Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine learning
This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out o...
Saved in:
| Published in | BMC genomics Vol. 26; no. 1; pp. 358 - 21 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
10.04.2025
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2164 1471-2164 |
| DOI | 10.1186/s12864-025-11553-6 |
Cover
| Abstract | This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data,
OAS1
was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model,
OAS1
maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples.
OAS1
was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including
ISG15
,
OAS1
,
IFI44
,
IFI27
,
IFIT2
,
IFIT3
,
IFI44L
,
MX1
,
MX2
,
OAS2
,
RSAD2
, and
OASL
) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as
CASP5
,
USP18
, and
DDX60
, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures. |
|---|---|
| AbstractList | This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures. Abstract This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures. This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15 , OAS1 , IFI44 , IFI27 , IFIT2 , IFIT3 , IFI44L , MX1 , MX2 , OAS2 , RSAD2 , and OASL ) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5 , USP18 , and DDX60 , which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures. This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures. This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures.This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures. This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures. Keywords: RNA sequencing, NanoString technology, Machine learning, Gene expression analysis, Concordance analysis |
| ArticleNumber | 358 |
| Audience | Academic |
| Author | Gurcan, Metin Nafi Mowery, Wyatt H. Narayanan, Aarthi Rezapour, Mostafa |
| Author_xml | – sequence: 1 givenname: Mostafa surname: Rezapour fullname: Rezapour, Mostafa email: mrezapou@wakehealth.edu organization: Center for Artificial Intelligence Research, Wake Forest University School of Medicine – sequence: 2 givenname: Aarthi surname: Narayanan fullname: Narayanan, Aarthi organization: Department of Biology, George Mason University – sequence: 3 givenname: Wyatt H. surname: Mowery fullname: Mowery, Wyatt H. organization: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology – sequence: 4 givenname: Metin Nafi surname: Gurcan fullname: Gurcan, Metin Nafi organization: Center for Artificial Intelligence Research, Wake Forest