolutionary Biology. “However, to really assess Mars’ habitability we need to consider the properties of its water. Not all of Earth’s waters are able to support life, and the limits of terrestrial life are sharply defined by water’s temperature, acidity, and salinity.”

Together with co-authors Andrew H. Knoll and Scott M. McLennan, Tosca analyzed salt deposits in four-billion-year-old Martian rock explored by NASA’s Mars Exploration Rover, Opportunity, and by orbiting spacecraft. It was the Mars Rover whose reports back to Earth stoked excitement over water on the ancient surface of the Red Planet.

The new analysis suggests that even billions of years ago, when there was unquestionably some water on Mars, its salinity commonly exceeded the levels in which terrestrial life can arise, survive, or thrive.

“Our sense has been that while Mars is a lousy environment for supporting life today, long ago it might have more closely resembled Earth,” says Knoll, Fisher Professor of Natural Sciences and professor of Earth and planetary sciences at Harvard. “But this result suggests quite strongly that even as long as four billion years ago, the surface of Mars would have been challenging for life. No matter how far back we peer into Mars’ history, we may never see a point at which the planet really looked like Earth.”

Tosca, Knoll, and McLennan studied mineral deposits in Martian rock to calculate the “water activity” of the water that once existed on Mars. Water activity is a quantity affected by how much solute is dissolved in water; since water molecules continuously adhere to and surround solute molecules, water activity reflects the amount of water that remains available for biological processes.

The water activity of pure water is 1.0, where all of its molecules are unaffected by dissolved solute and free to mediate biological processes. Terrestrial seawater has a water activity of 0.98. Decades of research, largely from the food industry, have shown that few known organisms can grow when water activity falls below 0.9, and very few can survive below 0.85.

Based on the chemical composition of salts that precipitated out of ancient Martian waters, Tosca and his colleagues project that the water activity of Martian water was at most 0.78 to 0.86, and quite possibly reaching below 0.5 as evaporation continued to concentrate the brines, making it an environment uninhabitable by terrestrial species.

“This doesn’t rule out life forms of a type we’ve never encountered,” Knoll says, “but life that could originate and persist in such a salty setting would require biochemistry distinct from any known among even the most robust halophiles on Earth.”

The scientists say that the handful of terrestrial halophiles — species that can tolerate high salinity — descended from ancestors that first evolved in purer waters. Based on what we know about Earth, they say that it’s difficult to imagine life arising in acidic, oxidizing brines like those inferred for ancient Mars.

“People have known for hundreds of years that salt prevents microbial growth,” Tosca says. “It’s why meat was salted in the days before refrigeration.”

Tosca and Knoll say it’s possible there may have been more dilute waters earlier in Mars’ history, or elsewhere on the planet. However, the area whose rocks they studied — called Meridiani Planum — is believed, based on Mars Rover data, to have been one of the wetter, more hospitable areas of ancient Mars.

A group of mathematicians at Michigan Technological University (MTU) have developed powerful new tools for winnowing out the genes behind some of humanity’s most intractable diseases.

With one, they can cast back through generations to pinpoint the genes behind inherited illness. With another, they have isolated 11 variations within genes—called single nucleotide polymorphisms, SNPs or “snips”—associated with type 2 diabetes.

“With chronic, complex diseases like Parkinson’s, diabetes and ALS [Lou Gehrig’s disease], multiple genes are involved,” said Qiuying Sha, an assistant professor of mathematical sciences. “You need a powerful test.”

That test is the Ensemble Learning Approach (ELA), software that can detect a set of SNPs that jointly have a significant effect on a disease.

With complex inherited conditions, including type 2 diabetes, single genes may precipitate the disease on their own, while other genes cause disease when they act together. In the past, finding these gene-gene combinations has been especially unwieldy, because the calculations needed to match up suspect genes among the 500,000 or so in the human genome have been virtually impossible.

ELA sidesteps this problem, first by drastically narrowing the field of potentially dangerous genes, and second, by applying statistical methods to determine which SNPs act on their own and which act in combination. “We thought it was pretty cool,” Sha said.

To test their model on real data, Sha’s team analyzed genes from over 1,000 people in the United Kingdom, half with type 2 diabetes and half without. They identified 11 SNPs that, singly or in pairs, are linked to the disease with a high degree of probability. Their work has been accepted by the journal Genetic Epidemiology and is available online at http://www3.interscience.wiley.com/cgi-bin/abstract/117890704/ABSTRACT .

