uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Biography [eng]

PhD in Bioinformatics from Uppsala University, 2009. Postdoctoral fellowships at Karolinska Institutet, Stockholm and Finnish Institute of Molecular Medicine (FIMM), Helsinki. Was co-director at the UPPMAX high performance computing center at Uppsala University 2011-2017, and Head of the Bioinformatics Compute and Storage facility at Science for Life Laboratory in Sweden 2011-2017. Currently employed as Senior Lecturer at Department of Pharmaceutical Biosciences in the fields of data-intensive and translational bioinformatics with a particular focus on how modern e-infrastructures enables the studying of complex phenomena, and predictive modeling in pharmacology, toxicology, and metabolism.

Biography [swe]

PhD in Bioinformatics from Uppsala University, 2009. Postdoctoral fellowships at Karolinska Institutet, Stockholm and Finnish Institute of Molecular Medicine (FIMM), Helsinki. Was co-director at the UPPMAX high performance computing center at Uppsala University 2011-2017, and Head of the Bioinformatics Compute and Storage facility at Science for Life Laboratory in Sweden 2011-2017. Currently employed as Senior Lecturer at Department of Pharmaceutical Biosciences in the fields of data-intensive and translational bioinformatics with a particular focus on how modern e-infrastructures enables the studying of complex phenomena, and predictive modeling in pharmacology, toxicology, and metabolism.

Publications (10 of 90) Show all publications
Herman, S., Niemelä, V., Emami Khoonsari, P., Sundblom, J., Burman, J., Landtblom, A.-M., . . . Kultima, K. (2019). Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects. Scientific Reports, 9, Article ID 4129.
Open this publication in new window or tab >>Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects
Show others...
2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 4129Article in journal (Refereed) Published
Abstract [en]

Huntington's disease (HD) is a severe neurological disease leading to psychiatric symptoms, motor impairment and cognitive decline. The disease is caused by a CAG expansion in the huntingtin (HTT) gene, but how this translates into the clinical phenotype of HD remains elusive. Using liquid chromatography mass spectrometry, we analyzed the metabolome of cerebrospinal fluid (CSF) from premanifest and manifest HD subjects as well as control subjects. Inter-group differences revealed that the tyrosine metabolism, including tyrosine, thyroxine, L-DOPA and dopamine, was significantly altered in manifest compared with premanifest HD. These metabolites demonstrated moderate to strong associations to measures of disease severity and symptoms. Thyroxine and dopamine also correlated with the five year risk of onset in premanifest HD subjects. The phenylalanine and the purine metabolisms were also significantly altered, but associated less to disease severity. Decreased levels of lumichrome were commonly found in mutated HTT carriers and the levels correlated with the five year risk of disease onset in premanifest carriers. These biochemical findings demonstrates that the CSF metabolome can be used to characterize molecular pathogenesis occurring in HD, which may be essential for future development of novel HD therapies.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2019
National Category
Neurology
Identifiers
urn:nbn:se:uu:diva-379886 (URN)10.1038/s41598-019-40186-5 (DOI)000460754600020 ()30858393 (PubMedID)
Funder
Åke Wiberg FoundationEU, Horizon 2020, 654241
Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-10-23Bibliographically approved
Herman, S., Åkerfeldt, T., Spjuth, O., Burman, J. & Kultima, K. (2019). Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis. Cells, 8(2), Article ID 84.
Open this publication in new window or tab >>Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis
Show others...
2019 (English)In: Cells, ISSN 2073-4409, Vol. 8, no 2, article id 84Article in journal (Refereed) Published
Abstract [en]

