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Lubovac-Pilav, ZelminaORCID iD iconorcid.org/0000-0001-6427-0315
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de Weerd, H. A., Guala, D., Gustafsson, M., Synnergren, J., Tegnér, J., Lubovac-Pilav, Z. & Magnusson, R. (2024). Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules. Patterns, 5(11), Article ID 101093.
Åpne denne publikasjonen i ny fane eller vindu >>Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules
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2024 (engelsk)Inngår i: Patterns, ISSN 2666-3899, Vol. 5, nr 11, artikkel-id 101093Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The human transcriptome is a highly complex system and is often the focus of research, especially when it fails to function properly, causing disease. Indeed, the amount of publicly available transcriptomic data has grown considerably with the advent of high-throughput techniques. Such special cases are often hard to fully dissect, since studies will be confined to limited data samples and multiple biases. An ideal approach would utilize all available data to learn the fundamentals of the human gene expression system and use these insights in the examination of the more limited sample sets relating to specific diseases. This study shows how a neural network model can be created and used to extract relevant disease genes when applied to limited disease datasets and to suggest relevant pharmaceutical compounds. Thus, it presents a step toward a future where artificial intelligence can advance the analysis of human high-throughput data.

sted, utgiver, år, opplag, sider
Elsevier, 2024
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-24648 (URN)10.1016/j.patter.2024.101093 (DOI)001355226900001 ()39568475 (PubMedID)2-s2.0-85208221759 (Scopus ID)
Forskningsfinansiär
Hedlund foundation, M-2023-2054University of SkövdeSwedish Research Council, 2019-04193Swedish Research Council, 2022-06725Stiftelsen Assar Gabrielssons fond, FB21-66Knowledge Foundation, 20200014
Merknad

CC BY 4.0

Available online 31 October 2024, 101093

Correspondence: hendrik.de.weerd@liu.se (H.A.d.W.), rasmus.magnusson@liu.se (R.M.)

This work was supported by the Systems Biology Research Centre at the University of Skövde under grants from the Swedish Knowledge Foundation (grant 20200014 to R.M, Z.L.-P., and J.S.), Petrus och Augusta Hedlunds Stiftelse (grant M-2023-2054 to R.M), the Assar Gabrielssons Fond (grant FB21-66 to R.M. and H.A.d.W.), and the Swedish Research Council (grant 2019-04193 to H.A.d.W. and M.G.). The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.

Tilgjengelig fra: 2024-11-04 Laget: 2024-11-04 Sist oppdatert: 2025-02-05bibliografisk kontrollert
Borgmästars, E., Jacobson, S., Simm, M., Johansson, M., Billing, O., Lundin, C., . . . Sund, M. (2024). Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank. Journal of Gastrointestinal Oncology, 15(2), 755-767
Åpne denne publikasjonen i ny fane eller vindu >>Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank
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2024 (engelsk)Inngår i: Journal of Gastrointestinal Oncology, ISSN 2078-6891, E-ISSN 2219-679X, Vol. 15, nr 2, s. 755-767Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.

Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.

Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714–0.854] compared to 0.681 (95% CI: 0.597–0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].

Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.

sted, utgiver, år, opplag, sider
AME Publishing Company, 2024
Emneord
Pancreatic neoplasms, biomarkers, hyperglycemia, risk, survival
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-23791 (URN)10.21037/jgo-23-930 (DOI)001284655300018 ()38756646 (PubMedID)2-s2.0-85192826642 (Scopus ID)
Forskningsfinansiär
Umeå UniversitySwedish Cancer Society, 19 0273Swedish Cancer Society, 2017-557Swedish Cancer Society, CAN 2017/332Swedish Cancer Society, CAN 2017/827Swedish Research Council, 2019-01690Swedish Research Council, 2016-02990Swedish Research Council, 2017-01531Region Västerbotten, RV-583411Region Västerbotten, RV-549731Region Västerbotten, RV-841551Region Västerbotten, RV-930167Region Västerbotten, VLL-643451Region Västerbotten, RV-930478Region Västerbotten, RV-930132Region Västerbotten, RV-9960708Region Västerbotten, RV-99607108Region Västerbotten, VLL-837731Sjöberg FoundationBengt Ihres Foundation, SLS-885861Bengt Ihres Foundation, SLS-960529Swedish Society of Medicine, SLS-960379Lions Cancerforskningsfond i NorrKnut and Alice Wallenberg FoundationThe Kempe Foundations
Merknad

