The photos are now available (click here).
Papers Submission Deadline
Submissions must be received in electronic form by 11:59 pm (GMT-4).
Please fill out the registration form provided by the Cold Spring Harbor Laboratory.Click to Register
We invite high-quality original full papers on topics related to comparative genomics. The submitted papers must have not been published or under the consideration for publication in any other journal or conference with formal proceedings. Papers are solicited on, but not limited to, the following topics:
The 12th edition of Recomb-CG will be held at Cold Spring Harbor Laboratory (CSHL) in Cold Spring Harbor, in the immediate vicinity of New York City, USA.
All accepted papers will have to be presented by one on the authors at the conference. Accepted papers will be invited to be published in the journal BMC Genomics, following the journals publication policy. BMC will charge publication fees independently from the conference registration. Download Call for Papers (click here). We also welcome poster submissions. Download Call for Posters (click here).
Submissions must be received in electronic form by 11:59 pm (GMT-4).
The final version of the papers must be sent in electronic form
Submissions must be received in electronic form by 11:59 pm (GMT-4)
Poster sessions are held in Bush Auditorium and Grace Lobby/Tent. Posters should fit within 1.22m x 1.22m (4 ft X 4 ft). Supplies for hanging posters are provided. Note that the poster areas are equipped with wireless internet access so you can demo software and web sites if you bring a laptop equipped with wireless modem. Laptops may be available from the Laboratory for a small rental/set-up charge.
Evolutionary analysis at the molecular level provide new tools to biology when considering the action of natural selection in genes and sets of genes in their functional setting of physiological pathways. Their analysis is illuminating by one hand the molecular bases of complex adaptations and, moreover, may help in advancing at a higher pace the basic understanding of function at the gene-product (or protein) level. These processes can be seen comparing genome data of different species or of populations within a single species: selection at large or short scale. Examples will be provided for humans and primates. The initial point is the interest of detecting natural selection in the form of positive (or adaptive) selection and purifying (or negative) selection. From the theoretical models now it is possible to interrogate whole genomes in the search of footprints of selection, among which there must lay the specific adaptations that define a species or a population. A new method that gathers information of many others will be presented. Comparative analysis of selective pressures on sets of genes involved in a complex pathway or functional network may help disentangle the fine tuned purifying selection pressures that may be converted in terms of “biological importance” or relative dispensability in sets of genes. Results in functional networks and gene families show differences in selective pressures (and thus in function) that are not being detected by standard experimental methods. As genes function in the context of molecular networks, with some occupying more important positions than others and thus being likely to be under stronger selective pressures, it is possible to interrogate how selection is distributes across the different parts of molecular networks. These analyses are telling us how evolution is shaping complex molecular pathways and networks, as the emerging function is a function of complex interactions. These analyses may be undertaken at the pathway level (with a low number of interacting units but a very detailed molecular knowledge) to relate selection and the specificity of the reactions and function, or at the general level of all interactions among proteins. At the level of the human interactome, selection does not act equally at short or long time depth: genes with higher centralities are more likely to have been targeted by recent positive selection during recent human evolution. Our results indicate that the relationship between centrality and the impact of adaptive evolution highly depends on the evolutionary time-scale. Most likely, network adaptation occurs through intra-specific adaptive leaps affecting key network genes, followed by fine-tuning adaptations in less important network regions.
