Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.
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http://dx.doi.org/10.1093/nar/gkac689 | DOI Listing |
Sensors (Basel)
January 2025
Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.
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View Article and Find Full Text PDFBioinformatics
January 2025
Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, 02-106, Poland.
Motivation: It is a challenging task to decipher the mechanisms of a complex system from observational data; especially in biology, where systems are sophisticated, measurements coarse and multi-modality is a common trait. The typical approaches of inferring a network of relationships between system's components struggle with the quality and feasibility of estimation, as well as with the interpretability of the results they yield.Said issues can be avoided, however, when dealing with a simpler problem of tracking only the influence paths, defined as circuits relying the information of an experimental perturbation as it spreads through the system.
View Article and Find Full Text PDFJ Proteome Res
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Functional Genomics Center Zurich (FGCZ) - University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
Mass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis () of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, interactive differential expression analysis tools become impractical. The addresses this challenge by providing a command-line interface that simplifies , making it accessible to nonprogrammers and seamlessly integrating it into workflow management systems.
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United Theranostics, Bethesda, Maryland.
Computational nuclear oncology for precision radiopharmaceutical therapy (RPT) is a new frontier for theranostic treatment personalization. A key strategy relies on the possibility to incorporate clinical, biomarker, image-based, and dosimetric information in theranostic digital twins (TDTs) of patients to move beyond a one-size-fits-all approach. The TDT framework enables treatment optimization by real-time monitoring of the real-world system, simulation of different treatment scenarios, and prediction of resulting treatment outcomes, as well as facilitating collaboration and knowledge sharing among health care professionals adopting a harmonized TDT.
View Article and Find Full Text PDFHeliyon
January 2025
Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602 105, Tamil Nadu, India.
AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration.
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