The road ahead in genetics and genomics

In celebration of the 20th anniversary of Nature Reviews Genetics, we asked 12 leading researchers to reflect on the key challenges and opportunities faced by the field of genetics and genomics. Keeping their particular research area in mind, they take stock of the current state of play and emphasize the work that remains to be done over the next few years so that, ultimately, the benefits of genetic and genomic research can be felt by everyone

Progress in Monte Carlo design and optimization of the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) will be an instrument covering a wide energy range in very-high-energy (VHE) gamma rays. CTA will include several types of telescopes, in order to optimize the performance over the whole energy range. Both large-scale Monte Carlo (MC) simulations of CTA super-sets (including many different possible CTA layouts as sub-sets) and smaller-scale simulations dedicated to individual aspects were carried out and are on-going. We summarize results of the prior round of large-scale simulations, show where the design has now evolved beyond the conservative assumptions of the prior round and present first results from the on-going new round of MC simulations.Comment: 4 pages, 5 figures. In Proceedings of the 33rd International Cosmic Ray Conference (ICRC2013), Rio de Janeiro (Brazil). All CTA contributions at arXiv:1307.223

In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles

Background: In silico candidate gene prioritisation (CGP) aids the discovery of gene functions by ranking genes according to an objective relevance score. While several CGP methods have been described for identifying human disease genes, corresponding methods for prokaryotic gene function discovery are lacking. Here we present two prokaryotic CGP methods, based on phylogenetic profiles, to assist with this task. Results: Using gene occurrence patterns in sample genomes, we developed two CGP methods (statistical and inductive CGP) to assist with the discovery of bacterial gene functions. Statistical CGP exploits the differences in gene frequency against phenotypic groups, while inductive CGP applies supervised machine learning to identify gene occurrence pattern across genomes. Three rediscovery experiments were designed to evaluate the CGP frameworks. The first experiment attempted to rediscover peptidoglycan genes with 417 published genome sequences. Both CGP methods achieved best areas under receiver operating characteristic curve (AUC) of 0.911 in Escherichia coli K-12 (EC-K12) and 0.978 Streptococcus agalactiae 2603 (SA-2603) genomes, with an average improvement in precision of >3.2-fold and a maximum of >27-fold using statistical CGP. A median AUC of >0.95 could still be achieved with as few as 10 genome examples in each group of genome examples in the rediscovery of the peptidoglycan metabolism genes. In the second experiment, a maximum of 109-fold improvement in precision was achieved in the rediscovery of anaerobic fermentation genes in EC-K12. The last experiment attempted to rediscover genes from 31 metabolic pathways in SA-2603, where 14 pathways achieved AUC >0.9 and 28 pathways achieved AUC >0.8 with the best inductive CGP algorithms. Conclusion: Our results demonstrate that the two CGP methods can assist with the study of functionally uncategorised genomic regions and discovery of bacterial gene-function relationships. Our rediscovery experiments also provide a set of standard tasks against which future methods may be compared.12 page(s

The Cherenkov Telescope Array Large Size Telescope

The two arrays of the Very High Energy gamma-ray observatory Cherenkov Telescope Array (CTA) will include four Large Size Telescopes (LSTs) each with a 23 m diameter dish and 28 m focal distance. These telescopes will enable CTA to achieve a low-energy threshold of 20 GeV, which is critical for important studies in astrophysics, astroparticle physics and cosmology. This work presents the key specifications and performance of the current LST design in the light of the CTA scientific objectives.Comment: 4 pages, 5 figures, In Proceedings of the 33rd International Cosmic Ray Conference (ICRC2013), Rio de Janeiro (Brazil). All CTA contributions at arXiv:1307.223

Regulation of plant stem cell quiescence by a brassinosteroid signaling module

Referred to by: Josep Vilarrasa-Blasi, Mary-Paz González-García, David Frigola, Norma Fàbregas-Vallvé, Konstantinos G. Alexiou, Nuria López-Bigas, Susana Rivas, Alain Jauneau, Jan U. Lohmann, Philip N. Benfey, Marta Ibañes, Ana I. Caño-Delgado Regulation of Plant Stem Cell Quiescence by a Brassinosteroid Signaling Module Developmental Cell, Volume 33, Issue 2, 20 April 2015, Pages 238.The quiescent center (QC) maintains the activity of the surrounding stem cells within the root stem cell niche, yet specific molecular players sustaining the low rate of QC cell division remain poorly understood. Here, we identified a R2R3-MYB transcription factor, BRAVO (BRASSINOSTEROIDS AT VASCULAR AND ORGANIZING CENTER), acting as a cell-specific repressor of QC divisions in the primary root of Arabidopsis. Ectopic BRAVO expression restricts overall root growth and ceases root regeneration upon damage of the stem cells, demonstrating the role of BRAVO in counteracting Brassinosteroid (BR)-mediated cell division in the QC cells. Interestingly, BR-regulated transcription factor BES1 (BRI1-EMS SUPRESSOR 1) directly represses and physically interacts with BRAVO in vivo, creating a switch that modulates QC divisions at the root stem cell niche. Together, our results define a mechanism for BR-mediated regulation of stem cell quiescence in plants.J.V.-B. and N.F.-V. are funded by FI PhD fellowship from the Generalitat de Catalunya (GC) in the A.I.C.-D. laboratory. J.V.-B. received a short-term fellowship (BE1-00924) in the Lohmann (J.U.L.) laboratory supported by the SFB873 of the DFG. Research by D.F and M.I. is funded by FIS2012-37655-C02-02 by the Spanish Ministry de Economy and Competitiveness and 2009SGR14 from GC, and D.F. has a PhD fellowship (FPU-AP2009-3736). S.R. is funded by the Laboratoire d’Excellence (LABEX) TULIP (ANR-10-LABX-41). M.-P.G.-G. received a “Juan de la Cierva” postdoctoral contract from the Spanish Ministry of Science in the Ana Caño (A.I.C.-D.) laboratory, and an HFSP short-term fellowship in the Benfey (P.N.B.) laboratory. P.N.B. is funded by NSF Arabidopsis 2010 grant. Work in the Ana Caño (A.I.C.-D.) laboratory is funded by a BIO2010/007 grant from the Spanish Ministry of Innovation and Science and a Marie-Curie Initial Training Network “BRAVISSIMO” (grant no. PITN-GA-2008-215118).Peer reviewe

