Automated Author ProfileLicht, Tine, Rask
Licht, Tine, Rask
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: 2.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
It is well known that the microbiota of high fat (HF) diet-induced obese mice differs from that of lean mice, but to what extent this difference reflects the obese state or the diet is unclear. To dissociate changes in the gut microbiota associated with high HF feeding from those associated with obesity, we took advantage of the different susceptibility of C57BL/6JBomTac (BL6) and 129S6/SvEvTac (Sv129) mice to diet-induced obesity and of their different responses to inhibition of cyclooxygenase (COX) activity, where inhibition of COX activity in BL6 mice prevents HF diet-induced obesity, but in Sv129 mice accentuates obesity.
Using HiSeq-based whole genome sequencing we identified taxonomic and functional differences in the gut microbiota of the two mouse strains fed regular low fat or HF diets with or without supplementation with the COX-inhibitor, indomethacin.
Here we present the sequence assemblies and annotations for those 54 samples, together with the gene catalogue and relevative abundance levels of both genes and OTUs. It is hoped these data can be used for comparison in future studies of a similar design.
Authors
- Xiao, Liang ;
- Sonne, Si, Brask ;
- Feng, Qiang ;
- Chen, Ning ;
- Xia, Zhongkui ;
- Li, Xiaoping ;
- Fang, Zhiwei ;
- Zhang, Dongya ;
- Fjære, Even ;
- Midtbø, Lisa, Kolden ;
- Derrien, Muriel ;
- Hugenholtz, Floor ;
- Li, Junhua ;
- Zhang, Jianfeng ;
- Liu, Chuan ;
- Hao, Qin ;
- Vogel, Ulla, Birgitte ;
- Mortensen, Alicja ;
- UR Kleerebezem, Michiel, Wageningen ;
- Licht, Tine, Rask ;
- Li, Yingrui ;
- Arumugam, Manimozhiyan ;
- Wang, Jun ;
- Madsen, Lise ;
- Kristiansen, Karsten
To increase the value of mice models studies, we have used HiSeq2000-based whole genome sequencing to establish a catalogue of 2.6 million non-redundant microbial genes derived from 1,130 gigabases of microbial sequences from faecal samples of 184 mice of different strains and from different providers and housing laboratories. More than 99% of the genes are bacterial indicating that the mouse gut microbiota comprises at least 800-900 prevalent bacterial species.This reference gene catalog was annotated to Non-redundant protein sequences (NR) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) databases.
Authors
- Xiao, Liang ;
- Feng, Qiang ;
- Liang, Suisha ;
- Sonne, Si Brask ;
- Xia, Zhongkui ;
- Qiu, Xinmin ;
- Li, Xiaoping ;
- Long, Hua ;
- Zhang, Jianfeng ;
- Zhang, Dongya ;
- Liu, Chuan ;
- Fang, Zhiwei ;
- Chou, Joyce ;
- Glanville, Jacob ;
- Hao, Qin ;
- Kotowska, Dorota ;
- Colding, Camilla ;
- Licht, Tine, Rask ;
- Wu, Donghai ;
- Yu, Jun ;
- Sung, Joseph, Jao Yiu ;
- Liang, Qiaoyi ;
- Li, Junhua ;
- Jia, Huijue ;
- Lan, Zhou ;
- Tremaroli, Valentina ;
- Backhed, Fredrik ;
- Doré, Joel ;
- Le Chatelier, Emmanuelle ;
- Ehrlich, S.Dusko ;
- Lin, John , C ;
- Arumugam, Manimozhiyan ;
- Wang, Jun ;
- Madsen, Lise ;
- Kristiansen, Karsten