For more than a century, Mendel’s laws of inheritance have guided studies of genetically simple traits, such as color of the seed coat in the common garden pea and inborn errors of metabolism in humans. Although initially measured as statistical properties, inheritance can now be followed as precise differences in DNA sequence, thereby enabling direct tests for associations between DNA variants (genotype) and biological variation (phenotype). Considerable effort is now being invested in humans, model organisms, and agricultural species to identify genetic variants that contribute to complex traits such as height and conditions such as autism. These variants have already provided important insights into the molecular basis for fundamental biological processes and changes that lead to disease. Unexpectedly however, these genetic variants typically account for only a modest fraction of the total phenotypic variation, suggesting that a simple extension of Mendelian genetics to genetically complex traits and common conditions is more complicated than imagined, or that alternative mechanisms of inheritance are involved. Three recent discoveries are revolutionizing our understanding of inheritance. First, epistasis is more common, strong and complex than expected, and likely creates networks of gene interactions that buffer biological systems from genetic and environmental perturbations. Developing analytical methods to detect epistasis and then characterize its impact of genetic variation on gene interaction networks is important but hard problem. Second, both environmental factors and genetic variants have been shown to induce epigenetic changes that can be transmitted across many generations, affecting phenotypic variation and disease risk, and suggesting that our understanding of the modes, mechanisms and molecules is remarkably incomplete. Third, both genetic and environmental (dietary) factors were recently shown to bias fertilization towards wild-type and away from mutant gametes, thereby violating one of the central tenets of Mendelian genetics. Work is urgently needed not only to discover the molecular mechanisms for biased fertilization and to explore the impact on models of genetic architecture of complex traits and predictions of disease risk. Together these observations suggest that inheritance is more complicated, and more interesting than imagined, and that inherited molecules, in addition to DNA, guide biological processes in health and disease in unexpected ways.
The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. I will describe a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of n chromosomes conditional on an ARG of n-1 chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the posterior distribution over ARGs and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. The patterns we observe near protein-coding genes are consistent with a primary influence from background selection rather than hitchhiking, although we cannot rule out a contribution from recurrent selective sweeps. Finally, I will describe recent work on adapting ARGweaver for parametric inference of demographic models for human populations.
Director and Group leader of the Unitat de Biologia Evolutiva
Pompeu Fabra University, Barcelona, Spain
Pacific Northwest Diabetes Research Institute, Seattle, WA, USA
Director of Computational Biology Center
IBM Research,Yorktown Heights, NY, USA
Cornell University, Ithaca, NY, USA
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
One Bungtown Road, Cold Spring Harbor, NY 11724
The Cold Spring Harbor Laboratory (CSHL) is a private, non-profit institution with research programs focusing on cancer, neurobiology, plant genetics, genomics and bioinformatics (more information at http://www.cshl.edu/).
Please see detailed additional meeting information regarding RECOMB-CG meeting at CSHL (click here).
Please see detailed location information at the CSHL campus map (click here).
If you have questions about logistic/travel regarding Recomb-CG 2014, please contact Katharine Bradley kbradley -at- cshl.edu
Take the Long Island Railroad (LIRR) to Syosset train station (Port Jefferson line). There is a shuttle service available from Syosset train station to CSHL on weekdays (see this pdf for the schedule:http://meetings.cshl.edu/shuttle.pdf. You can also take a taxi for the few miles trip from the train station to CSHL. For LIRR schedules and fares see http://lirr42.mta.info/ Note "peak" train fares are in effect during morning and afternoon rush hours (other times "off peak" fares). It is advisable to purchase a ticket at the station before boarding the train.
Please see detailed directions at the CSHL website: http://www.cshl.edu/About-Us/Directions/
CSHL is located 35 miles (55 kilometers) northeast from midtown Manhattan. Travel time from Manhattan is approximately 1.5 hours by public transport (including last 2 miles by taxi), or 1 hour by car (up to 2 hours with traffic). The closest airports are LaGuardia and JFK in New York and Newark in New Jersey.
The photos are now available (click here).
The list of accepted papers is now available (click here).
We welcome poster submissions. Submissions must be received in electronic form by 11:59pm (GMT-4), September 12th, 2014. Download Call for Posters (click here).
Now it is possible to register to attend Recomb-CG 2014 via this form (click here)
We welcome paper submissions and accepted articles will be published in the open access international online journal BMC Genomics. BMC will charge publication fees independently from the conference registration. All accepted papers will have to be presented by one of the authors at the conference. We also welcome poster submissions. Submissions must be received in electronic form by 11:59pm (GMT-4), June 27th, 2014. Download Call for Papers (click here).
The twelfth RECOMB-CG satellite workshop will be held at Cold Spring Harbor Laboratory (in the immediate vicinity of New York City, USA) 19 - 22 October 2014