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Dahlö, Martin
Publications (7 of 7) Show all publications
Lampa, S., Dahlö, M., Alvarsson, J. & Spjuth, O. (2018). SciPipe - A workflow library for agile development of complex and dynamic bioinformatics pipelines.
Open this publication in new window or tab >>SciPipe - A workflow library for agile development of complex and dynamic bioinformatics pipelines
2018 (English)In: Article in journal (Other academic) Epub ahead of print
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 and 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, re-usable 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 leaning, through a flexible programming API 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)10.1101/380808 (DOI)
Funder
eSSENCE - An eScience CollaborationSwedish e‐Science Research CenterEU, Horizon 2020, 654241
Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-08-28Bibliographically approved
Dahlö, M., Scofield, D., Schaal, W. & Spjuth, O. (2018). Tracking the NGS revolution: managing life science research on shared high-performance computing clusters. GigaScience, 7(5), Article ID giy028.
Open this publication in new window or tab >>Tracking the NGS revolution: managing life science research on shared high-performance computing clusters
2018 (English)In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 7, no 5, article id giy028Article in journal (Refereed) Published
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-350009 (URN)10.1093/gigascience/giy028 (DOI)000438566200001 ()29659792 (PubMedID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish National Infrastructure for Computing (SNIC)
Available from: 2018-04-05 Created: 2018-05-02 Last updated: 2018-09-24Bibliographically approved
Sütterlin, S., Dahlö, M., Tellgren-Roth, C., Schaal, W. & Melhus, Å. (2017). High frequency of silver resistance genes in invasive isolates of Enterobacter and Klebsiella species. Journal of Hospital Infection, 96(3), 256-261
Open this publication in new window or tab >>High frequency of silver resistance genes in invasive isolates of Enterobacter and Klebsiella species
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2017 (English)In: Journal of Hospital Infection, ISSN 0195-6701, E-ISSN 1532-2939, Vol. 96, no 3, p. 256-261Article in journal (Refereed) Published
Abstract [en]

Background: Silver-based products have been marketed as an alternative to antibiotics, and their consumption has increased. Bacteria may, however, develop resistance to silver.

Aim: To study the presence of genes encoding silver resistance (silE, silP, silS) over time in three clinically important Enterobacteriaceae genera.

Methods: Using polymerase chain reaction (PCR), 752 bloodstream isolates from the years 1990–2010 were investigated. Age, gender, and ward of patients were registered, and the susceptibility to antibiotics and silver nitrate was tested. Clonality and single nucleotide polymorphism were assessed with repetitive element sequence-based PCR, multi-locus sequence typing, and whole-genome sequencing.

Findings: Genes encoding silver resistance were detected most frequently in Enterobacter spp. (48%), followed by Klebsiella spp. (41%) and Escherichia coli 4%. Phenotypical resistance to silver nitrate was found in Enterobacter (13%) and Klebsiella (3%) isolates. The lowest carriage rate of sil genes was observed in blood isolates from the neonatology ward (24%), and the highest in blood isolates from the oncology/haematology wards (66%). Presence of sil genes was observed in international high-risk clones. Sequences of the sil and pco clusters indicated that a single mutational event in the silS gene could have caused the phenotypic resistance.

Conclusion: Despite a restricted consumption of silver-based products in Swedish health care, silver resistance genes are widely represented in clinical isolates of Enterobacter and Klebsiella species. To avoid further selection and spread of silver-resistant bacteria with a high potential for healthcare-associated infections, the use of silver-based products needs to be controlled and the silver resistance monitored.

National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-326299 (URN)10.1016/j.jhin.2017.04.017 (DOI)000403468000010 ()28506673 (PubMedID)
Funder
Swedish Research Council Formas, 2011-1692Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Available from: 2017-07-05 Created: 2017-07-05 Last updated: 2017-09-14Bibliographically approved
Spjuth, O., Bongcam-Rudloff, E., Dahlberg, J., Dahlö, M., Kallio, A., Pireddu, L., . . . Korpelainen, E. (2016). Recommendations on e-infrastructures for next-generation sequencing. GigaScience, 5, Article ID 26.
Open this publication in new window or tab >>Recommendations on e-infrastructures for next-generation sequencing
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2016 (English)In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 5, article id 26Article in journal (Refereed) Published
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-296012 (URN)10.1186/s13742-016-0132-7 (DOI)000377153700001 ()27267963 (PubMedID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish National Infrastructure for Computing (SNIC)Swedish e‐Science Research Center
Available from: 2016-06-07 Created: 2016-06-12 Last updated: 2018-01-10Bibliographically approved
Dahlö, M., Haziza, F., Kallio, A., Korpelainen, E., Bongcam-Rudloff, E. & Spjuth, O. (2015). BioImg.org: A catalog of virtual machine images for the life sciences. Bioinformatics and Biology Insights, 9, 125-128
Open this publication in new window or tab >>BioImg.org: A catalog of virtual machine images for the life sciences
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2015 (English)In: Bioinformatics and Biology Insights, ISSN 1177-9322, E-ISSN 1177-9322, Vol. 9, p. 125-128Article in journal (Refereed) Published
Abstract [en]

