+ You can select up to three traits from a saved trait collection to use as cofactors in the scatterplots, with each trait corresponding to point color, size, or symbol.
+ For symbol, traits must have no more than 4 distinct values.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Cofactor Color Range:
+
+
+
+
+
+
+
+
+
+ Cofactor 1:
+
+
+
+
+
+
+
+ Cofactor 2:
+
+
+
+
+
+
+
+ Cofactor 3:
+
+
+
+
+
+ {% else %}
+
No collections currently exist. Please create a collection first if you wish to include cofactors in the scatterplots.
Select any cell in the matrix to generate a scatter plot. Lower left cells list Pearson product-moment correlations; upper right cells list Spearman rank order correlations. Each cell also contains the n of cases in parenthesis. Values ranging from 0.4 to 1.0 range from orange to white, while values ranging from –0.4 to –1.0 range from dark blue to white.
+
+{% if lowest_overlap < 8 %}
+
Caution: This matrix of correlations contains some cells with small sample sizes of fewer than 8.
Values of record {{ this_trait.name }} in the {{ this_dataset.fullname }}
+ dataset were compared to all records in the {{ target_dataset.fullname }}
+ dataset. The top {{ return_results }} correlations ranked by the {{ formatted_corr_type }} are displayed.
+ You can resort this list by clicking the headers. Select the Record ID to open the trait data
+ and analysis page.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ with r >
+
+
+ r <
+ , with mean >
+
+
+ mean <
+
+
+
+
+
+ {% if request.form['trait_list'].split(",")|length < 2 %}
+
+ Error:
+
Too few traits as input
+ Please make sure you select enough traits to perform CTL. Your collection needs to contain at least 2 different traits. You provided {{request.form['trait_list'].split(',')|length}} traits as input.
+
+{% else %}
+
CTL analysis
+CTL analysis is published as open source software, if you are using this method in your publications, please cite:
+Arends D, Li Y, Brockmann GA, Jansen RC, Williams RW, Prins P
+Correlation trait locus (CTL) mapping: Phenotype network inference subject to genotype.
+The Journal of Open Source Software (2016)
+Published in
+
About the cases used to generate this set of data:
+
{{ info.About_Cases | safe}}
+
About the tissue used to generate this set of data:
+
{{ info.About_Tissue | safe }}
+
About downloading this data set:
+
All data links (right-most column above) will be made active as sooon as the global analysis of these data by the Consortium has been accepted for publication. Please see text on Data Sharing Policies, and Conditions and Limitations, and Contacts. Following publication, download a summary text file or Excel file of the PDNN probe set data. Contact RW Williams regarding data access problems.
+
+
About the array platform:
+
Affymetrix Mouse Genome 430 2.0 array: The 430v2 array consists of 992936 useful 25-nucleotide probes that estimate the expression of approximately 39,000 transcripts and the majority of known genes and expressed sequence tags. The array sequences were selected late in 2002 using Unigene Build 107 by Affymetrix. The UTHSC group has recently reannotated all probe sets on this array, producing more accurate data on probe and probe set targets. All probes were aligned to the most recent assembly of the Mouse Genome (Build 34, mm6) using Jim Kent's BLAT program. Many of the probe sets have been manually curated by Jing Gu and Rob Williams.
+
About data values and data processing:
+
Harshlight was used to examine the image quality of the array (CEL files). Bad areas (bubbles, scratches, blemishes) of arrays were masked.
+
+
First pass data quality control: Affymetrix GCOS provides useful array quality control data including:
+
+
The scale factor used to normalize mean probe intensity. This averaged 3.3 for the 179 arrays that passed and 6.2 for arrays that were excluded. The scale factor is not a particular critical parameter.
+
The average background level. Values averaged 54.8 units for the data sets that passed and 55.8 for data sets that were excluded. This factor is not important for quality control.
+
The percentage of probe sets that are associated with good signal ("present" calls). This averaged 50% for the 179 data sets that passed and 42% for those that failed. Values for passing data sets extended from 43% to 55%. This is a particularly important criterion.
+
The 3':5' signal ratios of actin and Gapdh. Values for passing data sets averaged 1.5 for actin and 1.0 for Gapdh. Values for excluded data sets averaged 12.9 for actin and 9.6 for Gapdh. This is a highly discriminative QC criterion, although one must keep in mind that only two transcripts are being tested. Sequence variation among strains (particularly wild derivative strains such as CAST/Ei) may affect these ratios.
