The HiSeq and MiSeq metagenomes were built using 20 sets of bacterial whole-genome shotgun reads. These reads were found either as part of the GAGE-B project [21 (link)] or in the NCBI Sequence Read Archive. Each metagenome contains sequences from ten genomes (Additional file 1 : Table S1). For both the 10,000 and 10 million read samples of each of these metagenomes, 10% of their sequences were selected from each of the ten component genome data sets (i.e., each genome had equal sequence abundance). All sequences were trimmed to remove low quality bases and adapter sequences.
The composition of these two metagenomes poses certain challenges to our classifiers. For example, Pelosinus fermentans, found in our HiSeq metagenome, cannot be correctly identified at the genus level by Kraken (or any of the other previously described classifiers), because there are no Pelosinus genomes in the RefSeq complete genomes database; however, there are seven such genomes in Kraken-GB’s database, including six strains of P. fermentans. Similarly, in our MiSeq metagenome, Proteus vulgaris is often classified incorrectly at the genus level because the only Proteus genome in Kraken’s database is a single Proteus mirabilis genome. Five more Proteus genomes are present in Kraken-GB’s database, allowing Kraken-GB to classify reads better from that genus. In addition, the MiSeq metagenome contains five genomes from the Enterobacteriaceae family (Citrobacter, Enterobacter, Klebsiella, Proteus and Salmonella). The high sequence similarity between the genera in this family can make distinguishing between genera difficult for any classifier.
The simBA-5 metagenome was created by simulating reads from the set of complete bacterial and archaeal genomes in RefSeq. Replicons from those genomes were used if they were associated with a taxon that had an entry associated with the genus rank, resulting in a set of replicons from 607 genera. We then used the Mason read simulator [22 ] with its Illumina model to produce 10 million 100-bp reads from these genomes. First we created simulated genomes for each species, using a SNP rate of 0.1% and an indel rate of 0.1% (both default parameters), from which we generated the reads. For the simulated reads, we multiplied the default mismatch and indel rates by five, resulting in an average mismatch rate of 2% (ranging from 1% at the beginning of reads to 6% at the ends) and an indel rate of 1% (0.5% insertion probability and 0.5% deletion probability). For the simBA-5 metagenome, the 10,000 read set was generated from a random sample of the 10 million read set.
The composition of these two metagenomes poses certain challenges to our classifiers. For example, Pelosinus fermentans, found in our HiSeq metagenome, cannot be correctly identified at the genus level by Kraken (or any of the other previously described classifiers), because there are no Pelosinus genomes in the RefSeq complete genomes database; however, there are seven such genomes in Kraken-GB’s database, including six strains of P. fermentans. Similarly, in our MiSeq metagenome, Proteus vulgaris is often classified incorrectly at the genus level because the only Proteus genome in Kraken’s database is a single Proteus mirabilis genome. Five more Proteus genomes are present in Kraken-GB’s database, allowing Kraken-GB to classify reads better from that genus. In addition, the MiSeq metagenome contains five genomes from the Enterobacteriaceae family (Citrobacter, Enterobacter, Klebsiella, Proteus and Salmonella). The high sequence similarity between the genera in this family can make distinguishing between genera difficult for any classifier.
The simBA-5 metagenome was created by simulating reads from the set of complete bacterial and archaeal genomes in RefSeq. Replicons from those genomes were used if they were associated with a taxon that had an entry associated with the genus rank, resulting in a set of replicons from 607 genera. We then used the Mason read simulator [22 ] with its Illumina model to produce 10 million 100-bp reads from these genomes. First we created simulated genomes for each species, using a SNP rate of 0.1% and an indel rate of 0.1% (both default parameters), from which we generated the reads. For the simulated reads, we multiplied the default mismatch and indel rates by five, resulting in an average mismatch rate of 2% (ranging from 1% at the beginning of reads to 6% at the ends) and an indel rate of 1% (0.5% insertion probability and 0.5% deletion probability). For the simBA-5 metagenome, the 10,000 read set was generated from a random sample of the 10 million read set.
Free full text: Click here
