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      Age‐associated variation in the gut microbiota of chinstrap penguins ( Pygoscelis antarctica) reveals differences in food metabolism

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          Abstract

          Age is known to affect the gut microbiota in various animals; however, this relationship is poorly understood in seabirds. We investigated the temporal succession of gut microbiota in captive chinstrap penguins of different ages using high‐throughput sequencing. The gut microbiota exhibited a significant age succession pattern, reaching maturity in adults and then declining with increasing age. Only 15 amplicon sequence variants were shared among the gut microbiota in chinstrap penguins at all studied ages, and these contributed to most of the age‐related variations in total gut microbiota. Co‐occurrence networks found that these key bacteria belonged to the genera Acinetobacter, Clostridium sensu stricto, and Fusobacterium, and more species interactions were found within the same taxonomy. Functional prediction indicated that most of the metabolic functions were more abundant in the gut microbiota in adult chinstrap penguins, except for carbohydrate metabolism, which was significantly more abundant in older individuals.

          Abstract

          The gut microbiota of chinstrap penguins exhibited a significant age succession pattern, reaching maturity in adults and then declining with increasing age. Only 15 bacterial strains were shared among the gut microbiota in chinstrap penguins at all studied ages, and these contributed to most of the age‐related variations in total gut microbiota. Most of the metabolic functions were more abundant in the gut microbiota of adult chinstrap penguins, except for carbohydrate metabolism, which was more abundant in older individuals.

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          Most cited references53

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          FLASH: fast length adjustment of short reads to improve genome assemblies.

          Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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            Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

            Background Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results We present q2-feature-classifier (https://github.com/qiime2/q2-feature-classifier), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated “novel” marker-gene sequences, are available in our extensible benchmarking framework, tax-credit (https://github.com/caporaso-lab/tax-credit-data). Conclusions Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.
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              PICRUSt2 for prediction of metagenome functions

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                Author and article information

                Contributors
                marine_ln@sina.com
                Journal
                Microbiologyopen
                Microbiologyopen
                10.1002/(ISSN)2045-8827
                MBO3
                MicrobiologyOpen
                John Wiley and Sons Inc. (Hoboken )
                2045-8827
                07 May 2021
                Mar-Apr 2021
                : 10
                : 2 ( doiID: 10.1002/mbo3.v10.2 )
                : e1190
                Affiliations
                [ 1 ] Dalian Key Laboratory of Conservation Biology for Endangered Marine mammals Liaoning Ocean and Fisheries Science Research Institute Dalian China
                [ 2 ] Dalian Sun Asia Tourism Holding Co., Ltd. Dalian China
                Author notes
                [*] [* ] Correspondence

                Jing Du, Dalian Key Laboratory of Conservation Biology for Endangered Marine mammals, Liaoning Ocean and Fisheries Science Research Institute, 50 Heishijiao Road, Shahekou District, Dalian, Liaoning, China.

                Email: marine_ln@ 123456sina.com

                Author information
                https://orcid.org/0000-0002-3606-6533
                Article
                MBO31190
                10.1002/mbo3.1190
                8103090
                33970544
                8cdce7cf-0df3-4e63-870b-b00a51156be8
                © 2021 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 April 2021
                : 25 February 2021
                : 13 April 2021
                Page count
                Figures: 7, Tables: 0, Pages: 10, Words: 6563
                Funding
                Funded by: Foundation of Liaoning Province Department of Ocean and Fisheries
                Award ID: 201812
                Award ID: 201822
                Funded by: China Environment and Zoology Protection for Offshore Oil and Ocean Foundation
                Award ID: CF‐MEEC/ER/2020‐19
                Award ID: CF‐MEEC/TR/2020‐04
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                March/April 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:07.05.2021

                Microbiology & Virology
                age,chinstrap penguin,gut microbiota,high‐throughput sequencing,metabolism,pygoscelis antarctica

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