Niko Beerenwinkel

Publications (73)158.72 Total impact

• Source

  Available from: Armin Töpfer

  Article: Probabilistic inference of viral quasispecies subject to recombination.

  Armin Töpfer, Osvaldo Zagordi, Sandhya Prabhakaran, Volker Roth, Eran Halperin, Niko Beerenwinkel
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  ABSTRACT: Abstract RNA viruses exist in their hosts as populations of different but related strains. The virus population, often called quasispecies, is shaped by a combination of genetic change and natural selection. Genetic change is due to both point mutations and recombination events. We present a jumping hidden Markov model that describes the generation of viral quasispecies and a method to infer its parameters from next-generation sequencing data. The model introduces position-specific probability tables over the sequence alphabet to explain the diversity that can be found in the population at each site. Recombination events are indicated by a change of state, allowing a single observed read to originate from multiple sequences. We present a specific implementation of the expectation maximization (EM) algorithm to find maximum a posteriori estimates of the model parameters and a method to estimate the distribution of viral strains in the quasispecies. The model is validated on simulated data, showing the advantage of explicitly taking the recombination process into account, and applied to reads obtained from a clinical HIV sample.
  Journal of computational biology: a journal of computational molecular cell biology 02/2013; 20(2):113-23. · 1.69 Impact Factor
• Source

  Available from: Armin Töpfer

  Conference Proceeding: Probabilistic inference of viral quasispecies subject to recombination

  Osvaldo Zagordi, Armin Töpfer, Sandhya Prabhakaran, Volker Roth, Eran Halperin, Niko Beerenwinkel
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  ABSTRACT: RNA viruses are present in a single host as a population of different but related strains. This population, shaped by the combination of genetic change and selection, is called quasispecies. Genetic change is due to both point mutations and recombination events. We present a jumping hidden Markov model that describes the generation of the viral quasispecies and a method to infer its parameters by analysing next generation sequencing data. The model introduces position-specific prob-ability tables over the sequence alphabet to explain the diversity that can be found in the population at each site. Recombination events are indi-cated by a change of state, allowing a single observed read to originate from multiple sequences. We present an implementation of the EM algorithm to find maximum likelihood estimates of the model parameters and a method to estimate the distribution of viral strains in the quasispecies. The model is validated on simulated data, showing the advantage of explicitly taking the recombination process into account, and applied to reads obtained from two experimental HIV samples.
  Recomb 2012, Barcelona; 04/2012
• Article: Reliable detection of subclonal single-nucleotide variants in tumour cell populations.

  Moritz Gerstung, Christian Beisel, Markus Rechsteiner, Peter Wild, Peter Schraml, Holger Moch, Niko Beerenwinkel
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  ABSTRACT: According to the clonal evolution model, tumour growth is driven by competing subclones in somatically evolving cancer cell populations, which gives rise to genetically heterogeneous tumours. Here we present a comparative targeted deep-sequencing approach combined with a customised statistical algorithm, called deepSNV, for detecting and quantifying subclonal single-nucleotide variants in mixed populations. We show in a rigorous experimental assessment that our approach is capable of detecting variants with frequencies as low as 1/10,000 alleles. In selected genomic loci of the TP53 and VHL genes isolated from matched tumour and normal samples of four renal cell carcinoma patients, we detect 24 variants at allele frequencies ranging from 0.0002 to 0.34. Moreover, we demonstrate how the allele frequencies of known single-nucleotide polymorphisms can be exploited to detect loss of heterozygosity. Our findings demonstrate that genomic diversity is common in renal cell carcinomas and provide quantitative evidence for the clonal evolution model.
  Nature Communications 01/2012; 3:811. · 7.40 Impact Factor
• Source

  Available from: Huldrych F Günthard

  Article: Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data.