To better understand the pathophysiological differences between secondary progressive multiple sclerosis (SPMS) and relapsing-remitting multiple sclerosis (RRMS), and to identify potential biomarkers of disease progression, we applied high-resolution mass spectrometry (HRMS) to investigate the metabolome of cerebrospinal fluid (CSF). The biochemical differences were determined using partial least squares discriminant analysis (PLS-DA) and connected to biochemical pathways as well as associated to clinical and radiological measures. Tryptophan metabolism was significantly altered, with perturbed levels of kynurenate, 5-hydroxytryptophan, 5-hydroxyindoleacetate, and N-acetylserotonin in SPMS patients compared with RRMS and controls. SPMS patients had altered kynurenine compared with RRMS patients, and altered indole-3-acetate compared with controls. Regarding the pyrimidine metabolism, SPMS patients had altered levels of uridine and deoxyuridine compared with RRMS and controls, and altered thymine and glutamine compared with RRMS patients. Metabolites from the pyrimidine metabolism were significantly associated with disability, disease activity and brain atrophy, making them of particular interest for understanding the disease mechanisms and as markers of disease progression. Overall, these findings are of importance for the characterization of the molecular pathogenesis of SPMS and support the hypothesis that the CSF metabolome may be used to explore changes that occur in the transition between the RRMS and SPMS pathologies.

Keywords
cerebrospinal fluid, mass spectrometry, metabolomics, multiple sclerosis, pyrimidine, tryptophan
National Category
Clinical Medicine
Identifiers
urn:nbn:se:uu:diva-375564 (URN)10.3390/cells8020084 (DOI)000460896000006 ()30678351 (PubMedID)
Funder
Åke Wiberg FoundationEU, Horizon 2020, 654241
Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-04-11Bibliographically approved
Novella, J. A., Emami Khoonsari, P., Herman, S., Whitenack, D., Capuccini, M., Burman, J., . . . Spjuth, O. (2019). Container-based bioinformatics with Pachyderm. Bioinformatics, 35, 839-846
Open this publication in new window or tab >>Container-based bioinformatics with Pachyderm
Show others...
2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, p. 839-846Article in journal (Refereed) Published
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:uu:diva-371628 (URN)10.1093/bioinformatics/bty699 (DOI)000467227300015 ()30101309 (PubMedID)
Projects
eSSENCE
Available from: 2018-08-08 Created: 2018-12-21 Last updated: 2019-09-26Bibliographically approved
Gupta, A., Harrison, P. J., Wieslander, H., Pielawski, N., Kartasalo, K., Partel, G., . . . Wählby, C. (2019). Deep Learning in Image Cytometry: A Review. Cytometry Part A, 95(6), 366-380
Open this publication in new window or tab >>Deep Learning in Image Cytometry: A Review
Show others...
2019 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 6, p. 366-380Article, review/survey (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-371631 (URN)10.1002/cyto.a.23701 (DOI)000466792700002 ()30565841 (PubMedID)
Funder
Swedish Foundation for Strategic Research , BD15-0008SB16-0046Swedish Research Council, 2014-6075EU, European Research Council, ERC-2015-CoG 683810
Available from: 2018-12-19 Created: 2018-12-21 Last updated: 2019-06-14Bibliographically approved
Emami Khoonsari, P., Moreno, P., Bergmann, S., Burman, J., Capuccini, M., Carone, M., . . . Spjuth, O. (2019). Interoperable and scalable data analysis with microservices: Applications in metabolomics. Bioinformatics, 35(19), 3752-3760
Open this publication in new window or tab >>Interoperable and scalable data analysis with microservices: Applications in metabolomics
Show others...
2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 19, p. 3752-3760Article in journal (Refereed) Published
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-390670 (URN)10.1093/bioinformatics/btz160 (DOI)
Available from: 2019-03-09 Created: 2019-08-13 Last updated: 2019-10-14Bibliographically approved
Peters, K., Bradbury, J., Bergmann, S., Capuccini, M., Cascante, M., de Atauri, P., . . . Steinbeck, C. (2019). PhenoMeNal: Processing and analysis of metabolomics data in the cloud. GigaScience, 8(2)
Open this publication in new window or tab >>PhenoMeNal: Processing and analysis of metabolomics data in the cloud
Show others...
2019 (English)In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 2Article in journal (Refereed) Published
National Category
Bioinformatics and Systems Biology Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-371635 (URN)10.1093/gigascience/giy149 (DOI)000462551600002 ()30535405 (PubMedID)
Funder
EU, Horizon 2020, EC654241
Available from: 2018-12-07 Created: 2018-12-21 Last updated: 2019-05-02Bibliographically approved
Lampa, S., Dahlö, M., Alvarsson, J. & Spjuth, O. (2019). SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines. GigaScience, 8(5), Article ID giz044.
Open this publication in new window or tab >>SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines
2019 (English)In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 5, article id giz044Article in journal (Refereed) Published
Abstract [en]

Background: The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning. Findings: SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline. Conclusions: SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting.

Keywords
Scientific Workflow Management Systems, Workflow tools, Workflows, Pipelines, Reproducibility, Machine Learning, Flow-based Programming, Go, Golang
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-358347 (URN)10.1093/gigascience/giz044 (DOI)000474856100002 ()31029061 (PubMedID)
Funder
eSSENCE - An eScience CollaborationSwedish e‐Science Research CenterEU, Horizon 2020, 654241
Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2019-08-13Bibliographically approved
Lampa, S., Dahlö, M., Alvarsson, J. & Spjuth, O. (2019). SciPipe-Turning Scientific Workflows into Computer Programs. Computing in science & engineering (Print), 21(3), 109-113
Open this publication in new window or tab >>SciPipe-Turning Scientific Workflows into Computer Programs
2019 (English)In: Computing in science & engineering (Print), ISSN 1521-9615, E-ISSN 1558-366X, Vol. 21, no 3, p. 109-113Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE Computer Society, 2019
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-384084 (URN)10.1109/MCSE.2019.2907814 (DOI)000466469900012 ()
Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-06-18Bibliographically approved
Kensert, A., Harrison, P. J. & Spjuth, O. (2019). Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes. SLAS discovery : advancing life sciences R & D, 24(4), 466-475
Open this publication in new window or tab >>Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
2019 (English)In: SLAS discovery : advancing life sciences R & D, ISSN 2472-5552, Vol. 24, no 4, p. 466-475Article in journal (Refereed) Published
Abstract [en]

The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which requires several nontrivial and independent analysis steps. Recently, convolutional neural networks have emerged as a compelling alternative, offering good predictive performance and the possibility to replace traditional workflows with a single network architecture. In this study, we applied the pretrained deep convolutional neural networks ResNet50, InceptionV3, and InceptionResnetV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profiling datasets from the Broad Bioimage Benchmark Collection. These networks were pretrained on ImageNet, enabling much quicker model training. We obtain higher predictive accuracy than previously reported, between 95% and 97%. The ability to quickly and accurately distinguish between different cell morphologies from a scarce amount of labeled data illustrates the combined benefit of transfer learning and deep convolutional neural networks for interrogating cell-based images.

Keywords
cell phenotypes, deep learning, high-content imaging, machine learning, transfer learning
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:uu:diva-375566 (URN)10.1177/2472555218818756 (DOI)000461840200004 ()30641024 (PubMedID)
Funder
Swedish Foundation for Strategic Research Swedish National Infrastructure for Computing (SNIC)
Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-05-06Bibliographically approved
Lapins, M., Arvidsson, S., Lampa, S., Berg, A., Schaal, W., Alvarsson, J. & Spjuth, O. (2018). A confidence predictor for logD using conformal regression and a support-vector machine. Journal of Cheminformatics, 10(1), Article ID 17.
Open this publication in new window or tab >>A confidence predictor for logD using conformal regression and a support-vector machine
Show others...
2018 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, no 1, article id 17Article in journal (Refereed) Published
Abstract [en]

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Keywords
Conformal prediction, LogD, Machine learning, QSAR, RDF, Support-vector machine
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-347779 (URN)10.1186/s13321-018-0271-1 (DOI)000429065900001 ()29616425 (PubMedID)
Funder
EU, Horizon 2020, 731075
Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-08-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8083-2864

Search in DiVA

Show all publications

Profile pages

Research group website