Virtualization is becoming increasingly important in bioscience, enabling assembly and provisioning of complete computer setups, including operating system, data, software, and services packaged as virtual machine images (VMIs). We present an open catalog of VMIs for the life sciences, where scientists can share information about images and optionally upload them to a server equipped with a large file system and fast Internet connection. Other scientists can then search for and download images that can be run on the local computer or in a cloud computing environment, providing easy access to bioinformatics environments. We also describe applications where VMIs aid life science research, including distributing tools and data, supporting reproducible analysis, and facilitating education.

Keywords
catalogue; virtual machine image; virtual appliance; container; software repository; cloud computing
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-262376 (URN)10.4137/BBI.S28636 (DOI)000365108200001 ()26401099 (PubMedID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceeSSENCE - An eScience CollaborationEU, FP7, Seventh Framework Programme, Bm1006EU, FP7, Seventh Framework Programme, allBioSwedish National Infrastructure for Computing (SNIC)
Available from: 2015-09-10 Created: 2015-09-14 Last updated: 2018-01-11Bibliographically approved
Lampa, S., Dahlö, M., Olason, P. I., Hagberg, J. & Spjuth, O. (2013). Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data. GigaScience, 2(1), 1-10
Open this publication in new window or tab >>Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data
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2013 (English)In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 2, no 1, p. 1-10Article in journal (Refereed) Published
Abstract [en]

Analyzing and storing data and results from next-generation sequencing (NGS) experiments is a challenging task, hampered by ever-increasing data volumes and frequent updates of analysis methods and tools. Storage and computation have grown beyond the capacity of personal computers and there is a need for suitable e-infrastructures for processing. Here we describe UPPNEX, an implementation of such an infrastructure, tailored to the needs of data storage and analysis of NGS data in Sweden serving various labs and multiple instruments from the major sequencing technology platforms. UPPNEX comprises resources for high-performance computing, large-scale and high-availability storage, an extensive bioinformatics software suite, up-to-date reference genomes and annotations, a support function with system and application experts as well as a web portal and support ticket system. UPPNEX applications are numerous and diverse, and include whole genome-, de novo- and exome sequencing, targeted resequencing, SNP discovery, RNASeq, and methylation analysis. There are over 300 projects that utilize UPPNEX and include large undertakings such as the sequencing of the flycatcher and Norwegian spruce. We describe the strategic decisions made when investing in hardware, setting up maintenance and support, allocating resources, and illustrate major challenges such as managing data growth. We conclude with summarizing our experiences and observations with UPPNEX to date, providing insights into the successful and less successful decisions made.

National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-202128 (URN)10.1186/2047-217X-2-9 (DOI)000321451400001 ()23800020 (PubMedID)
Funder
eSSENCE - An eScience Collaboration
Available from: 2013-06-19 Created: 2013-06-19 Last updated: 2018-05-18Bibliographically approved
Zhao, H., Dahlö, M., Isaksson, A., Syvänen, A.-C. & Pettersson, U. (2012). The transcriptome of the adenovirus infected cell. Virology, 424(2), 115-128
Open this publication in new window or tab >>The transcriptome of the adenovirus infected cell
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2012 (English)In: Virology, ISSN 0042-6822, E-ISSN 1096-0341, Vol. 424, no 2, p. 115-128Article in journal (Refereed) Published
Abstract [en]

Alternations of cellular gene expression following an adenovirus type 2 infection of human primary cells were studied by using superior sensitive cDNA sequencing. In total, 3791 cellular genes were identified as differentially expressed more than 2-fold. Genes involved in DNA replication, RNA transcription and cell cycle regulation were very abundant among the up-regulated genes. On the other hand, genes involved in various signaling pathways including TGF-β, Rho, G-protein, Map kinase, STAT and NF-κB stood out among the down-regulated genes. Binding sites for E2F, ATF/CREB and AP2 were prevalent in the up-regulated genes, whereas binding sites for SRF and NF-κB were dominant among the down-regulated genes. It is evident that the adenovirus has gained a control of the host cell cycle, growth, immune response and apoptosis at 24h after infection. However, efforts from host cell to block the cell cycle progression and activate an antiviral response were also observed.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-168669 (URN)10.1016/j.virol.2011.12.006 (DOI)000300752900004 ()22236370 (PubMedID)
Available from: 2012-02-14 Created: 2012-02-14 Last updated: 2017-12-07Bibliographically approved
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