Automated Author ProfileOta, Naruhisa
Roche (United States)
Ota, Naruhisa
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.5 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Additional file 2: Table S1-3. Differential Abundance Testing Results. To identify significantly enriched or depleted bacterial genera in the (S1) ileum, (S2) colon, and (S3) stool of mice treated with IL-22Fc compared to placebo, the DESeq2 R-package was used as described by McMurdie et al. [72]. We performed two additional, conceptually different statistical approaches for DA testing, nonparametric Wilcoxon rank-sum testing as well as compositional data analysis utilizing ALDEx2 to ensure our observations were robust to the statistical method implemented. Effect sizes, nominal p-values, and BH-adjusted p-values for all statistical approaches are displayed. Only genera with at least 10 reads detected in at least 20% of samples were considered for differential abundance testing. Table S4. Bacterial Strain Information. Strain ID, source, strain name, whole genome and 16S rRNA gene sequence accession number, preferred growth medium, and proxy taxonomy (i.e. experimentally identified bacterial taxa this strain is representing based on ≥97% 16S rRNA gene sequence similarity) for all bacterial strains used in this study. All strains sourced from Genentech (i.e.: Source = ‘Genentech’) were isolated directly from mice utilized in the described experiments while all other strains were acquired from the indicated culture collection. Table S5. Evidence for the capacity to produce tryptophan derived AhR ligands in sequenced genomes of putative AhR activating bacterial strains. Shown are the results of genome-wide BLAST comparisons of TnaA [E. coli], TrpA [E. coli], TrpB [E. coli], TDC [Staphylococcus epidermidis], DDC [Bacillus licheniformis], IpdC [Azospirillum], AofH [Bacillus subtilis] and AldA [E. coli] protein query sequences against sequenced genomes of bacterial strains GNE6609, GNE6603, GNE6686, and GNE6624. Each line within a field represents a high-scoring segment pair (HSP) yielded by tBLASTn searches of query protein sequences against the corresponding translated bacterial isolate genome with evalue ≤ 0.1. Genes are considered putatively present if BLAST searches yield at least one HSP satisfying query coverage ≥ 60%. Table S6. List of metabolomics panel analytes, stable isotope labeled internal standards, and additional protocol details.
Authors
- Mar, Jordan S. ;
- Ota, Naruhisa ;
- Pokorzynski, Nick D. ;
- Peng, Yutian ;
- Jaochico, Allan ;
- Sangaraju, Dewakar ;
- Skippington, Elizabeth ;
- Lekkerkerker, Annemarie N. ;
- Rothenberg, Michael E. ;
- Tan, Man-Wah ;
- Yi, Tangsheng ;
- Keir, Mary E.
Additional file 2: Table S1-3. Differential Abundance Testing Results. To identify significantly enriched or depleted bacterial genera in the (S1) ileum, (S2) colon, and (S3) stool of mice treated with IL-22Fc compared to placebo, the DESeq2 R-package was used as described by McMurdie et al. [72]. We performed two additional, conceptually different statistical approaches for DA testing, nonparametric Wilcoxon rank-sum testing as well as compositional data analysis utilizing ALDEx2 to ensure our observations were robust to the statistical method implemented. Effect sizes, nominal p-values, and BH-adjusted p-values for all statistical approaches are displayed. Only genera with at least 10 reads detected in at least 20% of samples were considered for differential abundance testing. Table S4. Bacterial Strain Information. Strain ID, source, strain name, whole genome and 16S rRNA gene sequence accession number, preferred growth medium, and proxy taxonomy (i.e. experimentally identified bacterial taxa this strain is representing based on ≥97% 16S rRNA gene sequence similarity) for all bacterial strains used in this study. All strains sourced from Genentech (i.e.: Source = ‘Genentech’) were isolated directly from mice utilized in the described experiments while all other strains were acquired from the indicated culture collection. Table S5. Evidence for the capacity to produce tryptophan derived AhR ligands in sequenced genomes of putative AhR activating bacterial strains. Shown are the results of genome-wide BLAST comparisons of TnaA [E. coli], TrpA [E. coli], TrpB [E. coli], TDC [Staphylococcus epidermidis], DDC [Bacillus licheniformis], IpdC [Azospirillum], AofH [Bacillus subtilis] and AldA [E. coli] protein query sequences against sequenced genomes of bacterial strains GNE6609, GNE6603, GNE6686, and GNE6624. Each line within a field represents a high-scoring segment pair (HSP) yielded by tBLASTn searches of query protein sequences against the corresponding translated bacterial isolate genome with evalue ≤ 0.1. Genes are considered putatively present if BLAST searches yield at least one HSP satisfying query coverage ≥ 60%. Table S6. List of metabolomics panel analytes, stable isotope labeled internal standards, and additional protocol details.
Authors
- Mar, Jordan S. ;
- Ota, Naruhisa ;
- Pokorzynski, Nick D. ;
- Peng, Yutian ;
- Jaochico, Allan ;
- Sangaraju, Dewakar ;
- Skippington, Elizabeth ;
- Lekkerkerker, Annemarie N. ;
- Rothenberg, Michael E. ;
- Tan, Man-Wah ;
- Yi, Tangsheng ;
- Keir, Mary E.