Journal of Biosciences

, Volume 41, Issue 3, pp 455–474 | Cite as

Identifying wrong assemblies in de novo short read primary sequence assembly contigs

  • Vandna Chawla
  • Rajnish Kumar
  • Ravi Shankar


With the advent of short-reads-based genome sequencing approaches, large number of organisms are being sequenced all over the world. Most of these assemblies are done using some de novo short read assemblers and other related approaches. However, the contigs produced this way are prone to wrong assembly. So far, there is a conspicuous dearth of reliable tools to identify mis-assembled contigs. Mis-assemblies could result from incorrectly deleted or wrongly arranged genomic sequences. In the present work various factors related to sequence, sequencing and assembling have been assessed for their role in causing mis-assembly by using different genome sequencing data. Finally, some mis-assembly detecting tools have been evaluated for their ability to detect the wrongly assembled primary contigs, suggesting a lot of scope for improvement in this area. The present work also proposes a simple unsupervised learning-based novel approach to identify mis-assemblies in the contigs which was found performing reasonably well when compared to the already existing tools to report mis-assembled contigs. It was observed that the proposed methodology may work as a complementary system to the existing tools to enhance their accuracy.


Assembly validation clustering contigs de novo assembly mis-assembly next generation sequencing reads 

Abbreviations used




bacterial artificial chromosome




fragment coverage distribution


false negative


false positive


Matthews correlation coefficient


National Center for Biotechnology Information


paired de Bruijn graphs


paired end




single end


sequence read archive


true negative


true positive


whole genome shotgun



We are thankful to Rohit Chauhan for his help during the analysis. VC is thankful to DST for SRF-INSPIRE fellowship, RK is thankful to DBT-BINC. We are thankful to all the researchers who provided their valuable data as an open access resource. The manuscript has IHBT communication ID: 3959. This work was supported under project funding BSC-121 (CSIR-12th FYP GENESIS project).

Supplementary material

12038_2016_9630_MOESM1_ESM.pdf (977 kb)
ESM 1 (PDF 977 kb)


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Copyright information

© Indian Academy of Sciences 2016

Authors and Affiliations

  1. 1.Studio of Computational Biology & Bioinformatics, Biotechnology DivisionCSIR-Institute of Himalayan Bioresource TechnologyPalampurIndia
  2. 2.Department of BiotechnologyGuru Nanak Dev UniversityAmritsarIndia

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