  Niko Beerenwinkel, Huldrych F Günthard, Volker Roth, Karin J Metzner
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  ABSTRACT: Many viruses, including the clinically relevant RNA viruses HIV (human immunodeficiency virus) and HCV (hepatitis C virus), exist in large populations and display high genetic heterogeneity within and between infected hosts. Assessing intra-patient viral genetic diversity is essential for understanding the evolutionary dynamics of viruses, for designing effective vaccines, and for the success of antiviral therapy. Next-generation sequencing (NGS) technologies allow the rapid and cost-effective acquisition of thousands to millions of short DNA sequences from a single sample. However, this approach entails several challenges in experimental design and computational data analysis. Here, we review the entire process of inferring viral diversity from sample collection to computing measures of genetic diversity. We discuss sample preparation, including reverse transcription and amplification, and the effect of experimental conditions on diversity estimates due to in vitro base substitutions, insertions, deletions, and recombination. The use of different NGS platforms and their sequencing error profiles are compared in the context of various applications of diversity estimation, ranging from the detection of single nucleotide variants (SNVs) to the reconstruction of whole-genome haplotypes. We describe the statistical and computational challenges arising from these technical artifacts, and we review existing approaches, including available software, for their solution. Finally, we discuss open problems, and highlight successful biomedical applications and potential future clinical use of NGS to estimate viral diversity.
  Frontiers in microbiology. 01/2012; 3:329.
• Article: Read length versus Depth of Coverage for Viral Quasispecies Reconstruction.

  Osvaldo Zagordi, Martin Däumer, Christian Beisel, Niko Beerenwinkel
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  ABSTRACT: Recent advancements of sequencing technology have opened up unprecedented opportunities in many application areas. Virus samples can now be sequenced efficiently with very deep coverage to infer the genetic diversity of the underlying virus populations. Several sequencing platforms with different underlying technologies and performance characteristics are available for viral diversity studies. Here, we investigate how the differences between two common platforms provided by 454/Roche and Illumina affect viral diversity estimation and the reconstruction of viral haplotypes. Using a mixture of ten HIV clones sequenced with both platforms and additional simulation experiments, we assessed the trade-off between sequencing coverage, read length, and error rate. For fixed costs, short Illumina reads can be generated at higher coverage and allow for detecting variants at lower frequencies. They can also be sufficient to assess the diversity of the sample if sequences are dissimilar enough, but, in general, assembly of full-length haplotypes is feasible only with the longer 454/Roche reads. The quantitative comparison highlights the advantages and disadvantages of both platforms and provides guidance for the design of viral diversity studies.
  PLoS ONE 01/2012; 7(10):e47046. · 4.09 Impact Factor
• Article: Genome-wide expression and copy number analysis identifies driver genes in gingivobuccal cancers.

  Srikant Ambatipudi, Moritz Gerstung, Manishkumar Pandey, Tanuja Samant, Asawari Patil, Shubhada Kane, Rajiv S Desai, Alejandro A Schäffer, Niko Beerenwinkel, Manoj B Mahimkar
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  ABSTRACT: The molecular mechanisms contributing to the development and progression of gingivobuccal complex (GBC) cancers-a sub-site of oral cancer, comprising the buccal mucosa, the gingivobuccal sulcus, the lower gingival region, and the retromolar trigone-remain poorly understood. Identifying the GBC cancer-related gene expression signature and the driver genes residing on the altered chromosomal regions is critical for understanding the molecular basis of its pathogenesis. Genome-wide expression profiling of 27 GBC cancers with known chromosomal alterations was performed to reveal differentially expressed genes. Putative driver genes were identified by integrating copy number and gene expression data. A total of 315 genes were found differentially expressed (P ≤ 0.05, logFC > 2.0) of which 11 genes were validated by real-time quantitative reverse transcriptase-PCR (qRT-PCR) in tumors (n = 57) and normal GBC tissues (n = 18). Overexpression of LY6K, in chromosome band 8q24.3, was validated by immunohistochemical (IHC) analysis. We found that 78.5% (2,417/3,079) of the genes located in regions of recurrent chromosomal alterations show copy number dependent expression indicating that copy number alteration has a direct effect on global gene expression. The integrative analysis revealed BIRC3 in 11q22.2 as a candidate driver gene associated with poor clinical outcome. Our study identified previously unreported differentially expressed genes in a homogeneous subtype of oral cancer and the candidate driver genes that may contribute to the development and progression of the disease. © 2011 Wiley Periodicals, Inc.
  Genes Chromosomes and Cancer 11/2011; 51(2):161-73. · 3.31 Impact Factor
• Source

  Available from: ethz.ch

  Article: Ultra-deep sequencing for the analysis of viral populations.

