Dataset Information

ViralFusionSeq: accurately discover viral integration events and reconstruct fusion transcripts at single-base resolution.

ABSTRACT: SUMMARY: Insertional mutagenesis from virus infection is an important pathogenic risk for the development of cancer. Despite the advent of high-throughput sequencing, discovery of viral integration sites and expressed viral fusion events are still limited. Here, we present ViralFusionSeq (VFS), which combines soft-clipping information, read-pair analysis and targeted de novo assembly to discover and annotate viral-human fusions. VFS was used in an RNA-Seq experiment, simulated DNA-Seq experiment and re-analysis of published DNA-Seq datasets. Our experiments demonstrated that VFS is both sensitive and highly accurate. AVAILABILITY: VFS is distributed under GPL version 3 at http://hkbic.cuhk.edu.hk/software/viralfusionseq

SUBMITTER: Li JW

PROVIDER: S-EPMC3582262 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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ViralFusionSeq: accurately discover viral integration events and reconstruct fusion transcripts at single-base resolution.

Li Jing-Woei JW Wan Raymond R Yu Chi-Shing CS Co Ngai Na NN Wong Nathalie N Chan Ting-Fung TF

Bioinformatics (Oxford, England) 20130112 5

<h4>Summary</h4>Insertional mutagenesis from virus infection is an important pathogenic risk for the development of cancer. Despite the advent of high-throughput sequencing, discovery of viral integration sites and expressed viral fusion events are still limited. Here, we present ViralFusionSeq (VFS), which combines soft-clipping information, read-pair analysis and targeted de novo assembly to discover and annotate viral-human fusions. VFS was used in an RNA-Seq experiment, simulated DNA-Seq exp ...[more]

PMID: 23314323

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Project description:Accurate representation of subject-specific bone anatomy in lower-limb musculoskeletal models is important for human movement analyses and simulations. Mathematical methods can reconstruct geometric bone models using incomplete imaging of bone by morphing bone model templates, but the validity of these methods has not been fully explored. The purpose of this study was to determine the minimal imaging requirements for accurate reconstruction of geometric bone models. Complete geometric pelvis and femur models of 14 healthy adults were reconstructed from magnetic resonance imaging through segmentation. From each complete bone segmentation, three sets of incomplete segmentations (set 1 being the most incomplete) were created to test the effect of imaging incompleteness on reconstruction accuracy. Geometric bone models were reconstructed from complete sets, three incomplete sets, and two motion capture-based methods. Reconstructions from (in)complete sets were generated using statistical shape modelling, followed by host-mesh and local-mesh fitting through the Musculoskeletal Atlas Project Client. Reconstructions from motion capture-based methods used positional data from skin surface markers placed atop anatomic landmarks and estimated joint centre locations as target points for statistical shape modelling and linear scaling. Accuracy was evaluated with distance error (mm) and overlapping volume similarity (%) between complete bone segmentation and reconstructed bone models, and statistically compared using a repeated measure analysis of variance (p<0.05). Motion capture-based methods produced significantly higher distance error than reconstructions from (in)complete sets. Pelvis volume similarity reduced significantly with the level of incompleteness: complete set (92.70±1.92%), set 3 (85.41±1.99%), set 2 (81.22±3.03%), set 1 (62.30±6.17%), motion capture-based statistical shape modelling (41.18±9.54%), and motion capture-based linear scaling (26.80±7.19%). A similar trend was observed for femur volume similarity. Results indicate that imaging two relevant bone regions produces overlapping volume similarity >80% compared to complete segmented bone models, and improve analyses and simulation over current standard practice of linear scaling musculoskeletal models.

Dataset Information

ViralFusionSeq: accurately discover viral integration events and reconstruct fusion transcripts at single-base resolution.

Publications

ViralFusionSeq: accurately discover viral integration events and reconstruct fusion transcripts at single-base resolution.

Similar Datasets

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