ELA is used to compare the genetic makeup of unrelated individuals to sort out disease-related genes. The team has also developed another approach, which uses a two-stage association test that incorporates founders’ phenotypes, called TTFP, that can examine the genomes of family members going back generations.

“In the past, researchers have dealt with the nuclear family, parents and children, but this could go back to grandparents, great-grandparents . . . as far back as you want.”

The team has published their findings in the European Journal of Human Genetics. An abstract is available at www.nature.com/ejhg/journal/v15/n11/abs/5201902a.html .

CHESTNUT HILL, MA – It took a global corps of scientists approximately $500 million and 13 years to identify the more than 35,000 genes of the human genome. Five years later, Boston College Biologist Gabor Marth and his research team have developed software that can analyze half a million DNA sequences in 10 minutes.The Marth laboratory’s proprietary PyroBayes software is one of a new breed of computer programs able to accurately process the mountains of genome data flowing from the latest generation of gene decoding machines, which have placed a premium on computational speed and accuracy in data-crunching fields known as bioinformatics and high-throughput biology, said Marth, an associate professor of Biology.

“We’re on the edge of a real technological revolution that I think will help us understand the genetic causes of diseases in humans and how genetic materials determine traits in animals,” said Marth. “It is going to lead to less expensive technologies that will allow researchers to decode any individual.”

PyroBayes will aid researchers involved in the 1,000 Genomes Project, which announced last month a plan to sequence the genomes of 1,000 individuals from around the world. The NIH, which helps direct the project, has awarded Marth more than $1.3 million to develop software over the next four years.

The advances of the Marth lab were revealed in two articles published by the professor and his assistants in the February issue of Nature Methods, the premier journal of scientific research methodology.

In an article co-authored by Marth, post-doctoral researcher Chip Stewart, and graduate students Aaron Quinlan and Mike Strömberg, the group unveiled the lab’s PyroBayes base caller software, which examines data from one of the latest generation of DNA decoding machines – from Roche / 454 Life Sciences – faster and with far greater accuracy than other programs for pyrosequencing, a technology that utilizes the detection of pyrophosphate for decoding the sequence of DNA, the carrier of genetic information in living organisms.

A second Nature Methods article, written in collaboration with colleagues from the Washington University School of Medicine, reported that three other computer programs developed by the Marth lab made it possible to quickly and accurately examine the whole genome of a laboratory worm and identify key differences between the sample strain and an earlier strain – a comparative process known as re-sequencing, now being applied to the genomes of humans and other organisms. This second study used another next-generation DNA sequencing platform, the Illumina/Solexa machine.

Advances are driving re-sequencing costs down, but researchers must still prove the effectiveness of the new technology by working with smaller organisms, which made the worm study critical, Marth said. “This brings us closer to a major milestone in human individual re-sequencing – the decoding of the genome of human beings in routine fashion,” said Marth.

Of the few computer programs available for the new sequencing machines, the software package developed by the Marth lab is the only one capable of working with a variety of decoding machines and offers greater accuracy, allowing researchers to separate true genetic variations from data errors, said Marth. PyroBayes, a Linux-based package, is made available to fellow academic researchers at no cost.

As a member of its analysis group, the Marth lab participates in the data analysis of the 1000 Genomes Project, which was launched last month. The goal of the project is to sequence the genomes of at least 1,000 people from around the world to create the most detailed and medically useful picture to date of human genetic variation.

Ultimately, advances in bioinformatics will help push genetic science forward, shedding new light on human health and disease. Marth sees his lab’s role in providing critical tools that help researchers to organize data, interpret them, and visualize genome variations.

“We are excited to develop the software that will help these super-fast, high-throughput sequencing machines to realize their potential to produce invaluable data for research,” Marth said.

Nature Methods: http://www.nature.com/nmeth/journal/v5/n2/full/nmeth.1172.html.

