Stitching gene fragments with a network matching algorithm improves gene assembly for metagenomics - PubMed (original) (raw)

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

Yu-Wei Wu et al. Bioinformatics. 2012.

Abstract

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.

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Figures

Fig. 1.

Fig. 1.

Alignment between a de Bruijn graph and a reference sequence. Blocks in the de Bruijn graph represent nodes, and black arrowheads represent the directed edges that connect nodes with overlapping k − 1 mers. Typically, a de Bruijn graph-based assembler will output each of the nodes as a contig. Red arrowheads constitute the optimal path of the nodes that aligns with the reference sequence derived by the network matching algorithm

Fig. 2.

Fig. 2.

Improvement of gene assembly by GeneStitch for the simulated and real community datasets, as evaluated by gene coverage (A) and the number of complete genes (B)

Fig. 3.

Fig. 3.

An example demonstrating the inference of a gene path from a connected component in the de Bruijn graph. The reference gene recruited by BLAST in this example is YP_812362. (A) In total, 17 nodes are present in this connected component. (B) The path found by GeneStitch using the reference gene. (C) The gene path

Fig. 4.

Fig. 4.

An example demonstrating the construction of a gene graph by merging gene paths. (A) only 19 nodes are shown in this figure for clarity (the actual component is larger). (B) Two paths are found by GeneStitch, using YP_003601430 and YP_004031707 as the reference genes. (C) The two paths are merged into a gene graph

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