You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Non-human primates, such as Rhesus macaques, are a powerful genetic model of human disease. mGAP seeks to fill several major voids to promote genomic analyses and the study of human genetic disease in this key pre-clinical model.
16
30
The database provides open access to rhesus macaque genomic and phenotypic data. Our dataset was originally generated using animals from the large, pedigreed colony of Indian-origin rhesus macaques housed at the Oregon National Primate Research Center (ONPRC); however, it has
17
-
since <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-externalData.view">been expanded to include data from other National Primate Research Centers and NHP colonies.</a>. Below are the key features of mGAP:
31
+
since <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-externalData.view">been expanded to include data from other National Primate Research Centers and NHP colonies.</a>.
32
+
<br><br>
33
+
<spanstyle="font-weight: bold">NEW: Click the video icons (<iclass="fa-solid fa-video mgap-video-icon"></i>) load help videos, or <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-tutorials.view">view our tutorials page</a> for more detail.</span>
The primary dataset generated by mGAP is a catalog of short variants (summarized to the right). Raw sequence data are <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mgap-dataProcessing.view">analyzed using a vetted pipeline</a> designed to produce high-confidence genotype calls.
21
37
The resulting variants are <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mgap-annotation.view">annotated using a wide range of data sources</a>, including predicted function, overlap with regulatory elements and association with phenotypes and diseases.
22
38
<br>
23
39
<ulstyle="padding-top: 10px;">
24
-
<li>Use the <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-genomeBrowser.view?">Genome Browser</a> to view and search data</li>
25
-
<!-- <li>Use our new <a class="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-genomeBrowser.view?target=variantSearch">Full-text Search</a> tool to query variants based on gene, or using our <a class="mgap-link" href="<%=contextPath%><%=containerPath%>/mgap-annotation.view">extensive annotations</a></li>-->
26
-
<li>View our list of <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-variantList.view">Predicted Damaging Variants</a>, which is a list of predicted high-impact or disease associated variants, generated from each release</li>
40
+
<li>Use the <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-genomeBrowser.view?">Genome Browser</a> to view and search data. <iclass="fa-solid fa-video mgap-video-icon" data-video="genome-browser" data-video-title="Genome Browser Overview"></i></li>
41
+
<li>Use our new <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-genomeBrowser.view?target=variantSearch">Full-text Search</a> tool to query variants based on gene, or using our <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mgap-annotation.view">extensive annotations</a><iclass="fa-solid fa-video mgap-video-icon" data-video="variant-search" data-video-title="Variant Full-text Search (BETA)"></i></li>
42
+
<li>View our list of <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-variantList.view">Predicted Damaging Variants</a>, which is a list of predicted high-impact or disease associated variants, generated from each release<iclass="fa-solid fa-video mgap-video-icon" data-video="predicted-damaging-variants" data-video-title="Predicted Damaging Variants"></i></li>
27
43
<li>Unlike many datasets, mGAP has genotype-level data, <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/project-begin.view?pageId=clinical">often connected to living animals from pedigreed breeding colonies</a></li>
28
44
<li>Download raw data, including <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/project-begin.view?pageId=datasets">sequence data</a> and <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/project-begin.view?pageId=variants">variant data</a></li>
We have released a draft dataset with structural variants generated from PacBio sequencing of 44 Rhesus macaques. These data complement the short variant catalog by detecting categories of variants not readily accomplished with short read Illumina data.
33
50
<br>
34
51
<ulstyle="padding-top: 10px;">
35
-
<li>Use the <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-genomeBrowser.view?activeTracks=mGAP Structural Variants 1.0">Genome Browser</a> to view and search structural variant data</li>
52
+
<li>Use the <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-genomeBrowser.view?activeTracks=mGAP Structural Variants 1.0&loc=4:137,917,074..137,937,929">Genome Browser</a> to view and search structural variant data</li>
36
53
</ul>
37
54
38
-
<h4style="text-decoration: underline;">NHP Models of Human Disease:</h4>
55
+
<h4><spanstyle="text-decoration: underline;">NHP Models of Human Disease:</span><imgstyle="vertical-align: sub" width="40px" src="<%=contextPath%>/mgap/images/phenotypes.png" alt="Phenotypes"></h4>
39
56
NHPs serve as essential pre-clinical models for a range of human diseases. The mGAP site supports this work by providing information about published NHP disease/phenotype models, and by providing summaries of the annotated variant catalog to help identify novel disease-associated variants.
40
57
<br>
41
58
<ulstyle="padding-top: 10px;">
59
+
<li>Tutorial on searching phenotypes and variants <iclass="fa-solid fa-video mgap-video-icon" data-video="phenotypes-and-models" data-video-title="Phenotypes and Models"></i></li>
42
60
<li>View our list of <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/project-begin.view?pageId=model">Published NHP Disease Models</a></li>
43
61
<li>View the list of <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mgap-phenotypeList.view">Genes and OMIM diseases/phenotypes</a> where predicted damaging variants were detected</li>
44
62
<li>View our list of <aclass="mgap-link" href="<%=contextPath%><%=containerPath%>/mGap-variantList.view">Predicted Damaging Variants</a>, which is a list of predicted high-impact or disease associated variants, generated from each release</li>
@@ -48,7 +66,7 @@ <h4 style="text-decoration: underline;">NHP Models of Human Disease:</h4>
Copy file name to clipboardExpand all lines: mGAP/resources/views/releaseNotes.html
+7-1Lines changed: 7 additions & 1 deletion
Original file line number
Diff line number
Diff line change
@@ -1,3 +1,9 @@
1
+
<h4>Release 2.5:</h4>
2
+
<ul>
3
+
<li>This release includes a major change in how we perform variant annotation, <ahref="mgap-annotation.view">described in more detail here.</a></li>
4
+
<li>This release includes 107 additional datasets. The total number of variants dropped relative to release 2.4 due to a change in our filtering. Previously, certain conditions would allow a variant to remain in the dataset, even if no subjects had this genotype. After this correction, ~4m sites were removed, although no genotype calls should be changed.</li>
5
+
</ul>
6
+
1
7
<h4>Release 2.4:</h4>
2
8
<ul>
3
9
<li>This is an additional 470 animals over the prior version. All processing and informatics steps are identical.</li>
@@ -9,7 +15,7 @@ <h4>Release 2.3:</h4>
9
15
<li>There are a sizable number of data processing changes, largely adaptations to handle the rapidly growing dataset size:</li>
10
16
<ol>
11
17
<li>All data used <ahref="https://gatk.broadinstitute.org/hc/en-us/articles/4405443600667-ReblockGVCF">GATK Reblocked gVCFs</a> as inputs. This reduces processing, but can reduce sensitivity at homozygous-reference sites (resulting in greater numbers of no-call genotypes at homozygous ref sites)</li>
12
-
<li>Also to adapt to larger data size, we changed the structure of data processing. Previously, samples were each aggregated into one GenomicsDB workspace per data type (WGS or WXS). Next, GenotypeGVCFs was run on each workspace, with one job per contig. The resulting VCFs were filtered and merged. In this release, the upfront aggregation step was dropped, and we instead: 1) use reblocked gVCFs as input (entire set of samples), 2) chunk the genome into ~1000 bins with one job/bin, 3) per bin, run GenomicsDbImport to make a transient workspace using the job's intervals +/- 1000bp, 4) run GenotypeGVCFs against that workspace, 5) filter the result, including technology-aware thresholds (i.e. different depth filters for WGS/WXS). This process is both considerably more efficient and has the advantage of joint-genotyping across the entire cohort at once.</li>
18
+
<li>To adapt to larger data size, we changed the structure of data processing. Previously, samples were each aggregated into one GenomicsDB workspace per data type (WGS or WXS). Next, GenotypeGVCFs was run on each workspace, with one job per contig. The resulting VCFs were filtered and merged. In this release, the upfront aggregation step was dropped, and we instead: 1) use reblocked gVCFs as input (entire set of samples), 2) chunk the genome into ~1000 bins with one job/bin, 3) per bin, run GenomicsDbImport to make a transient workspace using the job's intervals +/- 1000bp, 4) run GenotypeGVCFs against that workspace, 5) filter the result, including technology-aware thresholds (i.e. different depth filters for WGS/WXS). This process is both considerably more efficient and has the advantage of joint-genotyping across the entire cohort at once.</li>
0 commit comments