Today, scientists know more now than ever before about the microbes that inhabit our mouths. They know so much, in fact, that gathering all of the relevant bits of information into one place when designing experiments can be a job in itself. Now, grantees of the National Institute of Dental and Craniofacial Research (NIDCR), part of the National Institutes of Health, and their international colleagues intend to solve this problem with the launch of the first comprehensive database of the oral microbiome, or the approximately 600 distinct microorganisms currently known to live in the mouth.The free online compendium is called the Human Oral Microbiome Database (HOMD). The database goes live today as the digital equivalent of an Oxford dictionary of oral microorganisms, providing detailed biological entries for each species and an extensive catalogue of the thousands of genes that these microbes express. The site is located at http://www.homd.org and is overseen by scientists at The Forsyth Institute in Boston and King’s College London in England.

“The HOMD fills a critical research need,” said NIDCR director Lawrence Tabak, D.D.S., Ph.D. “The oral microbiome is extremely rich in data, and HOMD becomes the essential search engine for scientists to view and retrieve this information, generate novel hypotheses, make computational discoveries, and ultimately develop more biologically sound therapies to control oral diseases.”

According to Floyd Dewhirst, D.D.S., Ph.D., a leader of the project and a scientist at The Forsyth Institute, HOMD also introduces the first comprehensive nomenclature system to bring order to the naming of uncultured or previously unnamed oral microbes. The standardized numbering system helps to eliminate the Babel of confusing names and uninformative database designations that have frustrated scientists and sometimes hindered their research.

The database also categorizes each microbe by its 16S rRNA sequence, a distinctive fingerprint of genetic information that scientists have used for the past two decades to identify microorganisms. This sequence information allows the microbes to be placed in a family tree that shows how they are related to one another. For those organisms whose DNA has been sequenced, HOMD provides online tools to view and analyze all of their genes and proteins. Each category of information in the database is interlinked, readily searchable, appropriately annotated, and will be frequently updated to remain current.

Dewhirst noted that although HOMD has officially opened to scientists, the database remains an ongoing project. “We’ve already assembled a great deal of useful information for the research community, but we will continue to expand and refine the database for the next several years,” said Dewhirst. “I can see the Human Oral Microbiome Database serving as a valuable model for other microbiome databases now and in the years to come.”

Informally called “biology’s next revolution,” microbiome studies have opened a needed window into the complex microbial communities that occupy most parts of the human body. These studies will define how microbes contribute to sustaining health and, when their community dynamics are perturbed, play a role in common chronic disease, such as tooth decay and periodontal disease in the mouth. In December 2007, NIH launched the Human Microbiome Project that initially will sequence all of the genes, or genomes, of 600 representative microorganisms sampled from microbial communities in the mouth, skin, digestive tract, nose, and female urogenital tract. Additional studies are either under way or under development.

Among those already well under way is a NIDCR-supported project to compile a full catalogue of the complete genomes of all oral microbes. It has generated a tremendous amount of data and, coupled with the decades of more traditional studies of oral bacteria, the need for a comprehensive, user-friendly database has become a priority.

“The oral microbiome is currently better understood than those of other sites in the body, such as the intestine,” said Dr. Bruce Paster, Ph.D., also at The Forsyth Institute and another project scientist. “Since oral microorganisms appear in infections throughout the human body, the HOMD database certainly will be useful to physicians. Likewise, microbiologists in industry will find HOMD helpful because oral microbes sometimes contaminate food or the drug manufacturing process.”

The National Institute of Dental and Craniofacial Research (NIDCR) is the Nation’s leading funder of research on oral, dental, and craniofacial health.

LOS ALAMOS, New Mexico, March 10, 2008—A group of researchers has developed a novel way to view the world through the eyes of a common fly and partially decode the insect’s reactions to changes in the world around it. The research fundamentally alters earlier beliefs about how neural networks function and could provide the basis for intelligent computers that mimic biological processes.In an article published in the Public Library of Science Computational Biology Journal, Los Alamos physicist Ilya Nemenman joins Geoffrey Lewen, William Bialek and Rob de Ruyter van Steveninck of the Hun School of Princeton, Princeton University and Indiana University, respectively, in describing the research.

The team used tiny electrodes to tap into motion-sensitive neurons in the visual system of a common blowfly. Neurons are nerve cells that emit tiny electric spikes when stimulated. The electrodes detected pulses from the motion-sensitive neurons in the fly. The fly uses the neurons to estimate, and subsequently control, how it moves through the world.

