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

Chao Yang; Zhenmiao Zhang; Yufen Huang; Xuefeng Xie; Herui Liao; Jin Xiao; Werner Pieter Veldsman; Kejing Yin; Xiaodong Fang; Lu Zhang

doi:10.1093/gigascience/giae028

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

Chao Yang (Co-first Author), Zhenmiao Zhang (Co-first Author), Yufen Huang, Xuefeng Xie, Herui Liao, Jin Xiao, Werner Pieter Veldsman, Kejing Yin, Xiaodong Fang^*, Lu Zhang^*

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

3 Citations (Scopus)

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Abstract

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. © The Author(s) 2024. Published by Oxford University Press GigaScience.

Original language	English
Article number	giae028
Journal	GigaScience
Volume	13
Online published	13 Jun 2024
DOIs	https://doi.org/10.1093/gigascience/giae028
Publication status	Published - 2024

Funding

This research was partially supported by the open project of BGI-Shenzhen, Shenzhen 518000, China (BGIRSZ20220012); the Hong Kong Research Grant Council Early Career Scheme (HKBU 22201419); Young Collaborative Research Grant (C2004-23Y); Health and Medical Research Fund (11221026); HKBU Start-up Grant Tier 2 (RC-SGT2/19–20/SCI/007); HKBU IRCMS (No. IRCMS/19–20/D02); the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515012226); and the Science Technology and Innovation Committee of Shenzhen Municipality, China (SGDX20190919142801722).

Research Keywords

10x Genomics
human genome
linked-read sequencing
metagenome
stLFR
TELL-Seq

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

RGC-funded

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10.1093/gigascience/giae028

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

abstract = "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{\textquoteright}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. {\textcopyright} The Author(s) 2024. Published by Oxford University Press GigaScience.",

keywords = "10x Genomics, human genome, linked-read sequencing, metagenome, stLFR, TELL-Seq",

author = "Chao Yang and Zhenmiao Zhang and Yufen Huang and Xuefeng Xie and Herui Liao and Jin Xiao and Veldsman, {Werner Pieter} and Kejing Yin and Xiaodong Fang and Lu Zhang",

year = "2024",

doi = "10.1093/gigascience/giae028",

language = "English",

volume = "13",

journal = "GigaScience",

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TY - JOUR

T1 - LRTK

T2 - a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

AU - Yang, Chao

AU - Zhang, Zhenmiao

AU - Huang, Yufen

AU - Xie, Xuefeng

AU - Liao, Herui

AU - Xiao, Jin

AU - Veldsman, Werner Pieter

AU - Yin, Kejing

AU - Fang, Xiaodong

AU - Zhang, Lu

PY - 2024

Y1 - 2024

N2 - 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. © The Author(s) 2024. Published by Oxford University Press GigaScience.

AB - 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. © The Author(s) 2024. Published by Oxford University Press GigaScience.

KW - 10x Genomics

KW - human genome

KW - linked-read sequencing

KW - metagenome

KW - stLFR

KW - TELL-Seq

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U2 - 10.1093/gigascience/giae028

DO - 10.1093/gigascience/giae028

M3 - RGC 21 - Publication in refereed journal

C2 - 38869148

SN - 2047-217X

VL - 13

JO - GigaScience

JF - GigaScience

M1 - giae028

ER -

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

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

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