Dataset Information

Maximal viral information recovery from sequence data using VirMAP.

ABSTRACT: Accurate classification of the human virome is critical to a full understanding of the role viruses play in health and disease. This implies the need for sensitive, specific, and practical pipelines that return precise outputs while still enabling case-specific post hoc analysis. Viral taxonomic characterization from metagenomic data suffers from high background noise and signal crosstalk that confounds current methods. Here we develop VirMAP that overcomes these limitations using techniques that merge nucleotide and protein information to taxonomically classify viral reconstructions independent of genome coverage or read overlap. We validate VirMAP using published data sets and viral mock communities containing RNA and DNA viruses and bacteriophages. VirMAP offers opportunities to enhance metagenomic studies seeking to define virome-host interactions, improve biosurveillance capabilities, and strengthen molecular epidemiology reporting.

SUBMITTER: Ajami NJ

PROVIDER: S-EPMC6086868 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Maximal viral information recovery from sequence data using VirMAP.

Ajami Nadim J NJ Wong Matthew C MC Ross Matthew C MC Lloyd Richard E RE Petrosino Joseph F JF

Nature communications 20180810 1

Accurate classification of the human virome is critical to a full understanding of the role viruses play in health and disease. This implies the need for sensitive, specific, and practical pipelines that return precise outputs while still enabling case-specific post hoc analysis. Viral taxonomic characterization from metagenomic data suffers from high background noise and signal crosstalk that confounds current methods. Here we develop VirMAP that overcomes these limitations using techniques tha ...[more]

PMID: 30097567

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Project description:BackgroundCircadian rhythms comprise oscillating molecular interactions, the disruption of the homeostasis of which would cause various disorders. To understand this phenomenon systematically, an accurate technique to identify oscillating molecules among omics datasets must be developed; however, this is still impeded by many difficulties, such as experimental noise and attenuated amplitude.ResultsTo address these issues, we developed a new algorithm named Maximal Information Coefficient-based Oscillation Prediction (MICOP), a sine curve-matching method. The performance of MICOP in labeling oscillation or non-oscillation was compared with four reported methods using Mathews correlation coefficient (MCC) values. The numerical experiments were performed with time-series data with (1) mimicking of molecular oscillation decay, (2) high noise and low sampling frequency and (3) one-cycle data. The first experiment revealed that MICOP could accurately identify the rhythmicity of decaying molecular oscillation (MCC?>?0.7). The second experiment revealed that MICOP was robust against high-level noise (MCC?>?0.8) even upon the use of low-sampling-frequency data. The third experiment revealed that MICOP could accurately identify the rhythmicity of noisy one-cycle data (MCC?>?0.8). As an application, we utilized MICOP to analyze time-series proteome data of mouse liver. MICOP identified that novel oscillating candidates numbered 14 and 30 for C57BL/6 and C57BL/6 J, respectively.ConclusionsIn this paper, we presented MICOP, which is an MIC-based algorithm, for predicting periodic patterns in large-scale time-resolved protein expression profiles. The performance test using artificially generated simulation data revealed that the performance of MICOP for decaying data was superior to that of the existing widely used methods. It can reveal novel findings from time-series data and may contribute to biologically significant results. This study suggests that MICOP is an ideal approach for detecting and characterizing oscillations in time-resolved omics data sets.

Project description:BackgroundDue to the affordability of whole-genome sequencing, the genetic association design can now address rare diseases. However, some common statistical association methods only consider homozygosity mapping and need several criteria, such as sliding windows of a given size and statistical significance threshold setting, such as P-value < 0.05 to achieve good power in rare disease association detection.MethodsOur region-specific method, called expanded maximal segmental score (eMSS), converts p-values into continuous scores based on the maximal segmental score (MSS) (Lin et al., 2014) for detecting disease-associated segments. Our eMSS considers the whole genome sequence data, not only regions of homozygosity in candidate genes. Unlike sliding window methods of a given size, eMSS does not need predetermined parameters, such as window size or minimum or maximum number of SNPs in a segment. The performance of eMSS was evaluated by simulations and real data analysis for autosomal recessive diseases multiple intestinal atresia (MIA) and osteogenesis imperfecta (OI), where the number of cases is extremely small. For the real data, the results by eMSS were compared with a state-of-the-art method, HDR-del (Imai et al., 2016).ResultsOur simulation results show that eMSS had higher power as the number of non-causal haplotype blocks decreased. The type I error for eMSS under different scenarios was well controlled, p < 0.05. For our observed data, the bone morphogenetic protein 1 (BMP1) gene on chromosome 8, the Violaxanthin de-epoxidase-related chloroplast (VDR) gene on chromosome 12 associated with OI, and the tetratricopeptide repeat domain 7A (TTC7A) gene on chromosome 2 associated with MIA have previously been identified as harboring the relevant pathogenic mutations.ConclusionsWhen compared to HDR-del, our eMSS is powerful in analyzing even small numbers of recessive cases, and the results show that the method can further reduce numbers of candidate variants to a very small set of susceptibility pathogenic variants underlying OI and MIA. When we conduct whole-genome sequence analysis, eMSS used 3/5 the computation time of HDR-del. Without additional parameters needing to be set in the segment detection, the computational burden for eMSS is lower compared with that in other region-specific approaches.

Dataset Information

Maximal viral information recovery from sequence data using VirMAP.

Publications

Maximal viral information recovery from sequence data using VirMAP.

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