diff --git a/quarto-to-wiki.qmd b/quarto-to-wiki.qmd index fc2f09a..36820ea 100644 --- a/quarto-to-wiki.qmd +++ b/quarto-to-wiki.qmd @@ -12,7 +12,7 @@ https://scilifelab.atlassian.net/wiki/spaces/EBT/pages/2899836938/Quarto+example Give the page a name on this format with your name to make it unique: "Quarto_example-Tomas" ## Quarto document -Verena (reviewer Jason) +Verena (reviewer Jason): `Training-Tech-shorts/quarto-to-wiki/quarto-example-verena/quarto-example-verena.qmd` ## Quarto document Cormac (reviewer Guilherme) diff --git a/quarto-to-wiki/quarto-example-verena/_extensions/nbis/_extension.yml b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/_extension.yml new file mode 100644 index 0000000..6379b22 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/_extension.yml @@ -0,0 +1,23 @@ +title: NBIS Quarto Extension +author: Erik Fasterius +version: 1.0.0 +quarto-required: ">=1.2.0" +contributes: + formats: + html: + theme: [default, nbis.scss] + highlight-style: nbis.theme + toc: true + toc-location: left + toc-title: + df-print: paged + code-fold: true + smooth-scroll: true + embed-resources: true + code-line-numbers: true + revealjs: + theme: [default, nbis.scss] + logo: nbis-logo-green.svg + slide-number: false + scrollable: true + embed-resources: true diff --git a/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis-logo-green.svg b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis-logo-green.svg new file mode 100644 index 0000000..f829660 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis-logo-green.svg @@ -0,0 +1,3 @@ + + +image/svg+xml \ No newline at end of file diff --git a/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis.scss b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis.scss new file mode 100644 index 0000000..6e56576 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis.scss @@ -0,0 +1,162 @@ + +/*-- scss:defaults --*/ + +$theme: "nbis" !default; + +// NBIS base colours +$green: #92BB55 !default; +$blue: #0093BD !default; +$orange: #EF7C00 !default; + +// NBIS light colours +$lgreen: #F8FFE5 !default; +$lblue: #E5F9FF !default; +$lorange: #FFF2E5 !default; + +// Extra colours +$red: #BB5454 !default; +$yellow: #BBBB54 !default; +$purple: #AF75A7 !default; + +// Greyscale colours +$white: #FFFFFF !default; +$grey-100: #F5F5F5 !default; +$grey-90: #E5E5E5 !default; +$grey-80: #CCCCCC !default; +$grey-70: #B2B2B2 !default; +$grey-60: #999999 !default; +$grey-50: #7F7F7F !default; +$grey-40: #666666 !default; +$grey-30: #4C4C4C !default; +$grey-20: #333333 !default; +$grey-10: #191919 !default; +$black: #000000 !default; + +// Theme +$primary: $green !default; +$secondary: $blue !default; +$light: $grey-100 !default; +$dark: $grey-30 !default; +$success: $green !default; +$info: $purple !default; +$warning: $yellow !default; +$danger: $red !default; + +// Code +$code-color: $primary !default; +$code-bg: $light !default; +$code-block-bg: $light !default; + +// Links +$link-color: $primary !default; + +// Popover +$popover: $light !default; + +// Dropdowns +$dropdown-link-color: $grey-30 !default; +$dropdown-link-hover-color: $white !default; +$dropdown-link-hover-bg: $primary !default; + +// Fonts +$font-family-sans-serif: Lato, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol" !default; +$h1-font-size: 2.5rem !default; +$h2-font-size: 2.0rem !default; +$h3-font-size: 1.5rem !default; + +// Tables +$table-bg-scale: 0 !default; + +// Navs +$nav-link-padding-y: .5rem !default !default; +$nav-link-padding-x: 2rem !default; +$nav-link-disabled-color: $grey-40 !default !default; +$nav-tabs-border-color: $grey-80 !default; + +// Navbar +$navbar-padding-y: 1rem !default; +$navbar-light-bg: $primary !default; +$navbar-light-color: $white !default; +$navbar-light-hover-color: $success !default; +$navbar-light-active-color: $success !default; +$navbar-light-brand-color: $white !default; +$navbar-light-brand-hover-color: $navbar-light-brand-color !default; +$navbar-dark-color: $white !default; +$navbar-dark-hover-color: $primary !default; +$navbar-dark-active-color: $primary !default; +$navbar-dark-brand-color: $white !default; +$navbar-dark-brand-hover-color: $navbar-dark-brand-color !default; + +// Pagination +$pagination-color: $white !default; +$pagination-bg: $success !default; +$pagination-border-width: 0 !default; +$pagination-border-color: transparent !default; +$pagination-hover-color: $white !default; +$pagination-hover-bg: darken($success, 15%) !default; +$pagination-hover-border-color: transparent !default; +$pagination-active-bg: $pagination-hover-bg !default; +$pagination-active-border-color: transparent !default; +$pagination-disabled-color: $grey-80 !default; +$pagination-disabled-bg: lighten($success, 15%) !default; +$pagination-disabled-border-color: transparent !default; + +// List group +$list-group-hover-bg: $grey-80 !default; +$list-group-disabled-bg: $grey-80 !default; + +// Close +$btn-close-color: $white !default; +$btn-close-opacity: .4 !default; +$btn-close-hover-opacity: 1 !default; + +/*-- scss:rules --*/ + +// Variables +$web-font-path: "https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,400;0,700;1,400&display=swap" !default; +@if $web-font-path { + @import url($web-font-path); +} + +// Navbar +.bg-primary { + .navbar-nav .show > .nav-link, + .navbar-nav .nav-link.active, + .navbar-nav .nav-link:hover, + .navbar-nav .nav-link:focus { + color: $success !important; + } +} + +// Navs +.nav-tabs { + .nav-link.active, + .nav-link.active:focus, + .nav-link.active:hover, + .nav-item.open .nav-link, + .nav-item.open .nav-link:focus, + .nav-item.open .nav-link:hover { + color: $primary; + } +} + +// Pagination +.pagination { + a:hover { + text-decoration: none; + } +} + +// Blockquotes +.blockquote { + color: $lgreen; + border-left-color: $primary; +} + +// Cell Output +.cell-output-error > pre > code { + color: $red; +} +.cell-output-stderr > pre > code { + color: $yellow; +} diff --git a/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis.theme b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis.theme new file mode 100644 index 0000000..4e17af6 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/_extensions/nbis/nbis.theme @@ -0,0 +1,211 @@ +{ + "text-color": null, + "background-color": null, + "line-number-color": "#aaaaaa", + "line-number-background-color": null, + "text-styles": { + "Other": { + "text-color": "#9e9370", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Attribute": { + "text-color": "#000000", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "SpecialString": { + "text-color": "#bb6688", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, 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"bold": true, + "italic": false, + "underline": false + }, + "Variable": { + "text-color": "#005c86", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "BuiltIn": { + "text-color": "#005c86", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Extension": { + "text-color": null, + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Preprocessor": { + "text-color": "#BBBB54 ", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Information": { + "text-color": "#005c86", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + }, + "VerbatimString": { + "text-color": "#005c86", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Warning": { + "text-color": "#BBBB54 ", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + }, + "Documentation": { + "text-color": "#BB5454 ", + "background-color": null, + "bold": false, + "italic": true, + "underline": false + }, + "Import": { + "text-color": "#BB5454 ", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + }, + "Char": { + "text-color": "#af75a7 ", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "DataType": { + "text-color": "#9e9370", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Float": { + "text-color": "#EF7C00 ", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Comment": { + "text-color": "#B2B2B2 ", + "background-color": null, + "bold": false, + "italic": true, + "underline": false + }, + "CommentVar": { + "text-color": "#B2B2B2 ", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + }, + "Constant": { + "text-color": "#129379", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "SpecialChar": { + "text-color": "#129379", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "DecVal": { + "text-color": "#EF7C00 ", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Keyword": { + "text-color": "#BB5454 ", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + } + } +} diff --git a/quarto-to-wiki/quarto-example-verena/environment.yml b/quarto-to-wiki/quarto-example-verena/environment.yml new file mode 100644 index 0000000..a7fc69f --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/environment.yml @@ -0,0 +1,18 @@ +name: quarto-env +channels: + - bioconda + - conda-forge +dependencies: + - python=3.8.15 + - biopython=1.83 + - matplotlib=3.4.2 + - pandas=2.0.3 + - numpy=1.24.4 + - jupyter + - r-base + - r-ggplot2 + - r-rmarkdown + - r-reticulate + - nb_conda_kernels + - plotly + - scipy=1.7.0 diff --git a/quarto-to-wiki/quarto-example-verena/island.txt b/quarto-to-wiki/quarto-example-verena/island.txt new file mode 100644 index 0000000..64b9a89 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/island.txt @@ -0,0 +1,4 @@ +mlRho/O1.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt +mlRho/O11.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt +mlRho/O15.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt +mlRho/O16.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt \ No newline at end of file diff --git a/quarto-to-wiki/quarto-example-verena/mainland.txt b/quarto-to-wiki/quarto-example-verena/mainland.txt new file mode 100644 index 0000000..d24bcb1 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mainland.txt @@ -0,0 +1,5 @@ +mlRho/F34.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt +mlRho/F41.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt +mlRho/F42.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt +mlRho/F43.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt +mlRho/F44.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt \ No newline at end of file diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/F34.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/F34.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..c885aad --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/F34.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1119932672 7.35e-04<7.37e-04<7.38e-04 1.49e-04<1.49e-04<1.49e-04 1.54e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/F41.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/F41.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..8883823 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/F41.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1251211204 7.38e-04<7.39e-04<7.41e-04 1.42e-04<1.42e-04<1.43e-04 1.73e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/F42.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/F42.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..7b345f0 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/F42.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1140864574 7.16e-04<7.18e-04<7.20e-04 1.37e-04<1.37e-04<1.37e-04 1.58e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/F43.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/F43.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..1994e9e --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/F43.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1222080282 7.08e-04<7.09e-04<7.11e-04 1.32e-04<1.32e-04<1.33e-04 1.69e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/F44.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/F44.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..b97d205 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/F44.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1201034876 7.29e-04<7.31e-04<7.32e-04 1.37e-04<1.38e-04<1.38e-04 1.66e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/O1.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/O1.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..9c0589b --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/O1.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1178056169 6.99e-04<7.01e-04<7.03e-04 1.41e-04<1.42e-04<1.42e-04 1.63e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/O11.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/O11.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..12b46cd --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/O11.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1177834015 5.92e-04<5.94e-04<5.95e-04 1.21e-04<1.21e-04<1.21e-04 1.62e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/O15.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/O15.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..5a603c0 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/O15.merged.rmdup.merged.realn.mapped_q30.subs_dp6.5.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1188951750 7.35e-04<7.37e-04<7.39e-04 1.24e-04<1.24e-04<1.24e-04 1.64e+09 diff --git a/quarto-to-wiki/quarto-example-verena/mlRho/O16.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt b/quarto-to-wiki/quarto-example-verena/mlRho/O16.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt new file mode 100644 index 0000000..b5b7808 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/mlRho/O16.merged.rmdup.merged.realn.noCpG_ref.repma.autos.mlRho.txt @@ -0,0 +1,2 @@ +d n theta epsilon -log(L) +0 1071917495 6.86e-04<6.88e-04<6.89e-04 1.45e-04<1.45e-04<1.45e-04 1.48e+09 diff --git a/quarto-to-wiki/quarto-example-verena/quarto-example-verena.html b/quarto-to-wiki/quarto-example-verena/quarto-example-verena.html new file mode 100644 index 0000000..24a5a39 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/quarto-example-verena.html @@ -0,0 +1,3873 @@ + + + + + + + + + + + +Quarto example - Verena + + + + + + + + + + + + + + + + + + + + +
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Quarto example - Verena

