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| author | Pjotr Prins | 2022-12-06 08:59:12 -0600 |
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| committer | Pjotr Prins | 2022-12-06 08:59:12 -0600 |
| commit | 824058842cb80256430c9e3cb541e20a08d8dcd7 (patch) | |
| tree | 5d3128accbecd9ee92db66b36a7c438b8af1e80a | |
| parent | 1dffd84649e6f52d8eadfce9ff846ec714a2ff74 (diff) | |
| download | gn-docs-824058842cb80256430c9e3cb541e20a08d8dcd7.tar.gz | |
References
| -rw-r--r-- | features/introduction.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/features/introduction.md b/features/introduction.md index 999f01a..0a5e11a 100644 --- a/features/introduction.md +++ b/features/introduction.md @@ -1,9 +1,9 @@ # Introduction GeneNetwork (GN) is unique web-service offering over 25 years of experimental mouse and rat data. -According to Google Scholar the domain name 'genenetwork.org' is referenced in about [1200 publications](https://scholar.google.com/scholar?q=genenetwork.org) and about 60 publications are added every year (2022). Since 2018 genenetwork.org is referenced over [30 high impact publications](https://scholar.google.com/scholar?as_ylo=2018&q=genenetwork.org+nature.com&hl=en&as_sdt=0,43) in Nature and Science journals [cite]. +According to Google Scholar the domain name 'genenetwork.org' is referenced in about [1200 publications](https://scholar.google.com/scholar?q=genenetwork.org) and about 60 publications are added every year (2022). Since 2018 genenetwork.org is referenced over [30 high impact publications](https://scholar.google.com/scholar?as_ylo=2018&q=genenetwork.org+nature.com&hl=en&as_sdt=0,43) in Nature and Science journals [cite], proving its essential role in mouse and rat studies that target human health. -The bulk of the data reflects experiments on the 'immortal' BXD mouse model which means that experiments conducted 25 years ago can be reanalysed and compared with experiments today. +One of the interesting features of GN is that the bulk of the data reflects experiments on the genetically 'immortal' BXD mouse model which means that experiments conducted 25 years ago can be reanalysed and compared with experiments today. Also new studies on these model organisms add to the phenotype database and can be used for correlations and comparisons. Powerful features of GN include searching for dataset, genes, SNPs and QTL in different species, as well as [live mapping](mapping.md) using advanced tools, such as R/qtl and GEMMA that include LMMs and allow for selection of cofactors. GN can also compute correlations of data points and significant QTL. |