The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Christian Brandt - One of the best experts on this subject based on the ideXlab platform.
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Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN).
PLoS computational biology, 2021Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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: https://github.com/RVanDamme/MUFFIN.
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Metagenomics workflow for hybrid assembly, differential coverage binning, transcriptomics and pathway analysis (MUFFIN)
2020Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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 by providing RNA-Seq data (optionally) for quantification and annotation. 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 transcript annotations across the metagenomic sample and for each bin.
Renaud Van Damme - One of the best experts on this subject based on the ideXlab platform.
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Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN).
PLoS computational biology, 2021Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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: https://github.com/RVanDamme/MUFFIN.
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Metagenomics workflow for hybrid assembly, differential coverage binning, transcriptomics and pathway analysis (MUFFIN)
2020Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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 by providing RNA-Seq data (optionally) for quantification and annotation. 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 transcript annotations across the metagenomic sample and for each bin.
Alán Aspuru-guzik - One of the best experts on this subject based on the ideXlab platform.
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ChemOS: An Orchestration Software to democratize autonomous discovery.
PloS one, 2020Co-Authors: Loïc M Roch, Teresa Tamayo-mendoza, Lars P E Yunker, Christoph Kreisbeck, Florian Hase, Jason E Hein, Alán Aspuru-guzikAbstract:The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate Software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile Software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the Software package.
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Chapter 16:ChemOS: An Orchestration Software to Democratize Autonomous Discovery
Artificial Intelligence in Drug Discovery, 2020Co-Authors: Loïc M Roch, Florian Hase, Alán Aspuru-guzikAbstract:This chapter provides an overview of established algorithmic strategies for experiment planning for closed-loop experimentation highlighting their strengths and limitations through key examples from academia and industry. It also details the need for a transition from automation to autonomy in materials innovation and process optimization to accelerate discovery across sectors. In this context, we review the early realization of autonomous laboratories, and their associated strategies to optimization, and lay out a roadmap for deploying and orchestrating self-driving laboratories. As a specific tool to enable autonomy in technology innovation, we detail the architecture and suite of applications composing the ChemOS Software package. We complete our discussion by highlighting recent demonstrations of ChemOS in chemistry, materials science and process optimization and discuss the specific use of ChemOS to accelerate drug discovery.
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ChemOS: An Orchestration Software to Democratize Autonomous Discovery
ChemRxiv, 2018Co-Authors: Loïc M Roch, Teresa Tamayo-mendoza, Lars P E Yunker, Christoph Kreisbeck, Florian Hase, Jason E Hein, Alán Aspuru-guzikAbstract:Autonomous or “self-driving” laboratories combine robotic platforms with artificial intelligence to increase the rate of scientific discovery. They have the potential to transform our traditional approaches to experimentation. Although autonomous laboratories recently gained increased attention, the requirements imposed by engineering the Software packages often prevent their development. Indeed, autonomous laboratories require considerable effort in designing and writing advanced and robust Software packages to control, orchestrate and synchronize automated instrumentations, cope with databases, and interact with various artificial intelligence algorithms. To overcome this limitation, we introduce ChemOS, a portable, modular and versatile Software package, which supplies the structured layers indispensable for operating autonomous laboratories. Additionally, it enables remote control of laboratories, provides access to distributed computing resources, and comprises state-of-the-art machine learning methods. We believe that ChemOS will reduce the time-to-deployment from automated to autonomous discovery, and will provide the scientific community with an easy-to-use package to facilitate novel discovery, at a faster pace.
Adrian Viehweger - One of the best experts on this subject based on the ideXlab platform.
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Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN).
PLoS computational biology, 2021Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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: https://github.com/RVanDamme/MUFFIN.
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Metagenomics workflow for hybrid assembly, differential coverage binning, transcriptomics and pathway analysis (MUFFIN)
2020Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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 by providing RNA-Seq data (optionally) for quantification and annotation. 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 transcript annotations across the metagenomic sample and for each bin.
Bettina Müller - One of the best experts on this subject based on the ideXlab platform.
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Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN).
PLoS computational biology, 2021Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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: https://github.com/RVanDamme/MUFFIN.
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Metagenomics workflow for hybrid assembly, differential coverage binning, transcriptomics and pathway analysis (MUFFIN)
2020Co-Authors: Renaud Van Damme, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-rudloff, Christian BrandtAbstract: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 by providing RNA-Seq data (optionally) for quantification and annotation. 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 transcript annotations across the metagenomic sample and for each bin.