A reproducible approach to high-throughput biological data acquisition and integration
Modern biological research requires rapid, complex, and reproducible integration of multiple experimental results generated both internally and externally (e.g., from public repositories). Although large systematic meta-analyses are among the most effective approaches both for clinical biomarker dis...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2015-03-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/791.pdf |
| _version_ | 1797424233329983488 |
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| author | Daniela Börnigen Yo Sup Moon Gholamali Rahnavard Levi Waldron Lauren McIver Afrah Shafquat Eric A. Franzosa Larissa Miropolsky Christopher Sweeney Xochitl C. Morgan Wendy S. Garrett Curtis Huttenhower |
| author_facet | Daniela Börnigen Yo Sup Moon Gholamali Rahnavard Levi Waldron Lauren McIver Afrah Shafquat Eric A. Franzosa Larissa Miropolsky Christopher Sweeney Xochitl C. Morgan Wendy S. Garrett Curtis Huttenhower |
| author_sort | Daniela Börnigen |
| collection | DOAJ |
| description | Modern biological research requires rapid, complex, and reproducible integration of multiple experimental results generated both internally and externally (e.g., from public repositories). Although large systematic meta-analyses are among the most effective approaches both for clinical biomarker discovery and for computational inference of biomolecular mechanisms, identifying, acquiring, and integrating relevant experimental results from multiple sources for a given study can be time-consuming and error-prone. To enable efficient and reproducible integration of diverse experimental results, we developed a novel approach for standardized acquisition and analysis of high-throughput and heterogeneous biological data. This allowed, first, novel biomolecular network reconstruction in human prostate cancer, which correctly recovered and extended the NFκB signaling pathway. Next, we investigated host-microbiome interactions. In less than an hour of analysis time, the system retrieved data and integrated six germ-free murine intestinal gene expression datasets to identify the genes most influenced by the gut microbiota, which comprised a set of immune-response and carbohydrate metabolism processes. Finally, we constructed integrated functional interaction networks to compare connectivity of peptide secretion pathways in the model organisms Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa. |
| first_indexed | 2024-03-09T07:59:22Z |
| format | Article |
| id | doaj.art-2af1b205feed4303977b54e615be3277 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:59:22Z |
| publishDate | 2015-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-2af1b205feed4303977b54e615be32772023-12-03T00:49:02ZengPeerJ Inc.PeerJ2167-83592015-03-013e79110.7717/peerj.791791A reproducible approach to high-throughput biological data acquisition and integrationDaniela Börnigen0Yo Sup Moon1Gholamali Rahnavard2Levi Waldron3Lauren McIver4Afrah Shafquat5Eric A. Franzosa6Larissa Miropolsky7Christopher Sweeney8Xochitl C. Morgan9Wendy S. Garrett10Curtis Huttenhower11Biostatistics Department, Harvard School of Public Health, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USADana-Farber Cancer Institute, Boston, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USAThe Broad Institute of MIT and Harvard, Cambridge, MA, USABiostatistics Department, Harvard School of Public Health, Boston, MA, USAModern biological research requires rapid, complex, and reproducible integration of multiple experimental results generated both internally and externally (e.g., from public repositories). Although large systematic meta-analyses are among the most effective approaches both for clinical biomarker discovery and for computational inference of biomolecular mechanisms, identifying, acquiring, and integrating relevant experimental results from multiple sources for a given study can be time-consuming and error-prone. To enable efficient and reproducible integration of diverse experimental results, we developed a novel approach for standardized acquisition and analysis of high-throughput and heterogeneous biological data. This allowed, first, novel biomolecular network reconstruction in human prostate cancer, which correctly recovered and extended the NFκB signaling pathway. Next, we investigated host-microbiome interactions. In less than an hour of analysis time, the system retrieved data and integrated six germ-free murine intestinal gene expression datasets to identify the genes most influenced by the gut microbiota, which comprised a set of immune-response and carbohydrate metabolism processes. Finally, we constructed integrated functional interaction networks to compare connectivity of peptide secretion pathways in the model organisms Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa.https://peerj.com/articles/791.pdfHigh-throughput dataData integrationData acquisitionMeta-analysisHeterogeneous dataReproducibility |
| spellingShingle | Daniela Börnigen Yo Sup Moon Gholamali Rahnavard Levi Waldron Lauren McIver Afrah Shafquat Eric A. Franzosa Larissa Miropolsky Christopher Sweeney Xochitl C. Morgan Wendy S. Garrett Curtis Huttenhower A reproducible approach to high-throughput biological data acquisition and integration PeerJ High-throughput data Data integration Data acquisition Meta-analysis Heterogeneous data Reproducibility |
| title | A reproducible approach to high-throughput biological data acquisition and integration |
| title_full | A reproducible approach to high-throughput biological data acquisition and integration |
| title_fullStr | A reproducible approach to high-throughput biological data acquisition and integration |
| title_full_unstemmed | A reproducible approach to high-throughput biological data acquisition and integration |
| title_short | A reproducible approach to high-throughput biological data acquisition and integration |
| title_sort | reproducible approach to high throughput biological data acquisition and integration |
| topic | High-throughput data Data integration Data acquisition Meta-analysis Heterogeneous data Reproducibility |
| url | https://peerj.com/articles/791.pdf |
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