RT Journal Article SR Electronic T1 Falco: A quick and exible single-cell RNA-Seq processing framework on the cloud JF bioRxiv FD Cold Spring Harbor Laboratory SP 064006 DO 10.1101/064006 A1 Andrian Yang A1 Michael Troup A1 Joshua WK Ho YR 2016 UL http://biorxiv.org/content/early/2016/07/15/064006.abstract AB Summary Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designedto efficiently process scRNAseq data due to their limited scalability. Here we introduce Falco, a cloud-based framework for parallelised processing of large-scale transcriptomic data. The pipeline utilises state-of-the-art big data technology of Apache Hadoop and Apache Sparkto perform massively parallel alignment, quality control, and feature quantification of single-cell transcriptomic data in Amazon Web Service (AWS) cloud-computing environment. We have evaluated the performance of Falco using two public scRNA-seq datasets and demonstrated Falco’s scalability. The result shows Falco performs at least 2.6x faster against a highly optimized single node analysis and a reduction in runtime with increasing number ofcomputing nodes. Falco also allows user to the utilise lowcost spot instances of AWS, providing a 65% reduction in cost of analysis.Availability Falco is available via an open source license in https://github.com/VCCRI/Falco/.Contact j.ho{at}victorchang.edu.auSupplementary information Supplementary data are available at BioRXiv online.