+
+
+
The second step in our post-processing QC involves a count of the number of probe sets in each array that are more than 2 standard deviations (z score units) from the mean across the entire 206 array data sets. This was the most important criterion used to eliminate "bad" data sets. All 206 arrays were processed togther using standard RMA and PDNN methods. The count and percentage of probe sets in each array that were beyond the 2 z theshold was computed. Using the RMA transform the average percentage of probe sets beyond the 2 z threshold for the 179 arrays that finally passed of QC procedure was 1.76% (median of 1.18%). In contrast the 2 z percentage was more than 10-fold higher (mean of 22.4% and median 20.2%) for those arrays that were excluded. This method is not very sensitive to the transformation method that is used. Using the PDNN transform, the average percent of probe sets exceeding was 1.31% for good arrays and was 22.6% for those that were excluded. In our opinion, this 2 z criterion is the most useful criterion for the final decision of whether or not to include arrays, although again, allowances need to be made for wild strains that one expects to be different from the majority of conventional inbred strains. For example, if a data set has excellent characteristics on all of the Affymetrix GCOS metrics listed above, but generates a high 2 z percentage, then one would include the sample if one can verify that there are no problems in sample and data set identification.
+
+
The entire procedure can be reapplied once the initial outlier data sets have been eliminated to detect any remaining outlier data sets.
+
+
+
DataDesk was used to examine the statistical quality of the probe level (CEL) data after step 5 below. DataDesk allows the rapid detection of subsets of probes that are particularly sensitive to still unknown factors in array processing. Arrays can then be categorized at the probe level into "reaction classes." A reaction class is a group of arrays for which the expression of essentially all probes are colinear over the full range of log2 values. A single but large group of arrays (n = 32) processed in essentially the identical manner by a single operator can produce arrays belonging to as many as four different reaction classes. Reaction classes are NOT related to strain, age, sex, treatment, or any known biological parameter (technical replicates can belong to different reaction classes). We do not yet understand the technical origins of reaction classes. The number of probes that contribute to the definition of reaction classes is quite small (<10% of all probes). We have categorized all arrays in this data set into one of 5 reaction classes. These have then been treated as if they were separate batches. Probes in these data type "batches" have been aligned to a common mean as described below.
+
+
Probe (cell) level data from the CEL file: These CEL values produced by GCOS are 75% quantiles from a set of 91 pixel values per cell.
+
+
+
We added an offset of 1.0 unit to each cell signal to ensure that all values could be logged without generating negative values. We then computed the log base 2 of each cell.
+
+
We performed a quantile normalization of the log base 2 values for all arrays using the same initial steps used by the RMA transform.
+
+
We computed the Z scores for each cell value.
+
+
We multiplied all Z scores by 2.
+
+
We added 8 to the value of all Z scores. The consequence of this simple set of transformations is to produce a set of Z scores that have a mean of 8, a variance of 4, and a standard deviation of 2. The advantage of this modified Z score is that a two-fold difference in expression level (probe brightness level) corresponds approximately to a 1 unit difference.
+
+
Finally, we computed the arithmetic mean of the values for the set of microarrays for each strain. Technical replicates were averaged before computing the mean for independent biological samples. Note, that we have not (yet) corrected for variance introduced by differences in sex or any interaction terms. We have not corrected for background beyond the background correction implemented by Affymetrix in generating the CEL file. We eventually hope to add statistical controls and adjustments for some of these variables.
+
+
Probe set data from the CHP file: The expression values were generated using PDNN. The same simple steps described above were also applied to these values. Every microarray data set therefore has a mean expression of 8 with a standard deviation of 2. A 1 unit difference represents roughly a two-fold difference in expression level. Expression levels below 5 are usually close to background noise levels.
+
+
+
Probe level QC: Log2 probe data of all arrays were inspected in DataDesk before and after quantile normalization. Inspection involved examining scatterplots of pairs of arrays for signal homogeneity (i.e., high correlation and linearity of the bivariate plots) and looking at all pairs of correlation coefficients. XY plots of probe expression and signal variance were also examined. Probe level array data sets were organized into reaction groups. Arrays with probe data that were not homogeneous when compared to other arrays were flagged.
