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author | Bonface | 2024-02-13 23:52:26 -0600 |
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committer | Munyoki Kilyungi | 2024-08-09 13:30:43 +0300 |
commit | b2feda451ccfbeaed02dce9088d6dd228cf15861 (patch) | |
tree | 3dd2883524985114070a7770cd2e9f9bd7eb1848 /general/datasets/Cubr_rna_0219 | |
parent | d029d5d7f8ead1f1de8d318045004a4a6f68f5fb (diff) | |
download | gn-docs-b2feda451ccfbeaed02dce9088d6dd228cf15861.tar.gz |
Update dataset RTF Files.
Diffstat (limited to 'general/datasets/Cubr_rna_0219')
-rw-r--r-- | general/datasets/Cubr_rna_0219/citation.rtf | 5 | ||||
-rw-r--r-- | general/datasets/Cubr_rna_0219/contributors.rtf | 1 | ||||
-rw-r--r-- | general/datasets/Cubr_rna_0219/experiment-design.rtf | 1 | ||||
-rw-r--r-- | general/datasets/Cubr_rna_0219/notes.rtf | 1 | ||||
-rw-r--r-- | general/datasets/Cubr_rna_0219/specifics.rtf | 1 | ||||
-rw-r--r-- | general/datasets/Cubr_rna_0219/summary.rtf | 1 |
6 files changed, 10 insertions, 0 deletions
diff --git a/general/datasets/Cubr_rna_0219/citation.rtf b/general/datasets/Cubr_rna_0219/citation.rtf new file mode 100644 index 0000000..9406271 --- /dev/null +++ b/general/datasets/Cubr_rna_0219/citation.rtf @@ -0,0 +1,5 @@ +<ul>
+ <li>Rudra P, Shi WJ, Russell P, Vestal B et al. Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse. BMC Genomics 2018 Aug 29;19(1):639. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30157779" title="Link to PubMed record">30157779</a></li>
+ <li>Russell PH, Vestal B, Shi W, Rudra PD et al. miR-MaGiC improves quantification accuracy for small RNA-seq. BMC Res Notes 2018 May 15;11(1):296. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/29764489" title="Link to PubMed record">29764489</a></li>
+ <li>Rudra P, Shi WJ, Vestal B, Russell PH et al. Model based heritability scores for high-throughput sequencing data. BMC Bioinformatics 2017 Mar 2;18(1):143. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/28253840" title="Link to PubMed record">28253840</a></li>
+</ul>
diff --git a/general/datasets/Cubr_rna_0219/contributors.rtf b/general/datasets/Cubr_rna_0219/contributors.rtf new file mode 100644 index 0000000..a5ce46b --- /dev/null +++ b/general/datasets/Cubr_rna_0219/contributors.rtf @@ -0,0 +1 @@ +<p><a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Kechris%20K[Author]">Kechris K</a>, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Tabakoff%20B[Author]">Tabakoff B</a></p>
diff --git a/general/datasets/Cubr_rna_0219/experiment-design.rtf b/general/datasets/Cubr_rna_0219/experiment-design.rtf new file mode 100644 index 0000000..8ba40f7 --- /dev/null +++ b/general/datasets/Cubr_rna_0219/experiment-design.rtf @@ -0,0 +1 @@ +<p>This dataset includes small RNA NGS sequencing data from 59 strains from the Inbred Long Sleep (ILS) and Inbred Short Sleep (ISS) Recombinant inbred mouse whole brain RNA samples. 175 mice (2-3 from each strain) were untreated (naive). The naive samples were provided by Boris Tabakoff, University of Colorado Anschutz Medical Campus. All expression data generation and analysis were conducted by the Kechris Group, University of Colorado Anschutz Medical Campus.</p>
diff --git a/general/datasets/Cubr_rna_0219/notes.rtf b/general/datasets/Cubr_rna_0219/notes.rtf new file mode 100644 index 0000000..84096b8 --- /dev/null +++ b/general/datasets/Cubr_rna_0219/notes.rtf @@ -0,0 +1 @@ +<p>miRNA expression variance stabilized</p>
diff --git a/general/datasets/Cubr_rna_0219/specifics.rtf b/general/datasets/Cubr_rna_0219/specifics.rtf new file mode 100644 index 0000000..d301f7e --- /dev/null +++ b/general/datasets/Cubr_rna_0219/specifics.rtf @@ -0,0 +1 @@ +based on ens_mirna_expression_variance_stabilized
\ No newline at end of file diff --git a/general/datasets/Cubr_rna_0219/summary.rtf b/general/datasets/Cubr_rna_0219/summary.rtf new file mode 100644 index 0000000..57e5529 --- /dev/null +++ b/general/datasets/Cubr_rna_0219/summary.rtf @@ -0,0 +1 @@ +<p>We conducted a high-throughput sequencing study to measure whole brain miRNA expression levels in alcohol naïve animals in the LXS panel of recombinant inbred (RI) mouse strains. We then combined the sequencing data with genotype data, microarray gene expression data, and data on alcohol-related behavioral phenotypes such as 'Drinking in the dark', 'Sleep time', and 'Low dose activation' from the same RI panel.</p>
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