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Stitching gene fragments with a network matching algorithm improves gene assembly for metagenomics.

MOTIVATION: One of the difficulties in metagenomic assembly is that homologous genes from evolutionarily closely related species may behave like repeats and confuse assemblers. As a result, small contigs, each representing a short gene fragment, instead of complete genes, may be reported by an assembler. This further complicates annotation of metagenomic datasets, as annotation tools (such as gene predictors or similarity search tools) typically perform poorly on configs encoding short gene fragments. RESULTS: We present a novel way of using the de Bruijn graph assembly of metagenomes to improve the assembly of genes. A network matching algorithm is proposed for matching the de Bruijn graph of contigs against reference genes, to derive 'gene paths' in the graph (sequences of contigs containing gene fragments) that have the highest similarities to known genes, allowing gene fragments contained in multiple contigs to be connected to form more complete (or intact) genes. Tests on simulated and real datasets show that our approach (called GeneStitch) is able to significantly improve the assembly of genes from metagenomic sequences, by connecting contigs with the guidance of homologous genes-information that is orthogonal to the sequencing reads. We note that the improvement of gene assembly can be observed even when only distantly related genes are available as the reference. We further propose to use 'gene graphs' to represent the assembly of reads from homologous genes and discuss potential applications of gene graphs to improving functional annotation for metagenomics. AVAILABILITY: The tools are available as open source for download at http://omics.informatics.indiana.edu/GeneStitch CONTACT: yye@indiana.edu.

Pubmed ID: 22962453


  • Wu YW
  • Rho M
  • Doak TG
  • Ye Y


Bioinformatics (Oxford, England)

Publication Data

September 15, 2012

Associated Grants

  • Agency: NHGRI NIH HHS, Id: 1R01HG004908

Mesh Terms

  • Algorithms
  • Genes, Archaeal
  • Genes, Bacterial
  • Metagenome
  • Metagenomics