+
+
Probe set level QC: The final normalized individual array data were evaluated for outliers. This involved counting the number of times that the probe set value for a particular array was beyond two standard deviations of the mean. This outlier analysis was carried out using the PDNN, RMA and MAS5 transforms and outliers across different levels of expression. Arrays that were associated with an average of more than 8% outlier probe sets across all transforms and at all expression levels were eliminated. In contrast, most other arrays generated fewer than 5% outliers.
+
+
+
Validation of strains and sex of each array data set: A subset of probes and probe sets with a Mendelian pattern of inheritance were used to construct a expression correlation matrix for all arrays and the ideal Mendelian expectation for each strain constructed from the genotypes. There should naturally be a very high correlation in the expression patterns of transcripts with Mendelian phenotypes within each strain, as well as with the genotype strain distribution pattern of markers for the strain.
+
+
Sex of the samples was validated using sex-specific probe sets such as Xist and Dby.
+
Data source acknowledgment:
+
Data were generated with funds provided by a variety of public and private source to members of the Consortium. All of us thank Muriel Davisson, Cathy Lutz, and colleagues at the Jackson Laboratory for making it possible for us to add all of the CXB strains, and one or more samples from KK/HIJ, WSB/Ei, NZO/HILtJ, LG/J, CAST/Ei, PWD/PhJ, and PWK/PhJ to this study. We thank Yan Cui at UTHSC for allowing us to use his Linux cluster to align all M430 2.0 probes and probe sets to the mouse genome. We thank Hui-Chen Hsu and John Mountz for providing us BXD tissue samples, as well as many strains of BXD stock. We thanks Douglas Matthews (UMem in Table 1) and John Boughter (JBo in Table 1) for sharing BXD stock with us. Members of the Hippocampus Consortium thank the following sources for financial support of this effort:
+
+
+
David C. Airey, Ph.D.
+ Grant Support: Vanderbilt Institute for Integratie Genomics
+ Department of Pharmacology
+ david.airey at vanderbilt.edu
+
+
Lu Lu, M.D.
+ Grant Support: NIH U01AA13499, U24AA13513
+
+
Fred H. Gage, Ph.D.
+ Grant Support: Lookout Foundation
+
+
Dan Goldowitz, Ph.D.
+ Grant Support: NIAAA INIA AA013503
+ University of Tennessee Health Science Center
+ Dept. Anatomy and Neurobiology
+ email: dgold@nb.utmem.edu
+
+
Gerd Kempermann, M.D.
+ Grant Support: The Volkswagen Foundation Grant on Permissive and Persistent Factors in Neurogenesis in the Adult Central Nervous System
+ Humboldt-Universitat Berlin
+ Universitatsklinikum Charite
+ email: gerd.kempermann at mdc-berlin.de
+
+
Kenneth F. Manly, Ph.D.
+ Grant Support: NIH P20MH062009 and U01CA105417
+
+
Richard S. Nowakowski, Ph.D.
+ Grant Support: R01 NS049445-01
+
+
Glenn D. Rosen, Ph.D.
+ Grant Support: NIH P20
+
+
Leonard C. Schalkwyk, Ph.D.
+ Grant Support: MRC Career Establishment Grant G0000170
+ Social, Genetic and Developmental Psychiatry
+ Institute of Psychiatry,Kings College London
+ PO82, De Crespigny Park London SE5 8AF
+ L.Schalkwyk@iop.kcl.ac.uk
+
+
Guus Smit, Ph.D.
+ Dutch NeuroBsik Mouse Phenomics Consortium
+ Center for Neurogenomics & Cognitive Research
+ Vrije Universiteit Amsterdam, The Netherlands
+ e-mail: guus.smit at falw.vu.nl
+ Grant Support: BSIK 03053
+
+
Thomas Sutter, Ph.D.
+ Grant Support: INIA U01 AA13515 and the W. Harry Feinstone Center for Genome Research
+
+
Stephen Whatley, Ph.D.
+ Grant Support: XXXX
+
+
Robert W. Williams, Ph.D.
