GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Rautiainen, Mikko; Marschall, Tobias

doi:10.1101/810812

Cited by 49 publications

(99 citation statements)

References 57 publications

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“…However, Oxford Nanopore reads typically come with high sequencing error rates, complicating the calling process. In order to obtain reliable variant positions and genotypes from these error-prone reads, we ran an error correction pipeline [25] to reduce the number of sequencing errors (see Methods section). Figure 4c shows an exemplary IGV [23] screenshot of uncorrected reads (top) and corrected (bottom) for the FRIGIDA-like protein 5 isoform X2 gene.…”

Section: Resultsmentioning

confidence: 99%

Haplotype Threading: Accurate Polyploid Phasing from Long Reads

Schrinner

Mari

Ebler

et al. 2020

Preprint

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Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. As a highly complex computational problem, polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present W H , a novel two-stage approach that addresses these challenges by (i) clustering reads using a position-dependent scoring function and (ii) threading the haplotypes through the clusters by dynamic programming. We demonstrate on a simulated data set that this results in accurate haplotypes with switch error rates that are around three times lower than those obtainable by the current state-of-the-art and even around seven times lower in regions of collapsing haplotypes. Using a real data set comprising long and short read tetraploid potato sequencing data we show that W H is able to phase the majority of the potato genes a er error correction, which enables the assembly of local genomic regions of interest at haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap and ready to be included in production settings.

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Section: Resultsmentioning

confidence: 99%

Haplotype Threading: Accurate Polyploid Phasing from Long Reads

Schrinner

Mari

Ebler

et al. 2020

Preprint

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“…55 Contribution 56 Here, we introduce AERON, an alignment based pipeline for quantification and detection of gene-fusion 57 events using only long RNA reads. We adapt the sequence-to-graph alignment tool GraphAligner [33] 58 to align reads generated from noisy long read technologies, such as ONT, to a reference transcriptome 59 represented as gene-exon graphs. These graphs naturally allow to quantify gene and transcript expression 60 based on the overlap with paths of known transcripts.…”

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“…77 In the alignment step, a long read sequencing dataset is aligned to all gene-exon graphs (Fig. 1c) using 78 the GraphAligner [33]. Finally, through comparison of read alignments with graph paths of annotated 79 transcripts, expression values are computed ( Fig.…”

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confidence: 99%

“…AERON 219 comes with the first long-read specific fusion detection method. We use GraphAligner [33], a fast sequence-220 to-graph alignment method, to align ONT reads to a reference transcriptome and find better alignments 221 as compared to Minimap2, which is used as part of previous state-of-the-art quantification pipelines. 222 We assign reads to transcripts based on the position of the read mapping within the transcriptome and 223 the fraction of read sequence contained in the transcript sequence.…”

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“…The path sequence of a path is the concatenation of the node labels of p. 322 Given a read sequence and a graph, the sequence-to-graph alignment problem is to find the path in 323 the graph with the smallest edit distance between the path sequence and the read sequence. We use 324 GraphAligner for aligning the reads [33]. Briefly, GraphAligner is a seed-and-extend aligner that finds 325 maximal exact matches (MEMs) [45] between the read and the node sequences and then extends them 326 with a bit-parallel dynamic programming algorithm [46].…”

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AERON: Transcript quantification and gene-fusion detection using long reads

Rautiainen

Durai

Chen

et al. 2020

Preprint

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Single-molecule sequencing technologies have the potential to improve measurement and analysis of long RNA molecules expressed in cells. However, analysis of error-prone long RNA reads is a current challenge. We present AERON for the estimation of transcript expression and prediction of gene-fusion events. AERON uses an efficient read-to-graph alignment algorithm to obtain accurate estimates for noisy reads. We demonstrate AERON to yield accurate expression estimates on simulated and real datasets. It is the first method to reliably call gene-fusion events from long RNA reads. Sequencing the K562 transcriptome, we used AERON and found known as well as novel gene-fusion events. Introduction 1Whole-transcriptome sequencing has become an important method in many research projects. Due to the 2 revolution in short-read sequencing technologies and algorithm development, the detection of expressed 3 RNAs in biological samples with thousands of cells [1] and even single cells [2] is nowadays done routinely. 4 There are many important applications of such technologies, for example the detection of disease specific 5 gene expression patterns [3] or the detection of gene fusion events in cancer cells [4], which opens the door 6 for new therapeutic options and novel biological discoveries. However, short-read whole transcriptome 7 sequencing has its weaknesses. RNA molecules can be thousands of nucleotides long and recent studies 8 have revealed that more than 95% of multi-exonic genes undergo alternative splicing [5,6]. Short-read 9 sequencing thus has important limitations when it comes to the accurate quantification of gene isoform 10 expression levels and the detection of gene fusion events [7]. Recent long read sequencing technologies, 11 like developed by Pacific Biosciences (Pacbio) and Oxford Nanopore Technologies (ONT), have made 12 significant progress in sequencing output per run at dramatically reduced costs. Thus, an important 13 current challenge is to develop methods that can use long read RNA sequencing data for tasks where 14 long reads overpower short reads, such as transcript quantification and gene fusion detection. Given their 15 ability to cover large fractions of each transcript -and frequently complete transcripts -longer reads 16 hold the promise of more accurate expression estimates. Although there is much potential in using long 17 1/23 RNA reads for transcript quantification, limited work has been done in this area and, to our knowledge, 18 there are presently no tools for detecting gene-fusion events from long RNA reads. 19Modern bioinformatics tools designed for short read RNA sequencing such as Cufflinks [8], Kallisto [9] 20 and Salmon [10] map short reads to a reference transcriptome and estimate abundances. Similarly, for 21 fusion detection, algorithms such as TopHat-Fusion [11], SOAPfuse [12], MapSplice [13] and others 22 align reads to a reference using a "splice-aware" aligner. They detect fusion events by considering reads 23 overlapping two different genes [14]. All the above methods ...

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