Haplotype inference based on Hidden Markov Models in the QTL-MAS 2010 multi-generational dataset

<p>Abstract</p> <p>Background</p> <p>We have previously demonstrated an approach for efficient computation of genotype probabilities, and more generally probabilities of allele inheritance in inbred as well as outbred populations. That work also included an extension for haplotype inference, or phasing, using Hidden Markov Models. Computational phasing of multi-thousand marker datasets has not become common as of yet. In this communication, we further investigate the method presented earlier for such problems, in a multi-generational dataset simulated for QTL detection.</p> <p>Results</p> <p>When analyzing the dataset simulated for the 14th QTLMAS workshop, the phasing produced showed zero deviations compared to original simulated phase in the founder generation. In total, 99.93% of all markers were correctly phased. 97.68% of the individuals were correct in all markers over all 5 simulated chromosomes. Results were produced over a weekend on a small computational cluster. The specific algorithmic adaptations needed for the Markov model training approach in order to reach convergence are described.</p> <p>Conclusions</p> <p>Our method provides efficient, near-perfect haplotype inference allowing the determination of completely phased genomes in dense pedigrees. These developments are of special value for applications where marker alleles are not corresponding directly to QTL alleles, thus necessitating tracking of allele origin, and in complex multi-generational crosses. The cnF2freq codebase, which is in a current state of active development, is available under a BSD-style license.</p

qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software

<p>Abstract</p> <p>Background</p> <p><b>qtl.outbred </b>is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/<b>qtl</b>.</p> <p>Findings</p> <p>Using <b>qtl.outbred</b>, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL.</p> <p>Conclusion</p> <p><b>qtl.outbred </b>will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.</p

MAPfastR: Quantitative Trait Loci Mapping in Outbred Line Crosses

MAPfastR is a software package developed to analyze quantitative trait loci data from inbred and outbred line-crosses. The package includes a number of modules for fast and accurate quantitative trait loci analyses. It has been developed in the R language for fast and comprehensive analyses of large datasets. MAPfastR is freely available at: http://www.computationalgenetics.se/?page_id=7.Swedish Foundation for Strategic Research (Future Research Leader program), European Science Foundation (EURYI Award)

Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis

Background - A quantitative and a binary trait for the 14th QTLMAS 2010 workshop were simulated under a model which combined additive inheritance, epistasis and imprinting. This paper aimed to compare results submitted by the participants of the workshop.Methods - The results were compared according to three criteria: the success rate (ratio of mapped QTL to the total number of simulated QTL), and the error rate (ratio of false positives to the number of reported positions), and mean distance between a true mapped QTL and the nearest submitted position. Results - Seven groups submitted results for the quantitative trait and five for the binary trait. Among the 37 simulated QTL 17 remained undetected. Success rate ranged from 0.05 to 0.43, error rate was between 0.00 and 0.92, and the mean distance ranged from 0.26 to 0.77 Mb. Conclusions - Our comparison shows that differences among methods used by the participants increases with the complexity of genetic architecture. It was particularly visible for the quantitative trait which was determined partly by non-additive QTL. Furthermore, an imprinted QTL with a large effect may remain undetected if the applied model tests only for Mendelian genes

Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser

Citation: Ekeberg, T., Svenda, M., Abergel, C., Maia, F., Seltzer, V., Claverie, J. M., . . . Hajdu, J. (2015). Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser. Physical Review Letters, 114(9), 6. doi:10.1103/PhysRevLett.114.098102We present a proof-of-concept three-dimensional reconstruction of the giant mimivirus particle from experimentally measured diffraction patterns from an x-ray free-electron laser. Three-dimensional imaging requires the assembly of many two-dimensional patterns into an internally consistent Fourier volume. Since each particle is randomly oriented when exposed to the x-ray pulse, relative orientations have to be retrieved from the diffraction data alone. We achieve this with a modified version of the expand, maximize and compress algorithm and validate our result using new methods.Additional Authors: Andersson, I.;Loh, N. D.;Martin, A. V.;Chapman, H.;Bostedt, C.;Bozek, J. D.;Ferguson, K. R.;Krzywinski, J.;Epp, S. W.;Rolles, D.;Rudenko, A.;Hartmann, R.;Kimmel, N.;Hajdu, J

Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser

Citation: Ekeberg, T., Svenda, M., Abergel, C., Maia, F., Seltzer, V., Claverie, J. M., . . . Hajdu, J. (2015). Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser. Physical Review Letters, 114(9), 6. doi:10.1103/PhysRevLett.114.098102We present a proof-of-concept three-dimensional reconstruction of the giant mimivirus particle from experimentally measured diffraction patterns from an x-ray free-electron laser. Three-dimensional imaging requires the assembly of many two-dimensional patterns into an internally consistent Fourier volume. Since each particle is randomly oriented when exposed to the x-ray pulse, relative orientations have to be retrieved from the diffraction data alone. We achieve this with a modified version of the expand, maximize and compress algorithm and validate our result using new methods.Additional Authors: Andersson, I.;Loh, N. D.;Martin, A. V.;Chapman, H.;Bostedt, C.;Bozek, J. D.;Ferguson, K. R.;Krzywinski, J.;Epp, S. W.;Rolles, D.;Rudenko, A.;Hartmann, R.;Kimmel, N.;Hajdu, J

Three-dimensional reconstruction of the giant mimivirus particle with an X-ray free-electron laser

10.1103/PhysRevLett.114.098102Physical Review Letters114909810

Observation of a single protein by ultrafast X-ray diffraction

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory1, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes2. This was first demonstrated on biological samples a decade ago on the giant mimivirus3. Since then a large collaboration4 has been pushing the limit of the smallest sample that can be imaged5,6. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that of Escherichia coli GroEL which at 14 nm in diameter7 is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale