Mapping RNA-seq Data to a Transcript Graph via Approximate Pattern Matching to a Hypertext

Beretta, Stefano; Bonizzoni, Paola; Denti, Luca; Previtali, Marco; Rizzi, Raffaella

doi:10.1007/978-3-319-58163-7_3

Cited by 11 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Navarro [18] improved all previous results in both time and space complexity, proposing an algorithm which requires O(m(n + e)) time, where m is the length of the pattern, n is the length of the concatenation of all vertex labels, and e is the total number of edges. The method in [13] improves the latest result by Thachuk [19]: an algorithm with time complexity O m + γ 2 using succinct data structures to solve the exact version of matching a pattern to a graph -i.e. without errors -where γ is number of occurrences of the node texts as substrings of the pattern.…”

Section: Introductionmentioning

confidence: 74%

“…The ASGAL mapping algorithm improves a previous solution to the approximate pattern matching problem to a hypertext (an open problem faced in [13]). The approximate matching of a string to a graph with labeled vertices is a computational problem first introduced by Manber and Wu [14] and attacked by many researchers [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

“…without errors -where γ is number of occurrences of the node texts as substrings of the pattern. The algorithm in [13] is based on the concept of Maximal Exact Match and it uses a succinct data structure to solve the approximate matching of a pattern to a hypertext in O m + η 2 time, where η is the number of Maximal Exact Matches between the pattern and the concatenation of all vertex labels. In this paper we improve the results in [13] by extending the algorithm to implement an RNA-Seq data aligner for detecting general AS events from the splicing graph.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ASGAL: Aligning RNA-Seq Data to a Splicing Graph to Detect Novel Alternative Splicing Events

Denti

Rizzi

Beretta

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Background: While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. The latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph. Results: We present ASGAL (Alternative Splicing Graph ALigner): a tool for mapping RNA-Seq data to the splicing graph, with the main goal of detecting novel alternative splicing events. ASGAL receives in input the annotated transcripts of a gene and an RNA-Seq sample, and it computes (1) the spliced alignments of each read, and (2) a list of novel events with respect to the gene annotation.Conclusions: An experimental analysis shows that, by aligning reads directly to the splicing graph, ASGAL better predicts alternative splicing events when compared to tools requiring spliced alignments of the RNA-Seq data to a reference genome. To the best of our knowledge, ASGAL is the first tool that detects novel alternative splicing events by directly aligning reads to a splicing graph. Availability: Source code, documentation, and data are available for download at

show abstract

Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ASGAL: Aligning RNA-Seq Data to a Splicing Graph to Detect Novel Alternative Splicing Events

Denti

Rizzi

Beretta

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several sequence to graph aligners have been developed in recent years to map reads to variation graphs [11,12,18,21,28,35,37], de-Bruijn graphs [16,25,26] and splicing graphs [1,8]. Readers are referred to review articles, e.g., [5,34] for an expanded list of the tools.…”

Section: Introductionmentioning

confidence: 99%

Validating Paired-end Read Alignments in Sequence Graphs

Jain

Zhang

Dilthey

et al. 2019

Preprint

View full text Add to dashboard Cite

Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrixmatrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second. AcknowledgementsThe authors thank Abdurrahman Yasar, Siva Rajamanickam and Srinivas Eswar for sharing their insights on sparse matrix manipulations. Problem FormulationDefinition 1. Sequence Graph: A sequence graph G(V, E) is a directed graph with vertices V and edges E, where each vertex v ∈ V is labeled with a character from alphabet Σ.

show abstract

“…Aligning sequences to graphs is becoming increasingly important in the context of several applications in computational biology, including variant calling [22,6,7,9], genome assembly [2,33,8], long read error-correction [28,32,34], RNA-seq data analysis [4,13], and more recently, antimicrobial resistance profiling [27]. Much of this has been driven by the growing ease and ubiquity of sequencing at personal, population, and environmental-scale, leading to significant growth in availability of datasets.…”

Section: Introductionmentioning

confidence: 99%

On the Complexity of Sequence to Graph Alignment

Jain

Zhang

Gao

et al. 2019

Preprint

View full text Add to dashboard Cite

Availability of extensive genetics data across multiple individuals and populations is driving the growing importance of graph based reference representations. Aligning sequences to graphs is a fundamental operation on several types of sequence graphs (variation graphs, assembly graphs, pan-genomes, etc.) and their biological applications. Though research on sequence to graph alignments is nascent, it can draw from related work on pattern matching in hypertext. In this paper, we study sequence to graph alignment problems under Hamming and edit distance models, and linear and affine gap penalty functions, for multiple variants of the problem that allow changes in query alone, graph alone, or in both. We prove that when changes are permitted in graphs either standalone or in conjunction with changes in the query, the sequence to graph alignment problem is N P-complete under both Hamming and edit distance models for alphabets of size ≥ 2. For the case where only changes to the sequence are permitted, we present an O(|V | + m|E|) time algorithm, where m denotes the query size, and V and E denote the vertex and edge sets of the graph, respectively. Our result is generalizable to both linear and affine gap penalty functions, and improves upon the run-time complexity of existing algorithms. * Should be regarded as joint first-authors.

show abstract

Mapping RNA-seq Data to a Transcript Graph via Approximate Pattern Matching to a Hypertext

Cited by 11 publications

References 19 publications

ASGAL: Aligning RNA-Seq Data to a Splicing Graph to Detect Novel Alternative Splicing Events

ASGAL: Aligning RNA-Seq Data to a Splicing Graph to Detect Novel Alternative Splicing Events

Validating Paired-end Read Alignments in Sequence Graphs

On the Complexity of Sequence to Graph Alignment

Contact Info

Product

Resources

About