Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Comparative analysis of HiSeq3000 and BGISEQ-500 sequencing platform with shotgun metagenomic sequencing data

  • Animesh Kumar (Center for Bioinformatics, Department of Chemistry, UiT The Arctic University of Norway) ;
  • Espen M. Robertsen (Center for Bioinformatics, Department of Chemistry, UiT The Arctic University of Norway) ;
  • Nils P. Willassen (Center for Bioinformatics, Department of Chemistry, UiT The Arctic University of Norway) ;
  • Juan Fu (Faculty of Biosciences, Department of Livestock and Aquaculture Science, Norwegian University of Life Sciences) ;
  • Erik Hjerde (Center for Bioinformatics, Department of Chemistry, UiT The Arctic University of Norway)
  • Received : 2023.09.18
  • Accepted : 2023.12.04
  • Published : 2023.12.31
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Abstract

Recent advances in sequencing technologies and platforms have enabled to generate metagenomics sequences using different sequencing platforms. In this study, we analyzed and compared shotgun metagenomic sequences generated by HiSeq3000 and BGISEQ-500 platforms from 12 sediment samples collected across the Norwegian coast. Metagenomics DNA sequences were normalized to an equal number of bases for both platforms and further evaluated by using different taxonomic classifiers, reference databases, and assemblers. Normalized BGISEQ-500 sequences retained more reads and base counts after preprocessing, while a slightly higher fraction of HiSeq3000 sequences were taxonomically classified. Kaiju classified a higher percentage of reads relative to Kraken2 for both platforms, and comparison of reference database for taxonomic classification showed that MAR database outperformed RefSeq. Assembly using MEGAHIT produced longer assemblies and higher total contigs count in majority of HiSeq3000 samples than using metaSPAdes, but the assembly statistics notably improved with unprocessed or normalized reads. Our results indicate that both platforms perform comparably in terms of the percentage of taxonomically classified reads and assembled contig statistics for metagenomics samples. This study provides valuable insights for researchers in selecting an appropriate sequencing platform and bioinformatics pipeline for their metagenomics studies.

Keywords

Acknowledgement

Illumina HiSeq3000 sequencing was performed by the Norwegian Sequencing Centre (www.sequencing.uio.no), a national technology platform hosted by the University of Oslo and Oslo University Hospital and supported by the "Functional Genomics" and "Infrastructure" programs of the Research Council of Norway and the Southeastern Regional Health Authorities. BGI Tech Solutions (Hong Kong) Co., Ltd. provided assistance with sequencing using BGISEQ-500 sequencing platforms, acting as an overseas sample receiving site and temporary storage point. We would also like to acknowledge Dr. Tao Jin for help with sequencing at CNGB. The computations were performed on a local compute cluster and resources provided by Sigma2, the National Infrastructure for High-Performance Computing and Data Storage, Norway. This work was supported by UiT The Arctic University of Norway.

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