Choice of assemblers has a critical impact on de novo assembly of SARS-CoV-2 genome and characterizing variants

Abstract Background Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic following its initial emergence in China. SARS-CoV-2 has a positive-sense single-stranded RNA virus genome of around 30Kb. Using next-generation sequencing technologies, a large number of SARS-CoV-2 genomes are being sequenced at an unprecedented rate and being deposited in public repositories. For the de novo assembly of the SARS-CoV-2 genomes, a myriad of assemblers is being used, although their impact on the assembly quality has not been characterized for this virus. In this study, we aim to understand the variabilities on assembly qualities due to the choice of the assemblers. Results We performed 6648 de novo assemblies of 416 SARS-CoV-2 samples using eight different assemblers with different k-mer lengths. We used Illumina paired-end sequencing reads and compared the assembly quality of those assemblers. We showed that the choice of assembler plays a significant role in reconstructing the SARS-CoV-2 genome. Two metagenomic assemblers, e.g. MEGAHIT and metaSPAdes, performed better compared with others in most of the assembly quality metrics including, recovery of a larger fraction of the genome, constructing larger contigs and higher N50, NA50 values, etc. We showed that at least 09% (259/2873) of the variants present in the assemblies between MEGAHIT and metaSPAdes are unique to one of the assembly methods. Conclusion Our analyses indicate the critical role of assembly methods for assembling SARS-CoV-2 genome using short reads and their impact on variant characterization. This study could help guide future studies to determine the best-suited assembler for the de novo assembly of virus genomes.

[1]  Emiley A. Eloe-Fadrosh,et al.  Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity , 2017, PeerJ.

[2]  E. Holmes,et al.  A new coronavirus associated with human respiratory disease in China , 2020, Nature.

[3]  Trevor Bedford,et al.  Viral Phylodynamics , 2013, PLoS Comput. Biol..

[4]  Alexey A. Gurevich,et al.  QUAST: quality assessment tool for genome assemblies , 2013, Bioinform..

[5]  M. Baker.  De novo genome assembly: what every biologist should know , 2012, Nature Methods.

[6]  M. Schatz,et al.  Algorithms Gage: a Critical Evaluation of Genome Assemblies and Assembly Material Supplemental , 2008 .

[7]  Paul Medvedev,et al.  Informed and automated k-mer size selection for genome assembly , 2013, Bioinform..

[8]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[9]  Rafael Sanjuán,et al.  Mechanisms of viral mutation , 2016, Cellular and Molecular Life Sciences.

[10]  E. Holmes,et al.  Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding , 2020, The Lancet.

[11]  Trevor Bedford,et al.  Nextstrain: real-time tracking of pathogen evolution , 2017, bioRxiv.

[12]  John Vollmers,et al.  Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters! , 2017, PloS one.

[13]  Hideaki Sugawara,et al.  The Sequence Read Archive , 2010, Nucleic Acids Res..

[14]  Heng Li,et al.  Minimap2: pairwise alignment for nucleotide sequences , 2017, Bioinform..

[15]  P. Pevzner,et al.  metaSPAdes: a new versatile metagenomic assembler. , 2017, Genome research.

[16]  Marc L. Salit,et al.  Best practices for evaluating single nucleotide variant calling methods for microbial genomics , 2015, Front. Genet..

[17]  Yasubumi Sakakibara,et al.  MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads , 2012, Nucleic acids research.

[18]  E. Birney,et al.  Velvet: algorithms for de novo short read assembly using de Bruijn graphs. , 2008, Genome research.

[19]  N. Friedman,et al.  Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data , 2011, Nature Biotechnology.

[20]  Steven J. M. Jones,et al.  Abyss: a Parallel Assembler for Short Read Sequence Data Material Supplemental Open Access , 2022 .

[21]  T. Sutton,et al.  Choice of assembly software has a critical impact on virome characterisation , 2018, Microbiome.

[22]  Måns Magnusson,et al.  MultiQC: summarize analysis results for multiple tools and samples in a single report , 2016, Bioinform..

[23]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[24]  F. Raymond,et al.  which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Ray Meta: scalable de novo metagenome assembly and profiling , 2012 .

[25]  Kunihiko Sadakane,et al.  MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph , 2014, Bioinform..

[26]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[27]  Alexey A. Gurevich,et al.  MetaQUAST: evaluation of metagenome assemblies , 2016, Bioinform..

[28]  Samuel A. Assefa,et al.  A post-assembly genome-improvement toolkit (PAGIT) to obtain annotated genomes from contigs , 2012, Nature Protocols.

[29]  Sergey I. Nikolenko,et al.  SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing , 2012, J. Comput. Biol..