  Niko Beerenwinkel, Osvaldo Zagordi
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  ABSTRACT: Next-generation sequencing allows for cost-effective probing of virus populations at an unprecedented level of detail. The massively parallel sequencing approach can detect low-frequency mutations and it provides a snapshot of the entire virus population. However, analyzing ultra-deep sequencing data obtained from diverse virus populations is challenging because of PCR and sequencing errors and short read lengths, such that the experiment provides only indirect evidence of the underlying viral population structure. Recent computational and statistical advances allow for accommodating some of the confounding factors, including methods for read error correction, haplotype reconstruction, and haplotype frequency estimation. With these methods ultra-deep sequencing can be more reliably used to analyze, in a quantitative manner, the genetic diversity of virus populations.
  Current opinion in virology. 11/2011; 1(5):413-8.
• Source

  Available from: ArXiv

  Article: Evolutionary Games with Affine Fitness Functions: Applications to Cancer

  Moritz Gerstung, Hani Nakhoul, Niko Beerenwinkel
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  ABSTRACT: We analyze the dynamics of evolutionary games in which fitness is defined as an affine function of the expected payoff and a constant contribution. The resulting inhomogeneous replicator equation has an homogeneous equivalent with modified payoffs. The affine terms also influence the stochastic dynamics of a two-strategy Moran model of a finite population. We then apply the affine fitness function in a model for tumor-normal cell interactions to determine which are the most successful tumor strategies. In order to analyze the dynamics of concurrent strategies within a tumor population, we extend the model to a three-strategy game involving distinct tumor cell types as well as normal cells. In this model, interaction with normal cells, in combination with an increased constant fitness, is the most effective way of establishing a population of tumor cells in normal tissue.
  01/2011;
• Source

  Available from: PubMed Central

  Article: ShoRAH: estimating the genetic diversity of a mixed sample from next-generation sequencing data.

  Osvaldo Zagordi, Arnab Bhattacharya, Nicholas Eriksson, Niko Beerenwinkel
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  ABSTRACT: With next-generation sequencing technologies, experiments that were considered prohibitive only a few years ago are now possible. However, while these technologies have the ability to produce enormous volumes of data, the sequence reads are prone to error. This poses fundamental hurdles when genetic diversity is investigated. We developed ShoRAH, a computational method for quantifying genetic diversity in a mixed sample and for identifying the individual clones in the population, while accounting for sequencing errors. The software was run on simulated data and on real data obtained in wet lab experiments to assess its reliability. ShoRAH is implemented in C++, Python, and Perl and has been tested under Linux and Mac OS X. Source code is available under the GNU General Public License at http://www.cbg.ethz.ch/software/shorah.
  BMC Bioinformatics 01/2011; 12:119. · 2.75 Impact Factor
• Article: Clinicopathological and prognostic implications of genetic alterations in oral cancers.

  Swapnali M Pathare, Moritz Gerstung, Niko Beerenwinkel, Alejandro A Schäffer, Sadhana Kannan, Prathamesh Pai, K Alok Pathak, Anita M Borges, Manoj B Mahimkar
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  ABSTRACT: This study evaluated the clinicopathological and prognostic implications of genetic alterations characterizing oral squamous cell carcinoma(OSCC). Comparative genomic hybridization(CGH) was used to identify chromosomal alterations present in primary OSCCs obtained from 97 pateints. In this population, tobacco use was a significant risk factor for OSCC. By contrast, all 97 of our samples are negative for human papillomavirus (HPV) DNA integration, which is another known risk factor for OSCC in certain populations. Results of the Fisher's exact test followed by Benjamini-Hochberg correction for multiple testing, showed a correlation of 7p gain and 8p loss with node-positive OSCC (p≤0.04 for both genetic alterations) and association of 11q13 gain with high-grade OSCC (p≤0.05). Univariate Cox-proportional hazard models, also corrected for multiple testing, showed significant association of 11q13 gain and 18q loss with decreased survival (p≤0.05). These findings were supported by multivariate analysis which revealed that 11q13 gain and 18q loss together serve as a strong bivariate predictor of poor prognosis. In conclusion, our study has identified genetic alterations that correlate significantly with nodal status, grade, and poor survival status of OSCC. These potential biomarkers may aid the current TNM system for better prediction of clinical outcome.
  Oncology letters 01/2011; 2(3):445-451. · 0.11 Impact Factor
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  Available from: PubMed Central

  Article: The temporal order of genetic and pathway alterations in tumorigenesis.

