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CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.

ABSTRACT: Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.

SUBMITTER: Norouzi-Beirami MH

PROVIDER: S-EPMC7787360 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.

Norouzi-Beirami Mohammad H MH Marashi Sayed-Amir SA Banaei-Moghaddam Ali M AM Kavousi Kaveh K

NAR genomics and bioinformatics 20210106 1

Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at ...[more]

PMID: 33575649

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Project description:BackgroundThe study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of uncultivated microbial populations that may have important roles in their environments. Extracting individual draft genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis have become diverse and sophisticated, resulting in a significant burden for biologists to access and use them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their evaluation and visualization.ResultsTo address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from metagenomic data through bin consolidation and reassembly. MetaWRAP's hybrid bin extraction algorithm outperforms individual binning approaches and other bin consolidation programs in both synthetic and real data sets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including taxonomy assignment, abundance estimation, functional annotation, and visualization.ConclusionsMetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic analysis, while contributing significant improvements to the extraction and interpretation of high-quality metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software available at https://github.com/bxlab/metaWRAP .

Project description:The improvement of sequencing technologies has facilitated generalization of metagenomic sequencing, which has become a standard procedure for analyzing the structure and functionality of microbiomes. Bioinformatic analysis of sequencing results poses a challenge because it involves many different complex steps. SqueezeMeta is a fully automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support that enables co-assembly of related metagenomes and retrieval of individual genomes via binning procedures. SqueezeMeta features several unique characteristics: co-assembly procedure or co-assembly of unlimited number of metagenomes via merging of individual assembled metagenomes, both with read mapping for estimation of the abundances of genes in each metagenome. It also includes binning and bin checking for retrieving individual genomes. Internal checks for the assembly and binning steps provide information about the consistency of contigs and bins. Moreover, results are stored in a MySQL database, where they can be easily exported and shared, and can be inspected anywhere using a flexible web interface that allows simple creation of complex queries. We illustrate the potential of SqueezeMeta by analyzing 32 gut metagenomes in a fully automatic way, enabling retrieval of several million genes and several hundreds of genomic bins. One of the motivations in the development of SqueezeMeta was producing a software capable of running in small desktop computers and thus amenable to all users and settings. We were also able to co-assemble two of these metagenomes and complete the full analysis in less than one day using a simple laptop computer. This reveals the capacity of SqueezeMeta to run without high-performance computing infrastructure and in absence of any network connectivity. It is therefore adequate for in situ, real time analysis of metagenomes produced by nanopore sequencing. SqueezeMeta can be downloaded from https://github.com/jtamames/SqueezeMeta.

Project description:BackgroundMetagenomics is the application of modern genomic techniques to investigate the members of a microbial community directly in their natural environments and is widely used in many studies to survey the communities of microbial organisms that live in diverse ecosystems. In order to understand the metagenomic profile of one of the densest interaction spaces for millions of people, the public transit system, the MetaSUB international Consortium has collected and sequenced metagenomes from subways of different cities across the world. In collaboration with CAMDA, MetaSUB has made the metagenomic samples from these cities available for an open challenge of data analysis including, but not limited in scope to, the identification of unknown samples.ResultsTo distinguish the metagenomic profiling among different cities and also predict unknown samples precisely based on the profiling, two different approaches are proposed using machine learning techniques; one is a read-based taxonomy profiling of each sample and prediction method, and the other is a reduced representation assembly-based method. Among various machine learning techniques tested, the random forest technique showed promising results as a suitable classifier for both approaches. Random forest models developed from read-based taxonomic profiling could achieve an accuracy of 91% with 95% confidence interval between 80 and 93%. The assembly-based random forest model prediction also reached 90% accuracy. However, both models achieved roughly the same accuracy on the testing test, whereby they both failed to predict the most abundant label.ConclusionOur results suggest that both read-based and assembly-based approaches are powerful tools for the analysis of metagenomics data. Moreover, our results suggest that reduced representation assembly-based methods are able to simultaneous provide high-accuracy prediction on available data. Overall, we show that metagenomic samples can be traced back to their location with careful generation of features from the composition of microbes and utilizing existing machine learning algorithms. Proposed approaches show high accuracy of prediction, but require careful inspection before making any decisions due to sample noise or complexity.ReviewersThis article was reviewed by Eugene V. Koonin, Jing Zhou and Serghei Mangul.

Dataset Information

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.

Publications

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.

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