Cloud accelerated alignment and assembly of full-length single-cell RNA-seq data using Falco

Abstract

BackgroundRead alignment and transcript assembly are the core of RNA-seq analysis for transcript isoform discovery. Nonetheless, current tools are not designed to be scalable for analysis of full-length bulk or single cell RNA-seq (scRNA-seq) data. The previous version of our cloud-based tool Falco only focuses on RNA-seq read counting, but does not allow for more flexible steps such as alignment and read assembly.ResultsThe Falco framework can harness the parallel and distributed computing environment in modern cloud platforms to accelerate read alignment and transcript assembly of full-length bulk RNA-seq and scRNA-seq data. There are two new modes in Falco: alignment-only and transcript assembly. In the alignment-only mode, Falco can speed up the alignment process by 2.5–16.4x based on two public scRNA-seq datasets when compared to alignment on a highly optimised standalone computer. Furthermore, it also provides a 10x average speed-up compared to alignment using published cloud-enabled tool for read alignment, Rail-RNA. In the transcript assembly mode, Falco can speed up the transcript assembly process by 1.7–16.5x compared to performing transcript assembly on a highly optimised computer.ConclusionFalco is a significantly updated open source big data processing framework that enables scalable and accelerated alignment and assembly of full-length scRNA-seq data on the cloud. The source code can be found at https://github.com/VCCRI/Falco.

Big data analytics in single‐cell transcriptomics: Five grand opportunities

“…The verification layer analyses the biological significance of the input gene set. A cloud‐based framework Falco accelerates read alignment and transcript assembly of full‐length scRNA‐seq data (A. Yang, Kishore, Phipps, & Ho, 2019). It enables parallelization of existing RNA‐seq processing pipelines using big data platforms such as Apache Hadoop and Apache Spark, allowing for efficient processing of scRNA‐seq data.…”

Biophysical Review’s ‘meet the editors series’—a profile of Joshua W. K. Ho

“…My laboratory focused on methodological innovation in bioinformatics, which meant development of new computational methods to answer a range of biological or biomedical problems. Some of our representative contributions include the followings: a statistical method for cross-species gene set analysis (Djordjevic et al 2016), a scalable cloud-based single-cell RNA-seq (scRNA-seq) data processing big data framework (Yang et al 2017(Yang et al , 2019, a web-based real-time collaborative genome browser (Szot et al 2017), a fast dimensionality reduction and clustering method for scRNA-seq data (Lin et al 2017), a fast clustering method for millions of single cells in flow cytometry data (Ye and Ho 2019), and a method to mine meta-data in public gene expression repositories (Djordjevic et al 2019). These works were only possible thanks to all the postdoctoral fellows, students and interns in my laboratory over the years.…”