University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40211167$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkk1v1DAQhiNURD_gD3BAkbjAIcVjx4lzQquqwEpVkbpwthxnkvUqsbd20lKJH493t7RdhBCK5FjjZ16P553j5MA6i0nyGsgpgCg-BKCiyDNCeQbAOcuKZ8kR5CVkFIr84Mn-MDkOYUUIlILyF8lhTigAFOVR8nMWAoZgbJdqZ7XzjbIa0xrHW0SbXl3OsgVep8o26aWybjH6DTqiXlrXu85gSI1Nz2vXq8zYFvWITRrrXE6Dsunam0GNkZm2NwxKL43FtEflbQy8TJ63qg_46v5_knz_dP7t7Et28fXz_Gx2kWle8TGjJYWWQFOzuJZlxUnb6pIiaYVilIpKQMVRc8wrnVMeo1xoUbe5Vg0tcspOkvlOt3FqJbdF-TvplJHbgPOdVH40ukfZahCCMUWLpsqxqpRglWiYIlRDDS2PWmynNdm1urtVff8gCERufJE7X2T0RW59kUXM-rjLWk_1gI1GO3rV75Wyf2LNUnbuJgpUsQJeRoV39wreXU8YRjmYoLHvlUU3Bcli3QIKBlVE3_6BrtzkbeywZJQAp7Qg8Eh1Kr47eufixXojKmeCcSJKoHmkTv9Cxa_BwcSJwdbE-F7C-72EyIz4Y-zUFIKcL6722TdPu_LQjt_jGQG6A7R3IXhs_6_X9w6F9WZY0T8-_x9ZvwAzogVD |
| Cites_doi | 10.1371/journal.pone.0308849 10.1002/9781119214656 10.1186/gb-2010-11-3-r25 10.1109/TKDE.2019.2912815 10.1371/journal.pone.0293400 10.1126/scitranslmed.aaq1016 10.4103/0972-6748.62274 10.1186/s13059-016-1137-3 10.1002/widm.53 10.1111/j.2517-6161.1995.tb02031.x 10.3390/ijms252011142 10.1007/978-3-642-00296-0_5 10.1038/srep43144 10.1186/s13059-016-0881-8 10.3389/fmicb.2024.1342328 10.3390/ijms25137306 10.1186/s13059-014-0550-8 10.1189/jlb.0603288 10.1038/s41598-017-15145-7 10.11613/BM.2015.015 10.1038/nbt1385 10.1214/09-AOAS312 10.1128/JVI.00924-15 10.1186/s12864-019-6419-1 10.3389/fgene.2024.1327984 10.1186/1029-242X-2013-203 10.1186/gb-2010-11-2-r14 10.4097/kjae.2015.68.6.540 10.1371/journal.pone.0000898 10.2478/v10117-011-0021-1 10.2307/2344614 10.3389/frai.2024.1405332 10.1093/bioinformatics/btp616 10.1093/biomet/61.3.439 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). COPYRIGHT 2025 BioMed Central Ltd. 2025. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: COPYRIGHT 2025 BioMed Central Ltd. – notice: 2025. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QP 7QR 7SS 7TK 7U7 7X7 7XB 88E 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI C1K CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M7P P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS RC3 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/s12864-025-11553-6 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Entomology Abstracts (Full archive) Neurosciences Abstracts Toxicology Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) Medical Database ProQuest Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection Chemoreception Abstracts ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection Toxicology Abstracts ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Calcium & Calcified Tissue Abstracts ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: SpringerLink - Revues - OpenAccess url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: Consulter via DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 6 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2164 |
| EndPage | 21 |
| ExternalDocumentID | oai_doaj_org_article_fc18833a26d94e99a8398d3a02c1b1f5 10.1186/s12864-025-11553-6 PMC11983957 A835087124 40211167 10_1186_s12864_025_11553_6 |
