The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics
Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to est...
Gespeichert in:
| Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2017-11, Vol.33 (21), p.3489-3491 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3491 |
|---|---|
| container_issue | 21 |
| container_start_page | 3489 |
| container_title | Bioinformatics (Oxford, England) |
| container_volume | 33 |
| creator | Wiredja, Danica D Koyutürk, Mehmet Chance, Mark R |
| description | Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.
the KSEA App is a free online tool: casecpb.shinyapps.io/ksea/. The source code is on GitHub: github.com/casecpb/KSEA/. The application is also available as the R package "KSEAapp" on CRAN: CRAN.R-project.org/package=KSEAapp/.
mark.chance@case.edu.
Supplementary data are available at Bioinformatics online. |
| doi_str_mv | 10.1093/bioinformatics/btx415 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5860163</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1914580613</sourcerecordid><originalsourceid>FETCH-LOGICAL-c510t-7ad4478f3877e1a411be22c816c7e54237ccaed57d11f6f15fc9ceaf9cd09c73</originalsourceid><addsrcrecordid>eNqFUU1P3DAQtaqi8tWfUORjLymeOI6THiqtEC0IpB66d-M4465LEgfbu4V_j9HCCk4cRjOaefPmjR4hX4B9A9by0855N1kfRp2ciadduq9AfCAHwGtZVA3Ax13N-D45jPEfY0wwUX8i-2VTCwGCH5Cb5Qrp1Z_zBV3M83eq6X_sik5H7GnyfqD5Ar11U25QbZLbuPRA810MOBmkNviR3q31lFzKOjZI55WPOebgE_oxKzsme1YPET8_5yOy_Hm-PLsorn__ujxbXBdGAEuF1H1VycbyRkoEXQF0WJamgdpIFFXJpTEaeyF7AFtbENa0BrVtTc9aI_kR-bGlndfdiL3BKQU9qDm4UYcH5bVTbyeTW6m_fqNEUzOoeSb4-kwQ_N0aY1KjiwaHQU_o11GVnAlo2wx-FwotVKJhNTyxii3UBB9jQLtTBEw9-aje-qi2Pua9k9fv7LZejOOPoeehqQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1914580613</pqid></control><display><type>article</type><title>The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics</title><source>Oxford Journals Open Access Collection</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Wiredja, Danica D ; Koyutürk, Mehmet ; Chance, Mark R</creator><contributor>Valencia, Alfonso</contributor><creatorcontrib>Wiredja, Danica D ; Koyutürk, Mehmet ; Chance, Mark R ; Valencia, Alfonso</creatorcontrib><description>Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.
the KSEA App is a free online tool: casecpb.shinyapps.io/ksea/. The source code is on GitHub: github.com/casecpb/KSEA/. The application is also available as the R package "KSEAapp" on CRAN: CRAN.R-project.org/package=KSEAapp/.
mark.chance@case.edu.
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>ISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btx415</identifier><identifier>PMID: 28655153</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>algorithms ; Applications Notes ; bioinformatics ; computer software ; data collection ; drug therapy ; enzyme activity ; enzymes ; Internet ; phosphorylation ; proteomics</subject><ispartof>Bioinformatics (Oxford, England), 2017-11, Vol.33 (21), p.3489-3491</ispartof><rights>The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com</rights><rights>The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c510t-7ad4478f3877e1a411be22c816c7e54237ccaed57d11f6f15fc9ceaf9cd09c73</citedby><cites>FETCH-LOGICAL-c510t-7ad4478f3877e1a411be22c816c7e54237ccaed57d11f6f15fc9ceaf9cd09c73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860163/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860163/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,724,777,781,882,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28655153$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Valencia, Alfonso</contributor><creatorcontrib>Wiredja, Danica D</creatorcontrib><creatorcontrib>Koyutürk, Mehmet</creatorcontrib><creatorcontrib>Chance, Mark R</creatorcontrib><title>The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.
the KSEA App is a free online tool: casecpb.shinyapps.io/ksea/. The source code is on GitHub: github.com/casecpb/KSEA/. The application is also available as the R package "KSEAapp" on CRAN: CRAN.R-project.org/package=KSEAapp/.
mark.chance@case.edu.
