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Page 10 of 16  Lugli et al. Microbiome Res Rep 2023;2:15  https://dx.doi.org/10.20517/mrr.2022.21



 Table 2. MEGAnnotator2 report of 10 sequentially processed microbial genomes using long-read technology

 High                              Number                             Number of  Number of
 Sequencing   16S rRNA gene   Genome   Genome   Average   Genome   Number
 SRA  quality   ANI screening:     of                                 rRNA         tRNA
 output:  identity:  completeness:  contamination:  coverage:  length:  of genes:
 reads:                            contigs:                           genes:       genes:
 SRR12201911  500000  59283  Leuconostoc suionicum  Leuconostoc   98.41%  3.45%  148  71  2,724,779  2,995  21  86
 99.4%  mesenteroides
 96.7%
 SRR13648750 500000  47731  Lactococcus lactis   Lactococcus lactis   100%  3.83%  154  9  2,581,970  2,547  19  65
 99.9%  88.1%

 SRR15521836 500000  127174  Bacteroides salyersiae  Bacteroides   97.63%  1.49%  78  18  5,430,481  4,655  15  77
 99.9%  salyersiae 99.0%
 SRR17126341  500000  48110  Eubacterium eligens   Eubacterium   98.25%  8.13%  153  5  2,963,578  2,611  15  47
 100%  eligens 100%
 SRR17126949 500000  49224  Prevotella copri 99.8% Prevotella copri   97.97%  2.36%  114  11  3,688,102  3,133  20  63
 100%
 SRR17873544 500000  86673  Clostridium innocuum  Clostridium   100%  1.42%  53  14  5,032,638  4,937  12  48
 99.9%  innocuum 97.1%

 SRR17873548 500000  119899  Enterococcus hirae   Enterococcus hirae  96.16%  2.90%  76  89  3,181,147  3,053  23  84
 99.9%  98.9%
 SRR21075862 500000  45558  Streptococcus   Streptococcus   99.89%  4.44%  176  4  1,910,856  2,016  18  67
 salivarius subsp.   thermophilus
 thermophilus 99.9%  99.2%
 SRR21276823 500000  49609  Bifidobacterium   Bifidobacterium   100%  7.33%  180  6  2,041,333  1,653  9  58
 animalis subsp. lactis   animalis 95.7%
 100%
 SRR22159808 500000  49946  Klebsiella pneumoniae  Klebsiella   99.40%  0.13%  83  10  5,635,075  5,295  25  91
 100%  pneumoniae
 99.0%




 data. If the user is not interested in statistics, essential functions will take approximately 5.6 min for short reads, 49.2 min for long reads, and 40 min for the
 usage of hybrid reads. Furthermore, no additional time is spent between analyses using the additional function of MEGAnnotator2 to manage multiple

 samples.



 MEGAnnotator2 improvement with respect to the old version
 To highlight the enhancement made in MEGAnnotator2, a comparison against the first version (MEGAnnotator) was performed. Several features of the

 updated pipeline were not compared due to their absence in MEGAnnotator, e.g., quality reports of sequenced reads, genome quality assessments, and
 metabolic profiling. Furthermore, since the older pipeline version cannot manage long reads data, we only employed the short reads belonging to the 10
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