The team harnessed the wired fly into an elaborate turntable-like mechanism that mimics the kind of acrobatic flight a fly might undergo while evading a predator or chasing another fly. The mechanism can spin extremely fast and change velocities quickly. A fly in the mechanism sees changes in the world around it and its motion-sensitive neurons react much in the same way as they would if the insect were actually flying.

Under complex flight scenarios, the fly’s neurons fired very quickly. The researchers looked at the firing patterns and mapped them with a binary code of ones and zeroes, much like computer instructions, or binary messages in digital phone communications.

The team found that the impulses were like a primitive, but very regular “language”—with the neuron firing at precise times depending on what the fly’s visual sensors were trying to tell the rest of the fly about the visual stimulus. When they examined this language, it spoke volumes about how the harnessed fly reacted to its world.

“In this system, the motion-sensitive neurons emit spikes very often and very precisely,” said Nemenman. “Historically, people have observed a lot more random spike intervals. This research is a departure from the traditional understanding in that we see that the precision of spike timing that carries information about the fly’s rotation is a factor of ten higher than even the most daring previous estimates.”

Similar-though-much-simpler experiments on different subjects, including flies, and going back to the seminal work of E. D. Adrian and Yngve Zotterman in 1926, seemed to show that sensory neurons would fire a certain number of impulses during a given period, but that the precise timing of the impulses was largely irrelevant. Nemenman and his team believe the timing of the spikes was not as crucial during those early experiments largely because the artificial stimulation was in some sense unnatural, bordering on the monotonous and predictable.

“Biological organisms have an interest in conserving energy,” Nemenman said. “Fly eyes account for about one-tenth of the fly’s energy consumption. The fly wants to be very efficient, but it costs energy and molecular resources to emit many precise spikes in the neurons.

“If you are presenting simple stimuli where little changes with time, then the most efficient way to encode them may be to generate few randomly positioned spikes, which would be sufficient to convey whatever small changes, if any, happened. Similarly, if the stimulus is unnaturally fast, the neurons may not be able to encode it well.

“However, if you put an organism in an environment with fast and naturally changing velocity profiles, the fly starts using all the bandwidth available to it,” Nemenman said. “The motion-sensitive neuron adjusts its coding strategy and it uses the precise positioning of the spikes to tell the rest of the fly exactly what is happening.”

In addition to the complex motions possible with the team’s apparatus, they conducted their experiment in a wooded setting similar to the fly’s natural environment, adding to the complexity and realism of the experiment.

Nemenman and his colleagues’ research is significant because it re-examines fundamental assumptions that became the basis of neuromimetic approaches to artificial intelligence, such as artificial neural networks. These assumptions have developed networks based on reacting to a number of impulses within a given time period rather than the precise timing of those impulses.

“This may be one of the main reasons why artificial neural networks do not perform anywhere comparable to a mammalian visual brain,” said Nemenman, who is a member of Los Alamos’ Computer, Computational and Statistical Sciences Division. “In fact, the National Science Foundation has recognized the importance of this distinction and has recently funded a project, led by Garrett Kenyon of the Laboratory’s Physics Division, to enable creation of large, next-generation neural networks.”

New understanding of neural function in the design of computers could assist in analyses of satellite images and facial-pattern recognition in high-security environments, and could help solve other national and global security problems.

Nemenman’s work on this project at Los Alamos is funded by the Laboratory Directed Research and Development Program, which strategically invests less than six percent of the institution’s annual budget in early exploration or growth of creative scientific concepts selected at the discretion of the Laboratory director.

A group of researchers have developed a computer algorithm that can imitate the bat’s ability to classify plants using echolocation. The study, published March 21st in the open-access journal PLoS Computational Biology, represents a collaboration between machine learning scientists and biologists studying bat orientation.To detect plants, bats emit ultrasonic pulses and decipher the various echoes that return. Bats use plants daily as food sources and landmarks for navigation between foraging sites. Plant echoes are highly complex signals due to numerous reflections from leaves and branches. Classifying plants or other intricate objects, therefore, has been considered a troublesome task for bats and the scientific community was far from understanding how they do it.

Now, a research group in Tübingen, Germany, including University of Tübingen researchers Yossi Yovel, Peter Stilz and Hans Ulrich-Schnitzler, and Matthias Franz from the Max Planck Institute of Biological Cybernetics, has demonstrated that this process of plant classification is not as difficult as previously thought.