| Genre | Journal Article |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GrantInformation_xml | – fundername: U.S. Government under HDTRA 12310003, “Host signaling mechanisms contributing to endothelial damage in hemorrhagic fever virus infection,” PI: Narayanan grantid: HDTRA 12310003 – fundername: U.S. Government under HDTRA 12310003, "Host signaling mechanisms contributing to endothelial damage in hemorrhagic fever virus infection," PI: Narayanan grantid: HDTRA 12310003 |
| GroupedDBID | --- 0R~ 23N 2WC 2XV 53G 5VS 6J9 7X7 88E 8AO 8FE 8FH 8FI 8FJ AAFWJ AAHBH AAJSJ AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK IAO IGS IHR INH INR ISR ITC KQ8 LK8 M1P M7P M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS U2A UKHRP W2D WOQ WOW XSB AAYXX CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QP 7QR 7SS 7TK 7U7 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ K9. M48 P64 PKEHL PQEST PQUKI PRINS RC3 7X8 5PM 2VQ 4.4 ADRAZ ADTOC AHSBF C1A EJD H13 HYE IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c595t-2721f01db3f0177950ffc72e0f8a322898195ec5e49c425f8a58c8bf4cad26423 |
| IEDL.DBID | U2A |
| ISSN | 1471-2164 |
| IngestDate | Fri Oct 03 12:53:26 EDT 2025 Sun Oct 26 03:50:15 EDT 2025 Tue Sep 30 17:04:08 EDT 2025 Fri Sep 05 17:40:19 EDT 2025 Tue Oct 07 05:40:54 EDT 2025 Mon Oct 20 22:42:52 EDT 2025 Mon Oct 20 16:53:58 EDT 2025 Thu Oct 16 15:37:50 EDT 2025 Mon Apr 14 01:52:01 EDT 2025 Wed Oct 01 06:36:52 EDT 2025 Sat Sep 06 07:25:11 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | RNA sequencing Gene expression analysis Concordance analysis NanoString technology Machine learning |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. cc-by-nc-nd |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c595t-2721f01db3f0177950ffc72e0f8a322898195ec5e49c425f8a58c8bf4cad26423 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://link.springer.com/10.1186/s12864-025-11553-6 |
| PMID | 40211167 |
| PQID | 3201522601 |
| PQPubID | 44682 |
| PageCount | 21 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_fc18833a26d94e99a8398d3a02c1b1f5 unpaywall_primary_10_1186_s12864_025_11553_6 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11983957 proquest_miscellaneous_3188816319 proquest_journals_3201522601 gale_infotracmisc_A835087124 gale_infotracacademiconefile_A835087124 gale_incontextgauss_ISR_A835087124 pubmed_primary_40211167 crossref_primary_10_1186_s12864_025_11553_6 springer_journals_10_1186_s12864_025_11553_6 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025-04-10 |
| PublicationDateYYYYMMDD | 2025-04-10 |
| PublicationDate_xml | – month: 04 year: 2025 text: 2025-04-10 day: 10 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC genomics |
| PublicationTitleAbbrev | BMC Genomics |
