Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

Bokulich, Nicholas A.; Kaehler, Benjamin D.; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A.; Gregory Caporaso, J.

doi:10.1186/s40168-018-0470-z

Microbiome

Table 2 Optimized methods configurations for standard operating conditions

From: Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

				Mock			Cross-validated			Novel taxa
Target	Condition	Method	Parameters	F	P	R	F	P	R	F	P	R	Threshold
16S rRNA gene	Balanced	NB-bespoke	[6,6]:0.9	0.705	0.98	0.582	0.827	0.931	0.744	0.165	0.243	0.125	F = (0.49, 0.8, 0.1)
			[6,6]:0.92	0.705	0.98	0.581	0.825	0.936	0.737	0.165	0.251	0.123	F = (0.7, 0.8, 0.15)
			[6,6]:0.94	0.703	0.98	0.579	0.822	0.942	0.729	0.162	0.259	0.118
			[7,7]:0.92	0.712	0.978	0.592	0.831	0.931	0.751	0.151	0.221	0.115
			[7,7]:0.94	0.708	0.978	0.586	0.829	0.936	0.743	0.157	0.239	0.117
		Naive-Bayes	[7,7]:0.7	0.495	0.797	0.38	0.819	0.886	0.761	0.115	0.138	0.099
		rdp	0.6	0.564	0.798	0.457	0.815	0.868	0.768	0.102	0.128	0.084
			0.7	0.55	0.799	0.438	0.812	0.892	0.746	0.124	0.173	0.096
		Uclust	0.51:0.9:3	0.498	0.746	0.392	0.846	0.876	0.817	0.154	0.201	0.126
	Precision	NB-bespoke	[6,6]:0.98	0.676	0.987	0.537	0.803	0.956	0.692	0.163	0.303	0.111	P = (0.94, 0.95, 0.25)
			[7,7]:0.98	0.687	0.98	0.551	0.815	0.951	0.713	0.164	0.283	0.115
		rdp	1	0.239	0.941	0.16	0.632	0.968	0.469	0.12	0.457	0.069
	Recall	NB-bespoke	[12,12]:0.5	0.754	0.8	0.721	0.815	0.83	0.801	0.053	0.058	0.049	R = (0.47, 0.75, 0.04)
			[14,14]:0.5	0.758	0.802	0.726	0.811	0.826	0.797	0.052	0.057	0.048	R = (0.7, 0.75, 0.04)
			[16,16]:0.5	0.755	0.785	0.732	0.808	0.825	0.792	0.052	0.058	0.047
			[18,18]:0.5	0.772	0.803	0.748	0.805	0.823	0.789	0.055	0.061	0.05
			[32,32]:0.5	0.937	0.966	0.913	0.788	0.818	0.76	0.054	0.067	0.045
		Naive-Bayes	[11,11]:0.5	0.567	0.77	0.479	0.793	0.82	0.768	0.059	0.065	0.055
			[12,12]:0.5	0.567	0.769	0.479	0.79	0.816	0.765	0.059	0.064	0.055
			[18,18]:0.5	0.564	0.764	0.477	0.779	0.807	0.753	0.057	0.063	0.051
		rdp	0.5	0.577	0.791	0.48	0.816	0.848	0.787	0.068	0.079	0.06
	Novel	Blast+	10:0.51:0.8	0.436	0.723	0.325	0.816	0.896	0.749	0.225	0.332	0.171	F = (0.4, 0.8, 0.2)
		Uclust	0.76:0.9:5	0.467	0.775	0.348	0.84	0.938	0.76	0.219	0.358	0.158
		VSEARCH	10:0.51:0.8	0.45	0.74	0.342	0.814	0.891	0.75	0.226	0.333	0.171
			10:0.51:0.9	0.45	0.74	0.342	0.82	0.896	0.755	0.219	0.338	0.162
Fungi	Balanced	Naive-Bayes	[6,6]:0.94	0.874	0.935	0.827	0.481	0.57	0.416	0.374	0.438	0.327	F = (0.85, 0.45, 0.37)
			[6,6]:0.96	0.874	0.935	0.827	0.495	0.597	0.423	0.399	0.473	0.344
			[6,6]:0.98	0.874	0.935	0.827	0.505	0.629	0.423	0.426	0.52	0.361
