Hyperlink DOIs against preferred resolver (#793)

Author: katrinleinweber

topics/epigenetics/metadata.yaml

@@ -87,5 +87,5 @@ references:
   -
     authors: "I-Hsuan Lin"
     title: "Hierarchical Clustering of Breast Cancer Methylomes Revealed Differentially Methylated and Expressed Breast Cancer Genes"
-    link: "http://dx.doi.org/10.1371/journal.pone.0118453"
+    link: "https://doi.org/10.1371/journal.pone.0118453"
     summary: ""

topics/epigenetics/tutorials/methylation-seq/tutorial.md

@@ -19,7 +19,7 @@ tutorial_name: methylation-seq
 >
 {: .agenda}
 
-This tutorial is based on [I-Hsuan Lin et al.: 'Hierarchical Clustering of Breast Cancer Methylomes Revealed Differentially Methylated and Expressed Breast Cancer Genes'](https://dx.doi.org/10.1371/journal.pone.0118453).
+This tutorial is based on [I-Hsuan Lin et al.: 'Hierarchical Clustering of Breast Cancer Methylomes Revealed Differentially Methylated and Expressed Breast Cancer Genes'](https://doi.org/10.1371/journal.pone.0118453).
 
 The data we use in this tutorial is available at [Zenodo](https://zenodo.org/record/557099).
 

topics/metagenomics/metadata.yaml

@@ -79,7 +79,7 @@ references:
   -
     authors: "John C. Wooley, Adam Godzik, Iddo Friedberg"
     title: "A Primer on Metagenomics"
-    link: "http://dx.doi.org/10.1371/journal.pcbi.1000667"
+    link: "https://doi.org/10.1371/journal.pcbi.1000667"
     summary: ""
   -
     authors: "Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD."

topics/metagenomics/tutorials/mothur-miseq-sop/tutorial.md

@@ -779,7 +779,7 @@ This is not a perfect world. But this is pretty darn good!
 > Green, most or all species have been sampled; blue, this habitat has not been exhaustively sampled; red,
 > species rich habitat, only a small fraction has been sampled.
 >
-> (*A Primer on Metagenomics*, Wooley et al. 2010, https://dx.doi.org/10.1371/journal.pcbi.1000667)
+> (*A Primer on Metagenomics*, Wooley et al. 2010, https://doi.org/10.1371/journal.pcbi.1000667)
 {: .tip}
 
 Now that we have assessed our error rates we are ready for some real analysis.

topics/proteomics/tutorials/ntails/tutorial.md

@@ -8,7 +8,7 @@ N-Tails is a special Proteomics technique to analyze peptide abundancy changes o
 This is best explained by an example: directly after translation, a protein has exactly one N-terminus. When a protease is cutting the protein in half, each half has its own N-terminus. While the N-terminus of the first half protein is exactly the same as the one of the full protein precursor ("native N-terminus"), the N-terminus of the second half is different ("neo-N-terminus") and depends on the amino acid sequence where the protein was cut.
 The N-Tails technique includes the use of heavy isotope dimethyl labelling.
 
-The figure below illustrates the mechanism of N-Tails. It was originally published by Stefan Tholen (doctoral thesis, not available online). Further reading on N-Tails and other N-terminal techniques, see [Tholen et al., Springer Vienna, 2013](https://dx.doi.org/10.1007/978-3-7091-0885-7_5).
+The figure below illustrates the mechanism of N-Tails. It was originally published by Stefan Tholen (doctoral thesis, not available online). Further reading on N-Tails and other N-terminal techniques, see [Tholen et al., Springer Vienna, 2013](https://doi.org/10.1007/978-3-7091-0885-7_5).
 
 ![N-Tails technique](../../images/wf_ntails_technique.png "Overview of the N-Tails technique")
 

topics/sequence-analysis/tutorials/annotation-with-prokka/data-library.yaml

@@ -3,5 +3,5 @@ libraries:
     # This needs to be changed to reference your Zenodo data
     - name: "Assembled contigs for annotation"
       files:
-          - url: "http://doi.org/10.5281/zenodo.1156405"
+          - url: "https://doi.org/10.5281/zenodo.1156405"
             file_type: "fasta"

topics/transcriptomics/tutorials/ref-based/tutorial.md

@@ -46,7 +46,7 @@ We have extracted sequences from the Sequence Read Archive (SRA) files to build
 >
 >       To import the files, there are two options:
 >       - Option 1: From a shared data library if available (ask your instructor)
->       - Option 2: From [Zenodo](https://dx.doi.org/10.5281/zenodo.1185122)
+>       - Option 2: From [Zenodo](https://doi.org/10.5281/zenodo.1185122)
 >
 >           > ### {% icon tip %} Tip: Importing data via links
 >           >
@@ -211,7 +211,7 @@ We will map our RNA reads to the *Drosophila melanogaster* genome using STAR.
 