+ Grant Support: NIH U01AA013499, P20MH062009, U01AA013499, U01AA013513
+
+
+
Experiment Type:
+
Pooled RNA samples (usually one pool of male hippocampii and one pool of female hippocampii) were prepared using standard protocols. Samples were processed using a total of 206 Affymetrix GeneChip Mouse Expression 430 2.0 short oligomer arrays (MOE430 2.0 or M430v2; see GEO platform ID GPL1261), of which 201 passed quality control and error checking. This particular data set was processed using the PDNN protocol. To simplify comparisons among transforms, PDNN values of each array were adjusted to an average of 8 units and a standard deviation of 2 units.
+
+
Overall Design:
+
Pooled RNA samples (usually one pool of male hippocampii and one pool of female hippocampii) were prepared using standard protocols. Samples were processed using a total of 206 Affymetrix GeneChip Mouse Expression 430 2.0 short oligomer arrays (MOE430 2.0 or M430v2; see GEO platform ID GPL1261), of which 201 passed quality control and error checking. This particular data set was processed using the PDNN protocol. To simplify comparisons among transforms, PDNN values of each array were adjusted to an average of 8 units and a standard deviation of 2 units.
+
+
Contributor:
+
+
David C. Airey, Ph.D.
+ Grant Support: Vanderbilt Institute for Integratie Genomics
+ Department of Pharmacology
+ david.airey at vanderbilt.edu
+
+
Lu Lu, M.D.
+ Grant Support: NIH U01AA13499, U24AA13513
+
+
Fred H. Gage, Ph.D.
+ Grant Support: Lookout Foundation
+
+
Dan Goldowitz, Ph.D.
+ Grant Support: NIAAA INIA AA013503
+ University of Tennessee Health Science Center
+ Dept. Anatomy and Neurobiology
+ email: dgold@nb.utmem.edu
+
+
Gerd Kempermann, M.D.
+ Grant Support: The Volkswagen Foundation Grant on Permissive and Persistent Factors in Neurogenesis in the Adult Central Nervous System
+ Humboldt-Universitat Berlin
+ Universitatsklinikum Charite
+ email: gerd.kempermann at mdc-berlin.de
+
+
Kenneth F. Manly, Ph.D.
+ Grant Support: NIH P20MH062009 and U01CA105417
+
+
Richard S. Nowakowski, Ph.D.
+ Grant Support: R01 NS049445-01
+
+
Glenn D. Rosen, Ph.D.
+ Grant Support: NIH P20
+
+
Leonard C. Schalkwyk, Ph.D.
+ Grant Support: MRC Career Establishment Grant G0000170
+ Social, Genetic and Developmental Psychiatry
+ Institute of Psychiatry,Kings College London
+ PO82, De Crespigny Park London SE5 8AF
+ L.Schalkwyk@iop.kcl.ac.uk
+
+
Guus Smit, Ph.D.
+ Dutch NeuroBsik Mouse Phenomics Consortium
+ Center for Neurogenomics & Cognitive Research
+ Vrije Universiteit Amsterdam, The Netherlands
+ e-mail: guus.smit at falw.vu.nl
+ Grant Support: BSIK 03053
+
+
Thomas Sutter, Ph.D.
+ Grant Support: INIA U01 AA13515 and the W. Harry Feinstone Center for Genome Research
+
+
Stephen Whatley, Ph.D.
+ Grant Support: XXXX
+
+
Robert W. Williams, Ph.D.
+ Grant Support: NIH U01AA013499, P20MH062009, U01AA013499, U01AA013513
+
+
Citation:
+
+
Please cite: Overall RW, Kempermann G, Peirce J, Lu L, Goldowitz D, Gage FH, Goodwin S, Smit AB, Airey DC, Rosen GD, Schalkwyk LC, Sutter TR, Nowakowski RS, Whatley S, Williams RW (2009) Genetics of the hippocampal transcriptome in mice: a systematic survey and online neurogenomic resource. Front. Neurogen. 1:3 Full Text HTML doi:10.3389/neuro.15.003.2009
+
+
We appreciate your interest, but unfortunately, we don't have any additional information available for: {{ name }}. If you have other inquiries or need assistance with something else, please don't hesitate to get in touch with us. In the meantime you can explore other datasets here:
+ This error is not what we wanted to see. Unfortunately errors
+ are part of all software systems and we need to resolve this
+ together.
