Differences in sequencing technologies improve the retrieval of anammox bacterial genome from metagenomes

Abstract

BackgroundSequencing technologies have different biases, in single-genome sequencing and metagenomic sequencing; these can significantly affect ORFs recovery and the population distribution of a metagenome. In this paper we investigate how well different technologies represent information related to a considered organism of interest in a metagenome, and whether it is beneficial to combine information obtained using different technologies. We analyze comparatively three metagenomic datasets acquired from a sample containing the anammox bacterium Candidatus ’Brocadia fulgida’ (B. fulgida). These datasets were obtained using Roche 454 FLX and Sanger sequencing with two different libraries (shotgun and fosmid).ResultsIn each dataset, the abundance of the reads annotated to B. fulgida was much lower than the abundance expected from available cell count information. This was due to the overrepresentation of GC-richer organisms, as shown by GC-content distribution of the reads. Nevertheless, by considering the union of B. fulgida reads over the three datasets, the number of B. fulgida ORFs recovered for at least 80% of their length was twice the amount recovered by the best technology. Indeed, while taxonomic distributions of reads in the three datasets were similar, the respective sets of B. fulgida ORFs recovered for a large part of their length were highly different, and depth of coverage patterns of 454 and Sanger were dissimilar.ConclusionsPrecautions should be sought in order to prevent the overrepresentation of GC-rich microbes in the datasets. This overrepresentation and the consistency of the taxonomic distributions of reads obtained with different sequencing technologies suggests that, in general, abundance biases might be mainly due to other steps of the sequencing protocols. Results show that biases against organisms of interest could be compensated combining different sequencing technologies, due to the differences of their genome-level sequencing biases even if the species was present in not very different abundances in the metagenomes.

MetaMeta: integrating metagenome analysis tools to improve taxonomic profiling

“…An optional pre-processing step is provided to remove errors and improve sequence classification: Trimommatic [ 13 ] for read trimming and BayesHammer [ 14 ] for error correction. A sub-sampling step is also included, allowing the sub division of large read sets among several tools by equally dividing them or by taking smaller random samples with or without replacement, to reduce overall run-time.…”

Evaluation of the impact of Illumina error correction tools on de novo genome assembly

“…Sometimes, this process is further guided by using the per-base quality scores. Many standalone read error correction algorithms and implementations have been proposed for Illumina data, including ACE [ 5 ], BayesHammer [ 6 ], BFC [ 7 ], BLESS [ 8 ], BLESS 2 [ 9 ], Blue [ 10 ], EC [ 11 ], Fiona [ 12 ], Karect [ 13 ], Lighter [ 14 ], Musket [ 15 ], Pollux [ 16 ], Quake [ 17 ], QuorUM [ 18 ], RACER [ 19 ], SGA-EC [ 20 ] and Trowel [ 21 ]. For a comprehensive overview of the characteristics of these EC tools and those for other sequencing platforms, we refer to [ 22 ].…”

HyDA-Vista: towards optimal guided selection of k-mer size for sequence assembly

“…Analysis of the alignments revealed that 98% of the reads mapped to the reference genome, representing an average depth of approximately 600 × ; An analysis using BLAST against known contaminants revealed that the unmapped reads are attributed to minor contamination of the sample [ 41 ]. All reads were error corrected using BayesHammer [ 42 ] with default parameters.…”