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      Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic Islands

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          Abstract

          Metagenomic methods enable the simultaneous characterization of microbial communities without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods aim to reassemble individual genomes from this data. However, the recovery of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), by binning has not been well characterized. Given the association of antimicrobial resistance (AMR) genes and virulence factor (VF) genes with MGEs, studying their transmission is a public-health priority. The variable copy number and sequence composition of MGEs makes them potentially problematic for MAG binning methods. To systematically investigate this issue, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated. While 82–94 % of chromosomes could be correctly recovered and binned, only 38–44 % of GIs and 1–29 % of plasmid sequences were found. Strikingly, no plasmid-borne VF nor AMR genes were recovered, and only 0–45 % of AMR or VF genes within GIs. We conclude that short-read MAG approaches, without further optimization, are largely ineffective for the analysis of mobile genes, including those of public-health importance, such as AMR and VF genes. We propose that researchers should explore developing methods that optimize for this issue and consider also using unassembled short reads and/or long-read approaches to more fully characterize metagenomic data.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              BLAST+: architecture and applications

              Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                mgen
                mgen
                Microbial Genomics
                Microbiology Society
                2057-5858
                October 2020
                1 October 2020
                1 October 2020
                : 6
                : 10
                : mgen000436
                Affiliations
                [ 1] departmentFaculty of Computer Science , Dalhousie University, 6050 University Avenue , Halifax, Nova Scotia, B3H 4R2, Canada
                [ 2] departmentDepartment of Molecular Biology and Biochemistry , Simon Fraser University , 8888 University Drive, Burnaby, BC V5A 1S6, Canada
                Author notes
                [†]

                These authors contributed equally to this work

                *Correspondence: Fiona S. L. Brinkman, brinkman@ 123456sfu.ca
                Author information
                https://orcid.org/0000-0002-1203-9514
                https://orcid.org/0000-0002-4735-4709
                https://orcid.org/0000-0002-1962-189X
                https://orcid.org/0000-0003-3884-4009
                https://orcid.org/0000-0002-5065-4980
                https://orcid.org/0000-0002-0584-4099
                Article
                000436
                10.1099/mgen.0.000436
                7660262
                33001022
                a5d3b652-5b42-4d0b-bb51-173ccbda40de
                © 2020 The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.

                History
                : 23 May 2020
                : 04 September 2020
                Funding
                Funded by: Natural Sciences and Engineering Research Council of Canada
                Award Recipient : Fiona S.L. Brinkman
                Funded by: Genome Canada
                Award Recipient : Robert G Beiko
                Funded by: Simon Fraser University
                Award ID: Distinguished Professorship
                Award Recipient : Fiona S.L. Brinkman
                Funded by: Simon Fraser University
                Award ID: Omics and Data Sciences fellowship
                Award Recipient : Wing Yin Venus Lau
                Funded by: Simon Fraser University
                Award ID: Omics and Data Sciences fellowship
                Award Recipient : Kristen Gray
                Funded by: Simon Fraser University
                Award ID: Omics and Data Sciences fellowship
                Award Recipient : Baofeng Jia
                Funded by: Natural Sciences and Engineering Research Council of Canada
                Award ID: Collaborative Research and Training Experience (CREATE) Bioinformatics scholarship
                Award Recipient : Kristen Gray
                Funded by: Canadian Institutes of Health Research
                Award ID: Doctoral Scholarship
                Award Recipient : Wing Yin Venus Lau
                Funded by: Donald Hill Family Fellowship
                Award Recipient : Finlay Maguire
                Funded by: Canadian Institutes of Health Research
                Award ID: Doctoral Scholarship
                Award Recipient : Baofeng Jia
                Categories
                Research Article
                Genomic Methodologies: Data clustering methods
                Custom metadata
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                antimicrobial resistance,genomic islands,metagenomics,metagenome-assembled genomes,mobile genetic elements

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