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Some text, plots and tables from a NBIS user-fee project

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Author
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Verena Kutschera

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Published
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May 14, 2024

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Data

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Library construction and sequencing (from NGI report)

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Libraries were construction using the Illumina TruSeq PCR-free library prep kit. Samples were sequenced on NovaSeq6000 in a ‘S4’ mode flowcell. Bcl to FastQ conversion was performed using bcl2fastq_v2.20.0.422 from the CASAVA software suite.

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  • Raw data location on Rackham: /proj/snic2020-X-XXX/private/DataDelivery_2023-XX-XX_XX-XX-XX_ngisthlm00XXX/files/PXXXXX +
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    • Please note that NBIS recommends to submit raw data to ENA as soon as possible
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+Table 1: Sample metadata +
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Sample_IDNGI_IDMreads>=Q30DateSex
F34PXXXXX_113109.6590.042022F
F41PXXXXX_114119.4889.872022M
F42PXXXXX_11591.8889.912022F
F43PXXXXX_116106.5189.932022F
F44PXXXXX_117112.1589.752022M
O1PXXXXX_101110.4289.802022M
O11PXXXXX_203136.6190.222022M
O15PXXXXX_107110.2589.982022M
O16PXXXXX_108102.6789.622022F
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Sample_ID: sample identifier used for sampling and laboratory work. NGI_ID: NGI sample identifier, Mreads:total million read pairs for a sample,>=Q30: aggregated percentage of bases that have a quality score >= Q30, Longitude / Latitude: geographic sampling location, Date: sampling year, Sex: M=male, F=female

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Some methods

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Whole-genome sequencing data processing

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Whole-genome sequencing data from nine individuals (Table 1) were processed and analysed using GenErode v0.6.0 (Kutschera et al. 2022) with default settings if not otherwise described.

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Some results

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Population structure

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FST was calculated in 50 kb windows between island and mainland samples. The mean FST across all 50 kb windows (with zero missing data allowed) was 0.036.

+
+
+Code +
import os
+import pandas as pd
+import numpy as np
+
+def mean_fst(fst_file):
+    fst_df = pd.read_table(fst_file, delim_whitespace=True, header=0)
+    mean_fst = fst_df['MEAN_FST'].mean()
+    print(mean_fst)
+
+fst = "windowed.weir.fst"
+
+mean_fst(fst)
+
+
+
0.03610024109357622
+
+
+
+
+

Genome-wide heterozygosity

+

We estimated individual heterozygosity calculated as the number of heterozygote sites (i.e., SNPs) per site (theta = 4Neμ) using mlRho.