+
+
+ It is important to report this ERROR so we can fix it for everyone.
+
+
+
+ Report to the GeneNetwork team by recording the steps you take
+ to reproduce this ERROR. Next to those steps, copy-paste below
+ stack trace, either as
+ a new
+ issue or E-mail this full page to one of the developers
+ directly.
+
Sorry, the analysis was interrupted because your selections from GeneNetwork apparently include data from more than one array platform (i.e., Affymetrix U74A and M430 2.0). Most WebGestalt analyses assume that you are using a single array type and compute statistical values on the basis of that particular array. Please reselect traits from a signle platform and submit again.
+ {% elif chip_name == "not_microarray" %}
+
You need to select at least one microarray trait to submit.
+ {% elif '_NA' in chip_name %}
+
Sorry, the analysis was interrupted because your selections from GeneNetwork apparently include data from platform {{ chip_name }} which is unknown by GeneWeaver. Please reselect traits and submit again.
+ {% else %}
+
Sorry, an error occurred while submitting your traits to GeneWeaver.
+ {% endif %}
+
+ {% else %}
+
+
Opening GeneWeaver...
+
+
+ {% endif %}
+{% endblock %}
+{% block js %}
+{% if wrong_input == "False" %}
+
+{% endif %}
+{% endblock %}
\ No newline at end of file
diff --git a/gn2/wqflask/templates/genotype.html b/gn2/wqflask/templates/genotype.html
new file mode 100644
index 00000000..fc5b1ad7
--- /dev/null
+++ b/gn2/wqflask/templates/genotype.html
@@ -0,0 +1,87 @@
+{% extends "base.html" %}
+
+{% block css %}
+
+{% endblock %}
+
+{% block title %}Genotype: {{ name }}{% endblock %}
+
+{% block content %}
+
+
GN searched for the term(s) "{{ terms }}" in 754 datasets and 39,765,944 traits across 10 species
+ and found {{ trait_count }} results that match your query.
+ You can filter these results by adding key words in the fields below
+ and you can also sort results on most columns.
+
To study a record, click on its Record ID below. Check records below and click Add button to add to selection.
GN searched for the term(s) "{{ terms }}" in 51 datasets and 13763 traits across 10 species
+ and found {{ trait_count }} results that match your query.
+ You can filter these results by adding key words in the fields below
+ and you can also sort results on most columns.
+
To study a record, click on its ID below. Check records below and click Add button to add to selection.
+ The following heatmap is a work in progress. The heatmap for each trait runs horizontally (as opposed to vertically in GeneNetwork 1),
+ and hovering over a given trait's heatmap track will display its corresponding QTL chart below. White on the heatmap corresponds with a
+ low positive or negative z-score (darker when closer to 0), while light blue and red correspond to high negative and positive z-scores respectively.
+
+ There {%if anon_collections | length > 1%}are{%else%}is{%endif%}
+ {{anon_collections | length}} anonymous
+ collection{%if anon_collections | length > 1%}s{%endif%}
+ associated with your current session. What do you wish to do?
+
+
+
+ If you choose to ignore this, the anonymous collections will be
+ eventually deleted and lost.
+
+
+
+
+
+ {%endif%}
+
+
+
+
+
+
+
Select and Search
+
+
+
+
+
+
Advanced Commands
+
+
+
You can also use advanced commands. Copy these simple examples into the Get Any field for single term searches and Combined for searches with multiple terms:
+
+
+
POSITION=(chr1 25 30) finds genes, markers, or transcripts on
+ chromosome 1 between 25 and 30 Mb.
+
+
MEAN=(15 16) in the Combined field finds
+ highly expressed genes (15 to 16 log2 units)
+
+
RANGE=(1.5 2.5) in the Any field finds traits with values with a specified fold-range (minimum = 1).
+ Useful for finding "housekeeping genes" (1.0 1.2) or highly variable molecular assays (10 100).
+
+
LRS=(15 1000) or LOD=(2 8) finds all traits with peak LRS or LOD scores between lower and upper limits.
+
+
LRS=(9 999 Chr4 122 155) finds all traits on Chr 4 from 122 and 155 Mb with LRS scores between 9 and 999.
