Alternative splicing analysis benchmark with DICAST

Fenn, Amit; Tsoy, Olga; Faro, Tim; Roessler, Fanny; Dietrich, Alexander; Kersting, Johannes; Louadi, Zakaria; Lio, Chit Tong; Voelker, Uwe; Baumbach, Jan; Kacprowski, Tim; List, Markus

doi:10.1101/2022.01.05.475067

Cited by 4 publications

(10 citation statements)

References 44 publications

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“…Single-cell RNA-seq potentiates the discovery of cell-type-defining isoforms, including paralogous genes and those generated by alternative splicing. Many approaches for statistically rigorous detection of differential isoform expression have been recently developed, but all require reference alignments with aforementioned limitations ( 10, 28, 34 ), p-values require intensive computation, and each method has power to detect only certain events, for example, splicing but not SNPs. They also struggle to resolve multi-mapped reads ( 10, 34 ).…”

Section: Resultsmentioning

confidence: 99%

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SPLASH: a statistical, reference-free genomic algorithm unifies biological discovery

Chaung

Baharav

Zheludev

et al. 2022

Preprint

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We present a unifying statistical formulation for many fundamental problems in genome science and develop a reference-free, highly efficient algorithm that solves it. Sequence diversification - nucleic acid mutation, rearrangement, and reassortment - is necessary for the differentiation and adaptation of all replicating organisms. Identifying sample-dependent sequence diversification, e.g. adaptation or regulated isoform expression, is fundamental to many biological studies, and is achieved today with next-generation sequencing. Paradoxically, current analyses begin with attempts to align to or assemble necessarily incomplete reference genomes, a step that is at odds with detecting the most important examples of sequence diversification. In addition to being computationally expensive, reference-first approaches suffer from diminished discovery power: they are blind to unaligned or mis-aligned sequences. We provide a unifying formulation for detecting sample-dependent sequence diversification that subsumes core problems faced in diverse biological fields. This formulation allows us to construct an algorithm that performs inference on raw reads, avoiding references completely. We illustrate the power of our approach for new data-driven biological discovery with examples of novel single-cell resolved, cell-type-specific isoform expression, including expression in the major histocompatibility complex, and de novo prediction of viral protein adaptation including in SARS-CoV-2.

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Section: Resultsmentioning

confidence: 99%

“…The popular gapped aligner STAR (26) requires 60GB of memory to store the index of the human genome, for example, making analysis intractable for low-compute-resource scenarios. More recent aligners like HISAT and Bowtie2 require far less memory but the field continues to debate their sensitivity (27,28).…”

Section: Introductionmentioning

confidence: 99%

SPLASH: a statistical, reference-free genomic algorithm unifies biological discovery

Chaung

Baharav

Zheludev

et al. 2022

Preprint

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“…STAR (v2.7.8a) is used to align the reads to the genome (GRCh38 v107). We used STAR, which is currently among the best tools for splice-aware alignment [7]. Salmon (v1.7.0), kallisto (v0.44.0), RSEM and Cufflinks (v2.2.1) are used for transcript quantification.…”

Section: Methodsmentioning

confidence: 99%

“…Cufflinks performed best with acceptable precision (0.9) and recall (0.7) on the de novo splicing events discovery . DICAST, a docker-integrated alternative splicing benchmark tool, allows users to compare splicing-aware mapping tools and splicing event detection tools on simulated and real data sets [7,8]. Similarly, a large-scale study by Jiang et al focused on event-based tools applied to simulated datasets [9].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

Lio,

Düz,

Hoffmann

et al. 2024

Preprint

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RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.

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“…Instead of focusing on entire isoforms quantification, several approaches work at a finer-grained level and focus on exon-exon boundaries, also known as splice junctions, and check for changes in their usage. By analyzing splice junctions, these approaches can directly detect and quantify AS events providing more accurate results with respect to approaches based on transcript quantification [17]. Several tools have been proposed in the literature to differentially quantify AS events from RNA-Seq datasets [31,20,15,13,37,38,21,33].…”

Section: Introductionmentioning

confidence: 99%

Differential quantification of alternative splicing events on spliced pangenome graphs

Ciccolella,

Cozzi,

Vedova

et al. 2023

Preprint

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Pangenomes are becoming a powerful frameworks to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that ofannotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure.To investigate the usage of graph pangenomes for the quantification of AS events across conditions, we developedpantas, the first pangenomic method for differential analysis of AS events. A comparison with state-of-the-art linear reference-based approaches proves thatpantasachieves competitive accuracy, making spliced pangenomes effective for conducting AS events quantification and opening future directions for the analysis of population-based transcriptomes.pantasis open-source and freely available at github.com/algolab/pantas.

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Alternative splicing analysis benchmark with DICAST

Cited by 4 publications

References 44 publications

SPLASH: a statistical, reference-free genomic algorithm unifies biological discovery

SPLASH: a statistical, reference-free genomic algorithm unifies biological discovery

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

Differential quantification of alternative splicing events on spliced pangenome graphs

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