The group used a sonar system to emit bat-like, frequency-modulated ultrasonic pulses. The researchers recorded thousands of echoes from live plants of five species. An algorithm that uses the time-frequency information of these echoes was able to classify plants with high accuracy. This new algorithm also provides hints toward which echo characteristics might be best understood by the bats.

According to the group, these results enable us to improve our understanding of this fascinating ability of how bats classify plants, but do so without entering the bat’s brain.

http://www.ploscompbiol.org/doi/pcbi.1000032 (link will go live on Friday, March 21)

CITATION: Yovel Y, Franz MO, Stilz P, Schnitzler H-U (2008) Plant Classification from Bat-Like Echolocation Signals. PLoS Comput Biol 4(3): e1000032. doi:10.1371/journal.pcbi.1000032

Stockholm, Sweden and Seattle, Wash.  — March 17, 2008 — The International Neuroinformatics Coordinating Facility (INCF) and the Allen Institute for Brain Science announced today that the INCF will contribute infrastructure and support services to enhance global access to the Institute’s Allen Brain Atlas—Mouse Brain.  Publicly available for free to encourage widespread use and collaboration, the Allen Brain Atlas—Mouse Brain is a Web-based, genome-wide map of gene expression.  It is actively used by scientists worldwide to advance research on the brain in health and disease.

Through the partnership agreement, the INCF is operating a mirror, or direct copy, of the atlas from its Secretariat in Stockholm, Sweden.  The INCF will ensure the sustainability and technical maintenance of the mirror site, as well as optimal Internet connectivity, in order to guarantee the highest possible service performance and quality in Europe.

The Allen Institute is providing INCF with all content and data required for the mirror site.  The Allen Brain Atlas—Mouse Brain contains expression patterns of approximately 20,000 genes mapped throughout the entire adult mouse brain, revealing where in the brain each gene is expressed, or “turned on” down to the cellular level.

“Helping brain researchers worldwide augment and accelerate their research programs is central to our mission,” said Elaine Jones, chief operating officer at the Allen Institute for Brain Science.  “Providing free and easy global access to our data is, thus, a top priority for the Allen Institute.  We are thrilled to work with INCF to mirror the Allen Brain Atlas—Mouse Brain in Europe and thus enhance its performance for researchers overseas.”

“The Allen Brain Atlas—Mouse Brain is a unique neuroinformatics resource”, said Jan Bjaalie, executive director of the INCF.  “The INCF sees a future of highly valuable services like this becoming more and more interoperable and interlinked, to the benefit of neuroscience researchers.  By entering this collaboration with the Allen Institute for Brain Science the INCF aims to play a key role in making this happen.”

Challenge and the benefits

The Allen Brain Atlas—Mouse Brain is a uniquely comprehensive source of information about gene activity in the brain.  Each month, approximately 10,000 unique users from universities, research institutes, pharmaceutical companies and government laboratories, and others worldwide access the atlas.  The INCF’s mirroring of the atlas aims to balance the load of the global demands and relieve the Seattle-based servers, thus allowing for faster responses to queries.  Redirection to the European mirror will occur automatically and in response to traffic and load of the servers.  The service should counterbalance any increases in traffic and significantly improve the efficiency of the overall services provided by the Allen Brain Atlas—Mouse Brain.

This collaboration brings together an international outreach organization, the INCF, and a U.S.-based non-profit medical research organization, the Allen Institute for Brain Science.  Together, these organizations share the mission to provide new and improved resources and infrastructure intended to accelerate scientific progress towards a better understanding of the brain.

The launch of the brain atlas mirror inaugurates a three-year partnership with a main objective to extend and enhance the quality of services provided by the Allen Brain Atlas—Mouse Brain for neuroscientists within Europe.  In addition, the atlas database is undeniably a valuable resource that presents opportunities for further development of tools, models and resource integration services, a key element of the INCF mission.

Technical operation and server hosting is located at the Royal Institute of Technology (KTH) in Stockholm, Sweden, an organization with strong technology expertise and advanced computer operation facilities.