| PublicationTitleAlternate | BMC Genomics |
| PublicationYear | 2025 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | MD Young (11553_CR17) 2010; 11 Y Benjamini (11553_CR27) 1995; 57 11553_CR33 TK Kim (11553_CR26) 2015; 68 11553_CR31 A Banerjee (11553_CR30) 2009; 18 11553_CR36 11553_CR15 11553_CR12 11553_CR34 11553_CR13 E Speranza (11553_CR8) 2017; 7 JA Nelder (11553_CR14) 1972; 135 11553_CR29 11553_CR4 11553_CR28 11553_CR9 M Rezapour (11553_CR16) 2024; 19 11553_CR1 W Zhang (11553_CR7) 2020; 21 J Hauke (11553_CR11) 2011; 30 M Rezapour (11553_CR22) 2024; 25 A Bosworth (11553_CR2) 2017; 7 M Rezapour (11553_CR10) 2024; 15 11553_CR21 X Ying (11553_CR32) 2019; 1168 11553_CR24 11553_CR18 PA Ilinykh (11553_CR19) 2015; 89 MD Robinson (11553_CR23) 2010; 11 X Liu (11553_CR3) 2017; 18 T-T Wong (11553_CR25) 2019; 32 M Rezapour (11553_CR5) 2024; 7 M Rezapour (11553_CR20) 2024; 15 El-Gedaily (11553_CR35) 2004; 75 K Song (11553_CR6) 2023; 18 |
| References_xml | – volume: 19 start-page: e0308849 issue: 11 year: 2024 ident: 11553_CR16 publication-title: PLoS ONE doi: 10.1371/journal.pone.0308849 – ident: 11553_CR15 doi: 10.1002/9781119214656 – volume: 11 start-page: 1 year: 2010 ident: 11553_CR23 publication-title: Genome Biol doi: 10.1186/gb-2010-11-3-r25 – volume: 32 start-page: 1586 issue: 8 year: 2019 ident: 11553_CR25 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2019.2912815 – volume: 1168 start-page: 022022 year: 2019 ident: 11553_CR32 publication-title: J Phys: Conf Ser – volume: 18 start-page: e0293400 issue: 10 year: 2023 ident: 11553_CR6 publication-title: PLoS ONE doi: 10.1371/journal.pone.0293400 – ident: 11553_CR18 doi: 10.1126/scitranslmed.aaq1016 – volume: 18 start-page: 127 issue: 2 year: 2009 ident: 11553_CR30 publication-title: Industrial Psychiatry J doi: 10.4103/0972-6748.62274 – volume: 18 start-page: 1 year: 2017 ident: 11553_CR3 publication-title: Genome Biol doi: 10.1186/s13059-016-1137-3 – ident: 11553_CR33 doi: 10.1002/widm.53 – volume: 57 start-page: 289 issue: 1 year: 1995 ident: 11553_CR27 publication-title: J Roy Stat Soc: Ser B (Methodol) doi: 10.1111/j.2517-6161.1995.tb02031.x – ident: 11553_CR21 doi: 10.3390/ijms252011142 – ident: 11553_CR9 doi: 10.1007/978-3-642-00296-0_5 – volume: 7 start-page: 43144 issue: 1 year: 2017 ident: 11553_CR2 publication-title: Sci Rep doi: 10.1038/srep43144 – ident: 11553_CR1 doi: 10.1186/s13059-016-0881-8 – volume: 15 start-page: 1342328 year: 2024 ident: 11553_CR20 publication-title: Front Microbiol doi: 10.3389/fmicb.2024.1342328 – volume: 25 start-page: 7306 issue: 13 year: 2024 ident: 11553_CR22 publication-title: Int J Mol Sci doi: 10.3390/ijms25137306 – ident: 11553_CR29 doi: 10.1186/s13059-014-0550-8 – volume: 75 start-page: 495 issue: 3 year: 2004 ident: 11553_CR35 publication-title: J Leucocyte