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Banerjee G, Papri SR, Banerjee P. Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses. STAR Protoc 2024; 5:103167. [PMID: 38954516 PMCID: PMC11263634 DOI: 10.1016/j.xpro.2024.103167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/08/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024] Open
Abstract
Constructing metagenome-assembled genomes (MAGs) from complex metagenomic samples involves a series of bioinformatics operations, each requiring deep bioinformatics knowledge. Here, we present a protocol for constructing MAGs and conducting functional profiling to address biological questions. We describe steps for system configuration, data downloads, read processing, removal of human DNA contamination, metagenomic assembly, and statistical quality assessment of the final assembly. Additionally, we detail procedures for the construction and refinement of MAGs, as well as the functional profiling of MAGs.
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Affiliation(s)
- Goutam Banerjee
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - Suraya Rahman Papri
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Pratik Banerjee
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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Zhang Z, Xiao J, Wang H, Yang C, Huang Y, Yue Z, Chen Y, Han L, Yin K, Lyu A, Fang X, Zhang L. Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity. Nat Commun 2024; 15:4631. [PMID: 38821971 PMCID: PMC11143213 DOI: 10.1038/s41467-024-49060-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/17/2024] [Indexed: 06/02/2024] Open
Abstract
Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.
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Grants
- This research was partially supported by the Young Collaborative Research Grant (C2004-23Y, L.Z.), HMRF (11221026, L.Z.), the open project of BGI-Shenzhen, Shenzhen 518000, China (BGIRSZ20220012, L.Z.), the Hong Kong Research Grant Council Early Career Scheme (HKBU 22201419, L.Z.), HKBU Start-up Grant Tier 2 (RC-SGT2/19-20/SCI/007, L.Z.), HKBU IRCMS (No. IRCMS/19-20/D02, L.Z.).
- This research was partially supported by the open project of BGI-Shenzhen, Shenzhen 518000, China (BGIRSZ20220014, KJ.Y.).
- The study were partially supported by the Science Technology and Innovation Committee of Shenzhen Municipality, China (SGDX20190919142801722, XD.F.),
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Affiliation(s)
- Zhenmiao Zhang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Jin Xiao
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Hongbo Wang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Chao Yang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | | | - Zhen Yue
- BGI Research, Sanya, 572025, China
| | - Yang Chen
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese, Guangzhou, China
| | - Lijuan Han
- Department of Scientific Research, Kangmeihuada GeneTech Co., Ltd (KMHD), Shenzhen, China
| | - Kejing Yin
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
- Institute for Research and Continuing Education, Hong Kong Baptist University, Shenzhen, China
| | - Aiping Lyu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Xiaodong Fang
- BGI Research, Shenzhen, 518083, China
- BGI Research, Sanya, 572025, China
- Department of Scientific Research, Kangmeihuada GeneTech Co., Ltd (KMHD), Shenzhen, China
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
- Institute for Research and Continuing Education, Hong Kong Baptist University, Shenzhen, China.
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Sapoval N, Tanevski M, Treangen TJ. KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:506-520. [PMID: 38160303 PMCID: PMC10764071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
The microbes present in the human gastrointestinal tract are regularly linked to human health and disease outcomes. Thanks to technological and methodological advances in recent years, metagenomic sequencing data, and computational methods designed to analyze metagenomic data, have contributed to improved understanding of the link between the human gut microbiome and disease. However, while numerous methods have been recently developed to extract quantitative and qualitative results from host-associated microbiome data, improved computational tools are still needed to track microbiome dynamics with short-read sequencing data. Previously we have proposed KOMB as a de novo tool for identifying copy number variations in metagenomes for characterizing microbial genome dynamics in response to perturbations. In this work, we present KombOver (KO), which includes four key contributions with respect to our previous work: (i) it scales to large microbiome study cohorts, (ii) it includes both k-core and K-truss based analysis, (iii) we provide the foundation of a theoretical understanding of the relation between various graph-based metagenome representations, and (iv) we provide an improved user experience with easier-to-run code and more descriptive outputs/results. To highlight the aforementioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring less than 10 minutes and 10 GB RAM per sample to process these data. Furthermore, we highlight how graph-based approaches such as k-core and K-truss can be informative for pinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at: https://github.com/treangenlab/komb.
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Affiliation(s)
- Nicolae Sapoval
- Department of Computer Science, Rice University, Houston, TX 77005, USA,
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