+
+
+Code +
import pandas as pd
+import scipy.stats as stats
+
+def mlRho_dataframe(inputfiles):
+    # define a function to get the input data
+    dataframes = []
+    for infile in inputfiles:
+        dataset = str(infile).split(".")[0]
+        with open(infile) as f:
+            for i in f: # create one dataframe per input file
+                path_list = i.split("/")
+                sample = path_list[-1].split(".")[0]
+                genomeregion = path_list[-1].split(".")[-3]
+                if genomeregion == "genome":
+                    genomeregion = "genomewide"
+                elif genomeregion == "autos":
+                    genomeregion = "autosomes"
+                elif genomeregion == "sexchr":
+                    genomeregion = "sexchromosomes"
+                sample_df=pd.read_table(i.strip(), delim_whitespace=True, comment='#')
+                sample_df.insert(0, 'dataset', [dataset]) 
+                sample_df.insert(1, 'sample', [sample])
+                sample_df.insert(2, 'genomeregion', [genomeregion])
+                dataframes.append(sample_df)
+    conc_df = pd.concat(dataframes) # concatenate all dataframes
+    conc_df.reset_index(drop=True, inplace=True)
+    conc_df = conc_df.sort_values(by = ['dataset', 'sample'], ascending = [True, True])
+    return conc_df
+
+def process_mlrho_dataframe(mlrho_df):
+    # function to read in and process the data
+    mlrho_df['lower_theta'] = mlrho_df['theta'].str.split('<').str[0].astype(float)
+    mlrho_df['theta_est'] = mlrho_df['theta'].str.split('<').str[1].astype(float)
+    mlrho_df['upper_theta'] = mlrho_df['theta'].str.split('<').str[2].astype(float)
+    grouped_df = mlrho_df.groupby('dataset')
+    return mlrho_df, grouped_df
+
+def mann_whitney_u(statistic, dataset1, list1, dataset2, list2):
+    # perform Wilcoxon rank-sum test (equivalent to Mann-Whitney U test)
+    print('**** Mann-Whitney U test of',statistic,'****\n')
+    stat, p_value = stats.mannwhitneyu(list1, list2) 
+    print('\tU=%.2f, p=%.2f' % (stat, p_value)) 
+
+    # Level of significance 
+    alpha = 0.05
+    # conclusion
+    if p_value < alpha: 
+        print('\tRejecting Null Hypothesis (Significant difference between the two samples)') 
+    else: 
+        print('\tCannot Reject Null Hypothesis (No significant difference between the two samples)')
+
+    print('\n\tSamples and sample sizes:\n\t', dataset1,'n =',len(list1), '\n\t', dataset2, 'n =', len(list2))
+
+# read in the data
+infiles = ['mainland.txt', 'island.txt']
+
+# apply the functions
+mlRho_df = mlRho_dataframe(infiles)
+tabledf, groupeddf = process_mlrho_dataframe(mlRho_df)
+
+listdf = tabledf.groupby('dataset')['theta_est'].apply(list)
+
+print('Mainland:')
+print('\tMedian heterozygosity %.6f' % (pd.Series(listdf['mainland']).median()))
+print('\tMinimum heterozygosity %.6f' % (pd.Series(listdf['mainland']).min()))
+print('\tMaximum heterozygosity %.6f' % (pd.Series(listdf['mainland']).max()), '\n')
+
+print('Island:')
+print('\tMedian heterozygosity %.6f' % (pd.Series(listdf['island']).median()))
+print('\tMinimum heterozygosity %.6f' % (pd.Series(listdf['island']).min()))
+print('\tMaximum heterozygosity %.6f' % (pd.Series(listdf['island']).max()), '\n')
+
+mann_whitney_u('Heterozygosity', 'Mainland', listdf['mainland'], 'Island', listdf['island'])
+
+
+
Mainland:
+    Median heterozygosity 0.000731
+    Minimum heterozygosity 0.000709
+    Maximum heterozygosity 0.000739 
+
+Island:
+    Median heterozygosity 0.000695
+    Minimum heterozygosity 0.000594
+    Maximum heterozygosity 0.000737 
+
+**** Mann-Whitney U test of Heterozygosity ****
+
+    U=16.50, p=0.14
+    Cannot Reject Null Hypothesis (No significant difference between the two samples)
+
+    Samples and sample sizes:
+     Mainland n = 5 
+     Island n = 4
+
+
+
+
+Code +
import matplotlib.pyplot as plt
+import pandas as pd
+from matplotlib import cm
+from matplotlib.colors import ListedColormap, LinearSegmentedColormap
+
+def theta_plot(dataframe, grouped, dataset, ax, color):
+    # function for the subplots
+    data_df = grouped.get_group(dataset)
+    for lower,theta,upper,x in zip(data_df['lower_theta'],data_df['theta_est'],data_df['upper_theta'],range(len(data_df))):
+        ax.plot((x,x),(lower,upper),'-',color=color)
+        ax.plot((x),(theta),'o',color=color)
+    ax.set_xticks(range(len(data_df)))
+    ax.set_xticklabels(list(data_df['sample']), rotation = 45)
+
+def combine_theta_plots(dataframe, grouped):
+    # function to create the combined plot
+    cividis = cm.get_cmap('cividis', 256) # create the cividis colormap
+    fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)
+    ax1.set_xlabel("Mainland", fontsize=12)
+    ax2.set_xlabel("Island", fontsize=12)
+    theta_plot(dataframe, grouped, "mainland", ax1, cividis.colors[200])
+    theta_plot(dataframe, grouped, "island", ax2, cividis.colors[80])
+    fig.supxlabel('samples') # x axis label
+
+    fig.supylabel('theta', fontsize=12) # common y axis label
+    fig.align_labels() # align axis labels
+
+    plt.tight_layout()
+    plt.show()
+
+combine_theta_plots(tabledf, groupeddf)
+
+
+
+
+
+ +
+
+Figure 1: Genome-wide autosomal individual heterozygosity of nine individuals. Theta = 4Neμ = number of heterozygous sites per site. Error bars represent 95% confidence intervals. +
+
+
+
+
+
+
+
+

