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

Author:

Yang Chao1ORCID,Zhang Zhenmiao1ORCID,Huang Yufen23ORCID,Xie Xuefeng4ORCID,Liao Herui5ORCID,Xiao Jin1ORCID,Veldsman Werner Pieter1ORCID,Yin Kejing1ORCID,Fang Xiaodong34ORCID,Zhang Lu16ORCID

Affiliation:

1. Department of Computer Science, Hong Kong Baptist University , Hong Kong SAR 999077 , Hong Kong

2. BGI Research , Shenzhen 518083 , China

3. BGI Genomics , Shenzhen 518083 , China

4. BGI Research , Sanya 572025 , China

5. Department of Electrical Engineering, City University of Hong Kong , Hong Kong SAR 999077 , Hong Kong

6. Institute for Research and Continuing Education, Hong Kong Baptist University , Hong Kong SAR 999077 , Hong Kong

Abstract

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.

Funder

BGI-Shenzhen, Shenzhen

Hong Kong Research Grant Council Early Career Scheme

HKBU

Young Collaborative Research

Health and Medical Research Fund

HKBU Start-up Grant Tier 2

HKBU IRCMS

Guangdong Basic and Applied Basic Research Foundation

Science Technology and Innovation Committee of Shenzhen Municipality, China

Publisher

Oxford University Press (OUP)

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