HSRA: Hadoop-based spliced read aligner for RNA sequencing data

Abstract

Nowadays, the analysis of transcriptome sequencing (RNA-seq) data has become the standard method for quantifying the levels of gene expression. In RNA-seq experiments, the mapping of short reads to a reference genome or transcriptome is considered a crucial step that remains as one of the most time-consuming. With the steady development of Next Generation Sequencing (NGS) technologies, unprecedented amounts of genomic data introduce significant challenges in terms of storage, processing and downstream analysis. As cost and throughput continue to improve, there is a growing need for new software solutions that minimize the impact of increasing data volume on RNA read alignment. In this work we introduce HSRA, a Big Data tool that takes advantage of the MapReduce programming model to extend the multithreading capabilities of a state-of-the-art spliced read aligner for RNA-seq data (HISAT2) to distributed memory systems such as multi-core clusters or cloud platforms. HSRA has been built upon the Hadoop MapReduce framework and supports both single- and paired-end reads from FASTQ/FASTA datasets, providing output alignments in SAM format. The design of HSRA has been carefully optimized to avoid the main limitations and major causes of inefficiency found in previous Big Data mapping tools, which cannot fully exploit the raw performance of the underlying aligner. On a 16-node multi-core cluster, HSRA is on average 2.3 times faster than previous Hadoop-based tools. Source code in Java as well as a user's guide are publicly available for download at http://hsra.dec.udc.es.

Fig 1. Overall workflow of the MapReduce paradigm.

This workflow shows several map and reduce tasks working in parallel over different input splits.

Fig 2. Overview of the HSRA workflow for single-end alignment.

This mode executes a map-only job taking advantage of the HSP library to parse the reads directly from HDFS. Native pipes are used for efficient IPC communication between Hadoop and HISAT2.

Fig 3. Overview of the HSRA workflow for paired-end alignment using the reduce-side join approach.

This approach executes a MapReduce job using the single-end support provided by the HSP library, where a reduce-side join is needed to obtain the paired-end reads. Native pipes are used for efficient IPC communication between Hadoop and HISAT2.

Fig 4. Overview of the HSRA workflow for paired-end alignment using the map-side join approach.

This approach allows avoiding any data shuffling by executing a map-only job thanks to the specific support for paired-end datasets provided by the HSP library. Native pipes are used for efficient IPC communication between Hadoop and HISAT2.

Fig 5. Experimental results for single-end alignment.

Runtime results obtained by HSRA when varying the number of nodes using the (a) SRR1 and (b) DRR1 datasets.

Fig 6. Experimental results for paired-end alignment (SRR1 dataset).

Runtime results obtained by HSRA when varying the number of nodes using the (a) reduce-side and (b) map-side join approaches.

Fig 7. Experimental results for paired-end alignment (DRR1 dataset).

Runtime results obtained by HSRA when varying the number of nodes using the (a) reduce-side and (b) map-side join approaches.