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Additional file 5: Supplementary Figure S5. Clustering benchmarks on 64 two CAMI II Mouse gut datasets. The number of identified clusters with precision ��� 0.90 and different recall levels were shown. All programs were run with their default parameters. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. Assessments were evaluated using AMBER (version 2.0.2).

Additional file 8: Supplementary Figure S8. Clustering benchmarks on a cohort of T2D study. The number of identified clusters with contamination ��� 0.05 and different recall levels were shown. All programs were run with their default parameters. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. Assessments were evaluated using CheckM (version 1.0.13).

Additional file 7: Supplementary Figure S7. Clustering benchmarks on 24 Human Microbiome Project datasets. The number of identified clusters with contamination ��� 0.10 and different recall levels were shown. All programs were run with their default parameters. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. Assessments were evaluated using CheckM (version 1.0.13).

Additional file 3: Supplementary Figure S3. Clustering benchmarks on two CAMI I datasets. The number of identified bins on CAMI I (A) Medium and (B) High Complexity dataset with different score levels were shown. All programs were run in multi-sample mode with their default parameters except for MaxBin2 on the CAMI I High Complexity dataset. MaxBin2 was run on a single merged dataset duo to the excessive runtime with multiple samples. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. Assessments were evaluated using AMBER (version 2.0.2).

Additional file 4: Supplementary Figure S4. Clustering benchmarks on 64 two CAMI II Mouse gut datasets. The number of identified clusters with precision ��� 0.95 and different recall levels were shown. All programs were run with their default parameters. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. Assessments were evaluated using AMBER (version 2.0.2).

Additional file 2: Supplementary Figure S2. Clustering benchmarks on a simulated dataset. The number of identified bins with different score levels were shown. All programs were run with their default parameters in both single-sample (A) and multi-sample (B) modes. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. Assessments were evaluated using AMBER (version 2.0.2).

Additional file 12: Supplementary Figure S12. Euclidean distance distributions between contigs abstracted from the same genome (intra-genome) and from two different genomes (inter-genome). The 3,023 bacterial genomes downloaded from NCBI were randomly fragmented into contigs (1 Kb - 100 Kb), then one million simulations were performed. Two histograms denote the intra- and interdistances, respectively. Two curves represent Gaussian densities parametrized by the mean and variance of the intra- and inter- distances, respectively.

Additional file 6: Supplementary Figure S6. Clustering benchmarks on five CAMI II Human Microbiome Project datasets. The number of identified bins on (A) Airways, (B) Gastrointestinal tract, (C) Oral cavity, (D) Skin and (E) Urogenital tract dataset with different score levels were shown. All programs were run in multi-sample mode with their default parameters. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. DASTool was carried out on two combinations: 1) MetaDecoder and MetaBAT2, 2) CONCOCT and MetaBAT2. Assessments were evaluated using AMBER (version 2.0.2).

Additional file 9: Supplementary Figure S9. Clustering benchmarks on a cohort of T2D study. The number of identified clusters with contamination ��� 0.10 and different recall levels were shown. All programs were run with their default parameters. MetaDecoder with minimum sequence length setting to 1 Kb (MetaDecoder1000) was also added for benchmarking. Assessments were evaluated using CheckM (version 1.0.13).

Additional file 2. This file provides the details of Supplementary figures S1-S12.

Respiratory syncytial virus (RSV) is a common viral pathogen of the lower respiratory tract, which, in the absence of effective management, causes millions of cases of severe illness per year. Many of these infections develop into fatal pneumonia. In a review of English and Chinese medical literature, recent traditional Chinese medical herb- (TCMH-) based progress in the area of prevention and treatment was identified, and the potential anti-RSV compounds, herbs, and formulas were explored. Traditional Chinese medical herbs have a positive effect on inhibiting viral attachment, inhibiting viral internalization, syncytial formation, alleviation of airway inflammation, and stimulation of interferon secretion and immune system; however, the anti-RSV mechanisms of TCMHs are complicated, which should be further investigated.