References

+
+
+Kutschera, Verena E., Marcin Kierczak, Tom van der Valk, Johanna von Seth, Nicolas Dussex, Edana Lord, Marianne Dehasque, et al. 2022. “GenErode: A Bioinformatics Pipeline to Investigate Genome Erosion in Endangered and Extinct Species.” BMC Bioinformatics 23: 1–17. https://doi.org/10.1186/S12859-022-04757-0. +
+
+
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/quarto-to-wiki/quarto-example-verena/quarto-example-verena.qmd b/quarto-to-wiki/quarto-example-verena/quarto-example-verena.qmd new file mode 100644 index 0000000..a22b6a7 --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/quarto-example-verena.qmd @@ -0,0 +1,207 @@ +--- +title: Quarto example - Verena +subtitle: Some text, plots and tables from a NBIS user-fee project +author: Verena Kutschera +date: today +format: + nbis-html: default +engine: jupyter +jupyter: python3 +bibliography: ../../references.bib +--- + +## Data + +### Library construction and sequencing (from NGI report) + +Libraries were construction using the Illumina TruSeq PCR-free +library prep kit. Samples were sequenced on NovaSeq6000 in a +'S4' mode flowcell. Bcl to FastQ conversion was performed +using bcl2fastq_v2.20.0.422 from the CASAVA software suite. + +- Raw data location on Rackham: +`/proj/snic2020-X-XXX/private/DataDelivery_2023-XX-XX_XX-XX-XX_ngisthlm00XXX/files/PXXXXX` + - Please note that NBIS recommends to submit raw data to + ENA as soon as possible + +| Sample_ID | NGI_ID | Mreads | >=Q30 | Date | Sex | +|-----------|------------|--------|-------|-------|-----| +| F34 | PXXXXX_113 | 109.65 | 90.04 | 2022 | F | +| F41 | PXXXXX_114 | 119.48 | 89.87 | 2022 | M | +| F42 | PXXXXX_115 | 91.88 | 89.91 | 2022 | F | +| F43 | PXXXXX_116 | 106.51 | 89.93 | 2022 | F | +| F44 | PXXXXX_117 | 112.15 | 89.75 | 2022 | M | +| O1 | PXXXXX_101 | 110.42 | 89.80 | 2022 | M | +| O11 | PXXXXX_203 | 136.61 | 90.22 | 2022 | M | +| O15 | PXXXXX_107 | 110.25 | 89.98 | 2022 | M | +| O16 | PXXXXX_108 | 102.67 | 89.62 | 2022 | F | + +: Sample metadata {#tbl-metadata} + +*Sample_ID: sample identifier used for sampling and laboratory* +*work. NGI_ID: NGI sample identifier, Mreads:total million* +*read pairs for a sample,>=Q30: aggregated percentage of bases* +*that have a quality score >= Q30, Longitude / Latitude:* +*geographic sampling location, Date: sampling year,* +*Sex: M=male, F=female* + +## Some methods + +### Whole-genome sequencing data processing + +Whole-genome sequencing data from nine individuals (@tbl-metadata) +were processed and analysed using GenErode v0.6.0 [@Kutschera2022] +with default settings if not otherwise described. + +## Some results + +### Population structure + +F~ST~ was calculated in 50 kb windows between island and mainland +samples. The mean F~ST~ across all 50 kb windows (with zero +missing data allowed) was 0.036. + +```{python mean_fst} +#| echo: True +#| output: True + +import os +import pandas as pd +import numpy as np + +def mean_fst(fst_file): + fst_df = pd.read_table(fst_file, delim_whitespace=True, header=0) + mean_fst = fst_df['MEAN_FST'].mean() + print(mean_fst) + +fst = "windowed.weir.fst" + +mean_fst(fst) +``` + +### Genome-wide heterozygosity + +We estimated individual heterozygosity calculated as the +number of heterozygote sites (i.e., SNPs) per site (theta = +*4Neμ*) using mlRho. + +```{python mlRho_stats} +#| echo: True +#| output: True + +import pandas as pd +import scipy.stats as stats + +def mlRho_dataframe(inputfiles): + # define a function to get the input data + dataframes = [] + for infile in inputfiles: + dataset = str(infile).split(".")[0] + with open(infile) as f: + for i in f: # create one dataframe per input file + path_list = i.split("/") + sample = path_list[-1].split(".")