Biology doi: 10.1189/jlb.0603288 – volume: 7 start-page: 14756 issue: 1 year: 2017 ident: 11553_CR8 publication-title: Sci Rep doi: 10.1038/s41598-017-15145-7 – ident: 11553_CR13 doi: 10.11613/BM.2015.015 – ident: 11553_CR4 doi: 10.1038/nbt1385 – ident: 11553_CR12 doi: 10.1214/09-AOAS312 – volume: 89 start-page: 7567 issue: 15 year: 2015 ident: 11553_CR19 publication-title: J Virol doi: 10.1128/JVI.00924-15 – volume: 21 start-page: 1 year: 2020 ident: 11553_CR7 publication-title: BMC Genomics doi: 10.1186/s12864-019-6419-1 – ident: 11553_CR24 – volume: 15 start-page: 1327984 year: 2024 ident: 11553_CR10 publication-title: Front Genet doi: 10.3389/fgene.2024.1327984 – ident: 11553_CR34 doi: 10.1186/1029-242X-2013-203 – volume: 11 start-page: 1 year: 2010 ident: 11553_CR17 publication-title: Genome Biol doi: 10.1186/gb-2010-11-2-r14 – volume: 68 start-page: 540 issue: 6 year: 2015 ident: 11553_CR26 publication-title: Korean J Anesthesiology doi: 10.4097/kjae.2015.68.6.540 – ident: 11553_CR36 doi: 10.1371/journal.pone.0000898 – volume: 30 start-page: 87 issue: 2 year: 2011 ident: 11553_CR11 publication-title: Quaestiones Geographicae doi: 10.2478/v10117-011-0021-1 – volume: 135 start-page: 370 issue: 3 year: 1972 ident: 11553_CR14 publication-title: J Royal Stat Society: Ser (General) doi: 10.2307/2344614 – volume: 7 start-page: 1405332 year: 2024 ident: 11553_CR5 publication-title: Front Artif Intell doi: 10.3389/frai.2024.1405332 – ident: 11553_CR28 doi: 10.1093/bioinformatics/btp616 – ident: 11553_CR31 doi: 10.1093/biomet/61.3.439 |
| SSID | ssj0017825 |
| Score | 2.4588778 |
| Snippet | This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs)... Abstract This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 358 |
| SubjectTerms | 2',5'-Oligoadenylate Synthetase - genetics Agreements Animal Genetics and Genomics Animals Bias Binomial distribution Biomarkers Biomedical and Life Sciences Cancer Concordance analysis Correlation analysis Dendritic cells Dendritic Cells - metabolism Dendritic Cells - virology Ebola virus Ebola virus infections Ebolavirus Ebolavirus - genetics Ebolavirus - physiology Gene expression Gene expression analysis Gene Expression Profiling - methods Gene sequencing Generalized linear models Genes Genetic aspects Genetic research Hemorrhagic Fever, Ebola - genetics Hemorrhagic Fever, Ebola - virology Humans Immunoregulation Infections Learning algorithms Life Sciences Machine Learning Microarrays Microbial Genetics and Genomics Monocytes NanoString technology Physiological aspects Plant Genetics and Genomics Primates Proteomics Regression analysis Regression models Ribonucleic acid RNA RNA sequencing RNA-Seq - methods Statistical analysis Strains (organisms) USP18 protein Viral diseases Viral infections Viruses |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQJQQcEG9SCjIIiQONajtxYh8X1Kog0UOXSr1ZjmO3lUp26W6EKvHjmXEebEACDlz2EM9uYn8z9szO5BtCXgubBRkqjUl3keaKebA551LwnV2hpFQ-cnd-OioOT_KPp_J0o9UX1oR19MDdwu0Fx7EfrhVFrXOvtYUTXdWZZcLxiofIXsqUHoKpPn8A554cXpFRxd4KduEiT7F1K8dGOWkxOYYiW__ve_LGofRrweSYNb1DbrXN0l5_s5eXGwfTwT1yt_co6aybyX1ywzcPyM2ux-T1Q_K9S-vCz1AIfTHWRJxpX59Fj49m6dx_pbapKey0i_ka70jXwz_uEEjTi4buVxACp13llq9ps2hicz-6RLIK8FZpG-_wJZZmetr3ojh7RE4O9j-_P0z7lgupk1quUwEBYWC8rjL4LEstWQiuFJ4FZcH0lca0m3fS59qBtcNVqZyqQu5sDa6VyB6TLXgE_5RQEHc2y0NR-TyvRWnBHANztSsz6XnNEvJ2QMAsO2YNEyMSVZgOLwN4mYiXKRLyDkEaJZEVO14AXTG9rpi_6UpCXiHEBnkvGiysObPtamU-zI_NDDxRBsGjyBPyphcKCwDb2f49BZgVUmVNJHcmkmCYbjo8aJLpN4aVycDhQpeX8YS8HIfxm1js1vhFCzIwCwV-MtcJedIp3jhvsCiOqbOEqIlKThZmOtJcnEfacM61wqxsQnYH7f35XH9a-d1Rw_8BqO3_AdQzclugsSKnJtshW-ur1j8H529dvYh2_gPARVK4 priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3ra9RAEB_qFVE_iK9qtMoqgh9saF6b23wQucqVKnjInYV-Wzb7OAs1ufYuSME_3pm82igUv-RDdvLYzGNnMrO_AXgbqdhxl2eUdI_8RAQWdU5rH31nnQrOha2xO7_O0qPj5MsJP9mCWbcXhsoqO5tYG2pTavpHvh_jSkW-QhB-XJ371DWKsqtdCw3VtlYwH2qIsVuwHREy1gi2D6azb_M-r4DrIe-2zoh0f43WOU18aukaUgMdPx0sTzWK_7-2-tpi9XchZZ9NvQd3qmKlLn-ps7NrC9bhA7jfepps0ojGQ9iyxSO43fSevHwMv5t0L96GYUhMMSjxn7V1W2w-m_gLe85UYRha4HKxoSeyTfcnHgNsdlqwaY6hsd9UdFnDirKom_6xFYFYoBfLqvoJP-uSTcvaHhXLJ3B8OP3-6chvWzH4mmd840cYKLogNHmMx_E444FzehzZwAmFJkFklI6zmtsk02gF8CwXWuQu0cqgyxXFOzDCV7DPgCG5VnHi0twmiYnGCtXUBdroccxtaAIP3ncckKsGcUPWkYpIZcMvifySNb9k6sEBMamnJLTs-kR5sZSt8kmnQ-qprKLUZInNMoVeoTCxCiId5qHjHrwhFkvCwyio4GapqvVafl7M5QQ91ACDyijx4F1L5Epktlbt_gWcFUFoDSh3B5SosHo43EmSbA3GWl6Jtwev-2G6korgCltWSIOzEOg_h5kHTxvB6-eNmhZSSs0DMRDJwYcZjhSnP2o48TDMBGVrPdjrpPfqvW768nu9hP8Ho57fPOsXcDciNSQUzWAXRpuLyr5Ed2-Tv2p1-A9b4FDJ priority: 102 providerName: ProQuest – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrR1db9Mw0IJOCHjgc4zAQAYh8cDSxUmcOI8FbRpIVGil0niyHMcuFW3atYnQED-eu3yUZiAEEi99iC-q73x3vst9EfLCV4HlNk0w6O67ofAMyJzWLtjOOhKcC1P17nw_jE7G4bszftaUR2MtTDrX2Jx0PtXr_nYB-qyub8D5CWZ1uMxsLe4iOlyDho1CF8eyMhyC40ZXyU7EwTLvkZ3x8MPgU1VgFDPXB9egrZv57Yudu6lq4f-rot66qS5nUW5CqTfJ9TJfqouvajbbuq2Ob5N5i2edpPKlXxZpX3-71ALyfxHiDrnVmLV0UPPhXXLF5PfItXrQ5cV98r2OLcO2Kfjf6PAis9EmSYyeDgfuyJxTlWcU1P1iVCCGtGg_-4M3T6c5PUrBD3fr9DGT0XyRVxMG6RI7ZoDJTMvqH-ZVfqihzUCMyS4ZHx99fHPiNnMfXM0TXrg-eKXWY1kawG8cJ9yzVse-8axQoH9EgrE_o7kJEw0qB55yoUVqQ60ysO_84AHpwRbMQ0IBXKsgtFFqwjDzYwU6wXo603HADcs8h7xqT1wu6_YesnKLRCRrekqgp6zoKSOHvEam2EBia-7qwWI1kY2kS6sZDnBWfpQloUkSBSaoyALl-ZqlzHKHPEeWkth8I8fsnokq12v5dnQqB2AOe-DB-qFDXjZAdgGHrlVTLAFYYb-uDuR-BxK0g-4ut5wrG-20lgFYfWh3e8whzzbL-CZm3OVmUQIMYCHAWGeJQ_ZqRt_gDWLNMH7nENERgQ5huiv59HPVu5yxRGBo2CEHrbT83NefKH-wkai_OKhH_wb-mNzwUWywhae3T3rFqjRPwNYs0qeNCvkBPKx2uA priority: 102 providerName: Unpaywall |
| Title | Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine learning |
