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- Landi, Michael, et al.
(författare)
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EpiCass And CassavaNet4Dev Advanced Bioinformatics Workshop
- 2023
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Ingår i: EMBnet.journal. - 2226-6089. ; 29
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- EpiCass and CassavaNet4Dev are collaborative projects funded by the Swedish Research Council between the Swedish University of Agriculture (SLU) and the International Institute of Tropical Agriculture (IITA). The projects aim to investigate the influence of epigenetic changes on agricultural traits such as yield and virus resistance while also providing African students and researchers with advanced bioinformatics training and opportunities to participate in big data analysis events. The first advanced bioinformatics training workshop took place from May 16th to May 18th, 2022, followed by an online mini-symposium titled “Epigenetics and crop improvement” on May 19th. The symposium featured international speakers covering a wide range of topics related to plant epigenetics, cassava viral diseases, and cassava breeding strategies. A new online and on-site teaching model was developed for the three-day workshop to ensure maximum student participation across Western, Eastern, and Southern Africa. Initially planned in Nigeria, Kenya, Ethiopia, Tanzania, and Zambia, the workshop ultimately focused on Nigeria, Kenya, and Ethiopia due to a lack of qualified candidates in the other countries. Each classroom hosted 20 to 25 students, with at least one bioinformatician present for support. The classrooms were connected via video conferencing, whereas teachers located in different places in Africa and Europe joined the video stream to conduct teaching sessions. The workshop was divided into theoretical classes and hands-on sessions, where participants could run data analysis with support from online teachers and local bioinformaticians. To enable participants to run guided, CPU and RAM-intensive data analysis workflows and overcome local computing and internet access restrictions, a system of virtual machines (VMs) hosted in the cloud was developed. The teaching platform provided teaching and exercise materials to support the use of the VMs. Some students could not run heavy data analysis workflows due to unforeseen restrictions in the cloud. Currently, these issues have been solved and in the future all participants will have the opportunity to run the analysis steps independently in the cloud using the protocols hosted on the teaching platform.
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- Van Damme, Renaud, et al.
(författare)
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Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)
- 2021
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: .Author summaryDetermining the entire DNA of environmental samples (sequencing) is a fundamental approach to gain deep insights into complex bacterial communities and their functions. However, this approach produces enormous amounts of data, which makes analysis time intense and complicated. We developed the Software "MUFFIN," which effortlessly untangle the complex sequencing data to reconstruct individual bacterial species and determine their functions. Our software is performing multiple complicated steps in parallel, automatically allowing everyone with only basic informatics skills to analyze complex microbial communities.For this, we combine two sequencing technologies: "long-sequences" (nanopore, better reconstruction) and "short-sequences" (Illumina, higher accuracy). After the reconstruction, we group the fragments that belong together ("binning") via multiple approaches and refinement steps while also utilizing the information from other bacterial communities ("differential binning"). This process creates hundreds of "bins" whereas each represents a different bacterial species with a unique function. We automatically determine their species, assess each genome's completeness, and attribute their biological functions and activity ("transcriptomics and pathways"). Our Software is entirely freely available to everyone and runs on a good computer, compute cluster, or via cloud.
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- Wille, Michelle, et al.
(författare)
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Evolutionary genetics of canine respiratory coronavirus and recent introduction into Swedish dogs
- 2020
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Ingår i: Infection, Genetics and Evolution. - : Elsevier BV. - 1567-1348 .- 1567-7257. ; 82
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Tidskriftsartikel (refereegranskat)abstract
- Canine respiratory coronavirus (CRCoV) has been identified as a causative agent of canine infectious respiratory disease, an upper respiratory infection affecting dogs. The epidemiology is currently opaque, with an unclear understanding of global prevalence, pathology, and genetic characteristics. In this study, Swedish privately-owned dogs with characteristic signs of canine infectious respiratory disease (n = 88) were screened for CRCoV and 13 positive samples (14.7%, 8.4-23.7% [95% confidence interval (CI)]) were further sequenced. Sequenced Swedish CRCoV isolates were highly similar despite being detected in dogs living in geographically distant locations and sampled across 3 years (2013-2015). This is due to a single introduction into Swedish dogs in approximately 2010, as inferred by time structured phylogeny. Unlike other CRCoVs, there was no evidence of recombination in Swedish CRCoV viruses, further supporting a single introduction. Finally, there were low levels of polymorphisms, in the spike genes. Overall, we demonstrate that there is little diversity of CRCoV which is endemic in Swedish dogs.
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