Dataset Information

SummonChimera infers integrated viral genomes with nucleotide precision from NGS data.

ABSTRACT: Viral integration into a host genome is defined by two chimeric junctions that join viral and host DNA. Recently, computational tools have been developed that utilize NGS data to detect chimeric junctions. These methods identify individual viral-host junctions but do not associate chimeric pairs as an integration event. Without knowing the chimeric boundaries of an integration, its genetic content cannot be determined.Summonchimera is a Perl program that associates chimera pairs to infer the complete viral genomic integration event to the nucleotide level within single or paired-end NGS data. SummonChimera integration prediction was verified on a set of single-end IonTorrent reads from a purified Salmonella bacterium with an integrated bacteriophage. Furthermore, SummonChimera predicted integrations from experimentally verified Hepatitis B Virus chimeras within a paired-end Whole Genome Sequencing hepatocellular carcinoma tumor database.SummonChimera identified all experimentally verified chimeras detected by current computational methods. Further, SummonChimera integration inference precisely predicted bacteriophage integration. The application of SummonChimera to cancer NGS accurately identifies deletion of host and viral sequence during integration. The precise nucleotide determination of an integration allows prediction of viral and cellular gene transcription patterns.

SUBMITTER: Katz JP

PROVIDER: S-EPMC4210586 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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SummonChimera infers integrated viral genomes with nucleotide precision from NGS data.

Katz Joshua P JP Pipas James M JM

BMC bioinformatics 20141022

<h4>Background</h4>Viral integration into a host genome is defined by two chimeric junctions that join viral and host DNA. Recently, computational tools have been developed that utilize NGS data to detect chimeric junctions. These methods identify individual viral-host junctions but do not associate chimeric pairs as an integration event. Without knowing the chimeric boundaries of an integration, its genetic content cannot be determined.<h4>Results</h4>Summonchimera is a Perl program that associ ...[more]

PMID: 25331652

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Project description:BackgroundInformation obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of knowledge about protein function. The result is a weighted functional linkage network (FLN) in which linked neighbors share at least one function with high probability. Precision is, however, low. Aiming to provide precise functional annotation for as many proteins as possible, we explore and propose a two-step framework for functional annotation (1) construction of a high-coverage and reliable FLN via machine learning techniques (2) development of a decision rule for the constructed FLN to optimize functional annotation.ResultsWe first apply this framework to Saccharomyces cerevisiae. In the first step, we demonstrate that four commonly used machine learning methods, Linear SVM, Linear Discriminant Analysis, Naïve Bayes, and Neural Network, all combine heterogeneous data to produce reliable and high-coverage FLNs, in which the linkage weight more accurately estimates functional coupling of linked proteins than use individual data sources alone. In the second step, empirical tuning of an adjustable decision rule on the constructed FLN reveals that basing annotation on maximum edge weight results in the most precise annotation at high coverages. In particular at low coverage all rules evaluated perform comparably. At coverage above approximately 50%, however, they diverge rapidly. At full coverage, the maximum weight decision rule still has a precision of approximately 70%, whereas for other methods, precision ranges from a high of slightly more than 30%, down to 3%. In addition, a scoring scheme to estimate the precisions of individual predictions is also provided. Finally, tests of the robustness of the framework indicate that our framework can be successfully applied to less studied organisms.ConclusionWe provide a general two-step function-annotation framework, and show that high coverage, high precision annotations can be achieved by constructing a high-coverage and reliable FLN via data integration followed by applying a maximum weight decision rule.

Dataset Information

SummonChimera infers integrated viral genomes with nucleotide precision from NGS data.

Publications

SummonChimera infers integrated viral genomes with nucleotide precision from NGS data.

Similar Datasets

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