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Metagenomics - Crossref

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Last Updated: 23 April 2022

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High resolution shotgun metagenomics: the more data, the better?

Although the increase in data quality of the new iteration of high-throughput DNA sequencing systems has the potential to produce unprecedented sequence depth at a minimal cost, HRSM processes will be required to properly handle these ever-increasing sequence datasets. Our results indicate that shallow SM sequencing is a cost-effective route to gather useful information regarding microbial community structures and that high depth sequencing does not provide additional elements for ecological interpretation.

Source link: https://doi.org/10.1101/2022.04.19.488797


Advancing Mitochondrial Metagenomics: A New Assembly Strategy and Validating the Power of Seed-Based Approach

A novel MMG pipeline was used to various DNA extractions, one per species, and their sequence as a mixed sample for rapid mitogenome assembly is shown. Non-mitochondrial reads were filtered using a quicker and more accurate read mapper. In addition, we found that short barcodes used as references had almost identical detection capability when compared to mitogenomes, but required greater sequencing depth. To demonstrate its high success in mixed-species sample mitogenome assembly, we tested our MMG pipeline on two real and one simulated datasets.

Source link: https://doi.org/10.3390/d14050317


Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets

Without the assistance of any reference databases, Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning algorithms. We review 15 genome binning devices, 12 original binning devices, and 3 refining binning devices in this article by comparing the results of these tools on chicken gut metagenomic datasets and the first CAMI challenge datasets. Result;s Original genome binner MetaBat, Groopm2 and Autometa all performed better than any other original binner, and MetaWrap combined their findings into the most high-quality genome bins. DASTool formulated the most high-quality genome binners among all genome refining binners, according to Genome refining binners. Conclusions Weigh In conclusion, we tested a number of commonly available, state-of-the-art metagenomics hybrid binning devices, as well as a guide to choosing tools for metagenomic binning by comparing a variety of purity, completeness, adjusted rand index, and the number of high-quality reconstructed bins.

Source link: https://doi.org/10.1186/s12859-020-03667-3


Contribution of Clinical Metagenomics to the Diagnosis of Bone and Joint Infections

Bone and joint infections are complicated infections that necessitate precise microbiological documentation to optimize antibiotic therapy. We compared CMg to culture and 16S rDNA sequencing in 34 patients with suspected BJIs in 34 patients with suspected BJIs in the present study. 414 out of 539 species identified by CMg were classified as contaminants and 125 as present, based on the sequencing results of negative controls, 414 out of 539 bacterial species detected by CMg were identified as contaminants, with 125 being genuinely present. The sensitivity of CMg was 83. 3% as compared to culture, and 100% as compared to 16S rDNA. The sensitivity of CMg was 50% compared to culture and 100% compared to 16S rDNA. CMg detected 11 bacteria in ten samples from five different patients with samples lacking in culture. CMg performed a microbiological diagnosis where conventional treatments failed, in 5/34 patients, and CMg provided additional details in 16/34 patients.

Source link: https://doi.org/10.3389/fmicb.2022.863777


Analysis of sequencing strategies and tools for taxonomic annotation: Defining standards for progressive metagenomics

Abstract: Metagenomics research has flourished in recent years as a result of DNA sequencing technology advancement, spurring the introduction of new analytical software and the growth of taxonomic databases. We compared the findings from different software and database combinations in silico simulated data for 16S rRNA amplicons and whole metagenome shotgun results to find biases related to algorithms or database annotations. Researchers can analyze the expected error rate and coverage for their findings using our benchmark framework, regardless of the score used by each program.

Source link: https://doi.org/10.1038/s41598-018-30515-5

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions