docs: explicitly cite ACMG standards and related tools (#399) (#424)

docs/dev_quickstart.rst

@@ -13,7 +13,7 @@ The following assumes a Debian/Ubuntu machine; your mileage may vary.
 Prerequisites
 -------------
 
-You can use `pyenv <https://github.com/pyenv/pyenv>`__` for getting a specific python version.
+You can use `pyenv <https://github.com/pyenv/pyenv>`__ for getting a specific python version.
 
 .. code-block:: bash
 

docs/doc_manual.rst

@@ -159,9 +159,9 @@ Sequence Variants
 Clinical Significance
 =====================
 
-This card shows the semi-automated pathogenicity prediction based on InterVar.
+This card shows the semi-automated pathogenicity prediction based on InterVar\ :footcite:p:`Li:2017`.
 Using the buttons on the left you can...
-- Hide/show the terse mode of ACMG criteria display.
+- Hide/show the terse mode of ACMG criteria\ :footcite:p:`Richards:2015` display.
 On show this will provide you an overview of just the different criteria and their evidence level, on hide you will see the full display also providing a description on every ACMG criterion and how it should be used.
 - hide/show failed criteria (not set to "active" by the little switch displayed left to every criterion)
 
@@ -287,8 +287,8 @@ By clicking on the “Jump in local IGV” button on the bottom, you can also lo
 Clinical Significance
 =====================
 
-This card shows the semi-automated pathogenicity prediction based on AutoCNV.
-Using the buttons to the left of each criterion you can select or deselect every ACMG CNV criterion.
+This card shows the semi-automated pathogenicity prediction based on AutoCNV\ :footcite:p:`Fan:2021`.
+Using the buttons to the left of each criterion you can select or deselect every ACMG CNV criterion\ :footcite:p:`Riggs:2020`.
 The semi-automated prediction is providing an automated scoring for criteria 1-3 while you always have to select criteria 4 and 5 manually based on your clinical information on the case.
 On default you will see the automated selection of ACMG criteria.
 You can individually select and deselect every ACMG CNV criterion using the little switch displayed left to every criterion and also select the individual points you score on this criterion.

docs/doc_quickstart.rst

@@ -16,7 +16,7 @@ Introduction
 ------------
 
 We have developed REEV (Review, Evaluate and Explain Variants) to help clinicians and researchers in rare disease genetics with an open and free platform to explore comprehensive information about genes as well as sequence and structural variants.
-REEV is designed to help you to quickly yet comprehensively record all the important information on a variant and, on the basis of this, to evaluate it on the basis of the ACMG criteria.
+REEV is designed to help you to quickly yet comprehensively record all the important information on a variant and, on the basis of this, to evaluate it on the basis of the ACMG criteria\ :footcite:p:`Richards:2015`.
 To this end, REEV also integrates methods that allow users to (semi-)automatically fill the individual ACMG criteria sections to facilitate interpretation.
 
 Here, we provide a quick start as well as a full tutorial guiding you through the process on reviewing and evaluating genes and variants with the help of REEV.
@@ -131,7 +131,7 @@ We will thus focus on the second half of the page shown in the following figure.
 
 You can find the following elements on the page:
 
-1. Semi-automated ACMG variant class assessment based on the InterVar tool.
+1. Semi-automated ACMG variant class assessment based on the InterVar\ :footcite:p:`Li:2017` tool.
 2. A table with the impact of the variant on different transcripts.
 3. Information with ClinVar assertions on the variant.
    This displays the ClinVar reference assertion with the most pathogenic significance and its review status.
@@ -201,7 +201,7 @@ This is shown in the following figure.
 5. The variants will be sorted by reciprocal overlap (the fraction of the overlap of the variant - yours and the ClinVar one - and the large of the variant lenghts).
    This is useful to find the "best fitting" one.
 6. Open the location of the variant in an external genome browser or an external tool for further analysis.
-7. Semi-automated assessment of the variant following ACMG standards using the AutoCNV tool.
+7. Semi-automated assessment of the variant following ACMG standards\ :footcite:p:`Richards:2015` using the AutoCNV\ :footcite:p:`Fan:2021` tool.
 8. See the location of the variant in an internal genome browser with useful tracks for interpreting the variant.
 
 For more details, see the section :ref:`doc_tutorial_strucvar` of the :ref:`doc_tutorial` or use the little (?) help icons on the page.

docs/doc_tutorial.rst

@@ -125,7 +125,7 @@ In line with the low Z-score retrieved from the gnomAD database, we see that mos
 However, indeed there are also some pathogenic missense variants.
 Time to look at the variant level and find out whether our missense variant is one of these few!
 
-The semi-automated ACMG variant class assessment based on the InterVar tool tells us: we don't know…
+The semi-automated ACMG variant class assessment\ :footcite:p:`Richards:2015` based on the InterVar\ :footcite:p:`Li:2017` tool tells us: we don't know…
 
 .. figure:: img/tutorial/clinvar-significance-card-uncertain.png
    :width: 100%
@@ -232,10 +232,10 @@ Let's scroll back up to the ACMG rating tool and check the criteria ultimately a
    :width: 100%
    :align: center
 
-PM1, PM2, PP3 and BP1 had already been automatically selected by the InterVar tool.
+PM1, PM2, PP3 and BP1 had already been automatically selected by the InterVar\ :footcite:p:`Li:2017` tool.
 However, we were able to adjust PM2 to supporting and PP3 to strong level now with the help of REEV.
 Additionally, we applied the PS4 criterion on supporting level as we noticed that this variant is already listed in ClinVar as a pathogenic variant with one star.
-So solely by the help of the information provided by REEV we were able to correct the InterVar scoring as a variant of uncertain significance to a likely pathogenic variant!
+So solely by the help of the information provided by REEV we were able to correct the InterVar\ :footcite:p:`Li:2017` scoring as a variant of uncertain significance to a likely pathogenic variant!
 
 But, of course, since we also have some clinical information on our patient at hand, we can additionally state the patient's phenotype fits the *GLI3*-related spectrum.
 However, there are more genes than just *GLI3* causing polydactyly, so that we apply the PP4 criterion on supporting level only.
@@ -329,13 +329,13 @@ But the links to ENSEMBL and UCSC (2) tell us that this variant affects exon 3 o
 We also see that this deletion is absent from healthy controls in gnomAD (3).
 
 I think we can all agree: a very hot variant!
-But can we rate it pathogenic according to the `ACMG and ClinGen recommendations for copy-number variants <https://doi.org/10.1038/s41436-019-0686-8>`__?
+But can we rate it pathogenic according to the ACMG and ClinGen recommendations for copy-number variants\ :footcite:p:`Riggs:2020`?
 
 .. figure:: img/tutorial/strucvar-acmg-rating.png
    :width: 100%
    :align: center
 
-Semi-automated prediction from `autoCNV <https://doi.org/10.1186/s12864-021-08011-4>`__ implemented in REEV tells us it still remains unclear.
+Semi-automated prediction from AutoCNV\ :footcite:p:`Fan:2021` implemented in REEV tells us it still remains unclear.
 We have to use our knowledge gathered from the REEV information above and our clinical knowledge on this case, let's go through the CNV criteria!
 
 .. figure:: img/tutorial/strucvar-acmg-rating-show-failed.png
@@ -399,3 +399,5 @@ Just click the corresponding button the login screen.
   Go to  https://orcid.org for more information.
 - LifeScience Research Infrastructure allows researchers from the European Union to login with the account from their home organization.
   See https://lifescience-ri.eu/ls-login/ for more information.
+
+.. footbibliography::

docs/index.rst

@@ -26,18 +26,20 @@ It provides the following features as a central resource:
     - ClinVar variant information
     - gnomAD population frequency
     - UCSC 100 vertebrate conservation
-    - semi-automated ACMG classification from InterVar
+    - semi-automated ACMG classification from InterVar\ :footcite:p:`Li:2017`
     - link-out to external resources and tools
     - query the GA4GH beacon network for the variant
     - query the VariantValidator API for the variant
 
 - structural variant (currently copy number loss/gain only)-related functionality:
     - consequences on overlapping genes
     - information on overlapping structural variants in ClinVar
-    - semi-automated ACMG classification from AutoCNV
+    - semi-automated ACMG classification from AutoCNV\ :footcite:p:`Fan:2021`
     - link-out to external resources and tools
     - integrated genome browser with useful tracks for interpreting the variant
 
