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      Data integration and predictive modeling methods for multi-omics datasets

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          Abstract

          We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

          Abstract

          Translating data to knowledge and actionable insights is the Holy Grail for many scientific fields, including biology. The unprecedented massive and heterogeneous data have created as many challenges to store, process and analyze as the opportunities and promises they hold. Here, we provide an overview of these opportunities and challenges in multi-omics predictive analytics.

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          Performance comparison of benchtop high-throughput sequencing platforms.

          Nicholas Loman, Raju V. Misra, Timothy Dallman … (2012)
          Three benchtop high-throughput sequencing instruments are now available. The 454 GS Junior (Roche), MiSeq (Illumina) and Ion Torrent PGM (Life Technologies) are laser-printer sized and offer modest set-up and running costs. Each instrument can generate data required for a draft bacterial genome sequence in days, making them attractive for identifying and characterizing pathogens in the clinical setting. We compared the performance of these instruments by sequencing an isolate of Escherichia coli O104:H4, which caused an outbreak of food poisoning in Germany in 2011. The MiSeq had the highest throughput per run (1.6 Gb/run, 60 Mb/h) and lowest error rates. The 454 GS Junior generated the longest reads (up to 600 bases) and most contiguous assemblies but had the lowest throughput (70 Mb/run, 9 Mb/h). Run in 100-bp mode, the Ion Torrent PGM had the highest throughput (80–100 Mb/h). Unlike the MiSeq, the Ion Torrent PGM and 454 GS Junior both produced homopolymer-associated indel errors (1.5 and 0.38 errors per 100 bases, respectively).
          Article 0 comments Cited 470 times – based on 0 reviews     Review now
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            Is Open Access

            The BioGRID interaction database: 2017 update

            Andrew Chatr-aryamontri, Rose Oughtred, Lorrie Boucher … (2016)
            The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
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              The Gene Expression Omnibus Database.

              Emily Clough, Tanya Barrett (2016)
              The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/.
              Article 0 comments Cited 425 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (publisher-id): MOOMAW
                Title: Molecular Omics
                Abbreviated Title: Mol. Omics
                Publisher: Royal Society of Chemistry (RSC)
                ISSN (Electronic): 2515-4184
                Publication date Created: 2018
                Publication date (Electronic): 2018
                Volume: 14
                Issue: 1
                Pages: 8-25
                Affiliations
                [1 ]Department of Computer Science
                [2 ]University of California
                [3 ]Davis
                [4 ]USA
                [5 ]Genome Center
                Article
                DOI: 10.1039/C7MO00051K
                PubMed ID: 29725673
                SO-VID: d3ca76af-60e7-4d51-a734-0e9244f628e7
                Copyright © © 2018
                License:

                http://rsc.li/journals-terms-of-use

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