CC BY-NC-ND 4.0 DEED

Correspondence to: Emmy Borgmästars, PhD. Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Norrlands Universitetssjukhus, 6M, M31, 901 85 Umeå, Sweden. Email: emmy.borgmastars@umu.se

This work was supported by Umeå University, the Swedish Cancer Society (19 0273, 2017-557, CAN 2017/332, CAN 2017/827), the Swedish Research Council (2019-01690, 2016-02990, 2017-01531), Västerbotten Region (RV-583411, RV-549731, RV-841551, RV-930167, VLL-643451, RV-930478, RV-930132, RV-9960708, RV-99607108, VLL-837731), the Sjöberg Foundation, the Claes Groschinsky Memorial Foundation (M 19391), Bengt Ihre Foundation (SLS-885861, SLS-960529), Swedish Society of Medicine (SLS-960379), Lion’s Cancer Research Foundation, the Knut and Alice Wallenberg Foundation, Finska Läkaresällskapet, the Sigrid Juselius Foundation, Medicinska Understödsföreningen Liv och Hälsa, Bengt Ihre Fellowship Research Grant, and the JC Kempe Memorial Foundation Scholarship Fund.

Tilgjengelig fra: 2024-04-30 Laget: 2024-04-30 Sist oppdatert: 2024-08-15bibliografisk kontrollert
Borgmästars, E., Ulfenborg, B., Johansson, M., Jonsson, P., Billing, O., Franklin, O., . . . Sund, M. (2024). Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study. Translational Oncology, 48, Article ID 102059.
Åpne denne publikasjonen i ny fane eller vindu >>Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study
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2024 (engelsk)Inngår i: Translational Oncology, ISSN 1944-7124, E-ISSN 1936-5233, Vol. 48, artikkel-id 102059Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations.

A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant.

Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31–7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429–0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls.

CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
Pancreatic neoplasms, miRNomics, Metabolomics, Proteomics, Risk
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-24392 (URN)10.1016/j.tranon.2024.102059 (DOI)001272983200001 ()39018772 (PubMedID)2-s2.0-85198543877 (Scopus ID)
Forskningsfinansiär
Region VästerbottenSwedish Cancer Society, CAN 2016/643, 19 0273Swedish Research Council, 2016-02990, 2019-01690Sjöberg FoundationUmeå UniversityThe Royal Swedish Academy of Sciences, LM2021-0010, LM2023-0012Swedish Society of Medicine, SLS-960379Bengt Ihres Foundation, SLS-960529, SLS-986656
Merknad

CC BY 4.0

Corresponding author at: University Hospital of Umeå, 901 85 Umeå, Sweden. E-mail address: emmy.borgmastars@umu.se (E. Borgmästars)

The authors thank Hanna Nyström, and Daniel Öhlund at Umeå University for valuable assistance in data collection. We thank Xiaoshuang Feng at International Agency for Research of Cancer, Lyon, France for guidance in statistical analyses. The authors would also like to thank Swedish Metabolomics Centre, Umeå, Sweden (www.swedishmetabolomicscentre.se) and Biobanken Norr. Funding: This study was funded by Umeå University, the Swedish Research Council [2016-02990, 2019-01690], the Swedish Cancer Society [CAN 2016/643, 19 0273], Region Västerbotten [RV-583411, RV-549731, RV-583411, RV-841551, RV 967602], Finska Läkaresällskapet, Medicinska Understödsföreningen Liv och Hälsa, the Sjöberg Foundation, The JC Kempe Memorial Foundation Scholarship Fund, The Royal Swedish Academy of Sciences (PE Lindahl Foundation, LM2021-0010 and LM2023-0012), The Swedish Society of Medicine (SLS-960379), Cancerforskningsfonden i Norrland (LP 23-2337), Bengt Ihre foundation (SLS-960529 and SLS-986656), and Bengt Ihre Research Fellowship Grant. The sponsors had no role in the study design, collection, analysis and interpretation of data, writing of the report, or in the decision to submit the article for publication.

Tilgjengelig fra: 2024-07-17 Laget: 2024-07-17 Sist oppdatert: 2025-02-05bibliografisk kontrollert
Åkesson, J., Hojjati, S., Hellberg, S., Raffetseder, J., Khademi, M., Rynkowski, R., . . . Gustafsson, M. (2023). Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. Nature Communications, 14(1), Article ID 6903.
Åpne denne publikasjonen i ny fane eller vindu >>Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis
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2023 (engelsk)Inngår i: Nature Communications, E-ISSN 2041-1723, Vol. 14, nr 1, artikkel-id 6903Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.

sted, utgiver, år, opplag, sider
Springer Nature, 2023
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-23344 (URN)10.1038/s41467-023-42682-9 (DOI)001129872400021 ()37903821 (PubMedID)2-s2.0-85175444895 (Scopus ID)
Forskningsfinansiär
Swedish Foundation for Strategic Research, SB16-0011The Swedish Brain FoundationKnut and Alice Wallenberg FoundationSwedish Research Council, 2019-04193Swedish Research Council, 2018-02776Swedish Research Council, 2020-02700Swedish Research Council, 2020-00014Swedish Research Council, 2021-03092Medical Research Council of Southeast Sweden (FORSS), FORSS-315121Swedish Association of Persons with Neurological Disabilities, F2018-0052
Merknad

CC BY 4.0

e-mail: mika.gustafsson@liu.se

The study was funded by the Swedish Foundation for Strategic Research (SB16-0011 [M.G., J.E.]), the Swedish Brain Foundation, Knut and Alice Wallenberg Foundation, and Margareth AF Ugglas Foundation, Swedish Research Council (2019-04193 [M.G.], 2018-02776 [J.E.], 2020-02700 [F.P.], 2020-00014 [Z.L.P.], 2021-03092 [J.E.]), the Medical Research Council of Southeast Sweden (FORSS-315121 [J.E.]), NEURO Sweden (F2018-0052 [J.E.]), ALF grants, Region Östergötland, the Swedish Foundation for MS Research and the European Union’s Marie Sklodowska-Curie (813863 [J.E.]). The authors would like to acknowledge support of the Clinical biomarker facility at SciLifeLab Sweden for providing assistance in protein analyses.

Open access funding provided by Linköping University.

Tilgjengelig fra: 2023-11-08 Laget: 2023-11-08 Sist oppdatert: 2025-02-05bibliografisk kontrollert
Jurcevic, S., Keane, S., Borgmästars, E., Lubovac-Pilav, Z. & Ejeskär, K. (2022). Bioinformatics analysis of miRNAs in the neuroblastoma 11q-deleted region reveals a role of miR-548l in both 11q-deleted and MYCN amplified tumour cells. Scientific Reports, 12(1), Article ID 19729.
Åpne denne publikasjonen i ny fane eller vindu >>Bioinformatics analysis of miRNAs in the neuroblastoma 11q-deleted region reveals a role of miR-548l in both 11q-deleted and MYCN amplified tumour cells
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2022 (engelsk)Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 12, nr 1, artikkel-id 19729Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Neuroblastoma is a childhood tumour that is responsible for approximately 15% of all childhood cancer deaths. Neuroblastoma tumours with amplification of the oncogene MYCN are aggressive, however, another aggressive subgroup without MYCN amplification also exists; rather, they have a deleted region at chromosome arm 11q. Twenty-six miRNAs are located within the breakpoint region of chromosome 11q and have been checked for a possible involvement in development of neuroblastoma due to the genomic alteration. Target genes of these miRNAs are involved in pathways associated with cancer, including proliferation, apoptosis and DNA repair. We could show that miR-548l found within the 11q region is downregulated in neuroblastoma cell lines with 11q deletion or MYCN amplification. In addition, we showed that the restoration of miR-548l level in a neuroblastoma cell line led to a decreased proliferation of these cells as well as a decrease in the percentage of cells in the S phase. We also found that miR-548l overexpression suppressed cell viability and promoted apoptosis, while miR-548l knockdown promoted cell viability and inhibited apoptosis in neuroblastoma cells. Our results indicate that 11q-deleted neuroblastoma and MYCN amplified neuroblastoma coalesce by downregulating miR-548l.

sted, utgiver, år, opplag, sider
Springer Nature, 2022
HSV kategori
Forskningsprogram
Infektionsbiologi; Translationell medicin TRIM; Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-22068 (URN)10.1038/s41598-022-24140-6 (DOI)000885172100065 ()36396668 (PubMedID)2-s2.0-85142197814 (Scopus ID)
Forskningsfinansiär
Swedish Childhood Cancer Foundation
Merknad

CC BY 4.0

© 2022 Springer Nature Limited

We thank the Swedish Childhood Cancer Fund and Assar Gabrielsson Found for financial support.