			[7,7]:0.98	0.874	0.935	0.827	0.485	0.596	0.409	0.388	0.47	0.33
		NB-bespoke	[6,6]:0.94	0.928	0.968	0.915	0.48	0.567	0.416	0.371	0.433	0.325
			[6,6]:0.96	0.928	0.968	0.915	0.491	0.59	0.42	0.393	0.466	0.34
			[6,6]:0.98	0.927	0.97	0.913	0.504	0.624	0.422	0.421	0.512	0.358
			[7,7]:0.98	0.935	0.97	0.921	0.487	0.596	0.412	0.386	0.466	0.329
		rdp	0.7	0.929	0.939	0.922	0.479	0.572	0.413	0.382	0.451	0.332
			0.8	0.924	0.939	0.915	0.507	0.633	0.422	0.434	0.534	0.366
			0.9	0.922	0.937	0.913	0.517	0.698	0.411	0.47	0.617	0.379
	Precision	Naive-Bayes	[6,6]:0.98	0.874	0.935	0.827	0.505	0.629	0.423	0.426	0.52	0.361	P = (0.92, 0.6, 0.3)
		NB-bespoke	[6,6]:0.98	0.927	0.97	0.913	0.504	0.624	0.422	0.421	0.512	0.358
		rdp	0.8	0.924	0.939	0.915	0.507	0.633	0.422	0.434	0.534	0.366
			0.9	0.922	0.937	0.913	0.517	0.698	0.411	0.47	0.617	0.379
			1	0.821	0.943	0.742	0.461	0.81	0.322	0.459	0.774	0.327
	Recall	NB-bespoke	[6,6]:0.92	0.938	0.971	0.924	0.467	0.544	0.409	0.353	0.407	0.312	R = (0.9, 0.4, 0.3)
			[6,6]:0.94	0.928	0.968	0.915	0.48	0.567	0.416	0.371	0.433	0.325
			[6,6]:0.96	0.928	0.968	0.915	0.491	0.59	0.42	0.393	0.466	0.34
			[6,6]:0.98	0.927	0.97	0.913	0.504	0.624	0.422	0.421	0.512	0.358
			[7,7]:0.96	0.935	0.969	0.921	0.47	0.56	0.404	0.357	0.422	0.31
			[7,7]:0.98	0.935	0.97	0.921	0.487	0.596	0.412	0.386	0.466	0.329
		rdp	0.7	0.929	0.939	0.922	0.479	0.572	0.413	0.382	0.451	0.332
			0.8	0.924	0.939	0.915	0.507	0.633	0.422	0.434	0.534	0.366
			0.9	0.922	0.937	0.913	0.517	0.698	0.411	0.47	0.617	0.379
	Novel	Naive-Bayes	[6,6]:0.98	0.874	0.935	0.827	0.505	0.629	0.423	0.426	0.52	0.361	F = (0.85, 0.45, 0.4)
		NB-bespoke	[6,6]:0.98	0.927	0.97	0.913	0.504	0.624	0.422	0.421	0.512	0.358
		rdp	0.8	0.923	0.939	0.915	0.507	0.633	0.422	0.434	0.534	0.366
			0.9	0.921	0.937	0.913	0.517	0.698	0.411	0.47	0.617	0.379

^aF, F-measure; P, precision; R, recall
^bNaive Bayes parameters: k-mer range, confidence
^cRDP parameters: confidence
^dBLAST+/VSEARCH parameters: max accepts, minimum consensus, minimum percent identity
^eUCLUST parameters: minimum consensus, similarity, max accepts
^fThreshold describes the score cut-offs used to define optimal method ranges, in the following format: [metric = (mock score, cross-validated score, novel-taxa score)]. If two cut-offs are given, the second indicates a higher cut-off used to select parameters for the developmental NB-bespoke method, and the configurations listed are the union of the two cutoffs: the second cutoff for selecting NB-bespoke, the first for selecting all other methods

Back to article page

ISSN: 2049-2618

Contact us

General enquiries: journalsubmissions@springernature.com