A Comprehensive Study of De Novo Genome Assemblers: Current Challenges and Future Prospective

Background: Current advancements in next-generation sequencing technology have made possible to sequence whole genome but assembling a large number of short sequence reads is still a big challenge. In this article, we present the comparative study of seven assemblers, namely, ABySS, Velvet, Edena, SGA, Ray, SSAKE, and Perga, using prokaryotic and eukaryotic paired-end as well as single-end data sets from Illumina platform. Results: Results showed that in case of single-end data sets, Velvet and ABySS outperformed in all the seven assemblers with comparatively low assembling time and high genome fraction. Velvet consumed the least amount of memory than any other assembler. In case of paired-end data sets, Velvet consumed least amount of time and produced high genome fraction after ABySS and Ray. In terms of low memory usage, SGA and Edena outperformed in all the assemblers. Ray also showed good genome fraction; however, extremely high assembling time consumed by the Ray might make it prohibitively slow on larger data sets of single and paired-end data. Conclusions: Our comparison study will provide assistance to the scientists for selecting the suitable assembler according to their data sets and will also assist the developers to upgrade or develop a new assembler for de novo assembling.

[1]  S. Koren,et al.  Assembly algorithms for next-generation sequencing data. , 2010, Genomics.

[2]  M. Schatz,et al.  Hybrid error correction and de novo assembly of single-molecule sequencing reads , 2012, Nature Biotechnology.

[3]  K. Borgwardt,et al.  Accurate indel prediction using paired-end short reads , 2013, BMC Genomics.

[4]  François Laviolette,et al.  Ray: Simultaneous Assembly of Reads from a Mix of High-Throughput Sequencing Technologies , 2010, J. Comput. Biol..

[5]  Walter Pirovano,et al.  BIOINFORMATICS APPLICATIONS , 2022 .

[6]  Nan Li,et al.  Comparison of the two major classes of assembly algorithms: overlap-layout-consensus and de-bruijn-graph. , 2012, Briefings in functional genomics.

[7]  Huntington F. Willard,et al.  Thompson and Thompson Genetics in Medicine , 2001 .

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

[9]  Siu-Ming Yiu,et al.  IDBA - A Practical Iterative de Bruijn Graph De Novo Assembler , 2010, RECOMB.

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

[11]  R. Durbin,et al.  Efficient de novo assembly of large genomes using compressed data structures. , 2012, Genome research.

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

[13]  J. T. Dunnen,et al.  Next generation sequencing technology: Advances and applications. , 2014, Biochimica et biophysica acta.

[14]  Yongchao Liu,et al.  Parallelized short read assembly of large genomes using de Bruijn graphs , 2011, BMC Bioinformatics.

[15]  Ying Cheng,et al.  The European Nucleotide Archive , 2010, Nucleic Acids Res..

[16]  Pavel A Pevzner,et al.  How to apply de Bruijn graphs to genome assembly. , 2011, Nature biotechnology.

[17]  Hanlee P. Ji,et al.  Next-generation DNA sequencing , 2008, Nature Biotechnology.

[18]  Mihai Pop,et al.  Genome Sequence Assembly: Algorithms and Issues , 2002, Computer.

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

[20]  Inanç Birol,et al.  Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species , 2013, GigaScience.

[21]  Guangri Quan,et al.  PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach , 2014, PloS one.

[22]  Sanguthevar Rajasekaran,et al.  A memory-efficient data structure representing exact-match overlap graphs with application for next-generation DNA assembly , 2010, Bioinform..

[23]  Afiahayati,et al.  MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning , 2014, DNA research : an international journal for rapid publication of reports on genes and genomes.

[24]  I. Rigoutsos,et al.  Evaluation of Methods for De Novo Genome Assembly from High-Throughput Sequencing Reads Reveals Dependencies That Affect the Quality of the Results , 2011, PloS one.

[25]  René L. Warren,et al.  Assembling millions of short DNA sequences using SSAKE , 2006, Bioinform..

[26]  Arend Hintze,et al.  Scaling metagenome sequence assembly with probabilistic de Bruijn graphs , 2011, Proceedings of the National Academy of Sciences.

[27]  Yaping Wang,et al.  De Bruijn Graph-Based Whole-Genomic Sequence Assembly Algorithms and Applications , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[28]  David Hernández,et al.  De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer. , 2008, Genome research.