Imagine the power of knowing the three-dimensional structures of all proteins.  The 3D-structure can provide information about critical protein-protein interactions both from a global perspective as well as all the way down to the level of minuscule molecular and biochemical detail.  In much the same way, structural information can reveal a lot about the protein’s evolutionary relationships and functions.  Even to provide this information about all the proteins in one organism—its proteome—would offer a more global view of these relationships, but solving each structure individually would be a formidable task.

However, in a new study published online this week in the open access journal PloS Biology, Lars Malmström, David Baker, and colleagues have done precisely this for the model organism yeast.  These researchers divided all Saccharomyces cerevisiae proteins into nearly 15,000 distinct “domains” (regions of a protein that fold into a distinct quaternary globular structure).  They then applied their own de novo structure prediction methods together with worldwide distributed computing to predict three-dimensional structures for all domains lacking sequence similarity to proteins of known structure.

To overcome the uncertainties in de novo structure prediction, Lars Malmström and colleagues combined these predictions with data on the biological process, function, and localization of the proteins from previous experimental studies to assign the domains to families of evolutionarily related proteins.  These genome-wide domain predictions and superfamily assignments provide the basis for the generation of experimentally testable hypotheses about the mechanism of action for a large number of yeast proteins.

Citation: Malmstro¨m L, Riffle M, Strauss CEM, Chivian D, Davis TN, et al.  (2007) Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology.  PloS Biol 5(4): e76.  Doi:10.1371/journal.pbio.0050076.

Uncovering the Structural Alphabet of RNA

A team of bioinformaticians at the Université de Montréal (UdeM) report in the March 6th edition of Nature the discovery of a structural alphabet that can be used to infer the 3D structure of ribonucleic acid (RNA) from sequence data, providing new tools to understand the role of this important class of cellular regulators.

The folding of a single-stranded RNA molecule is determined by the interactions between its constituent nucleotides. The classical approach to RNA modelling suffers from an important limitation: it only takes into account the canonical Watson-Crick interactions A:U and G:C, that is those where the nucleotides are facing each other. The non-canonical Hoogsteen and sugar interactions, those where the nucleotides are side by side or on top of each other, are not taken into account by conventional modelling algorithms. The result can be incomplete or erroneous models which can mislead researchers.

The attempt to remedy this problem led François Major, principal investigator at the Institute for Research in Immunology and Cancer of the UdeM and professor in the Department of Computer Science and Operations Research and Marc Parisien, a graduate student in his laboratory, to propose a radically different approach to model RNA structure. Their idea: assemble the structure in silico starting from motifs that combine all the possible interactions between a nucleotide and its neighbors.

The researchers implemented a first algorithm, MC-Fold, that systematically assigns the different motifs to each segment of the sequence and selects the most probable pair based on its frequency in known structures. A second algorithm, MC-Sym, then assembles the set of selected motifs, taking into account the constraints that are found in known structures.

“We introduced a new first-order object to represent nucleotide relationships, the nucleotide cyclic motif (NCM). We reasoned that using NCMs could allow us to arrive at better models of the 3D structure of RNA molecules, ” explains François Major. “Compared to the thermodynamic approach, our algorithms make less false positives and negatives and predict structures that are closer to the empirical data in the case of sequences for which it is available. The improvement is due to the fact that NCMs incorporate more base-pairing context-dependent information.”

The biological importance of RNA and the growing recognition of its therapeutic potential mean that the new modelling algorithms have many applications in biomedical research. For instance, Major and Parisien have shown that these tools can be used to study the biology of RNA viruses such as HIV. They have also used the MC-Fold:MC-Sym pipeline to identify microRNAs, an important class of regulatory molecules which is currently the focus of intense investigation. microRNAs inhibit target genes both efficiently and specifically and are often considered to be the next generation of therapeutic agents. Since microRNAs are notoriously difficult to identify based on sequence alone, the use of RNA modelling algorithms and structural features to do so represents an important breakthrough.

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Work in the laboratory of François Major is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR). Marc Parisien holds Ph.D. scholarships from the NSERC, the Fonds québécois de la recherche sur la nature et les technologies (FQRNT) and the UdeM Faculty for Graduate and Postdoctoral Studies. François Major is a member of the Robert-Cedergren Centre at the Université de Montréal.

The MC-Fold and MC-Sym RNA modelling tools are available on the Internet at www.major.iric.ca.