Author: Anna Shcherbina; Darrell O. Ricke; Nelson Chiu
Title: Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim Document date: 2016_3_31
ID: csokkcqq_35
Snippet: and MetaPhlAn was second best, identifying 22,647 (10.4%) of the reads. None of the algorithms 235 identified any of the 2,045 A. odontolyticus genes (Figure 5b) . This poor performance likely results from 236 the fact that A. odontolyticus genome annotation in GenBank is incomplete [36] . Conversely, at the 237 species level, five of the six algorithms mapped a high number of reads to Streptococcus agalactiae for 238 both the HMP even and HMP st.....
Document: and MetaPhlAn was second best, identifying 22,647 (10.4%) of the reads. None of the algorithms 235 identified any of the 2,045 A. odontolyticus genes (Figure 5b) . This poor performance likely results from 236 the fact that A. odontolyticus genome annotation in GenBank is incomplete [36] . Conversely, at the 237 species level, five of the six algorithms mapped a high number of reads to Streptococcus agalactiae for 238 both the HMP even and HMP staggered datasets (Figure 2f, 2g) , but only a small number of reads for 239 this organism were present in the truth data. The relative abundance of Streptococcus mutans is lower 240
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