+
+
cisLRS=(15 1000 5) or cisLOD=(2 8 5) finds all cis eQTLs with peak LRS or LOD scores between lower and upper limits,
+ with an inclusion zone of 5 Mb around the parent gene.
+
+
transLRS=(15 1000 5) or transLOD=(2 8 5) finds all trans eQTLs with peak LRS or LOD scores between lower and upper limits,
+ with an exclusion zone of 5 Mb around the parent gene. You can also add a fourth term specifying which chromosome you want the transLRS to be on
+ (for example transLRS=(15 1000 5 7) would find all trans eQTLs with peak LRS on chromosome 7 that is also a trans eQTL with exclusionary zone of 5Mb).
+
+
POSITION=(Chr4 122 130) cisLRS=(9 999 10)
+ finds all traits on Chr 4 from 122 and 155 Mb with cisLRS scores
+ between 9 and 999 and an inclusion zone of 10 Mb.
+
+
RIF=mitochondrial searches RNA databases for
+ GeneRIF links.
+
+
WIKI=nicotine searches
+ GeneWiki for genes that you or other users have annotated
+ with the word nicotine.
+
+
GO:0045202 searches for synapse-associated genes listed in the
+
+ Gene Ontology.
+
+
RIF=diabetes LRS=(9 999 Chr2 100 105) transLRS=(9 999 10)
+ finds diabetes-associated transcripts with peak
+ trans eQTLs on Chr 2 between 100 and 105 Mb with LRS
+ scores between 9 and 999.
+
+
+
+
+
+
+
+
Tutorials
+
+
+
+ Webinars & Courses
+ In-person courses, live webinars and webinar recordings
+
+
+
+ Tutorials
+ Tutorials: Training materials in HTML, PDF and video formats
+
+
+ Please contact Zach Sloan (zachary.a.sloan@gmail.com) or Arthur Centeno
+ (acenteno@gmail.com) about the error.
+
+ {%else:%}
+
There is likely an issue with the genotype file associated with this group/RISet. Please contact Zach Sloan (zachary.a.sloan@gmail.com) or Arthur Centeno (acenteno@gmail.com) about the data set in question.
+
+
+
+
Try mapping using interval mapping instead; some genotype files with many columns of NAs have issues with GEMMA or R/qtl.
+
+
+ {% for marker in qtl_results %}
+ {% if (score_type == "LOD" and marker.lod_score > cutoff) or
+ (score_type == "LRS" and marker.lrs_value > cutoff) %}
+
+ {%if user_error is defined%}
+
+
+
+ {{user_error.error}}
+ {{user_error.error_description}}
+ {%else%}
+ No users found for this group
+ {%endif%}
+
+
+ {{flash_me()}}
+
+ {%if group_join_request is defined and group_join_request.exists %}
+
+
+
+ You have an active request to join a group.
+
+
+
+ You cannot create a group, or request to join a new group until your
+ currently active request has been either accepted or rejected.
+
+ {%else%}
+
You can
+
+ {%if groups | length > 0 %}
+
+
+ For most users, this is the preffered choice. You request access to an
+ existing group, and the group leader will chose whether or not to add you to
+ their group.
+
+
You can only be a member of a single group.
+
+
+
+
+
or
+ {%else%}
+
+
+
+ There an currently no groups to join.
+
+ {%endif%}
+
+
+
+ Creating a new group automatically makes you that group's administrator.
+
+
+ {%if "group:role:create-role" in user_privileges%}
+ New Group Role
+ {%endif%}
+
+ {%if group_roles_error is defined%}
+ {{display_error("Group Roles", group_role_error)}}
+ {%else%}
+
+ This error is not what we wanted to see. Unfortunately errors
+ are part of all software systems and we need to resolve this
+ together.
+
+
+ It is important to report this ERROR so we can fix it for everyone.
+
+
+
+ Report to the GeneNetwork team by recording the steps you take
+ to reproduce this ERROR. Next to those steps, copy-paste below
+ stack trace, either as
+ a new
+ issue or E-mail this full page to one of the developers
+ directly.
+
+
+
+
+ GeneNetwork error:
+ {{message}}
+
+
+ {%if command_id %}
+
+ Please provide the following information to help with
+ troubleshooting:
+ Command ID: {{command_id}}
+
+ {%endif%}
+
+
+ To check if this already a known issue, search the
+ issue
+ tracker.
