The Experts below are selected from a list of 50271 Experts worldwide ranked by ideXlab platform
Osvaldo Zagordi - One of the best experts on this subject based on the ideXlab platform.
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MinVar: A rapid and versatile tool for HIV-1 drug resistance genotyping by Deep Sequencing
Journal of Virological Methods, 2016Co-Authors: Michael Huber, Karin J. Metzner, Fabienne D. Geissberger, Cyril Shah, Christine Leemann, Thomas Klimkait, Jürg Böni, Alexandra Trkola, Osvaldo ZagordiAbstract:Genotypic monitoring of drug-resistance mutations (DRMs) in HIV-1 infected individuals is strongly recommended to guide selection of the initial antiretroviral therapy (ART) and changes of drug regimens. Traditionally, mutations conferring drug resistance are detected by population Sequencing of the reverse transcribed viral RNA encoding the HIV-1 enzymes target by ART, followed by manual analysis and interpretation of Sanger Sequencing traces. This process is labor intensive, relies on subjective interpretation from the operator, and offers limited sensitivity as only mutations above 20% frequency can be reliably detected. Here we present MinVar, a pipeline for the analysis of Deep Sequencing data, which allows reliable and automated detection of DRMs down to 5%. We evaluated MinVar with data from amplicon Sequencing of defined mixtures of molecular virus clones with known DRM and plasma samples of viremic HIV-1 infected individuals and we compared it to VirVarSeq, another virus variant detection tool exclusively working on Illumina Deep Sequencing data. MinVar was designed to be compatible with a diverse range of Sequencing platforms and allows the detection of DRMs and insertions/deletions from Deep Sequencing data without the need to perform additional bioinformatics analysis, a prerequisite to a widespread implementation of HIV-1 genotyping using Deep Sequencing in routine diagnostic settings.
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ultra Deep Sequencing for the analysis of viral populations
Current Opinion in Virology, 2011Co-Authors: Niko Beerenwinkel, Osvaldo ZagordiAbstract: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.
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Ultra-Deep Sequencing for the analysis of viral populations
Current Opinion in Virology, 2011Co-Authors: Niko Beerenwinkel, Osvaldo ZagordiAbstract: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. © 2011 Elsevier B.V. All rights reserved.
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Deep Sequencing of a genetically heterogeneous sample: local haplotype reconstruction and read error correction.
Journal of Computational Biology, 2010Co-Authors: Osvaldo Zagordi, Lukas Geyrhofer, Volker Roth, Niko BeerenwinkelAbstract:: 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.
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RECOMB - Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction
Lecture Notes in Computer Science, 2009Co-Authors: Osvaldo Zagordi, Lukas Geyrhofer, Volker Roth, Niko BeerenwinkelAbstract: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.
Niko Beerenwinkel - One of the best experts on this subject based on the ideXlab platform.
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ultra Deep Sequencing for the analysis of viral populations
Current Opinion in Virology, 2011Co-Authors: Niko Beerenwinkel, Osvaldo ZagordiAbstract: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.
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Ultra-Deep Sequencing for the analysis of viral populations
Current Opinion in Virology, 2011Co-Authors: Niko Beerenwinkel, Osvaldo ZagordiAbstract: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. © 2011 Elsevier B.V. All rights reserved.
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Deep Sequencing of a genetically heterogeneous sample: local haplotype reconstruction and read error correction.
Journal of Computational Biology, 2010Co-Authors: Osvaldo Zagordi, Lukas Geyrhofer, Volker Roth, Niko BeerenwinkelAbstract:: 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.
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RECOMB - Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction
Lecture Notes in Computer Science, 2009Co-Authors: Osvaldo Zagordi, Lukas Geyrhofer, Volker Roth, Niko BeerenwinkelAbstract: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.
Nicholas T Ingolia - One of the best experts on this subject based on the ideXlab platform.
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the ribosome profiling strategy for monitoring translation in vivo by Deep Sequencing of ribosome protected mrna fragments
Nature Protocols, 2012Co-Authors: Nicholas T Ingolia, Gloria A Brar, Silvia Rouskin, Anna M Mcgeachy, Jonathan S WeissmanAbstract:The ribosome profiling strategy for monitoring translation in vivo by Deep Sequencing of ribosome-protected mRNA fragments
Sam Griffithsjones - One of the best experts on this subject based on the ideXlab platform.
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mirbase annotating high confidence micrornas using Deep Sequencing data
Nucleic Acids Research, 2014Co-Authors: Ana Kozomara, Sam GriffithsjonesAbstract:We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA Deep Sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of Deep Sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.
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mirbase integrating microrna annotation and Deep Sequencing data
Nucleic Acids Research, 2011Co-Authors: Ana Kozomara, Sam GriffithsjonesAbstract:miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-Sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA Deep-Sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.
Jonathan S Weissman - One of the best experts on this subject based on the ideXlab platform.
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the ribosome profiling strategy for monitoring translation in vivo by Deep Sequencing of ribosome protected mrna fragments
Nature Protocols, 2012Co-Authors: Nicholas T Ingolia, Gloria A Brar, Silvia Rouskin, Anna M Mcgeachy, Jonathan S WeissmanAbstract:The ribosome profiling strategy for monitoring translation in vivo by Deep Sequencing of ribosome-protected mRNA fragments
