Accurate spliced alignment of long RNA sequencing reads

Sahlin, Kristoffer; Mäkinen, Veli

doi:10.1101/2020.09.02.279208

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…Some of the identified novel transcripts result from misalignment of long reads at short exons. [37]. IsoTools helps identifying these cases, such that alignment algorithms can be improved in this regard.…”

Section: Discussionmentioning

confidence: 99%

Long-read transcriptome sequencing analysis with IsoTools

Lienhard

Beucken

Timmermann

et al. 2021

Preprint

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Long-read transcriptome sequencing (LRTS) holds the promise to boost our understanding of alternative splicing. Recent advances in accuracy and throughput have diminished the major limitations and enabled the direct quantification of isoforms. Considering the complexity of the data and the broad range of potential applications, it is clear that highly flexible, accurate analysis tools are crucial. Here, we present IsoTools, a comprehensive Python-based analysis package, for the improvement of alternative and differential splicing analysis. IsoTools provides a comprehensive data structure that integrates genomic information from LRTS transcripts together with the reference annotation, and enables broad functionality to quality control, visualize and analyze the data. Additionally, we implemented a graph-based method for the identification of alternative splicing events and a statistical approach based on the beta binomial distribution for the detection of differential events. To demonstrate our methods, we generated PacBio Iso-Seq data of human hepatocytes treated with the HDAC inhibitor valproic acid, a compound known to induce widespread transcriptional changes. Contrasted with short read RNA-Seq of the same samples, this analysis shows that LRTS provides valuable additional insights for a better understanding of alternative splicing, in particular with respect to complex novel and differential splicing events. IsoTools is made available for the community along with extensive documentation at https://github.com/MatthiasLienhard/isotools.

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Section: Discussionmentioning

confidence: 99%

Long-read transcriptome sequencing analysis with IsoTools

Lienhard

Beucken

Timmermann

et al. 2021

Preprint

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“…Extension and chaining runtimes Given sorted anchors, let T Chain be the time spent finding an optimal chain. T Chain depends on the objective function (Mäkinen and Sahlin, 2020;Jain et al, 2022;Abouelhoda and Ohlebusch, 2005;Otto et al, 2011). Since our gap costs are linear, T Chain = O(N log N ) where N is the number of anchors (Abouelhoda and Ohlebusch, 2005).…”

Section: Assumptions and Modelsmentioning

confidence: 99%

Sequence aligners can guarantee accuracy in almostO(mlogn) time: a rigorous average-case analysis of the seed-chain-extend heuristic

Shaw

2022

Preprint

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Seed-chain-extend with k-mer seeds is a powerful heuristic technique for sequence alignment employed by modern sequence aligners. While effective in practice for both runtime and accuracy, theoretical guarantees on the resulting alignment do not exist for seed-chain-extend. In this work, we give the first rigorous bounds for the efficacy of seed-chain-extend with k-mers in expectation. Assume we are given a random nucleotide sequence of length ≈ n that is indexed (or seeded) and a mutated substring of length ≈ m ≤ n with mutation rate θ < 0.206. We prove that we can find a k = Θ(log n) for the k-mer size such that the expected runtime of seed-chain-extend under optimal linear gap cost chaining and quadratic time gap extension is O(mnf(θ)log n) where f(θ) < 2.43·θ holds as a loose bound. In fact, for reasonable θ = 0.05, f(θ) < 0.08, indicating nearly quasilinear running time in practice. The alignment also turns out to be good; we prove that more than 1 − O(1/√m) fraction of the homologous bases are recoverable under an optimal chain. We also show that our bounds work when k-mers are sketched, i.e. only a subset of all k-mers is selected. Under the open syncmer sketching method, one can sketch with decreasing density as a function of n and achieve asymptotically smaller chaining time, yet the same bounds for extension time and recoverability hold. In other words, sketching reduces chaining time without increasing alignment time or decreasing accuracy too much, justifying the effectiveness of sketching as a practical speedup in sequence alignment. We verify our results in simulation and conjecture that f(θ) can be further reduced.

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“…1). Techniques along these lines are used in many tools, such as long-read mappers like Minimap2 (Li (2018)), Minigraph (Li et al (2020)), Winnowmap2 (Jain et al (2020)), uLTRA RNA-seq aligner (Sahlin and Makinen (2020)), IRA (Ren and Chaisson (2020)), NGMLR (Sedlazeck et al (2018)), BLASR (Chaisson and Tesler (2012)) and generic alignment tools like Nucmer (Kurtz et al (2004), Marçais et al (2018)), CLASP (Otto et al (2011)), and CoCoNUT ( Abouelhoda et al (2008)). We will focus on the following problem (described formally in Section 2): Given a set of n anchors, determine an optimal ordered subset (or chain) of these anchors.…”

Section: Introductionmentioning

confidence: 99%

Co-linear Chaining with Overlaps and Gap Costs

Jain

Gibney

Thankachan

2021

Preprint

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Co-linear chaining has proven to be a powerful technique for finding approximately optimal alignments and approximating edit distance. It is used as an intermediate step in numerous mapping tools that follow seed-and-extend strategy. Despite this popularity, subquadratic time algorithms for the case where chains support anchor overlaps and gap costs are not currently known. Moreover, a theoretical connection between co-linear chaining cost and edit distance remains unknown. We present algorithms to solve the co-linear chaining problem with anchor overlaps and gap costs in ~O(n) time, where n denotes the count of anchors. We establish the first theoretical connection between co-linear chaining cost and edit distance. Specifically, we prove that for a fixed set of anchors under a carefully designed chaining cost function, the optimal 'anchored' edit distance equals the optimal co-linear chaining cost. Finally, we demonstrate experimentally that optimal co-linear chaining cost under the proposed cost function can be computed significantly faster than edit distance, and achieves high correlation with edit distance for closely as well as distantly related sequences.

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Accurate spliced alignment of long RNA sequencing reads

Cited by 5 publications

References 45 publications

Long-read transcriptome sequencing analysis with IsoTools

Long-read transcriptome sequencing analysis with IsoTools

Sequence aligners can guarantee accuracy in almostO(mlogn) time: a rigorous average-case analysis of the seed-chain-extend heuristic

Co-linear Chaining with Overlaps and Gap Costs

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