Dataset Information

HPV-FRAME: A consensus statement and quality framework for modelled evaluations of HPV-related cancer control.

ABSTRACT: Intense research activity in HPV modelling over this decade has prompted the development of additional guidelines to those for general modelling. A specific framework is required to address different policy questions and unique complexities of HPV modelling. HPV-FRAME is an initiative to develop a consensus statement and quality-based framework for epidemiologic and economic HPV models. Its development involved an established process. Reporting standards have been structured according to seven domains reflecting distinct policy questions in HPV and cancer prevention and categorised by relevance to a population or evaluation. Population-relevant domains are: 1) HPV vaccination in pre-adolescent and young adolescent individuals; 2) HPV vaccination in older individuals; 3) targeted vaccination in men who have sex with men; 4) considerations for individuals living with HIV and 5) considerations for low- and middle-income countries. Additional considerations applicable to specific evaluations are: 6) cervical screening or integrated cervical screening and HPV vaccination approaches and 7) alternative vaccine types and alternative dosing schedules. HPV-FRAME aims to promote the development of models in accordance with an explicit framework, to better enable target audiences to understand a model's strength and weaknesses in relation to a specific policy question and ultimately improve the model's contribution to informed decision-making.

SUBMITTER: Canfell K

PROVIDER: S-EPMC6804684 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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HPV-FRAME: A consensus statement and quality framework for modelled evaluations of HPV-related cancer control.

Canfell Karen K Kim Jane J JJ Kulasingam Shalini S Berkhof Johannes J Barnabas Ruanne R Bogaards Johannes A JA Campos Nicole N Jennett Chloe C Sharma Monisha M Simms Kate T KT Smith Megan A MA Velentzis Louiza S LS Brisson Marc M Jit Mark M

Papillomavirus research (Amsterdam, Netherlands) 20190907

Intense research activity in HPV modelling over this decade has prompted the development of additional guidelines to those for general modelling. A specific framework is required to address different policy questions and unique complexities of HPV modelling. HPV-FRAME is an initiative to develop a consensus statement and quality-based framework for epidemiologic and economic HPV models. Its development involved an established process. Reporting standards have been structured according to seven d ...[more]

PMID: 31505258

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Project description:Motivation:Protein coding genes can be studied using long-read next generation sequencing. However, high rates of indel sequencing errors are problematic, corrupting the reading frame. Even the consensus of multiple independent sequence reads retains indel errors. To solve this problem, we introduce Reference-Informed Frame-Resolving multiple-Alignment Free template inference algorithm (RIFRAF), a sequence consensus algorithm that takes a set of error-prone reads and a reference sequence and infers an accurate in-frame consensus. RIFRAF uses a novel structure, analogous to a two-layer hidden Markov model: the consensus is optimized to maximize alignment scores with both the set of noisy reads and with a reference. The template-to-reads component of the model encodes the preponderance of indels, and is sensitive to the per-base quality scores, giving greater weight to more accurate bases. The reference-to-template component of the model penalizes frame-destroying indels. A local search algorithm proceeds in stages to find the best consensus sequence for both objectives. Results:Using Pacific Biosciences SMRT sequences from an HIV-1 env clone, NL4-3, we compare our approach to other consensus and frame correction methods. RIFRAF consistently finds a consensus sequence that is more accurate and in-frame, especially with small numbers of reads. It was able to perfectly reconstruct over 80% of consensus sequences from as few as three reads, whereas the best alternative required twice as many. RIFRAF is able to achieve these results and keep the consensus in-frame even with a distantly related reference sequence. Moreover, unlike other frame correction methods, RIFRAF can detect and keep true indels while removing erroneous ones. Availability and implementation:RIFRAF is implemented in Julia, and source code is publicly available at https://github.com/MurrellGroup/Rifraf.jl. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

HPV-FRAME: A consensus statement and quality framework for modelled evaluations of HPV-related cancer control.

Publications

HPV-FRAME: A consensus statement and quality framework for modelled evaluations of HPV-related cancer control.

Similar Datasets

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