Browsing by Subject "probabilistic modelling"

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  • Mäklin, Tommi; Kallonen, Teemu; Alanko, Jarno; Samuelsen, Ørjan; Hegstad, Kristin; Mäkinen, Veli; Corander, Jukka; Heinz, Eva; Honkela, Antti (2021)
    Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.
  • Kaikkonen, Laura; Helle, Inari; Kostamo, Kirsi; Kuikka, Sakari; Törnroos, Anna; Nygård, Henrik; Venesjärvi, Riikka; Uusitalo, Laura (American Chemical Society, 2021)
    Environmental Science & Technology 55: 13, 8502-8513
    Seabed mining is approaching the commercial mining phase across the world’s oceans. This rapid industrialization of seabed resource use is introducing new pressures to marine environments. The environmental impacts of such pressures should be carefully evaluated prior to permitting new activities, yet observational data is mostly missing. Here, we examine the environmental risks of seabed mining using a causal, probabilistic network approach. Drawing on a series of interviews with a multidisciplinary group of experts, we outline the cause-effect pathways related to seabed mining activities to inform quantitative risk assessments. The approach consists of (1) iterative model building with experts to identify the causal connections between seabed mining activities and the affected ecosystem components, and (2) quantitative probabilistic modelling to provide estimates of mortality of benthic fauna in the Baltic Sea. The model is used to evaluate alternative mining scenarios, offering a quantitative means to highlight the uncertainties around the impacts of mining. We further outline requirements for operationalizing quantitative risk assessments, highlighting the importance of a cross-disciplinary approach to risk identification. The model can be used to support permitting processes by providing a more comprehensive description of the potential environmental impacts of seabed resource use, allowing iterative updating of the model as new information becomes available.
  • Mäklin, Tommi; Kallonen, Teemu; David, Sophia; Boinett, Christine J.; Pascoe, Ben; Méric, Guillaume; Aanensen, David M.; Feil, Edward J.; Baker, Stephen; Parkhill, Julian; Sheppard, Samuel K.; Corander, Jukka; Honkela, Antti (2021)
    Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP pipeline for identifying and estimating the relative sequence abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our pipeline facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.