Open access funding provided by University of Skövde.

Correspondence and requests for materials should be addressed to S.J.

Tilgjengelig fra: 2022-11-21 Laget: 2022-11-21 Sist oppdatert: 2025-02-05bibliografisk kontrollert
de Weerd, H. A., Åkesson, J., Guala, D., Gustafsson, M. & Lubovac-Pilav, Z. (2022). MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data. Bioinformatics Advances, 2(1), Article ID vbac006.
Åpne denne publikasjonen i ny fane eller vindu >>MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data
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2022 (engelsk)Inngår i: Bioinformatics Advances, E-ISSN 2635-0041, Vol. 2, nr 1, artikkel-id vbac006Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

MotivationNetwork-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.

ResultsWe developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.

Availability and implementationMODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.

Supplementary informationSupplementary data are available at Bioinformatics Advances online.

sted, utgiver, år, opplag, sider
Oxford University Press, 2022
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-21058 (URN)10.1093/bioadv/vbac006 (DOI)001153137500002 ()36699378 (PubMedID)2-s2.0-85148565848 (Scopus ID)
Forskningsfinansiär
Knowledge Foundation, dnr HSK219/26Swedish Foundation for Strategic Research, SB16-0011Swedish Research Council, 2019-04193
Merknad

CC BY 4.0

Correspondence: Mika Gustafsson

Advance Access Publication Date: 25 January 2022

Funding: This work was supported by the Knowledge Foundation [dnr HSK219/26]; Swedish Foundation for Strategic Research [SB16-0011]; and Swedish Research Council [grant 2019-04193].

Tilgjengelig fra: 2022-04-13 Laget: 2022-04-13 Sist oppdatert: 2025-02-07bibliografisk kontrollert
Badam, T. V. S., de Weerd, H. A., Martínez-Enguita, D., Olsson, T., Alfredsson, L., Kockum, I., . . . Gustafsson, M. (2021). A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis. BMC Genomics, 22(1), Article ID 631.
Åpne denne publikasjonen i ny fane eller vindu >>A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
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2021 (engelsk)Inngår i: BMC Genomics, E-ISSN 1471-2164, Vol. 22, nr 1, artikkel-id 631Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases. 

sted, utgiver, år, opplag, sider
BioMed Central, 2021
Emneord
Benchmark, Data integration, Disease modules, Genome-wide association analysis, Methylomics, Multi-omics, Multiple sclerosis, Network analysis, Network modules, Protein network analysis, Risk factors, Transcriptomics
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-20535 (URN)10.1186/s12864-021-07935-1 (DOI)000692402600002 ()34461822 (PubMedID)2-s2.0-85113734842 (Scopus ID)
Forskningsfinansiär
Swedish Research Council, 2015–03807Swedish Research Council, 2018–02638EU, Horizon 2020, grant 818170Knut and Alice Wallenberg Foundation, 2019.0089Knowledge Foundation, 20170298Swedish Foundation for Strategic Research , SB16–0095Swedish National Infrastructure for Computing (SNIC), SNIC 2020/5–177, LiU-2018-12 and LiU-2019-25
Merknad

CC BY 4.0

© 2021, The Author(s)

This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Correspondence: mika.gustafsson@liu.se

This work was supported by the Swedish Research Council (grant 2015–03807(M.G.), grant 2018–02638(M.J.)), the Swedish foundation for strategic research (grant SB16–0095(M.G.)), the Center for Industrial IT (CENIIT)(M.G.), European Union Horizon 2020/European Research Council Consolidator grant (Epi4MS, grant 818170(M.J.)), Knut and Alice Wallenberg Foundation (grant 2019.0089(M.J.)) and the Knowledge Foundation (grant 20170298(Z.L.)). Computational resources were granted by Swedish National Infrastructure for Computing (SNIC; SNIC 2020/5–177, LiU-2018-12 and LiU-2019-25). The funding bodies had no role in the study and collection, ana-lysis, and interpretation of data and in writing the manuscript. Open Accessfunding provided by Linköping University.

