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OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics.

ABSTRACT: Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of 'omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new 'omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography-mass spectrometry (LC-MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.

SUBMITTER: Zhou G

PROVIDER: S-EPMC9252810 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ContextHepatitis C virus (HCV) genotyping at our institution is performed using the Versant HCV genotype 2.0 Line Probe Assay (LiPA). The last steps of this procedure are manual, laborious, and error-prone process that involves the comparison of the banding pattern on a test strip to a physical reference table.AimWe developed a web-based HCV genotype interpretation platform that utilizes a scanned image to generate the genotypes, thus minimizing interpretation time and reducing error.Subjects and methodsHCV Genie 2 utilizes a database of banding patterns in conjuncture with image analysis algorithms to determine the genotype for any number of scanned LiPA strips. HCV Genie 2 is built with client-side JavaScript; allowing the program to run in the user' browser rather than on an unknown server, essentially eliminating data and patient privacy concerns.ResultsHCV Genie 2 was tested over 2 months and proved identical to human expert interpretation for 148 samples (>1000 bands identified). Manual intervention was required only for two faint bands and one false-positive band; this was done utilizing the built-in-user interface. Utilizing the original method, the trained laboratory technician interpretation time for 16 samples was 13.8 (±0.96) min as compared to 5.0 (±1.09) min with HCV Genie 2, a 63.8% decrease. In addition to the time savings, the new method provides an additional validation step, which decreases the potential for errors.ConclusionsOur institution has moved exclusively to utilize the new techniques and tools described here. Both experienced technicians and the molecular pathologists at our institution prefer the workflow using HCV Genie. It is easier for the technicians to prepare and document, and the pathologists are more rapidly able to review and confirm results. The use of this tool will lead to increase the quality of patient care delivered through this test methodology by decreasing the potential for error. The algorithms developed here can be ported to similar band identification platforms, most directly to other LiPAs.

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OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics.

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