 > ### {% icon hands_on %} Hands-on: Spliced mapping
 >
-> 1. Import the Ensembl gene annotation for *Drosophila melanogaster* (`Drosophila_melanogaster.BDGP6.87.gtf`) from the shared data library or from [Zenodo](https://dx.doi.org/10.5281/zenodo.1185122) into your current Galaxy history
+> 1. Import the Ensembl gene annotation for *Drosophila melanogaster* (`Drosophila_melanogaster.BDGP6.87.gtf`) from the shared data library or from [Zenodo](https://doi.org/10.5281/zenodo.1185122) into your current Galaxy history
 >    - Rename the dataset if necessary
 >    - Verify that the datatype is `gtf` and not `gff`
 >
@@ -496,7 +496,7 @@ In the previous section, we counted reads that mapped to genes for two sample. T
 
 > ### {% icon hands_on %} (Optional) Hands-on: Re-run on the other datasets
 >
-> You can do the same process on the other sequence files available on [Zenodo](https://dx.doi.org/10.5281/zenodo.1185122)
+> You can do the same process on the other sequence files available on [Zenodo](https://doi.org/10.5281/zenodo.1185122)
 >
 > - Paired-end data
 >     - `GSM461178_1` and `GSM461178_2`
@@ -509,7 +509,7 @@ In the previous section, we counted reads that mapped to genes for two sample. T
 > This is really interesting to redo on the other datasets, specially to check how the parameters are inferred given the different type of data.
 {: .hands_on}
 
-To save time, we have run the necessary steps for you and obtained 7 count files, available on [Zenodo](https://dx.doi.org/10.5281/zenodo.1185122).
+To save time, we have run the necessary steps for you and obtained 7 count files, available on [Zenodo](https://doi.org/10.5281/zenodo.1185122).
 
 These files contain for each gene of *Drosophila* the number of reads mapped to it. We could compare the files directly and calculate the extent of differential gene expression, but the number of sequenced reads mapped to a gene depends on:
 
@@ -548,7 +548,7 @@ Here treatment is the primary factor which we are interested in. The sequencing
 > ### {% icon hands_on %} Hands-on: Determines differentially expressed features
 >
 > 1. Create a new history
-> 2. Import the seven count files from [Zenodo](https://dx.doi.org/10.5281/zenodo.1185122) or the data library
+> 2. Import the seven count files from [Zenodo](https://doi.org/10.5281/zenodo.1185122) or the data library
 >    - `GSM461176_untreat_single.counts`
 >    - `GSM461177_untreat_paired.counts`
 >    - `GSM461178_untreat_paired.counts`
@@ -901,15 +901,15 @@ DEXSeq generates a file similar to the one generated by featureCounts, but with
 
 DEXSeq usage is similar to DESeq2. It uses similar statistics to find differentially used exons.
 
-As for DESeq2, in the previous step, we counted only reads that mapped to exons on chromosome 4 and for only one sample. To be able to identify differential exon usage induced by PS depletion, all datasets (3 treated and 4 untreated) must be analyzed following the same procedure. To save time, we did that for you. The results are available on [Zenodo](https://dx.doi.org/10.5281/zenodo.1185122):
+As for DESeq2, in the previous step, we counted only reads that mapped to exons on chromosome 4 and for only one sample. To be able to identify differential exon usage induced by PS depletion, all datasets (3 treated and 4 untreated) must be analyzed following the same procedure. To save time, we did that for you. The results are available on [Zenodo](https://doi.org/10.5281/zenodo.1185122):
 
 - The results of running DEXSeq-count in 'Prepare annotation' mode
 - Seven count files generated in 'Count reads' mode
 
 > ### {% icon hands_on %} Hands-on:
 >
 > 1. Create a new history
-> 2. Import the seven exon count files and the annotation GTF file from [Zenodo](https://dx.doi.org/10.5281/zenodo.1185122) or the data library
+> 2. Import the seven exon count files and the annotation GTF file from [Zenodo](https://doi.org/10.5281/zenodo.1185122) or the data library
 >    - `Drosophila_melanogaster.BDGP6.87.dexseq.gtf`
 >    - `GSM461176_untreat_single.exon.counts`
 >    - `GSM461177_untreat_paired.exon.counts`

topics/variant-analysis/metadata.yaml

@@ -57,7 +57,7 @@ material:
     title: "Exome sequencing data analysis"
     type: "tutorial"
     name: "exome-seq"
-    zenodo_link: "http://dx.doi.org/10.5281/zenodo.60520"
+    zenodo_link: "https://doi.org/10.5281/zenodo.60520"
     galaxy_tour: no
     hands_on: yes
     slides: no

topics/variant-analysis/tutorials/diploid-variant-calling/tutorial.md

@@ -140,7 +140,7 @@ In this example we will perform variant calling and annotation using [genome in
 * HG003- NA24149 - hu6E4515 (father)
 * HG004- NA24143 - hu8E87A9 (mother)
 
-Yet for a quick tutorial these datasets are way too big, so we created a [downsampled dataset](https://dx.doi.org/10.5281/zenodo.60520). This dataset was produced by mapping the trio reads against `hg19` version of the human genome, merging the resulting bam files together (we use readgroups to label individual reads so they can be traced to each of the original individuals), and restricting alignments to a small portion of chromosome 19 containing the [*POLRMT*](http://www.ncbi.nlm.nih.gov/gene?cmd=Retrieve&dopt=Graphics&list_uids=5442) gene.
+Yet for a quick tutorial these datasets are way too big, so we created a [downsampled dataset](https://doi.org/10.5281/zenodo.60520). This dataset was produced by mapping the trio reads against `hg19` version of the human genome, merging the resulting bam files together (we use readgroups to label individual reads so they can be traced to each of the original individuals), and restricting alignments to a small portion of chromosome 19 containing the [*POLRMT*](http://www.ncbi.nlm.nih.gov/gene?cmd=Retrieve&dopt=Graphics&list_uids=5442) gene.
 
 > ### {% icon hands_on %} Hands-on: Variant calling
 >