  Moritz Gerstung, Nicholas Eriksson, Jimmy Lin, Bert Vogelstein, Niko Beerenwinkel
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  ABSTRACT: Cancer evolves through the accumulation of mutations, but the order in which mutations occur is poorly understood. Inference of a temporal ordering on the level of genes is challenging because clinically and histologically identical tumors often have few mutated genes in common. This heterogeneity may at least in part be due to mutations in different genes having similar phenotypic effects by acting in the same functional pathway. We estimate the constraints on the order in which alterations accumulate during cancer progression from cross-sectional mutation data using a probabilistic graphical model termed Hidden Conjunctive Bayesian Network (H-CBN). The possible orders are analyzed on the level of genes and, after mapping genes to functional pathways, also on the pathway level. We find stronger evidence for pathway order constraints than for gene order constraints, indicating that temporal ordering results from selective pressure acting at the pathway level. The accumulation of changes in core pathways differs among cancer types, yet a common feature is that progression appears to begin with mutations in genes that regulate apoptosis pathways and to conclude with mutations in genes involved in invasion pathways. H-CBN models provide a quantitative and intuitive model of tumorigenesis showing that the genetic events can be linked to the phenotypic progression on the level of pathways.
  PLoS ONE 01/2011; 6(11):e27136. · 4.09 Impact Factor
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  Available from: Alejandro A Schaffer

  Article: Genomic profiling of advanced-stage oral cancers reveals chromosome 11q alterations as markers of poor clinical outcome.

  Srikant Ambatipudi, Moritz Gerstung, Ravindra Gowda, Prathamesh Pai, Anita M Borges, Alejandro A Schäffer, Niko Beerenwinkel, Manoj B Mahimkar
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  ABSTRACT: Identifying oral cancer lesions associated with high risk of relapse and predicting clinical outcome remain challenging questions in clinical practice. Genomic alterations may add prognostic information and indicate biological aggressiveness thereby emphasizing the need for genome-wide profiling of oral cancers. High-resolution array comparative genomic hybridization was performed to delineate the genomic alterations in clinically annotated primary gingivo-buccal complex and tongue cancers (n = 60). The specific genomic alterations so identified were evaluated for their potential clinical relevance. Copy-number changes were observed on chromosomal arms with most frequent gains on 3q (60%), 5p (50%), 7p (50%), 8q (73%), 11q13 (47%), 14q11.2 (47%), and 19p13.3 (58%) and losses on 3p14.2 (55%) and 8p (83%). Univariate statistical analysis with correction for multiple testing revealed chromosomal gain of region 11q22.1-q22.2 and losses of 17p13.3 and 11q23-q25 to be associated with loco-regional recurrence (P = 0.004, P = 0.003, and P = 0.0003) and shorter survival (P = 0.009, P = 0.003, and P 0.0001) respectively. The gain of 11q22 and loss of 11q23-q25 were validated by interphase fluorescent in situ hybridization (I-FISH). This study identifies a tractable number of genomic alterations with few underlying genes that may potentially be utilized as biological markers for prognosis and treatment decisions in oral cancers.
  PLoS ONE 01/2011; 6(2):e17250. · 4.09 Impact Factor
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  Available from: Martin Däumer

  Article: Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies.

