High Performance Computing for Haplotyping: Models and Platforms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)


The reconstruction of the haplotype pair for each chromosome is a hot topic in Bioinformatics and Genome Analysis. In Haplotype Assembly (HA), all heterozygous Single Nucleotide Polymorphisms (SNPs) have to be assigned to exactly one of the two chromosomes. In this work, we outline the state-of-the-art on HA approaches and present an in-depth analysis of the computational performance of GenHap, a recent method based on Genetic Algorithms. GenHap was designed to tackle the computational complexity of the HA problem by means of a divide-et-impera strategy that effectively leverages multi-core architectures. In order to evaluate GenHap’s performance, we generated different instances of synthetic (yet realistic) data exploiting empirical error models of four different sequencing platforms (namely, Illumina NovaSeq, Roche/454, PacBio RS II and Oxford Nanopore Technologies MinION). Our results show that the processing time generally decreases along with the read length, involving a lower number of sub-problems to be distributed on multiple cores.


Future-generation sequencing Genome Analysis Haplotype Assembly High Performance Computing Master-Slave paradigm 



We acknowledge the CINECA for the availability of High Performance Computing resources and support.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanItaly
  2. 2.Institute of Biomedical Technologies, Italian National Research CouncilSegrateItaly
  3. 3.Department of Human and Social SciencesUniversity of BergamoBergamoItaly
  4. 4.Computer LaboratoryUniversity of CambridgeCambridgeUK
  5. 5.Institute of Molecular Bioimaging and Physiology, Italian National Research CouncilCefalùItaly
  6. 6.SYSBIO.IT Centre of Systems BiologyMilanoItaly

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