Assembly and analytical validation of a metagenomic reference catalog of human gut microbiota based on co-barcoding sequencing

Huang, Yingwei; Jiang, Puzi; Liang, Zhengjiao; Chen, R C; Yue, Zhen; Xie, Xuefeng; Guan, Changge; Fang, Xiaodong

doi:10.3389/fmicb.2023.1145315

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…Previous studies have amply demonstrated the huge potential of linked reads in metagenomic studies [ 3 , 8 , 9 , 11 ]. Here, we evaluated the performance of linked reads in detecting taxonomic abundance and genomic variants using LRTK.…”

Section: Resultsmentioning

confidence: 99%

“…The hidden long-range information they provide enables local assembly of co-barcoded reads and thus significantly increases the number of high-quality metagenome-assembled genomes (MAGs) [ 8 ]. In longitudinal sequencing datasets [ 3 , 9 , 10 ], the barcodes associated with linked reads promote the phasing of genomic variants and refine the identification of intrahost evolution of gut microbiota. In some complex environments, such as soil, linked-read sequencing has been shown to aid in the investigation of the involved microbial genomes [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

Yang,

Zhang,

Huang

et al. 2024

GigaScience

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Background Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of linked-read technologies are well known and have been demonstrated in many human genomic and metagenomic studies. However, existing linked-read analysis pipelines (e.g., Long Ranger) were primarily developed to process sequencing data from the human genome and are not suited for analyzing metagenomic sequencing data. Moreover, linked-read analysis pipelines are typically limited to 1 specific sequencing platform. Findings To address these limitations, we present the Linked-Read ToolKit (LRTK), a unified and versatile toolkit for platform agnostic processing of linked-read sequencing data from both human genome and metagenome. LRTK provides functions to perform linked-read simulation, barcode sequencing error correction, barcode-aware read alignment and metagenome assembly, reconstruction of long DNA fragments, taxonomic classification and quantification, and barcode-assisted genomic variant calling and phasing. LRTK has the ability to process multiple samples automatically and provides users with the option to generate reproducible reports during processing of raw sequencing data and at multiple checkpoints throughout downstream analysis. We applied LRTK on linked reads from simulation, mock community, and real datasets for both human genome and metagenome. We showcased LRTK’s ability to generate comparative performance results from preceding benchmark studies and to report these results in publication-ready HTML document plots. Conclusions LRTK provides comprehensive and flexible modules along with an easy-to-use Python-based workflow for processing linked-read sequencing datasets, thereby filling the current gap in the field caused by platform-centric genome-specific linked-read data analysis tools.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

Yang,

Zhang,

Huang

et al. 2024

GigaScience

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Elucidating the Intricate Roles of Gut and Breast Microbiomes in Breast Cancer Metastasis to the Bone

Naik,

Godbole

2024

Cancer Reports

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BackgroundBreast cancer is the most predominant and heterogeneous cancer in women. Moreover, breast cancer has a high prevalence to metastasize to distant organs, such as the brain, lungs, and bones. Patients with breast cancer metastasis to the bones have poor overall and relapse‐free survival. Moreover, treatment using chemotherapy and immunotherapy is ineffective in preventing or reducing cancer metastasis.Recent FindingsMicroorganisms residing in the gut and breast, termed as the resident microbiome, have a significant influence on the formation and progression of breast cancer. Recent studies have identified some microorganisms that induce breast cancer metastasis to the bone. These organisms utilize multiple mechanisms, including induction of epithelial–mesenchymal transition, steroid hormone metabolism, immune modification, bone remodeling, and secretion of microbial products that alter tumor microenvironment, and enhance propensity of breast cancer cells to metastasize. However, their involvement makes these microorganisms suitable as novel therapeutic targets. Thus, studies are underway to prevent and reduce breast cancer metastasis to distant organs, including the bone, using chemotherapeutic or immunotherapeutic drugs, along with probiotics, antibiotics or fecal microbiota transplantation.ConclusionsThe present review describes association of gut and breast microbiomes with bone metastases. We have elaborated on the mechanisms utilized by breast and gut microbiomes that induce breast cancer metastasis, especially to the bone. The review also highlights the current treatment options that may target both the microbiomes for preventing or reducing breast cancer metastases. Finally, we have specified the necessity of maintaining a diverse gut microbiome to prevent dysbiosis, which otherwise may induce breast carcinogenesis and metastasis especially to the bone. The review may facilitate more detailed investigations of the causal associations between these microbiomes and bone metastases. Moreover, the potential treatment options described in the review may promote discussions and research on the modes to improve survival of patients with breast cancer by targeting the gut and breast microbiomes.

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LRTK: A platform agnostic toolkit for linked-read analysis of both human genomes and metagenomes

Yang

Zhang

Liao

et al. 2022

Preprint

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Summary: Linked-read sequencing technologies offering reads with both high base quality and long-range DNA connectedness have shown great success in genomic studies. The mainstream platforms include 10x Genomics linked-read (10x), Single Tube Long Fragment Read (stLFR) and Transposase Enzyme-Linked Long-read Sequencing (TELL-Seq). The existing data analysis pipelines, e.g., Long Ranger, have been developed to process sequencing data from particular platforms and so are unable to fully utilize the unique characteristics of other platforms; thus, users have limited tools to choose for downstream analysis. To address these limitations, we present Linked-Read ToolKit (LRTK), a unified and versatile toolkit to process linked-read sequencing data from different platforms. LRTK provides flexible functions to perform data simulation, format conversion, data preprocessing, barcode-aware read alignment, variant calling and phasing. It also allows multi-sample batch processing and generates a HTML report with key statistics and plots. We applied LRTK to the linked-read data of NA24385 obtained from all three platforms, where the results showed the advancement of LRTK in structural variation recall rate for 10x linked-reads and in increasing phase block N50 for 10x and stLFR linked-reads. Availability: Source codes are available at https://github.com/ericcombiolab/LRTK. Anaconda supports the installation of LRTK and its dependencies. Contact: ericluzhang@hkbu.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.

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Assembly and analytical validation of a metagenomic reference catalog of human gut microbiota based on co-barcoding sequencing

Cited by 3 publications

References 57 publications

LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

Elucidating the Intricate Roles of Gut and Breast Microbiomes in Breast Cancer Metastasis to the Bone

LRTK: A platform agnostic toolkit for linked-read analysis of both human genomes and metagenomes

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