+
We appreciate your interest, but unfortunately, we don't have any additional information available for: {{ name }}. If you have any other questions or need assistance with something else, please feel free to reach out to us.
+ {%else%}
+ {%if user.name == "Anonymous User"%}
+ {{display_error("Access Denied", {"error": "AuthorisationError", "error_description": "This trait is not accessible for the general public yet. Please log in."})}}
+ {%else%}
+ {{display_error("Access Denied", {"error": "AuthorisationError", "error_description": "The user '" + user.name + "', does not currently possess the appropriate privileges to view this trait. If you know the owner of this trait, please request that they grant you access, or wait until it is made public."})}}
+ {%endif%}
+ {%endif%}
+
+
+
+{% endblock %}
+
+{% block js %}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+{% endblock %}
diff --git a/gn2/wqflask/templates/show_trait_calculate_correlations.html b/gn2/wqflask/templates/show_trait_calculate_correlations.html
new file mode 100644
index 00000000..22fe6142
--- /dev/null
+++ b/gn2/wqflask/templates/show_trait_calculate_correlations.html
@@ -0,0 +1,165 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Chr:
+ Mb: to
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sample Correlation
+
The Sample Correlation
+ is computed
+ between trait data and any
+ other traits in the sample database selected above. Use
+ Spearman
+ Rank
+ when the sample size is small (<20) or when there are influential outliers.
+
Literature Correlation
+
The Literature Correlation
+ (Lit r) between
+ this gene and all other genes is computed
+ using the
+ Semantic Gene Organizer
+ and human, rat, and mouse data from PubMed.
+ Values are ranked by Lit r, but Sample r and Tissue r are also displayed.
+ More on using Lit r
+
Tissue Correlation
+
The Tissue Correlation
+ (Tissue r)
+ estimates the similarity of expression of two genes
+ or transcripts across different cells, tissues, or organs
+ (glossary).
+ Tissue correlations
+ are generated by analyzing expression in multiple samples usually taken from single cases.
+ Pearson and Spearman Rank correlations have been
+ computed for all pairs of genes using data from mouse samples.
+
+
+
+
diff --git a/gn2/wqflask/templates/show_trait_details.html b/gn2/wqflask/templates/show_trait_details.html
new file mode 100644
index 00000000..9c12393d
--- /dev/null
+++ b/gn2/wqflask/templates/show_trait_details.html
@@ -0,0 +1,255 @@
+
+
+
Group
+
{{ this_trait.dataset.group.species[0]|upper }}{{ this_trait.dataset.group.species[1:] }}: {{ this_trait.dataset.group.name }} group
+ {% for mapping_method in dataset.group.mapping_names %}
+ {% if mapping_method == "GEMMA" %}
+
+
+
+
+
+
+
+
+ {% if genofiles and genofiles|length>0 %}
+
+
+
+
+
+
+ {% endif %}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {% elif mapping_method == "QTLReaper" %}
+
+
+
+
+
+
+
+
+ {% if genofiles and genofiles|length>0 %}
+
+
+
+
+
+
+
+
+
+
+
+
+ {% else %}
+
+
+
+
+
+
+ {% endif %}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {% elif mapping_method == "R/qtl" %}
+
+
+
+
+
+
+
+
+ {% if genofiles and genofiles|length > 0 %}
+
+
+
+
+
+
+
+
+
+
+
+
+ {% else %}
+
+
+
+
+
+
+ {% endif %}
+
+
+
+
+
+
+ {% if sample_groups[0].attributes|length > 0 %}
+
+
+
+
+
+
+
+ {% endif %}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {% if genofiles and genofiles|length > 0 %}
+
+
+
+
+
+
+ {% endif %}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {% endif %}
+ {% endfor %}
+
+
+
+
+
+
+ {% for mapping_method in dataset.group.mapping_names %}
+ {% if mapping_method == "GEMMA" %}
+
GEMMA
+
GEMMA maps with correction for kinship using a linear mixed model and can include covariates such as sex and age. Defaults include a minor allele frequency of 0.05 and the leave-one-chromosome-out method (PMID: 2453419, and GitHub code).
