{"id":72,"date":"2018-11-18T00:00:00","date_gmt":"2018-11-18T00:00:00","guid":{"rendered":"https:\/\/www.thermofisher.com\/blog\/connectedlab\/differential-gene-expression-on-the-platform-for-science\/"},"modified":"2024-07-25T08:25:18","modified_gmt":"2024-07-25T08:25:18","slug":"differential-gene-expression-on-the-platform-for-science","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/connectedlab\/differential-gene-expression-on-the-platform-for-science\/","title":{"rendered":"Differential Gene Expression on the Platform for Science"},"content":{"rendered":"<h2>Platform Interoperability Drives Science<\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-323 size-medium\" src=\"https:\/\/www.thermofisher.com\/blog\/wp-content\/uploads\/sites\/14\/2019\/07\/Differential-Gene-Expression-on-the-Platform-for-Science-300x265.jpg\" alt=\"Driving Science with Differential Gene Expression Data Analysis\" width=\"300\" height=\"265\" \/><br \/>\nDifferential gene expression, the comparison of gene expression in healthy and diseased states, continues to be a growing area of focus for biomedical research.\u00a0 Understanding not only what genes are present, but what triggers their expression enables scientists to better understand the disease and to develop improved treatment options for patients.\u00a0 Many scientists rely on the R programming language to perform gene expression profiling.\u00a0 Shiny<sup>TM <\/sup>from RStudio, which is now integrated into Thermo Fisher<sup>TM <\/sup>Platform for Science<sup>TM<\/sup> software, enables scientists to perform gene expression analysis in the platform.<\/p>\n<h2>Driving Science with Differential Gene Expression Data Analysis<\/h2>\n<p>One of the challenges all scientists face is the need to integrate data across a variety of proprietary databases, programming languages, and applications.\u00a0 qPCR, microarray, and <a href=\"https:\/\/www.thermofisher.com\/blog\/connectedlab\/ngs-into-sanger-lab\/\">Next Generation Sequencing techniques<\/a> are all commonly used to determine gene expression.\u00a0 Genomics data needs to be aggregated into a single platform to enable comparison between the baseline and the disease state.\u00a0\u00a0 The Platform for Science software is able to connect to other systems through our OData API as part of <a href=\"https:\/\/www.thermofisher.com\/uk\/en\/home\/digital-solutions\/lab-informatics\/lab-information-management-systems-lims\/solutions\/core.html\">Thermo Scientific<sup>TM<\/sup> Core LIMS<sup>TM<\/sup><\/a>. The OData API is an open standards-based API that improves interoperability and simplifies integration across applications. \u00a0It effectively unlocks data that resides in Platform for Science software to be used by other OData services and software, like Shiny.<\/p>\n<p>Shiny has multiple data analysis options for differential gene expression. \u00a0By incorporating the DEApp opensource code<sup>1<\/sup> and count-based sequence data into our existing Next Gen Sequencing (NGS) workflow, we provide our customers with the tools they need to prepare, run and analyze genomic samples.\u00a0 Samples move seamlessly from one step to the next, allowing researchers to track where samples are in their process. \u00a0This new functionality empowers labs to leverage R-based NGS applications for differential gene expression analysis without relying on R developers, data scientists or bioinformaticians.<\/p>\n<h2>Welcome to the R Community<\/h2>\n<p>R is best known for statistics and data analysis. Users now have access to the open source R community including access to a whopping 10,000+ statistical packages from the Comprehensive R Archive Network (CRAN), Bioconductor and GitHub and can communicate with developers, domain experts, and testers to help drive their science. When a new technique or algorithm is peer reviewed and published, this solution can be configured into a Shiny data analysis application that integrates directly with Platform for Science software.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>References<\/strong><\/p>\n<ol>\n<li>Yan Li and Jorge Andrade; <em>DEApp: An Interactive Web Interface for Differential Expression Analysis of Next Generation Sequence; Source Code for Biology and Medicine<\/em>, 03 Feb 2017 12:2; <a href=\"https:\/\/doi.org\/10.1186\/s13029-017-0063-4\">https:\/\/doi.org\/10.1186\/s13029-017-0063-4<\/a><\/li>\n<\/ol>\n<p><em>Marc Siladi leads the Data Science Team and oversees the strategy and development of statistical analysis software at Thermo Fisher Scientific. His scientific expertise and experience allow him to better understand the problems facing our customers. Before joining Thermo Fisher Scientific, Marc was a Research Scientist at Vertex Pharmaceuticals with hands on involvement in innovative drug discovery efforts to benefit patients with deadly diseases.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Platform Interoperability Drives Science Differential gene expression, the comparison of gene expression in healthy and diseased states, continues to be a growing area of focus for biomedical research.\u00a0 Understanding not only what genes are present, but what triggers their expression enables scientists to better understand the disease and to develop improved treatment options for patients.\u00a0<\/p>\n","protected":false},"author":1028,"featured_media":331,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[107,89],"tags":[167,119,163,164,97,98,99,109,165,166,120],"division":[],"class_list":{"0":"post-72","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-genomics","8":"category-technology","9":"tag-data-analysis","10":"tag-data-analysis-and-visualization","11":"tag-differential-gene-expression","12":"tag-microarray","13":"tag-next-generation-sequencing","14":"tag-ngs","15":"tag-platform-for-science","16":"tag-qpcr","17":"tag-rstudio","18":"tag-shiny","19":"tag-visualization","20":"entry"},"_selected_authors":"","_selected_reviewers":"","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.8 (Yoast SEO v27.8) - 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