Aquila enables reference-assisted diploid personal genome assembly and comprehensive variant detection based on linked reads

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

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.

BLR: a flexible pipeline for haplotype analysis of multiple linked-read technologies

Linked-read sequencing promises a one-method approach for genome-wide insights including single nucleotide variants (SNVs), structural variants, and haplotyping. We introduce Barcode Linked Reads (BLR), an open-source haplotyping pipeline capable of handling millions of barcodes and data from multiple linked-read technologies including DBS, 10× Genomics, TELL-seq and stLFR. Running BLR on DBS linked-reads yielded megabase-scale phasing with low (<0.2%) switch error rates. Of 13616 protein-coding genes phased in the GIAB benchmark set (v4.2.1), 98.6% matched the BLR phasing. In addition, large structural variants showed concordance with HPRC-HG002 reference assembly calls. Compared to diploid assembly with PacBio HiFi reads, BLR phasing was more continuous when considering switch errors. We further show that integrating long reads at low coverage (∼10×) can improve phasing contiguity and reduce switch errors in tandem repeats. When compared to Long Ranger on 10× Genomics data, BLR showed an increase in phase block N50 with low switch-error rates. For TELL-Seq and stLFR linked reads, BLR generated longer or similar phase block lengths and low switch error rates compared to results presented in the original publications. In conclusion, BLR provides a flexible workflow for comprehensive haplotype analysis of linked reads from multiple platforms.

Haplotyping-Assisted Diploid Assembly and Variant Detection with Linked Reads

Phasing is essential for determining the origins of each set of alleles in the whole-genome sequencing data of individuals. As such, it provides essential information for the causes of hereditary diseases and the sources of individual variability. Recent technical breakthroughs in linked-read (referred to as co-barcoding in other chapters of the book) and long-read sequencing and downstream analysis have brought the goal of accurate and complete phasing within reach. Here we review recent progress related to the assembly and phasing of personal genomes based on linked-reads and related applications. Motivated by current limitations in generating high-quality diploid assemblies and detecting variants, a new suite of software tools, Aquila, was developed to fully take advantage of linked-read sequencing technology. The overarching goal of Aquila is to exploit the strengths of linked-read technology including long-range connectivity and inherent phasing of variants for reference-assisted local de novo assembly at the whole-genome scale. The diploid nature of the assemblies facilitates detection and phasing of genetic variation, including single nucleotide variations (SNVs), small insertions and deletions (indels), and structural variants (SVs). An extension of Aquila, Aquila_stLFR, focuses on another newly developed linked-reads sequencing technology, single-tube long-fragment read (stLFR). AquilaSV, a region-based diploid assembly approach, is used to characterize structural variants and can achieve diploid assembly in one target region at a time. Lastly, we introduce HAPDeNovo, a program that exploits phasing information from linked-read sequencing to improve detection of de novo mutations. Use of these tools is expected to harness the advantages of linked-reads technology, improve phasing, and advance variant discovery.

stLFRsv: A Germline Structural Variant Analysis Pipeline Using Co-barcoded Reads

Co-barcoded reads originating from long DNA fragments (mean length >30 kbp) maintain both single base level accuracy and long-range genomic information. We propose a pipeline, stLFRsv, to detect structural variation using co-barcoded reads. stLFRsv identifies abnormal large gaps between co-barcoded reads to detect potential breakpoints and reconstruct complex structural variants (SVs). Haplotype phasing by co-barcoded reads increases the signal to noise ratio, and barcode sharing profiles are used to filter out false positives. We integrate the short read SV caller smoove for smaller variants with stLFRsv. The integrated pipeline was evaluated on the well-characterized genome HG002/NA24385, and 74.5% precision and a 22.4% recall rate were obtained for deletions. stLFRsv revealed some large variants not included in the benchmark set that were verified by long reads or assembly. For the HG001/NA12878 genome, stLFRsv also achieved the best performance for both resource usage and the detection of large variants. Our work indicates that co-barcoded read technology has the potential to improve genome completeness.