+ {% elif mapping_method == "R/qtl" %}
+
R/qtl (version 1.44.9)
+
R/qtl maps using several models and uniquely support 4-way intercrosses such as the "Aging Mouse Lifespan Studies" (NIA UM-HET3). We will add support for R/qtl2 (PMID: 30591514) in 2023—a version that handles complex populations with admixture and many haplotypes.
+
Pair Scan (R/qtl v 1.44.9)
+
The Pair Scan mapping tool performs a search for joint effects of two separate loci that may influence a trait. This search typically requires large sample sizes. Pair Scans can included covariates such as age and sex. For more on this function by K. Broman and colleagues see www.rdocumentation.org/packages/qtl/versions/1.60/topics/scantwo
+ {% elif mapping_method == "QTLReaper" %}
+
Haley-Knott Regression
+
HK regression (QTL Reaper) is a fast mapping method with permutation that works well with F2 intercrosses and backcrosses (PMID 16718932), but is not recommended for admixed populations, advanced intercrosses, or strain families such as the BXDs (QTL Reaper code).
+ {% endif %}
+ {% endfor %}
+
+
+ More information on R/qtl mapping models and methods can be found here.
+
+
+
+
+
+
+ {% else %}
+ Mapping options are disabled for data not matched with genotypes.
+ {% endif %}
+
diff --git a/gn2/wqflask/templates/show_trait_progress_bar.html b/gn2/wqflask/templates/show_trait_progress_bar.html
new file mode 100644
index 00000000..f9a34070
--- /dev/null
+++ b/gn2/wqflask/templates/show_trait_progress_bar.html
@@ -0,0 +1,35 @@
+
+
+
+
+
Loading...
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Loading... (Estimated time ~10-15m)
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/gn2/wqflask/templates/show_trait_statistics.html b/gn2/wqflask/templates/show_trait_statistics.html
new file mode 100644
index 00000000..9ee0de5c
--- /dev/null
+++ b/gn2/wqflask/templates/show_trait_statistics.html
@@ -0,0 +1,106 @@
+
Sorry, the analysis was interrupted because your selections from GeneNetwork apparently include data from more than one array platform (i.e., Affymetrix U74A and M430 2.0). Most WebGestalt analyses assume that you are using a single array type and compute statistical values on the basis of that particular array. Please reselect traits from a signle platform and submit again.
+ {% elif chip_name == "not_microarray" %}
+
You need to select at least one microarray trait to submit.
+ {% elif '_NA' in chip_name %}
+
Sorry, the analysis was interrupted because your selections from GeneNetwork apparently include data from platform {{ chip_name }} which is unknown by WebGestalt. Please reselect traits and submit again.
+ {% else %}
+
Sorry, an error occurred while submitting your traits to WebGestalt.
+ {% if request.form['trait_list'].split(",")|length < 4 %}
+
+ Error:
+
Too few phenotypes as input
+ Please make sure you select enough phenotypes / genes to perform WGCNA. Your collection needs to contain at least 4 different phenotypes. You provided {{request.form['trait_list'].split(',')|length}} phenotypes as input.
+
+ {% else %}
+
+ {% endif %}
+
+
+
+
+
+
+
+
+
+
+
+
+{% endblock %}
\ No newline at end of file
diff --git a/gn2/wqflask/templates/with-trait-items.html b/gn2/wqflask/templates/with-trait-items.html
new file mode 100644
index 00000000..66d6fd22
--- /dev/null
+++ b/gn2/wqflask/templates/with-trait-items.html
@@ -0,0 +1,18 @@
+{%for trait in traits_list:%}
+
+ 
+ 
+ 
+ 
+ 
+ 
\ No newline at end of file
diff --git a/gn2/wqflask/templates/show_trait.html b/gn2/wqflask/templates/show_trait.html
new file mode 100644
index 00000000..dd054ffc
--- /dev/null
+++ b/gn2/wqflask/templates/show_trait.html
@@ -0,0 +1,276 @@
+{% extends "base.html" %}
+{%from "oauth2/display_error.html" import display_error%}
+
+{% block title %}Trait Data and Analysis{% endblock %}
+
+{% block css %}
+
+
+
+
+
+
+
+
+
+
+
+{% endblock %}
+
+{% block content %}
+
+ {{flash_me()}}
+ {%if "group:resource:view-resource" in trait_privileges or "system:resource:public-read" in trait_privileges%}
+ 