| URI | https://link.springer.com/article/10.1186/s12864-025-11553-6 https://www.ncbi.nlm.nih.gov/pubmed/40211167 https://www.proquest.com/docview/3201522601 https://www.proquest.com/docview/3188816319 https://pubmed.ncbi.nlm.nih.gov/PMC11983957 https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-025-11553-6 https://doaj.org/article/fc18833a26d94e99a8398d3a02c1b1f5 |
| UnpaywallVersion | publishedVersion |
| Volume | 26 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: KQ8 dateStart: 20000701 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: KQ8 dateStart: 20000101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: Consulter via DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: ABDBF dateStart: 20000101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: DIK dateStart: 20000101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: Consulter via PubMed Central customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: RPM dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: AAJSJ dateStart: 20001201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink - Revues - OpenAccess customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: C6C dateStart: 20000112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1471-2164 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017825 issn: 1471-2164 databaseCode: U2A dateStart: 20001201 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3rb9MwED-xTQj4gHgTGJVBSHxg0fJy4nzsqk4DiWpqqTQ-WY5jl0kjLWsrNIk_njvnwQIIwZdKjS8P5x7-Xe58B_A6UrHltsgp6B75iQgM6pzWPmJnnQrOhXG1Oz9M0pN58v6MnzWbwtZttnsbknSW2qm1SA_XaEnTxKf2qyE1u_HTHdjjVM4LpXgeDbvYAa55vN0e88fzekuQq9T_uz2-tiD9mizZRUzvwK1ttVJX39TFxbVF6fge3G3QJBvW7L8PN0z1AG7W_SWvHsL3OqSLl2Ho9pKfSTxmTW4Wm06G_sx8ZaoqGVrZ5WxDd2Sb9ms7OtHsvGLjAt1fv87aMiWrlpVr7MdWVKgCkSrbujt8cWmZhjV9KBaPYH48_jg68Zt2C77mOd_4ETqDNgjLIsbfLMt5YK3OIhNYoVDtRU4hN6O5SXKNmo5HudCisIlWJcKqKH4Mu_gI5ikwJNcqTmxamCQpo0yhKtpAlzqLuQnLwIO3LQfkqq6qIZ03IlJZ80siv6Tjl0w9OCImdZRUEdsdWF4uZKNg0uqQ-iarKC3zxOS5QuQnylgFkQ6L0HIPXhGLJdW8qCipZqG267V8N5vKIaLQAB3HKPHgTUNkl8hsrZo9CjgrKpPVo9zvUaJS6v5wK0myMQprGSPYIrgbhB687IbpTEp0q8xyizQ4C4EYOcw9eFILXjdv1KaQwmYeiJ5I9l5Mf6Q6_-xKhodhLigi68FBK70_n-tvb_6gk_B_YNSz_7v6c7gdkVpS5cxgH3Y3l1vzAiHephjATnaWDWDvaDw5neK_UToauM8lA6frODKfnA4__QCCXU1w |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF5VrVDhgHhjKLAgEAdq1Y9dZ32oUAqpEtpGKGml3rbr9W6oVOy0SVRF4rfx25jxqzVIFZdecrDHsdfz9szOR8j7QIWW2yTGonvgMuEZ0DmtXYiddSQ4F6aY3XkwjPpH7NsxP14hv-u9MNhWWdvEwlCnucZv5FsheCqMFTz_8_TcRdQorK7WEBqqglZIt4sRY9XGjj2zvIQUbrY9-Ar8_hAEu73DL323QhlwNY_53A0gB7KenyYh_HY6Mfes1Z3AeFYokHYRY6XJaG5YrEHA4SgXWiSWaZVCNIGDD8AFrLGQxZD8re30ht9HTR0D_C-vt-qIaGsG3iBiLkLI-gjY40Ytd1igBvzrG645x78bN5vq7T2yvsimanmpzs6uOcjdB-R-FdnSbimKD8mKyR6ROyXW5fIx-VWWl-FvKKTgmPOivNGqT4yOhl13bM6pylIKFj8fz_GOdF5_-YeEnp5mtJdAKu6WHWQmpVmeFSCDdIpDMyBqpoviDj-LFlFDK0yMyRNydCtMeUpW4RHMc0KBXKuQ2SgxjKVBR4FZsJ5OdSfkxk89h3yqOSCn5YQPWWRGIpIlvyTwSxb8kpFDdpBJDSVO5y4O5BcTWSm7tNpHDGcVRGnMTBwriEJFGiov0H7iW-6Qd8hiifM3MmzwmajFbCYH45HsQkTsQRIbMId8rIhsDszWqtovAavCkV0tyo0WJRgI3T5dS5KsDNRMXqmTQ942p_FKbLrLTL4AGliFgHjdjx3yrBS8Zt2g2T6W8BwiWiLZejHtM9npj2J8ue_HAqvDDtmspffquW5685uNhP8Ho17cvOo3ZL1_eLAv9wfDvZfkboAqiRM8vQ2yOr9YmFcQas6T15U-U3Jy2ybkD4Y3jOs |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9QwELagiOsBcRMoYBASDzRqnNiJ87gsXbUcK9SlUt8sx7GXSiW7dLNClfjxzDgHG0AIXvIQTw5nDs9kxt8Q8iLWiROuyDHpHodcRhZ0zpgQfGeTSiGk9didH6bp_hF_eyyON3bx-2r3LiXZ7GlAlKaq3l2WrlFxme6uwKqmPMRWrAwb34TpRXKJw-qGPQzG6bjPI8D6J7qtMn-8brAcedT-323zxuL0a-Fknz29Tq6uq6U-_6ZPTzcWqMlNcqP1LOmoEYVb5IKtbpPLTa_J8zvke5PehdtQmCvGnMhv2tZp0cPpKJzZr1RXJQWLu5jV-ERad3_eIaCmJxXdKyAUDpsKLlvSalH5Jn90iaAV4LXStX_CF1-iaWnbk2J-lxxN9j6N98O29UJoRC7qMIbA0EWsLBI4ZlkuIudMFtvISQ0mQOaYfrNGWJ4b0Ho4K6SRheNGl-Bixck9sgWvYB8QCuRGJ9ylheW8jDMNaukiU5osEZaVUUBedRxQywZhQ_nIRKaq4ZcCfinPL5UG5DUyqadEdGx_YnE2V62yKWcY9lDWcVrm3Oa5Bi9QlomOYsMK5kRAniOLFeJfVFhgM9fr1UodzA7VCDzSCILImAfkZUvkFsBso9v9CjArhMwaUG4PKEFBzXC4kyTVGoiVSsDxQtc3YgF51g_jlVj0VtnFGmhgFhL8ZZYH5H4jeP28QbMYptACIgciOfgww5Hq5LOHD2csl5idDchOJ70_3-tvX36nl_B_YNTD_7v7U3Ll45uJen8wffeIXItRQxFQM9omW_XZ2j4Gz68unnjl_gFQY05_ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrR1db9Mw0IJOCHjgc4zAQAYh8cDSxUmcOI8FbRpIVGil0niyHMcuFW3atYnQED-eu3yUZiAEEi99iC-q73x3vst9EfLCV4HlNk0w6O67ofAMyJzWLtjOOhKcC1P17nw_jE7G4bszftaUR2MtTDrX2Jx0PtXr_nYB-qyub8D5CWZ1uMxsLe4iOlyDho1CF8eyMhyC40ZXyU7EwTLvkZ3x8MPgU1VgFDPXB9egrZv57Yudu6lq4f-rot66qS5nUW5CqTfJ9TJfqouvajbbuq2Ob5N5i2edpPKlXxZpX3-71ALyfxHiDrnVmLV0UPPhXXLF5PfItXrQ5cV98r2OLcO2Kfjf6PAis9EmSYyeDgfuyJxTlWcU1P1iVCCGtGg_-4M3T6c5PUrBD3fr9DGT0XyRVxMG6RI7ZoDJTMvqH-ZVfqihzUCMyS4ZHx99fHPiNnMfXM0TXrg-eKXWY1kawG8cJ9yzVse-8axQoH9EgrE_o7kJEw0qB55yoUVqQ60ysO_84AHpwRbMQ0IBXKsgtFFqwjDzYwU6wXo603HADcs8h7xqT1wu6_YesnKLRCRrekqgp6zoKSOHvEam2EBia-7qwWI1kY2kS6sZDnBWfpQloUkSBSaoyALl-ZqlzHKHPEeWkth8I8fsnokq12v5dnQqB2AOe-DB-qFDXjZAdgGHrlVTLAFYYb-uDuR-BxK0g-4ut5wrG-20lgFYfWh3e8whzzbL-CZm3OVmUQIMYCHAWGeJQ_ZqRt_gDWLNMH7nENERgQ5huiv59HPVu5yxRGBo2CEHrbT83NefKH-wkai_OKhH_wb-mNzwUWywhae3T3rFqjRPwNYs0qeNCvkBPKx2uA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Assessing+concordance+between+RNA-Seq+and+NanoString+technologies+in+Ebola-infected+nonhuman+primates+using+machine+learning&rft.jtitle=BMC+genomics&rft.au=Rezapour%2C+Mostafa&rft.au=Narayanan%2C+Aarthi&rft.au=Mowery%2C+Wyatt+H.&rft.au=Gurcan%2C+Metin+Nafi&rft.date=2025-04-10&rft.pub=BioMed+Central&rft.eissn=1471-2164&rft.volume=26&rft.issue=1&rft_id=info:doi/10.1186%2Fs12864-025-11553-6&rft.externalDocID=10_1186_s12864_025_11553_6 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2164&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2164&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2164&client=summon |