Tilgjengelig fra: 2021-09-09 Laget: 2021-09-09 Sist oppdatert: 2025-02-05bibliografisk kontrollert
Åkesson, J., Lubovac-Pilav, Z., Magnusson, R. & Gustafsson, M. (2021). ComHub: Community predictions of hubs in gene regulatory networks. BMC Bioinformatics, 22(1), Article ID 58.
Åpne denne publikasjonen i ny fane eller vindu >>ComHub: Community predictions of hubs in gene regulatory networks
2021 (engelsk)Inngår i: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 22, nr 1, artikkel-id 58Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

BACKGROUND: Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.

RESULTS: We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.

CONCLUSIONS: In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

sted, utgiver, år, opplag, sider
Springer Nature, 2021
Emneord
Gene regulatory networks, Hubs, Master regulators, Network inference
HSV kategori
Forskningsprogram
Bioinformatik; INF502 Biomarkörer
Identifikatorer
urn:nbn:se:his:diva-19478 (URN)10.1186/s12859-021-03987-y (DOI)000617736000001 ()33563211 (PubMedID)2-s2.0-85100810993 (Scopus ID)
Merknad

CC BY 4.0

The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. CC0 1.0

Tilgjengelig fra: 2021-02-18 Laget: 2021-02-18 Sist oppdatert: 2025-02-07bibliografisk kontrollert
Magnusson, R. & Lubovac-Pilav, Z. (2021). TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes. BMC Bioinformatics, 22(1), Article ID 440.
Åpne denne publikasjonen i ny fane eller vindu >>TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes
2021 (engelsk)Inngår i: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 22, nr 1, artikkel-id 440Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. While a core strategy to understand the biological context of genes and proteins includes annotation enrichment analysis, such as Gene Ontology term enrichment, these methods are not well suited for analysing groups of TFs. This is particularly true since such methods do not aim to include downstream processes, and given a set of TFs, the expected top ontologies would revolve around transcription processes.

Results: We present the TFTenricher, a Python toolbox that focuses specifically at identifying gene ontology terms, cellular pathways, and diseases that are over-represented among genes downstream of user-defined sets of human TFs. We evaluated the inference of downstream gene targets with respect to false positive annotations, and found an inference based on co-expression to best predict downstream processes. Based on these downstream genes, the TFTenricher uses some of the most common databases for gene functionalities, including GO, KEGG and Reactome, to calculate functional enrichments. By applying the TFTenricher to differential expression of TFs in 21 diseases, we found significant terms associated with disease mechanism, while the gene set enrichment analysis on the same dataset predominantly identified processes related to transcription.

Conclusions and availability: The TFTenricher package enables users to search for biological context in any set of TFs and their downstream genes. The TFTenricher is available as a Python 3 toolbox at https://github.com/rasma774/Tftenricher, under a GNU GPL license and with minimal dependencies.

sted, utgiver, år, opplag, sider
Springer Nature, 2021
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:his:diva-20586 (URN)10.1186/s12859-021-04357-4 (DOI)000696540200002 ()34530727 (PubMedID)2-s2.0-85115057681 (Scopus ID)
Merknad

CC BY 4.0

Correspondence: rasmus.magnusson@his.se School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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 mate‑rial. 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/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdo‑main/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Tilgjengelig fra: 2021-09-23 Laget: 2021-09-23 Sist oppdatert: 2025-02-05bibliografisk kontrollert
de Weerd, H. A., Badam, T. V. S., Martínez-Enguita, D., Åkesson, J., Muthas, D., Gustafsson, M. & Lubovac-Pilav, Z. (2020). MODifieR: an ensemble R package for inference of disease modules from transcriptomics networks. Bioinformatics, 36(12), 3918-3919
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2020 (engelsk)Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 36, nr 12, s. 3918-3919Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

MOTIVATION: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best performing method. Hence, there is a need for combining these methods to generate robust disease modules.

RESULTS: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.

AVAILABILITY: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

sted, utgiver, år, opplag, sider
Oxford University Press, 2020
HSV kategori
Forskningsprogram
Bioinformatik; INF501 Integrering av -omicsdata
Identifikatorer
urn:nbn:se:his:diva-18387 (URN)10.1093/bioinformatics/btaa235 (DOI)000550127500051 ()32271876 (PubMedID)2-s2.0-85087321319 (Scopus ID)
Merknad

CC BY 4.0

"Applications Note". "Systems biology". 

Tilgjengelig fra: 2020-04-15 Laget: 2020-04-15 Sist oppdatert: 2025-02-07bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-6427-0315