MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes

Péricard, Pierre; Dufresne, Yoann; Couderc, Loïc; Blanquart, Samuel; Touzet, Hélène

doi:10.1093/bioinformatics/btx644

Cited by 50 publications

(58 citation statements)

References 30 publications

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“…We compared three different assemblers, two targeted-assembly tools Emirge (15) and Matam (16) and the general-purpose genome assembler SPAdes (32), on SSU rRNA reads extracted with SortMeRNA using the filtered SILVA 132 SSURef NR96 reference database and E-value cutoff 10 -5 .…”

Section: General-purpose Assembler Spades Yields Longer and More Divementioning

confidence: 99%

“…Ideally, we would like to leverage the vast existing knowledge base of the SSU rRNA gene in (meta-)genomics projects for several different outcomes: taxonomic profiling without assembly (6,7), targeted assembly of full-length sequences for phylogenetics and probe design (15,16,25,26), and linking SSU rRNA sequences to complete genomes (27). For each of these aims, separate software tools have already been developed, each with their own merits and shortcomings (16). However, these are ultimately different aspects of the same problem and should be considered together.…”

Section: Introductionmentioning

confidence: 99%

“…phyloFlash quickly processes Illumina (meta)genomic data, is straightforward to use, even as part of high-throughput quality control, and has user-friendly output reports. The software is available at https://github.com/HRGV/phyloFlash (GPL3 license) and is documented with an online manual.reads (12)(13)(14), and some tools also go beyond classification to perform targeted assembly of specific genes (15,16). Regardless of the method that is used for taxonomic or functional profiling, the results will be limited by the reference data available.…”

mentioning

confidence: 99%

“…reads (12)(13)(14), and some tools also go beyond classification to perform targeted assembly of specific genes (15,16). Regardless of the method that is used for taxonomic or functional profiling, the results will be limited by the reference data available.…”

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confidence: 99%

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phyloFlash – Rapid SSU rRNA profiling and targeted assembly from metagenomes

Gruber‐Vodicka

Seah

Pruesse

2019

Preprint

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The SSU rRNA gene is the key marker in molecular ecology for all domains of life, but is largely absent from metagenome-assembled genomes that often are the only resource available for environmental microbes. Here we present phyloFlash, a pipeline to overcome this gap with rapid, SSU rRNA-centered taxonomic classification, targeted assembly, and graph-based binning of full metagenomic assemblies. We show that a cleanup of artifacts is pivotal even with a curated reference database. With such a filtered database, the general-purpose mapper BBmap extracts SSU rRNA reads five times faster than the rRNA-specialized tool SortMeRNA with similar sensitivity and higher selectivity on simulated metagenomes. Reference-based targeted assemblers yielded either highly fragmented assemblies or high levels of chimerism, so we employ the general-purpose genomic assembler SPAdes. Our optimized implementation is independent of reference database composition and has satisfactory levels of chimera formation. Using the phyloFlash workflow we could recover the first complete genomes of several enigmatic taxa, including Marinamargulisbacteria from surface ocean seawater. phyloFlash quickly processes Illumina (meta)genomic data, is straightforward to use, even as part of high-throughput quality control, and has user-friendly output reports. The software is available at https://github.com/HRGV/phyloFlash (GPL3 license) and is documented with an online manual.reads (12)(13)(14), and some tools also go beyond classification to perform targeted assembly of specific genes (15,16). Regardless of the method that is used for taxonomic or functional profiling, the results will be limited by the reference data available. For example, target organisms or their close relatives may not yet be represented in the database, and horizontal gene transfer can result in conflicting phylogenetic signal, especially in prokaryotic microbes (17,18).In molecular ecology, the gene for the small subunit ribosomal RNA (SSU rRNA) is the most important marker because it can be readily used to link sequences to actual cells. The SSU rRNA is available in high copy numbers in ribosomes and can be accessed through the well-established molecular probing technique of fluorescence in situ hybridization (FISH) (19, 20) Its value in imaging based analyses together with its high phylogenetic reliability due to low rates of horizontal transfer has made the SSU rRNA gene the best-sampled marker in terms of phylogenetic diversity (21). Even with the current advances of metagenomics, where drafts of microbial genomes can automatically project (https://www.arb-silva.de) (28). Sequences containing fragments of the large subunit (LSU) rRNA are detected with a hidden Markov model (HMM) from a customized version of Barrnap (Evalue cutoff 10 -10 , >10% of total model length) (https://github.com/tseemann/barrnap), and are removed. Sequence regions containing low-complexity sequence or repeat k-mers are masked with bbmask.sh (from BBmap, https://sourceforge.net/projects/bbmap/) using k-m...

