Chaining for accurate alignment of erroneous long reads to acyclic variation graphs

Abstract

Motivation Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit (Garrison et al., 2018) is a popular aligner of short reads, GraphAligner (Rautiainen and Marschall, 2020) is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. Results We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12% to 17% more reads, and 21% to 28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22 and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph (Li et al., 2020) and minichain (Chandra and Jain, 2023) obtain an accuracy of less than 60% on this setting. Availability GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address. Supplementary information Supplementary data are available at Bioinformatics online.

Technological Development and Advances for Constructing and Analyzing Plant Pangenomes

“…For instance, Minigraph ( Li et al 2020 ) is robust in mapping sequences directly onto the pangenome graph, while Vg Giraffe ( Sirén et al 2021 ) is noted for its effectiveness in aligning short-read sequencing data to the pangenome graph. Additionally, tools like GraphChainer ( Ma et al 2023 ) and GraphAligner ( Rautiainen and Marschall 2020 ) offer advantages in aligning long-read data, demonstrating the tailored functionalities of these algorithms in addressing different aspects of pangenome analysis. Currently, three approaches are available for generating different types of pangenome graphs, including the genetic variation graph, reference-based pangenome graph, and reference-unbiased pangenome graph.…”

Aligning distant sequences to graphs using long seed sketches

“…There are also several areas for improvement that are beyond the scope of this work. For instance, we have not implemented any seed filtering techniques such as colinear chaining ( Li et al 2020 ; Almodaresi et al 2021 ; Karasikov et al 2022 ; Chandra and Jain 2023 ; Ma et al 2023 ). These approaches use a cut-off threshold rather than the number of neighbors for anchoring, which can reduce the number of anchors in sparse areas of the sketch space and decrease the number of false matches.…”

“…Because the time complexity of optimal sequence-to-graph alignment grows linearly with the number of edges in the graph ( Jain et al 2020 ; Gibney et al 2022 ), many approaches instead follow an approximate seed-and-extend strategy ( Altschul et al 1990 ), which operates in four main steps: (1) seed extraction , which in its simplest form involves finding all substrings with a certain length; (2) seed anchoring , finding matching nodes in the graph, (3) seed filtration , often involving clustering ( Chang et al 2020 ; Rautiainen and Marschall 2020 ) or colinear chaining ( Almodaresi et al 2021 ; Karasikov et al 2022 ; Chandra and Jain 2023 ; Ma et al 2023 ) of seeds, and (4) seed extension , involving performing semiglobal pairwise sequence alignment forward and backward from each anchored seed ( Li 2013 ). We will review the usage of exact seeds used in tools such as vg ( Garrison et al 2018 ) and GraphAligner (GA) ( Rautiainen and Marschall 2020 ) and discuss their limitations in a high mutation-rate setting.…”

Finding maximal exact matches in graphs

“…Extending alignment between sequences to sequence-to-graph alignment is an emerging and central challenge of computational pangenomics [ 12 ], as labeled graphs are a popular representation of pangenomes used in recent bioinformatics tools [ 13 – 16 ]. We assume that a labeled graph ( ) is the reference pangenome of interest.…”

“…To circumvent this difficulty, research efforts have concentrated on finding parameterized solutions to (exact) pattern matching in labeled graphs [ 19 – 22 ]. Moreover, the use of MEMs and co-linear chaining has also been extended to graphs [ 13 – 16 ].…”