Integration of multiple data sources to prioritize candidate genes using discounted rating system

Background: Identifying disease gene from a list of candidate genes is an important task in bioinformatics. The main strategy is to prioritize candidate genes based on their similarity to known disease genes. Most of existing gene prioritization methods access only one genomic data source, which is noisy and incomplete. Thus, there is a need for the integration of multiple data sources containing different information. Results: In this paper, we proposed a combination strategy, called discounted rating system (DRS). We performed leave one out cross validation to compare it with N-dimensional order statistics (NDOS) used in Endeavour. Results showed that the AUC (Area Under the Curve) values achieved by DRS were comparable with NDOS on most of the disease families. But DRS worked much faster than NDOS, especially when the number of data sources increases. When there are 100 candidate genes and 20 data sources, DRS works more than 180 times faster than NDOS. In the framework of DRS, we give different weights for different data sources. The weighted DRS achieved significantly higher AUC values than NDOS. Conclusion: The proposed DRS algorithm is a powerful and effective framework for candidate gene prioritization. If weights of different data sources are proper given, the DRS algorithm will perform better

Progress in Monte Carlo design and optimization of the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) will be an instrument covering a wide energy range in very-high-energy (VHE) gamma rays. CTA will include several types of telescopes, in order to optimize the performance over the whole energy range. Both large-scale Monte Carlo (MC) simulations of CTA super-sets (including many different possible CTA layouts as sub-sets) and smaller-scale simulations dedicated to individual aspects were carried out and are on-going. We summarize results of the prior round of large-scale simulations, show where the design has now evolved beyond the conservative assumptions of the prior round and present first results from the on-going new round of MC simulations.Fil: Bernlöhr, K.. Max-Planck-Institut fur Kernphysik; AlemaniaFil: Barnacka, A.. Polish Academy of Sciences; ArgentinaFil: Becherini, Y.. École Polytechnique; FranciaFil: Blanch Bigas, O.. IFAE; EspañaFil: Bouvier, A.. University of California; Estados UnidosFil: Carmona, E.. Max-Planck-Institut fur Physik; AlemaniaFil: Colin, P.. Max-Planck-Institut fur Physik; AlemaniaFil: Decerprit, G.. DESY; AlemaniaFil: di Pierro, F.. Osservatorio Astrofisico di Torino dell’Istituto Nazionale di Astrofisica; ItaliaFil: Dubois, F.. Universidad Complutense de Madrid; EspañaFil: Farnier, C.. Stockholm University; SueciaFil: Funk, S.. Kavli Institute for Particle Astrophysics and Cosmology; Estados UnidosFil: Hermann, G.. Max-Planck-Institut fur Kernphysik; AlemaniaFil: Hinton, J. A.. The University of Leicester; Reino UnidoFil: Humensky, T. B.. Columbia University; Estados UnidosFil: Jogler, T.. Kavli Institute for Particle Astrophysics and Cosmology; Estados UnidosFil: Khélifi, B.. École Polytechnique; FranciaFil: Kihm, T.. Max-Planck-Institut fur Kernphysik; AlemaniaFil: Komin, N.. Universite de Savoie; FranciaFil: Lenain, J. -P.. Université Denis Diderot Paris 7; FranciaFil: López Coto, R.. IFAE; EspañaFil: Maier, G.. DESY; AlemaniaFil: Mazin, D.. Max-Planck-Institut fur Physik; AlemaniaFil: Medina, Maria Clementina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Moralejo, A.. IFAE; EspañaFil: Moderski, R.. Polish Academy of Sciences; ArgentinaFil: Nolan, S. J.. Durham University; Reino UnidoFil: Ohm, S.. The University of Leicester; Reino UnidoFil: de Oña Wilhelmi, E.. Max-Planck-Institut fur Kernphysik; Alemania33rd International Cosmic Ray ConferenceRío de JaneiroBrasilBrazilian Physical Societ

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

<p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p

Targeted Genome-Wide Enrichment of Functional Regions

Only a small fraction of large genomes such as that of the human contains the functional regions such as the exons, promoters, and polyA sites. A platform technique for selective enrichment of functional genomic regions will enable several next-generation sequencing applications that include the discovery of causal mutations for disease and drug response. Here, we describe a powerful platform technique, termed “functional genomic fingerprinting” (FGF), for the multiplexed genomewide isolation and analysis of targeted regions such as the exome, promoterome, or exon splice enhancers. The technique employs a fixed part of a uniquely designed Fixed-Randomized primer, while the randomized part contains all the possible sequence permutations. The Fixed-Randomized primers bind with full sequence complementarity at multiple sites where the fixed sequence (such as the splice signals) occurs within the genome, and multiplex amplify many regions bounded by the fixed sequences (e.g., exons). Notably, validation of this technique using cardiac myosin binding protein-C (MYBPC3) gene as an example strongly supports the application and efficacy of this method. Further, assisted by genomewide computational analyses of such sequences, the FGF technique may provide a unique platform for high-throughput sample production and analysis of targeted genomic regions by the next-generation sequencing techniques, with powerful applications in discovering disease and drug response genes