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Section: General-purpose Assembler Spades Yields Longer and More Divementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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phyloFlash – Rapid SSU rRNA profiling and targeted assembly from metagenomes

Gruber‐Vodicka

Seah

Pruesse

2019

Preprint

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“…There exist several gene-centric targeted assemblers that perform de-novo reconstruction, e.g. via an overlap graph of candidate reads (Kucuk et al, 2017;Pericard et al, 2017;Steinegger et al, 2018;Gruber-Vodicka et al, 2019). While several of those only reconstruct the 16S rRNA gene or are limited to transcriptomic data, the MEGAN gene-centric assembler reconstructs contigs based on the alignment of reads to any reference protein sequence (Huson et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

PhyloMagnet: Fast and accurate screening of short-read meta-omics data using gene-centric phylogenetics

Schön

Eme

Ettema

2019

Preprint

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MotivationMetagenomic and metatranscriptomic sequencing analyses have become increasingly popular tools for producing massive amounts of short-read data, often used for the reconstruction of draft genomes or the detection of (active) genes in microbial communities. Unfortunately, sequence assemblies of such datasets generally remain a computationally challenging task. Frequently, researchers are only interested in a specific group of organisms or genes; yet, the assembly of multiple datasets only to identify candidate sequences for a specific question is sometimes prohibitively slow, forcing researchers to select a subset of available datasets to address their question. Here we present PhyloMagnet, a workflow to screen meta-omics datasets for taxa and genes of interest using gene-centric assembly and phylogenetic placement of sequences.ResultsUsing PhyloMagnet, we could identify up to 87% of the genera in an in vitro mock community with variable abundances, while the false positive predictions per single gene tree ranged from 0% to 23%. When applied to a group of metagenomes for which a set of MAGs have been published, we could detect the majority of the taxonomic labels that the MAGs had been annotated with. In a metatranscriptomic setting the phylogenetic placement of assembled contigs corresponds to that of transcripts obtained from transcriptome assembly.AvailabilityPhyloMagnet is built using Nextflow, available at github.com/maxemil/PhyloMagnet and is developed and tested on Linux. It is released under the open source GNU GPL license and documentation is available at phylomagnet.readthedocs.io. Version 0.5 of PhyloMagnet was used for all benchmarks experiments.

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Microbial genomes retrieved from High Arctic lake sediments encode for adaptation to cold and oligotrophic environments

Ruuskanen

Colby

Pierre

et al. 2019

Limnology & Oceanography

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The Arctic is currently warming at an unprecedented rate, which may affect environmental constraints on the freshwater microbial communities found there. Yet, our knowledge of the community structure and functional potential of High Arctic freshwater microbes remains poor, even though they play key roles in nutrient cycling and other ecosystem services. Here, using high-throughput metagenomic sequencing and genome assembly, we show that sediment microbial communities in the High Arctic's largest lake by volume, Lake Hazen, are phylogenetically diverse, ranging from Proteobacteria, Verrucomicrobia, Planctomycetes, to members of the newly discovered Candidate Phyla Radiation groups. These genomes displayed a high prevalence of pathways involved in lipid chemistry, and a low prevalence of nutrient uptake pathways, which might represent adaptations to the specific, cold ($ 3.5 C) and extremely oligotrophic conditions in Lake Hazen. Despite these potential adaptations, it is unclear how ongoing environmental changes will affect microbial communities, the makeup of their genomic idiosyncrasies, as well as the possible implications at higher trophic levels.

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MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 50 publications

References 30 publications

phyloFlash – Rapid SSU rRNA profiling and targeted assembly from metagenomes

phyloFlash – Rapid SSU rRNA profiling and targeted assembly from metagenomes

PhyloMagnet: Fast and accurate screening of short-read meta-omics data using gene-centric phylogenetics

Microbial genomes retrieved from High Arctic lake sediments encode for adaptation to cold and oligotrophic environments

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