Related Experiment Video
Updated: Apr 6, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
Published on: May 9, 2017
SuRankCo: supervised ranking of contigs in de novo assemblies
Mathias Kuhring1,2, Piotr Wojtek Dabrowski3,4, Vitor C Piro5
1Central Administration 4 (IT), Robert Koch Institute, Berlin, Germany. KuhringK@rki.de.
Background:
Evaluating the quality and reliability of a de novo assembly and of single contigs in particular is challenging since commonly a ground truth is not readily available and numerous factors may influence results. Currently available procedures provide assembly scores but lack a comparative quality ranking of contigs within an assembly.
Results:
We present SuRankCo, which relies on a machine learning approach to predict quality scores for contigs and to enable the ranking of contigs within an assembly. The result is a sorted contig set which allows selective contig usage in downstream analysis. Benchmarking on datasets with known ground truth shows promising sensitivity and specificity and favorable comparison to existing methodology.
Conclusions:
SuRankCo analyzes the reliability of de novo assemblies on the contig level and thereby allows quality control and ranking prior to further downstream and validation experiments.
Related Concept Videos
Genome Annotation and Assembly
RNA-seq
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
Next-generation Sequencing
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
Ranks
Protein Complex Assembly
Many viruses self-assemble into a fully functional unit using the infected host cell to...
Protein Complex Assembly