Supplementary data are available at Bioinformatics online.</description><subject>algorithms</subject><subject>Applications Notes</subject><subject>bioinformatics</subject><subject>computer software</subject><subject>data collection</subject><subject>drug therapy</subject><subject>enzyme activity</subject><subject>enzymes</subject><subject>Internet</subject><subject>phosphorylation</subject><subject>proteomics</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqFUU1P3DAQtaqi8tWfUORjLymeOI6THiqtEC0IpB66d-M4465LEgfbu4V_j9HCCk4cRjOaefPmjR4hX4B9A9by0855N1kfRp2ciadduq9AfCAHwGtZVA3Ax13N-D45jPEfY0wwUX8i-2VTCwGCH5Cb5Qrp1Z_zBV3M83eq6X_sik5H7GnyfqD5Ar11U25QbZLbuPRA810MOBmkNviR3q31lFzKOjZI55WPOebgE_oxKzsme1YPET8_5yOy_Hm-PLsorn__ujxbXBdGAEuF1H1VycbyRkoEXQF0WJamgdpIFFXJpTEaeyF7AFtbENa0BrVtTc9aI_kR-bGlndfdiL3BKQU9qDm4UYcH5bVTbyeTW6m_fqNEUzOoeSb4-kwQ_N0aY1KjiwaHQU_o11GVnAlo2wx-FwotVKJhNTyxii3UBB9jQLtTBEw9-aje-qi2Pua9k9fv7LZejOOPoeehqQ</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>Wiredja, Danica D</creator><creator>Koyutürk, Mehmet</creator><creator>Chance, Mark R</creator><general>Oxford University Press</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope></search><sort><creationdate>20171101</creationdate><title>The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics</title><author>Wiredja, Danica D ; Koyutürk, Mehmet ; Chance, Mark R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c510t-7ad4478f3877e1a411be22c816c7e54237ccaed57d11f6f15fc9ceaf9cd09c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>algorithms</topic><topic>Applications Notes</topic><topic>bioinformatics</topic><topic>computer software</topic><topic>data collection</topic><topic>drug therapy</topic><topic>enzyme activity</topic><topic>enzymes</topic><topic>Internet</topic><topic>phosphorylation</topic><topic>proteomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wiredja, Danica D</creatorcontrib><creatorcontrib>Koyutürk, Mehmet</creatorcontrib><creatorcontrib>Chance, Mark R</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wiredja, Danica D</au><au>Koyutürk, Mehmet</au><au>Chance, Mark R</au><au>Valencia, Alfonso</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>33</volume><issue>21</issue><spage>3489</spage><epage>3491</epage><pages>3489-3491</pages><issn>1367-4803</issn><issn>1460-2059</issn><eissn>1367-4811</eissn><abstract>Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.
the KSEA App is a free online tool: casecpb.shinyapps.io/ksea/. The source code is on GitHub: github.com/casecpb/KSEA/. The application is also available as the R package "KSEAapp" on CRAN: CRAN.R-project.org/package=KSEAapp/.
mark.chance@case.edu.
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>28655153</pmid><doi>10.1093/bioinformatics/btx415</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1367-4803 |
| ispartof | Bioinformatics (Oxford, England), 2017-11, Vol.33 (21), p.3489-3491 |
| issn | 1367-4803 1460-2059 1367-4811 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5860163 |
| source | Oxford Journals Open Access Collection; EZB-FREE-00999 freely available EZB journals; PubMed Central; Alma/SFX Local Collection |
| subjects | algorithms Applications Notes bioinformatics computer software data collection drug therapy enzyme activity enzymes Internet phosphorylation proteomics |
| title | The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T20%3A16%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20KSEA%20App:%20a%20web-based%20tool%20for%20kinase%20activity%20inference%20from%20quantitative%20phosphoproteomics&rft.jtitle=Bioinformatics%20(Oxford,%20England)&rft.au=Wiredja,%20Danica%20D&rft.date=2017-11-01&rft.volume=33&rft.issue=21&rft.spage=3489&rft.epage=3491&rft.pages=3489-3491&rft.issn=1367-4803&rft.eissn=1367-4811&rft_id=info:doi/10.1093/bioinformatics/btx415&rft_dat=%3Cproquest_pubme%3E1914580613%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1914580613&rft_id=info:pmid/28655153&rfr_iscdi=true |