[0] + genomeregion = path_list[-1].split(".")[-3] + if genomeregion == "genome": + genomeregion = "genomewide" + elif genomeregion == "autos": + genomeregion = "autosomes" + elif genomeregion == "sexchr": + genomeregion = "sexchromosomes" + sample_df=pd.read_table(i.strip(), delim_whitespace=True, comment='#') + sample_df.insert(0, 'dataset', [dataset]) + sample_df.insert(1, 'sample', [sample]) + sample_df.insert(2, 'genomeregion', [genomeregion]) + dataframes.append(sample_df) + conc_df = pd.concat(dataframes) # concatenate all dataframes + conc_df.reset_index(drop=True, inplace=True) + conc_df = conc_df.sort_values(by = ['dataset', 'sample'], ascending = [True, True]) + return conc_df + +def process_mlrho_dataframe(mlrho_df): + # function to read in and process the data + mlrho_df['lower_theta'] = mlrho_df['theta'].str.split('<').str[0].astype(float) + mlrho_df['theta_est'] = mlrho_df['theta'].str.split('<').str[1].astype(float) + mlrho_df['upper_theta'] = mlrho_df['theta'].str.split('<').str[2].astype(float) + grouped_df = mlrho_df.groupby('dataset') + return mlrho_df, grouped_df + +def mann_whitney_u(statistic, dataset1, list1, dataset2, list2): + # perform Wilcoxon rank-sum test (equivalent to Mann-Whitney U test) + print('**** Mann-Whitney U test of',statistic,'****\n') + stat, p_value = stats.mannwhitneyu(list1, list2) + print('\tU=%.2f, p=%.2f' % (stat, p_value)) + + # Level of significance + alpha = 0.05 + # conclusion + if p_value < alpha: + print('\tRejecting Null Hypothesis (Significant difference between the two samples)') + else: + print('\tCannot Reject Null Hypothesis (No significant difference between the two samples)') + + print('\n\tSamples and sample sizes:\n\t', dataset1,'n =',len(list1), '\n\t', dataset2, 'n =', len(list2)) + +# read in the data +infiles = ['mainland.txt', 'island.txt'] + +# apply the functions +mlRho_df = mlRho_dataframe(infiles) +tabledf, groupeddf = process_mlrho_dataframe(mlRho_df) + +listdf = tabledf.groupby('dataset')['theta_est'].apply(list) + +print('Mainland:') +print('\tMedian heterozygosity %.6f' % (pd.Series(listdf['mainland']).median())) +print('\tMinimum heterozygosity %.6f' % (pd.Series(listdf['mainland']).min())) +print('\tMaximum heterozygosity %.6f' % (pd.Series(listdf['mainland']).max()), '\n') + +print('Island:') +print('\tMedian heterozygosity %.6f' % (pd.Series(listdf['island']).median())) +print('\tMinimum heterozygosity %.6f' % (pd.Series(listdf['island']).min())) +print('\tMaximum heterozygosity %.6f' % (pd.Series(listdf['island']).max()), '\n') + +mann_whitney_u('Heterozygosity', 'Mainland', listdf['mainland'], 'Island', listdf['island']) +``` + +```{python mlRho-fig} +#| label: fig-mlrho +#| fig-cap: "Genome-wide autosomal individual heterozygosity of nine individuals. Theta = *4Neμ* = number of heterozygous sites per site. Error bars represent 95% confidence intervals." + +import matplotlib.pyplot as plt +import pandas as pd +from matplotlib import cm +from matplotlib.colors import ListedColormap, LinearSegmentedColormap + +def theta_plot(dataframe, grouped, dataset, ax, color): + # function for the subplots + data_df = grouped.get_group(dataset) + for lower,theta,upper,x in zip(data_df['lower_theta'],data_df['theta_est'],data_df['upper_theta'],range(len(data_df))): + ax.plot((x,x),(lower,upper),'-',color=color) + ax.plot((x),(theta),'o',color=color) + ax.set_xticks(range(len(data_df))) + ax.set_xticklabels(list(data_df['sample']), rotation = 45) + +def combine_theta_plots(dataframe, grouped): + # function to create the combined plot + cividis = cm.get_cmap('cividis', 256) # create the cividis colormap + fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True) + ax1.set_xlabel("Mainland", fontsize=12) + ax2.set_xlabel("Island", fontsize=12) + theta_plot(dataframe, grouped, "mainland", ax1, cividis.colors[200]) + theta_plot(dataframe, grouped, "island", ax2, cividis.colors[80]) + fig.supxlabel('samples') # x axis label + + fig.supylabel('theta', fontsize=12) # common y axis label + fig.align_labels() # align axis labels + + plt.tight_layout() + plt.show() + +combine_theta_plots(tabledf, groupeddf) +``` + +## References + +::: {#refs} +::: \ No newline at end of file diff --git a/quarto-to-wiki/quarto-example-verena/windowed.weir.fst b/quarto-to-wiki/quarto-example-verena/windowed.weir.fst new file mode 100644 index 0000000..32f14cb --- /dev/null +++ b/quarto-to-wiki/quarto-example-verena/windowed.weir.fst @@ -0,0 +1,1044 @@ +CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST MEAN_FST +HiC_scaffold_1 1 50000 6 -0.00244648 -0.00544218 +HiC_scaffold_1 50001 100000 1 0.3 0.3 +HiC_scaffold_1 100001 150000 4 0.0149254 0.0149254 +HiC_scaffold_1 150001 200000 1 -0.0326531 -0.0326531 +HiC_scaffold_1 200001 250000 1 0.1 0.1 +HiC_scaffold_1 300001 350000 1 0.3 0.3 +HiC_scaffold_1 450001 500000 1 -0.0246575 -0.0246575 +HiC_scaffold_1 700001 750000 2 0.368085 0.367355 +HiC_scaffold_1 900001 950000 1 0.198765 0.198765 +HiC_scaffold_1 950001 1000000 1 0.243262 0.243262 +HiC_scaffold_1 1000001 1050000 4 0.164865 0.114587 +HiC_scaffold_1 1100001 1150000 1 0.283721 0.283721 +HiC_scaffold_1 1200001 1250000 2 0.0260417 0.00766808 +HiC_scaffold_1 1650001 1700000 2 -0.0323077 -0.0333814 +HiC_scaffold_1 1700001 1750000 1 0.0313433 0.0313433 +HiC_scaffold_1 1750001 1800000 1 0.0232759 0.0232759 +HiC_scaffold_1 1950001 2000000 3 0.153265 0.111936 +HiC_scaffold_1 2000001 2050000 2 0.0833333 0.0491803 +HiC_scaffold_1 2050001 2100000 3 0.209375 0.164313 +HiC_scaffold_1 2200001 2250000 1 -0.0326531 -0.0326531 +HiC_scaffold_1 2250001 2300000 1 0.0327586 0.0327586 +HiC_scaffold_1 2350001 2400000 1 0.168504 0.168504 +HiC_scaffold_1 2400001 2450000 1 0.12193 0.12193 +HiC_scaffold_1 2500001 2550000 2 0.323735 0.270777 +HiC_scaffold_1 2900001 2950000 2 -0.00571429 0.0229167 +HiC_scaffold_1 3000001 3050000 3 0.00864198 0.00864198 +HiC_scaffold_1 3200001 3250000 1 0.00864198 0.00864198 +HiC_scaffold_1 3550001 3600000 1 0.05 0.05 +HiC_scaffold_1 3600001 3650000 2 0.05 0.05 +HiC_scaffold_1 3650001 3700000 4 0.336364 0.335417 +HiC_scaffold_1 3750001 3800000 1 0.05 0.05 +HiC_scaffold_1 3800001 3850000 1 -0.055102 -0.055102 +HiC_scaffold_1 3900001 3950000 2 0.241667 0.241667 +HiC_scaffold_1 4000001 4050000 1 -0.0272727 -0.0272727 +HiC_scaffold_1 4100001 4150000 4 0.126471 0.117686 +HiC_scaffold_1 4150001 4200000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 4200001 4250000 1 0.1 0.1 +HiC_scaffold_1 4250001 4300000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 4300001 4350000 1 0.05 0.05 +HiC_scaffold_1 4350001 4400000 1 0.05 0.05 +HiC_scaffold_1 4400001 4450000 1 0.05 0.05 +HiC_scaffold_1 4450001 4500000 3 0.05 0.05 +HiC_scaffold_1 4500001 4550000 2 -0.0112903 0.0025 +HiC_scaffold_1 4550001 4600000 4 0.0151163 0.0177419 +HiC_scaffold_1 4700001 4750000 5 0.0969136 0.0513364 +HiC_scaffold_1 4750001 4800000 1 0.05 0.05 +HiC_scaffold_1 4850001 4900000 1 0.05 0.05 +HiC_scaffold_1 4900001 4950000 1 -0.0426087 -0.0426087 +HiC_scaffold_1 4950001 5000000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 5000001 5050000 1 0.233333 0.233333 +HiC_scaffold_1 5050001 5100000 1 0.05 0.05 +HiC_scaffold_1 5100001 5150000 1 0.0359551 0.0359551 +HiC_scaffold_1 5150001 5200000 2 0.0164706 0.0155084 +HiC_scaffold_1 5200001 5250000 1 -0.0290323 -0.0290323 +HiC_scaffold_1 5250001 5300000 3 0.0663415 0.0467354 +HiC_scaffold_1 5300001 5350000 1 0.366667 0.366667 +HiC_scaffold_1 5400001 5450000 2 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 