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- Lv, Zhihan, Dr. 1984-, et al.
(author)
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Augmented Reality for Bioinformatics
- 2022
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In: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 26:6, s. 2403-2404
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Journal article (other academic/artistic)
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| 4. |
- Lv, Zhihan, Dr. 1984-, et al.
(author)
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Editorial : 5G for Augmented Reality
- 2022
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In: Mobile Networks and Applications. - : Springer. - 1383-469X .- 1572-8153.
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Journal article (peer-reviewed)
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| 5. |
- Mas-Lloret, J, et al.
(author)
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Gut microbiome diversity detected by high-coverage 16S and shotgun sequencing of paired stool and colon sample
- 2020
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In: Scientific data. - : Springer Science and Business Media LLC. - 2052-4463. ; 7:1, s. 92-
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Journal article (peer-reviewed)abstract
- The gut microbiome has a fundamental role in human health and disease. However, studying the complex structure and function of the gut microbiome using next generation sequencing is challenging and prone to reproducibility problems. Here, we obtained cross-sectional colon biopsies and faecal samples from nine participants in our COLSCREEN study and sequenced them in high coverage using Illumina pair-end shotgun (for faecal samples) and IonTorrent 16S (for paired feces and colon biopsies) technologies. The metagenomes consisted of between 47 and 92 million reads per sample and the targeted sequencing covered more than 300 k reads per sample across seven hypervariable regions of the 16S gene. Our data is freely available and coupled with code for the presented metagenomic analysis using up-to-date bioinformatics algorithms. These results will add up to the informed insights into designing comprehensive microbiome analysis and also provide data for further testing for unambiguous gut microbiome analysis.
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- Obon-Santacana, M, et al.
(author)
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Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes
- 2022
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In: Cancers. - : MDPI AG. - 2072-6694. ; 14:17
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Journal article (peer-reviewed)abstract
- The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78–0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66–0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41–0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.
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