7 | | multiple levels: gene expression (eQTL), protein abundance (pQTL), |
8 | | metabolite abundance (mQTL) and phenotype (phQTL) levels. All |
9 | | popular QTL mapping methods are accessible via the web user |
10 | | interface, large calculations scale easily on to multi-core computers, |
11 | | clusters, and Cloud, and all data involved can be uploaded and |
12 | | queried online: markers, genotypes, microarrays, NGS, LC-MS, |
13 | | GC-MS, NMR, etc. When new data types come available, xQTL |
14 | | workbench can be rapidly customized using the Molgenis toolkit. |
| 7 | multiple levels: |
| 8 | |
| 9 | * gene expression (eQTL), |
| 10 | * protein abundance (pQTL), |
| 11 | * metabolite abundance (mQTL) and |
| 12 | * phenotype (phQTL) levels. |
| 13 | |
| 14 | Main features: |
| 15 | * Popular QTL mapping methods are accessible via the web user |
| 16 | interface, |
| 17 | * large calculations scale easily on to multi-core computers, |
| 18 | clusters, and Cloud, and |
| 19 | * all data involved can be uploaded and |
| 20 | queried online: |
| 21 | * markers, |
| 22 | * genotypes, |
| 23 | * microarrays, |
| 24 | * NGS, |
| 25 | * LC-MS, |
| 26 | * GC-MS, |
| 27 | * NMR, etc. |
| 28 | When new data types come available, xQTL |
| 29 | workbench can be rapidly customized using the Molgenis toolkit. See MolgenisGuide. |