  Osvaldo Zagordi, Rolf Klein, Martin Däumer, Niko Beerenwinkel
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  ABSTRACT: Next-generation sequencing technologies can be used to analyse genetically heterogeneous samples at unprecedented detail. The high coverage achievable with these methods enables the detection of many low-frequency variants. However, sequencing errors complicate the analysis of mixed populations and result in inflated estimates of genetic diversity. We developed a probabilistic Bayesian approach to minimize the effect of errors on the detection of minority variants. We applied it to pyrosequencing data obtained from a 1.5-kb-fragment of the HIV-1 gag/pol gene in two control and two clinical samples. The effect of PCR amplification was analysed. Error correction resulted in a two- and five-fold decrease of the pyrosequencing base substitution rate, from 0.05% to 0.03% and from 0.25% to 0.05% in the non-PCR and PCR-amplified samples, respectively. We were able to detect viral clones as rare as 0.1% with perfect sequence reconstruction. Probabilistic haplotype inference outperforms the counting-based calling method in both precision and recall. Genetic diversity observed within and between two clinical samples resulted in various patterns of phenotypic drug resistance and suggests a close epidemiological link. We conclude that pyrosequencing can be used to investigate genetically diverse samples with high accuracy if technical errors are properly treated.
  Nucleic Acids Research 11/2010; 38(21):7400-9. · 8.03 Impact Factor
• Source

  Available from: Volker Roth

  Article: Deep sequencing of a genetically heterogeneous sample: local haplotype reconstruction and read error correction.

  Osvaldo Zagordi, Lukas Geyrhofer, Volker Roth, Niko Beerenwinkel
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  ABSTRACT: We present a computational method for analyzing deep sequencing data obtained from a genetically diverse sample. The set of reads obtained from a deep sequencing experiment represents a statistical sample of the underlying population. We develop a generative probabilistic model for assigning observed reads to unobserved haplotypes in the presence of sequencing errors. This clustering problem is solved in a Bayesian fashion using the Dirichlet process mixture to define a prior distribution on the unknown number of haplotypes in the mixture. We devise a Gibbs sampler for sampling from the joint posterior distribution of haplotype sequences, assignment of reads to haplotypes, and error rate of the sequencing process, to obtain estimates of the local haplotype structure of the population. The method is evaluated on simulated data and on experimental deep sequencing data obtained from HIV samples.
  Journal of computational biology: a journal of computational molecular cell biology 03/2010; 17(3):417-28. · 1.69 Impact Factor
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  Available from: ArXiv

  Article: Waiting Time Models of Cancer Progression

  MORITZ GERSTUNG, NIKO BEERENWINKEL
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  ABSTRACT: Cancer progression is an evolutionary process driven by mutation and selection in a population of tumor cells. In multistage models of cancer progression, each stage is associated with the occurrence of genetic alterations and their fixation in the population. The accumulation of mutations is described using conjunctive Bayesian networks, an exponential family of waiting time models in which the occurrence of mutations is constrained by a partial temporal order. Two opposing limit cases arise if mutations either follow a linear order or occur independently. Exact analytical expressions for the waiting time until a specific number of mutations have accumulated are derived in these limit cases as well as for the general conjunctive Bayesian network. In a stochastic population genetics model that accounts for mutation and selection, waves of clonal expansions sweep through the population at equidistant intervals. An approximate analytical expression for the waiting time is compared to the results obtained with conjunctive Bayesian networks.
  Mathematical Population Studies 01/2010; 17(3):115-135. · 0.24 Impact Factor
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  Available from: Michael Baudis

  Article: Quantifying cancer progression with conjunctive Bayesian networks.

  Moritz Gerstung, Michael Baudis, Holger Moch, Niko Beerenwinkel
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  ABSTRACT: Cancer is an evolutionary process characterized by accumulating mutations. However, the precise timing and the order of genetic alterations that drive tumor progression remain enigmatic. We present a specific probabilistic graphical model for the accumulation of mutations and their interdependencies. The Bayesian network models cancer progression by an explicit unobservable accumulation process in time that is separated from the observable but error-prone detection of mutations. Model parameters are estimated by an Expectation-Maximization algorithm and the underlying interaction graph is obtained by a simulated annealing procedure. Applying this method to cytogenetic data for different cancer types, we find multiple complex oncogenetic pathways deviating substantially from simplified models, such as linear pathways or trees. We further demonstrate how the inferred progression dynamics can be used to improve genetics-based survival predictions which could support diagnostics and prognosis. The software package ct-cbn is available under a GPL license on the web site cbg.ethz.ch/software/ct-cbn moritz.gerstung@bsse.ethz.ch.
  Bioinformatics 09/2009; 25(21):2809-15. · 5.47 Impact Factor
• Article: Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database.