5500001 5550000 1 0.0666667 0.0666667 +HiC_scaffold_1 5550001 5600000 2 0.05 0.0333333 +HiC_scaffold_1 5600001 5650000 2 -0.0414894 -0.0270833 +HiC_scaffold_1 5650001 5700000 1 0.05 0.05 +HiC_scaffold_1 5700001 5750000 1 0.05 0.05 +HiC_scaffold_1 5750001 5800000 2 0.0333333 0.025 +HiC_scaffold_1 5850001 5900000 3 0.125828 0.0956863 +HiC_scaffold_1 5900001 5950000 4 0.0222222 0.0166667 +HiC_scaffold_1 5950001 6000000 1 0.16 0.16 +HiC_scaffold_1 6050001 6100000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 6200001 6250000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 6250001 6300000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 6300001 6350000 2 0.118103 0.0652381 +HiC_scaffold_1 6400001 6450000 2 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 6450001 6500000 3 0.154375 0.112482 +HiC_scaffold_1 6500001 6550000 2 0.2375 0.135714 +HiC_scaffold_1 6550001 6600000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 6650001 6700000 2 -0.0214286 -0.0145161 +HiC_scaffold_1 6700001 6750000 1 -1.43115e-17 -1.43115e-17 +HiC_scaffold_1 6750001 6800000 1 -0.0323077 -0.0323077 +HiC_scaffold_1 6800001 6850000 1 -0.055102 -0.055102 +HiC_scaffold_1 6850001 6900000 2 0.130476 0.0728723 +HiC_scaffold_1 6900001 6950000 3 -0.031677 -0.0138956 +HiC_scaffold_1 6950001 7000000 5 0.00668896 0.0133493 +HiC_scaffold_1 7000001 7050000 2 0.025 0.025 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Yet, genomic erosion estimates of modern-day populations often lack concordance with dwindling population sizes and conservation status of threatened species. One way to directly quantify the genomic consequences of population declines is to compare genome-wide data from pre-decline museum samples and modern samples. However, doing so requires computational data processing and analysis tools specifically adapted to comparative analyses of degraded, ancient or historical, DNA data with modern DNA data as well as personnel trained to perform such analyses. Here, we present a highly flexible, scalable, and modular pipeline to compare patterns of genomic erosion using samples from disparate time periods. The GenErode pipeline uses state-of-the-art bioinformatics tools to simultaneously process whole-genome re-sequencing data from ancient/historical and modern samples, and to produce comparable estimates of several genomic erosion indices. No programming knowledge is required to run the pipeline and all bioinformatic steps are well-documented, making the pipeline accessible to users with different backgrounds. GenErode is written in Snakemake and Python3 and uses Conda and Singularity containers to achieve reproducibility on high-performance compute clusters. The source code is freely available on GitHub ( https://github.com/NBISweden/GenErode ). GenErode is a user-friendly and reproducible pipeline that enables the standardization of genomic erosion indices from temporally sampled whole genome re-sequencing data.}, + author = {Verena E. Kutschera and Marcin Kierczak and Tom van der Valk and Johanna von Seth and Nicolas Dussex and Edana Lord and Marianne Dehasque and David W.G. Stanton and Payam Emami Khoonsari and Björn Nystedt and Love Dalén and David Díez-del-Molino}, + doi = {10.1186/S12859-022-04757-0}, + issn = {1471-2105}, + issue = {1}, + journal = {BMC Bioinformatics}, + keywords = {Algorithms,Bioinformatics,Computational Biology/Bioinformatics,Computer Appl. in Life Sciences,Microarrays}, + pages = {1-17}, + publisher = {BioMed Central}, + title = {GenErode: a bioinformatics pipeline to investigate genome erosion in endangered and extinct species}, + volume = {23}, + url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04757-0}, + year = {2022}, +}