SAMNetWeb: identifying condition-specific networks linking signaling and transcription
Motivation: High-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identif...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Oxford University Press
2017
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| Online Access: | http://hdl.handle.net/1721.1/108517 https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-6534-4774 |
| _version_ | 1811069248654540800 |
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| author | Oh, Coyin Fraenkel, Ernest Gosline, Sara Calafell |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Oh, Coyin Fraenkel, Ernest Gosline, Sara Calafell |
| author_sort | Oh, Coyin |
| collection | MIT |
| description | Motivation: High-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identification of biological processes and pathways that are most likely to explain the measured results. These tools are primarily designed to analyse data from a single experiment (e.g. drug treatment versus control), creating a need for computational algorithms that can handle heterogeneous datasets across multiple experimental conditions at once.
Summary: We introduce SAMNetWeb, a web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment ‘share’ underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments.
Availability and implementation: SAMNetWeb is freely available at http://fraenkel.mit.edu/samnetweb. |
| first_indexed | 2024-09-23T08:08:05Z |
| format | Article |
| id | mit-1721.1/108517 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:08:05Z |
| publishDate | 2017 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/1085172022-09-30T07:47:32Z SAMNetWeb: identifying condition-specific networks linking signaling and transcription Oh, Coyin Fraenkel, Ernest Gosline, Sara Calafell Massachusetts Institute of Technology. Department of Biological Engineering Fraenkel, Ernest Oh, Coyin Fraenkel, Ernest Gosline, Sara Calafell Motivation: High-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identification of biological processes and pathways that are most likely to explain the measured results. These tools are primarily designed to analyse data from a single experiment (e.g. drug treatment versus control), creating a need for computational algorithms that can handle heterogeneous datasets across multiple experimental conditions at once. Summary: We introduce SAMNetWeb, a web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment ‘share’ underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments. Availability and implementation: SAMNetWeb is freely available at http://fraenkel.mit.edu/samnetweb. United States. National Institutes of Health (U54CA112967) United States. National Institutes of Health (R01GM089903) National Science Foundation (U.S.) (DB1-0821391) 2017-04-28T20:39:31Z 2017-04-28T20:39:31Z 2014-11 2014-09 Article http://purl.org/eprint/type/JournalArticle 1367-4803 1460-2059 http://hdl.handle.net/1721.1/108517 Gosline, S. J. C.; Oh, C. and Fraenkel, E.. “SAMNetWeb: Identifying Condition-Specific Networks Linking Signaling and Transcription.” Bioinformatics 31, no. 7 (November 2014): 1124–1126. https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-6534-4774 en_US http://dx.doi.org/10.1093/bioinformatics/btu748 Bioinformatics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press Prof. Fraenkel via Howard Silver |
| spellingShingle | Oh, Coyin Fraenkel, Ernest Gosline, Sara Calafell SAMNetWeb: identifying condition-specific networks linking signaling and transcription |
| title | SAMNetWeb: identifying condition-specific networks linking signaling and transcription |
| title_full | SAMNetWeb: identifying condition-specific networks linking signaling and transcription |
| title_fullStr | SAMNetWeb: identifying condition-specific networks linking signaling and transcription |
| title_full_unstemmed | SAMNetWeb: identifying condition-specific networks linking signaling and transcription |
| title_short | SAMNetWeb: identifying condition-specific networks linking signaling and transcription |
| title_sort | samnetweb identifying condition specific networks linking signaling and transcription |
| url | http://hdl.handle.net/1721.1/108517 https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-6534-4774 |
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