  André Altmann, Martin Däumer, Niko Beerenwinkel, Yardena Peres, Eugen Schülter, Joachim Büch, Soo-Yon Rhee, Anders Sönnerborg, W Jeffrey Fessel, Robert W Shafer, Maurizio Zazzi, Rolf Kaiser, Thomas Lengauer
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  ABSTRACT: Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure. We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega. The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed. Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org.
  The Journal of Infectious Diseases 03/2009; 199(7):999-1006. · 6.41 Impact Factor
• Article: Construction of oncogenetic tree models reveals multiple pathways of oral cancer progression.

  Swapnali Pathare, Alejandro A Schäffer, Niko Beerenwinkel, Manoj Mahimkar
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  ABSTRACT: Oral cancer develops and progresses by accumulation of genetic alterations. The interrelationship between these alterations and their sequence of occurrence in oral cancers has not been thoroughly understood. In the present study, we applied oncogenetic tree models to comparative genomic hybridization (CGH) data of 97 primary oral cancers to identify pathways of progression. CGH revealed the most frequent gains on chromosomes 8q (72.4%) and 9q (41.2%) and frequent losses on 3p (49.5%) and 8p (47.5%). Both mixture and distance-based tree models suggested multiple progression pathways and identified +8q as an early event. The mixture model suggested two independent pathways namely a major pathway with -8p and a less frequent pathway with +9q. The distance-based tree identified three progression pathways, one characterized by -8p, another by -3p and the third by alterations +11q and +7p. Differences were observed in cytogenetic pathways of node-positive and node-negative oral cancers. Node-positive cancers were characterized by more non-random aberrations (n = 11) and progressed via -8p or -3p. On the other hand, node-negative cancers involved fewer non-random alterations (n = 6) and progressed along -3p. In summary, the tree models for oral cancers provided novel information about the interactions between genetic alterations and predicted their probable order of occurrence.
  International Journal of Cancer 02/2009; 124(12):2864-71. · 5.44 Impact Factor
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  Available from: ArXiv

  Article: Multiple Sequence Alignment System for Pyrosequencing Reads

  Fahad Saeed, Ashfaq Khokhar, Osvaldo Zagordi, Niko Beerenwinkel
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  ABSTRACT: Pyrosequencing is among the emerging sequencing techniques, capable of generating upto 100,000 overlapping reads in a single run. This technique is much faster and cheaper than the existing state of the art sequencing technique such as Sanger. However, the reads generated by pyrosequencing are short in size and contain numerous errors. Furthermore, each read has a specific position in the reference genome. In order to use these reads for any subsequent analysis, the reads must be aligned . Existing multiple sequence alignment methods cannot be used as they do not take into account the specific positions of the sequences with respect to the genome, and are highly inefficient for large number of sequences. Therefore, the common practice has been to use either simple pairwise alignment despite its poor accuracy for error prone pyroreads, or use computationally expensive techniques based on sequential gap propagation. In this paper, we develop a computationally efficient method based on domain decomposition, referred to as pyro-align, to align such large number of reads. The proposed alignment algorithm accurately aligns the erroneous reads in a short period of time, which is orders of magnitude faster than any existing method. The accuracy of the alignment is confirmed from the consensus obtained from the multiple alignments. Comment: 14 pages, 8 figures, Bioinformatics and Computational Biology (BICoB) conference 09, LNCS
  01/2009;
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  Available from: PubMed Central

  Article: Viral population estimation using pyrosequencing.

  Nicholas Eriksson, Lior Pachter, Yumi Mitsuya, Soo-Yon Rhee, Chunlin Wang, Baback Gharizadeh, Mostafa Ronaghi, Robert W Shafer, Niko Beerenwinkel
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  ABSTRACT: The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate-based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug-resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an expectation-maximization (EM) algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.
  PLoS Computational Biology 05/2008; 4(4):e1000074. · 5.22 Impact Factor