Fortuna Detects Novel Splicing in Drosophila scRNASeq Data

Recent developments in single-cell RNA sequencing techniques (scRNASeq) have made large quantities of sequenced data available across numerous species and tissues. Alternative splicing (AS) of pre-mRNA introns varies between tissues and even between cell-types and can be altered in disease. The study of novel AS, using standard RNASeq data, has been extensively studied for many years, while similar work on scRNASeq data has been scarce, despite its potential to offer a broader insight into cell-type specific processes. In this paper, we propose a novel pipeline that uses fortuna, a method that efficiently classifies and quantifies novel AS events, to process scRNASeq samples. Due to its short lifespan, high number of progeny, low maintenance cost, and intricate alternative splicing patterns similar in complexity to those of mammals, Drosophila Melanogaster (fruit fly) is a species of particular interest to researchers. Therefore, we experimentally evaluate our pipeline on real-world Drosophila single-cell data samples from the Fly Cell Atlas.

I. Introduction

Multiple different mRNA molecules (transcripts) can be transcribed from the same genomic region [1] [2]. These overlapping transcripts, which contain codes for protein synthesis, can be the result of the mechanisms that regulate alternative splicing (AS) [3]. Known AS events are recorded in the transcriptome annotation, while novel and aberrant AS events can occur in disease [4] and are often unannotated. Deviations from the regular splicing process may have a significant impact on the organism, as discussed in [5]. Therefore, analysis of AS events is an important topic in the field of computational molecular biology. With the advent of single-cell RNA sequencing (scRNASeq) [6], an opportunity to study AS events from a different perspective has presented itself to researchers. As it is discussed in [7], the study of AS in the context of scRNASeq is challenging and the related research has been scarce, despite its clear benefits. The authors of [8] have created a comprehensive reference atlas comprising of nearly 500,000 cells from 24 different tissues and organs and have dedicated a part of their research to AS events. A similar reference atlas has been created for the fruit fly (Drosophila Melanogaster) [9], called the F1y Cell Atlas. It contains approximately 580,000 cells from 15 different tissues, but no analysis of AS events has been conducted. In data obtained by the means of traditional RNA sequencing (RNASeq), novel AS events can be indentified and quantified [10] after conducting the computationally challenging alignment process. Pseudoalignment methods such as [11] and [12], significantly outperform traditional alignment methods such as [13], [14], and [15] in terms of running time. Though, they are usually limited to pseudoaligning short reads to annotated transcripts and are unable to detect novel AS events. Only a single tool exists, to our best knowledge, that combines the speed of a pseudoaligner with an ability to detect novel splicing [16]. In the scRNASeq case, additional computation is required to account for differentiation between cells, correcting for in-vitro [17] or PCR [18] amplification and related sequencing errors.