+.. footbibliography::
+
 .. toctree::
     :hidden:
     :maxdepth: 1

docs/refs.bib

@@ -1,3 +1,47 @@
+@Article{Richards:2015,
+   Author="Richards, S.  and Aziz, N.  and Bale, S.  and Bick, D.  and Das, S.  and Gastier-Foster, J.  and Grody, W. W.  and Hegde, M.  and Lyon, E.  and Spector, E.  and Voelkerding, K.  and Rehm, H. L. ",
+   Title="{{S}tandards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the {A}merican {C}ollege of {M}edical {G}enetics and {G}enomics and the {A}ssociation for {M}olecular {P}athology}",
+   Journal="Genet Med",
+   Year="2015",
+   Volume="17",
+   Number="5",
+   Pages="405--424",
+   Month="May"
+}
+
+@Article{Li:2017,
+   Author="Li, Q.  and Wang, K. ",
+   Title="{{I}nter{V}ar: {C}linical {I}nterpretation of {G}enetic {V}ariants by the 2015 {A}{C}{M}{G}-{A}{M}{P} {G}uidelines}",
+   Journal="Am J Hum Genet",
+   Year="2017",
+   Volume="100",
+   Number="2",
+   Pages="267--280",
+   Month="Feb"
+}
+
+@Article{Riggs:2020,
+   Author="Riggs, E. R.  and Andersen, E. F.  and Cherry, A. M.  and Kantarci, S.  and Kearney, H.  and Patel, A.  and Raca, G.  and Ritter, D. I.  and South, S. T.  and Thorland, E. C.  and Pineda-Alvarez, D.  and Aradhya, S.  and Martin, C. L. ",
+   Title="{{T}echnical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the {A}merican {C}ollege of {M}edical {G}enetics and {G}enomics ({A}{C}{M}{G}) and the {C}linical {G}enome {R}esource ({C}lin{G}en)}",
+   Journal="Genet Med",
+   Year="2020",
+   Volume="22",
+   Number="2",
+   Pages="245--257",
+   Month="Feb"
+}
+
+@Article{Fan:2021,
+   Author="Fan, C.  and Wang, Z.  and Sun, Y.  and Sun, J.  and Liu, X.  and Kang, L.  and Xu, Y.  and Yang, M.  and Dai, W.  and Song, L.  and Wei, X.  and Xiang, J.  and Huang, H.  and Zhou, M.  and Zeng, F.  and Huang, L.  and Xu, Z.  and Peng, Z. ",
+   Title="{{A}uto{C}{N}{V}: a semiautomatic {C}{N}{V} interpretation system based on the 2019 {A}{C}{M}{G}/{C}lin{G}en {T}echnical {S}tandards for {C}{N}{V}s}",
+   Journal="BMC Genomics",
+   Year="2021",
+   Volume="22",
+   Number="1",
+   Pages="721",
+   Month="Oct"
+}
+
 @Article{pejaver:2022,
    Author="Pejaver, V.  and Byrne, A. B.  and Feng, B. J.  and Pagel, K. A.  and Mooney, S. D.  and Karchin, R.  and O'Donnell-Luria, A.  and Harrison, S. M.  and Tavtigian, S. V.  and Greenblatt, M. S.  and Biesecker, L. G.  and Radivojac, P.  and Brenner, S. E.  and Biesecker, L. G.  and Harrison, S. M.  and Tayoun, A. A.  and Berg, J. S.  and Brenner, S. E.  and Cutting, G. R.  and Ellard, S.  and Greenblatt, M. S.  and Kang, P.  and Karbassi, I.  and Karchin, R.  and Mester, J.  and O'Donnell-Luria, A.  and Pesaran, T.  and Plon, S. E.  and Rehm, H. L.  and Strande, N. T.  and Tavtigian, S. V.  and Topper, S. ",
    Title="{{C}alibration of computational tools for missense variant pathogenicity classification and {C}lin{G}en recommendations for {P}{P}3/{B}{P}4 criteria}",
@@ -19,4 +63,3 @@ @Article{walker:2023
    Pages="1046--1067",
    Month="Jul"
 }
-