TideHunter: efficient and sensitive tandem repeat detection from noisy long-reads using seed-and-chain

Abstract

Motivation Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) sequencing technologies can produce long-reads up to tens of kilobases, but with high error rates. In order to reduce sequencing error, Rolling Circle Amplification (RCA) has been used to improve library preparation by amplifying circularized template molecules. Linear products of the RCA contain multiple tandem copies of the template molecule. By integrating additional in silico processing steps, these tandem sequences can be collapsed into a consensus sequence with a higher accuracy than the original raw reads. Existing pipelines using alignment-based methods to discover the tandem repeat patterns from the long-reads are either inefficient or lack sensitivity. Results We present a novel tandem repeat detection and consensus calling tool, TideHunter, to efficiently discover tandem repeat patterns and generate high-quality consensus sequences from amplified tandemly repeated long-read sequencing data. TideHunter works with noisy long-reads (PacBio and ONT) at error rates of up to 20% and does not have any limitation of the maximal repeat pattern size. We benchmarked TideHunter using simulated and real datasets with varying error rates and repeat pattern sizes. TideHunter is tens of times faster than state-of-the-art methods and has a higher sensitivity and accuracy. Availability and implementation TideHunter is written in C, it is open source and is available at https://github.com/yangao07/TideHunter

Dynamic turnover of centromeres drives karyotype evolution in Drosophila

“…We first used Tandem Repeat Finder (TRF; Benson 1999) to identify satellite sequences in our assemblies that are enriched in putative centromeric regions (i.e., ends of telocentric chromosomes and scaffolding breakpoints in the middle of metacentric chromosomes). Additionally, we identify short simple satellites (<20 bp) directly from raw Illumina reads using k--Seek , and we also identify tandem repeats directly from long--read data (Nanopore and PacBio) using TideHunter (Gao et al 2019). The latter two approaches provided analyses of satellite and repeat enrichment independent of our genome assemblies.…”

“…S5). Using this pipeline, our TRF analysis recovered the same 21--bp and 99--bp motif (or higher order variants of the two motifs) in D. miranda, with a higher order variant (Benson 1999), k--Seek , and TideHunter (Gao et al 2019) for each Drosophila species. HOR = higher order repeat.…”

“…Drosophila guanche Illumina data used in TRF and k--Seek analyses was downloaded from the NCBI (SRA accession, ERX2095402). To characterize tandem repeats directly from long reads we used TideHunter (Gao et al 2019) with default settings and only kept consensus repeats that were > 4bp. Since the TideHunter output includes a consensus repeat sequence for each read, similar repeats and HOR variants will be found repeatedly in the output.…”

TRiCoLOR: tandem repeat profiling using whole-genome long-read sequencing data

“…Few TR detection methods for long-read sequencing data have been developed so far. Examples include PacmonSTR [ 9 ], NCRF [ 10 ], TideHunter [ 11 ], NanoSatellite [ 12 ], and Tandem-genotypes [ 13 ]. However, these tools have some limitations, either because they are technology-specific (PacmonSTR and NanoSatellite), because they are not intended to be used genome-wide (NCRF, TideHunter, and NanoSatellite), or because they require substantial preprocessing steps preventing their large-scale use (Tandem-genotypes).…”

abPOA: an SIMD-based C library for fast partial order alignment using adaptive band

“…Multiple consensus sequences are then generated by applying the heaviest bundling algorithm to the alignment graph . Recently, with the advent and growing popularity of long-read sequencing technologies on the Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) platforms, there is a renewed appreciation and interest of POA, and this algorithm is now broadly used for error correction and assembly of error-prone long reads (Loman et al, 2015;Vaser et al, 2017;Volden et al, 2018;Gao et al, 2019;Ruan and Li, 2019).…”