Analyzing Big Datasets of Genomic Sequences: Fast and Scalable Collection of k-mer Statistics

TL;DR

This paper introduces FastKmer, a scalable Spark-based method for efficient k-mer counting in large genomic datasets, emphasizing the importance of parameter tuning and workload balancing for optimal performance.

Contribution

The paper presents FastKmer, a novel distributed approach with workload balancing to improve efficiency and scalability in k-mer statistics extraction from large biological sequences.

Findings

FastKmer outperforms existing Big Data methods in speed.

Workload balancing significantly improves scalability.

Careful framework-specific engineering enhances analysis efficiency.

Abstract

Distributed approaches based on the map-reduce programming paradigm have started to be proposed in the bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of map-reduce and related Big Data technologies and frameworks (e.g., Apache Hadoop and Spark) does not necessarily produce satisfactory results, in terms of both efficiency and effectiveness. We discuss how the development of distributed and Big Data management technologies has affected the analysis of large datasets of biological sequences. Moreover, we show how the choice of different parameter configurations and the careful engineering of the software with respect to the specific framework under consideration may be crucial in order to achieve good performance, especially on very large amounts of data. We choose k-mers counting as a case study for our analysis, and Spark as the framework to implement FastKmer, a novel approach for the extraction of k-mer statistics from large collection of biological sequences, with arbitrary values of k. One of the most relevant contributions of FastKmer is the introduction of a module for balancing the statistics aggregation workload over the nodes of a computing cluster, in order to overcome data skew while allowing for a fully exploitation of the underly- ing distributed architecture. We also present the results of a comparative experimental analysis showing that our approach is currently the fastest among the ones based on Big Data technologies, while exhibiting a very good scalability. We provide evidence that the usage of technologies such as Hadoop or Spark for the analysis of big datasets of biological sequences is productive only if the architectural details and the peculiar aspects of the considered framework are carefully taken into account for the algorithm design and implementation.