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

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

doi:10.1101/260372

Cited by 7 publications

(9 citation statements)

References 39 publications

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“…Since Shark can be used as a preliminary step in pipelines for the detection of novel alternative splicing events from samples of RNA-Seq data, future work will be devoted to an in-depth experimental analysis of Shark as a preliminary step of a pipeline that includes computationallydemanding tools such as ASGAL [8], that relies on mapping reads against a splicing graph, and KISSPLICE [18], that assembles reads and identifies alternative splicing events by analyzing bubbles in the resulting de Bruijn graph.…”

Section: Discussionmentioning

confidence: 99%

“…At the end of this step, each 1 in BF is associated with a subset of genes back-references (represented as a list of IDs) stored in memory. Then, we scan all the k-mers in each gene of G, we compute the corresponding 1 in BF and (via a rank) the corresponding list L r to which the gene ID must be appended (lines [8][9][10][11][12]. Finally all duplicates are removed from the lists L r .…”

Section: Methodsmentioning

confidence: 99%

“…RNA-Seq analysis plays an important role in the biological and medical research aimed at deepening our understanding of cellular biological processes and their relationships with pathological conditions. As such, several research initiatives had the objective of producing RNA-Seq data, while a number of tools have been proposed to analyze such datasets and gained a widespread adoption in the community [4,8,16,18,19,24]. Traditional approaches generally rely on RNA-Seq read mapping to the annotated gene isoforms or on the spliced alignment of reads to the genome.…”

Section: Introductionmentioning

confidence: 99%

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Shark: fishing in a sample to discard useless RNA-Seq reads

Bonizzoni

Ceccato

Vedova

et al. 2019

Preprint

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Recent advances in high throughput RNA-Seq technologies allow to produce massive datasets. When a study focuses only on a handful of genes, most reads are not relevant and degrade the performance of the tools used to analyze the data. Removing such useless reads from the input dataset leads to improved efficiency without compromising the results of the study.To this aim, in this paper we introduce a novel computational problem, called gene assignment and we propose an efficient alignment-free approach to solve it. Given a RNA-Seq sample and a panel of genes, a gene assignment consists in extracting from the sample the reads that most probably were sequenced from those genes. The problem becomes more complicated when the sample exhibits evidence of novel alternative splicing events.We implemented our approach in a tool called Shark and assessed its effectiveness in speeding up differential splicing analysis pipelines. This evaluation shows that Shark is able to significantly improve the performance of RNA-Seq analysis tools without having any impact on the final results.The tool is distributed as a stand-alone module and the software is freely available at https://github.com/AlgoLab/shark.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shark: fishing in a sample to discard useless RNA-Seq reads

Bonizzoni

Ceccato

Vedova

et al. 2019

Preprint

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“…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

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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.

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“…Graphs with nodes representing nucleotide characters and edges representing adjacencies 51 have successfully been used for variant calling [24][25][26], genome assembly [27][28][29][30], and short tandem 52 repeat resolution [31]. Furthermore, alternative splicing events can be detected by aligning short reads 53 to splicing graphs [32]. So far, to our knowledge, no algorithm has been proposed to use sequence graphs 54 for long read transcript quantification and gene-fusion detection.…”

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confidence: 99%

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

Cited by 7 publications

References 39 publications

Shark: fishing in a sample to discard useless RNA-Seq reads

Shark: fishing in a sample to discard useless RNA-Seq reads

Validating Paired-end Read Alignments in Sequence Graphs

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

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