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Efficient sampling for Bayesian inference of conjunctive Bayesian networks (2012)

Sakoparnig, T., Beerenwinkel, N.

Oxford University Press

In: Bioinformatics

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Publication Date: 2012-09-08

Description: Motivation: Cancer development is driven by the accumulation of advantageous mutations and subsequent clonal expansion of cells harbouring these mutations, but the order in which mutations occur remains poorly understood. Advances in genome sequencing and the soon-arriving flood of cancer genome data produced by large cancer sequencing consortia hold the promise to elucidate cancer progression. However, new computational methods are needed to analyse these large datasets. Results: We present a Bayesian inference scheme for Conjunctive Bayesian Networks, a probabilistic graphical model in which mutations accumulate according to partial order constraints and cancer genotypes are observed subject to measurement noise. We develop an efficient MCMC sampling scheme specifically designed to overcome local optima induced by dependency structures. We demonstrate the performance advantage of our sampler over traditional approaches on simulated data and show the advantages of adopting a Bayesian perspective when reanalyzing cancer datasets and comparing our results to previous maximum-likelihood-based approaches. Availability: An R package including the sampler and examples is available at http://www.cbg.ethz.ch/software/bayes-cbn . Contacts: niko.beerenwinkel@bsse.ethz.ch

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Full-length haplotype reconstruction to infer the structure of heterogeneous virus populations (2014)

Giallonardo, F. D., Topfer, A., Rey, M., Prabhakaran, S., Duport, Y., Leemann, C., Schmutz, S., Campbell, N. K., Joos, B., Lecca, M. R., Patrignani, A., Daumer, M., Beisel, C., Rusert, P., Trkola, A., Gunthard, H. F., Roth, V., Beerenwinkel, N., Metzner, K. J.

Oxford University Press

In: Nucleic Acids Research

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Publication Date: 2014-08-15

Description: Next-generation sequencing (NGS) technologies enable new insights into the diversity of virus populations within their hosts. Diversity estimation is currently restricted to single-nucleotide variants or to local fragments of no more than a few hundred nucleotides defined by the length of sequence reads. To study complex heterogeneous virus populations comprehensively, novel methods are required that allow for complete reconstruction of the individual viral haplotypes. Here, we show that assembly of whole viral genomes of ~8600 nucleotides length is feasible from mixtures of heterogeneous HIV-1 strains derived from defined combinations of cloned virus strains and from clinical samples of an HIV-1 superinfected individual. Haplotype reconstruction was achieved using optimized experimental protocols and computational methods for amplification, sequencing and assembly. We comparatively assessed the performance of the three NGS platforms 454 Life Sciences/Roche, Illumina and Pacific Biosciences for this task. Our results prove and delineate the feasibility of NGS-based full-length viral haplotype reconstruction and provide new tools for studying evolution and pathogenesis of viruses.

Keywords: Computational Methods, Massively Parallel (Deep) Sequencing, Genomics

Print ISSN: 0305-1048

Electronic ISSN: 1362-4962

Topics: Biology

Published by Oxford University Press

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Stability of gene rankings from RNAi screens (2012)

Siebourg, J., Merdes, G., Misselwitz, B., Hardt, W.-D., Beerenwinkel, N.

Oxford University Press

In: Bioinformatics

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Publication Date: 2012-06-12

Description: Motivation: Genome-wide RNA interference (RNAi) experiments are becoming a widely used approach for identifying intracellular molecular pathways of specific functions. However, detecting all relevant genes involved in a biological process is challenging, because typically only few samples per gene knock-down are available and readouts tend to be very noisy. We investigate the reliability of top scoring hit lists obtained from RNAi screens, compare the performance of different ranking methods, and propose a new ranking method to improve the reproducibility of gene selection. Results: The performance of different ranking methods is assessed by the size of the stable sets they produce, i.e. the subsets of genes which are estimated to be re-selected with high probability in independent validation experiments. Using stability selection, we also define a new ranking method, called stability ranking, to improve the stability of any given base ranking method. Ranking methods based on mean, median, t -test and rank-sum test, and their stability-augmented counterparts are compared in simulation studies and on three microscopy image RNAi datasets. We find that the rank-sum test offers the most favorable trade-off between ranking stability and accuracy and that stability ranking improves the reproducibility of all and the accuracy of several ranking methods. Availability: Stability ranking is freely available as the R/Bioconductor package staRank at http://www.cbg.ethz.ch/software/staRank . Contact: niko.beerenwinkel@bsse.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Challenges in RNA virus bioinformatics (2014)

Marz, M., Beerenwinkel, N., Drosten, C., Fricke, M., Frishman, D., Hofacker, I. L., Hoffmann, D., Middendorf, M., Rattei, T., Stadler, P. F., Topfer, A.

Oxford University Press

In: Bioinformatics

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Publication Date: 2014-06-27

Description: Motivation: Computer-assisted studies of structure, function and evolution of viruses remains a neglected area of research. The attention of bioinformaticians to this interesting and challenging field is far from commensurate with its medical and biotechnological importance. It is telling that out of 〉200 talks held at ISMB 2013, the largest international bioinformatics conference, only one presentation explicitly dealt with viruses. In contrast to many broad, established and well-organized bioinformatics communities (e.g. structural genomics, ontologies, next-generation sequencing, expression analysis), research groups focusing on viruses can probably be counted on the fingers of two hands. Results: The purpose of this review is to increase awareness among bioinformatics researchers about the pressing needs and unsolved problems of computational virology. We focus primarily on RNA viruses that pose problems to many standard bioinformatics analyses owing to their compact genome organization, fast mutation rate and low evolutionary conservation. We provide an overview of tools and algorithms for handling viral sequencing data, detecting functionally important RNA structures, classifying viral proteins into families and investigating the origin and evolution of viruses. Contact: manja@uni-jena.de Supplementary information: Supplementary data are available at Bioinformatics online. The references for this article can be found in the Supplementary Material.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction (2014)

Dumcke, S., Brauer, J., Anchang, B., Spang, R., Beerenwinkel, N., Tresch, A.

Oxford University Press

In: Bioinformatics

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Publication Date: 2014-01-29

Description: Motivation: For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network. Results: In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development. Availability and implementation: The algorithm is implemented in the statistical language R. Code and documentation are available at Bioinformatics online. Contact: duemcke@mpipz.mpg.de or tresch@mpipz.mpg.de Supplementary information: Supplementary Materials are available at Bioinfomatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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