update README and CHANGELOG

sigven · Sep 29, 2024 · 0cb0d76 · 0cb0d76
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diff --git a/README.md b/README.md
@@ -24,24 +24,29 @@ Example screenshots from the [quarto](https://quarto.org)-based cancer genome re
 ![PCGR screenshot 2](pcgrr/pkgdown/assets/img/sc1.png)
 ![PCGR screenshot 3](pcgrr/pkgdown/assets/img/sc3.png)
 
-PCGR originates from the [Norwegian Cancer Genomics Consortium (NCGC)](http://cancergenomics.no), at the [Institute for Cancer Research, Oslo University Hospital, Norway](http://radium.no).
+PCGR originates from the [Norwegian Cancer Genomics Consortium (NCGC)](https://cancergenomics.no), at the [Institute for Cancer Research, Oslo University Hospital, Norway](https://radium.no).
 
 ### Top News
 
+- *September 29th 2024*: **2.1.0 release**
+  - updated bundle, more oncogenic variants, CNA visualization, 
+    improved RNA-seq support, bug fixes, and more
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
+
 - *August 1st 2024*: **2.0.3 release** 
   - patch to fix purity/ploidy propagation, MAF output for tumor-only runs, and other minor issues
-  - [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
 
 - *July 16th 2024*: **2.0.2 release** 
   - patch to ensure correct reference to actionability guidelines
-  - [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
 
 - *July 7th 2024*: **2.0.1 release** 
   - patch with bug fix for mitochondrial input variants ([pr245](https://github.com/sigven/pcgr/pull/245))
-  - [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
 
 - *June 2024*: **2.0.0 release**
-  - Details in [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - Details in [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
   - Massive reference data bundle upgrade, new report layout, oncogenicity classification++
   - Support for Singularity/Apptainer
   - Major data/software updates:
@@ -52,19 +57,9 @@ PCGR originates from the [Norwegian Cancer Genomics Consortium (NCGC)](http://ca
     - CancerMine `v50` (2023-03)
     - UniProt KB `v2024_03`
 
-- *February 2023*: **1.3.0 release**
-  - Details in [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
-  - prioritize protein-coding BIOTYPE csq ([pr201](https://github.com/sigven/pcgr/pull/201))
-  - expose `--pcgrr_conda` option to flexibly activate pcgrr env via a non-default pcgrr name
-  - `cpsr_validate_input.py`: refactor for efficient custom gene egrep
-
-- *November 2022*: **1.2.0 release**
-  -    Keep only autosomal, X, Y, M/MT chromosomes
-  -    Import bcftools as dependency
-
 ### Example reports
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.12752833.svg)](https://doi.org/10.5281/zenodo.12752833)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13855988.svg)](https://doi.org/10.5281/zenodo.13855988)
 
 ### Why use PCGR?
 
@@ -94,10 +89,12 @@ PCGR integrates a [comprehensive set of knowledge resources](https://sigven.gith
 
 ### Citation
 
-If you use PCGR, please cite our publication:
+If you use PCGR or CPSR, please cite our publications:
 
 Sigve Nakken, Ghislain Fournous, Daniel Vodák, Lars Birger Aaasheim, Ola Myklebost, and Eivind Hovig. **Personal Cancer Genome Reporter: variant interpretation report for precision oncology** (2017). *Bioinformatics*. 34(10):1778--1780. [doi.org/10.1093/bioinformatics/btx817](https://doi.org/10.1093/bioinformatics/btx817)
 
+Sigve Nakken, Vladislav Saveliev, Oliver Hofmann, Pål Møller, Ola Myklebost, and Eivind Hovig. **Cancer Predisposition Sequencing Reporter (CPSR): a flexible variant report engine for high-throughput germline screening in cancer** (2021). *Int J Cancer*. [doi:[10.1002/ijc.33749](doi:%5B10.1002/ijc.33749)](https://doi.org/10.1002/ijc.33749)
+
 ## Contact
 
 sigven AT ifi.uio.no
diff --git a/pcgrr/inst/templates/pcgr_quarto_report/msi.qmd b/pcgrr/inst/templates/pcgr_quarto_report/msi.qmd
@@ -2,7 +2,7 @@
 
 Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers ([Cortes-Ciriano et al., 2017](https://www.ncbi.nlm.nih.gov/pubmed/28585546)). We have built a statistical MSI classifier that only considers features of the somatic mutation profile (e.g. _fraction of indels_, _load of indels_, _mutations in MMR genes_ etc.) in order to separate _MSI.H_ (MSI-high) from _MSS_ (MS stable) tumors. 
 
-he MSI classifier was trained using __N = 1,065__ exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a [positive predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Positive_predictive_value) of 97.9% and a [negative predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Negative_predictive_value) of 99.4% on an independent test set of __N = 435 samples__. Details of the MSI classification approach can be found <a href="https://rpubs.com/sigven/msi_classifier" target="_blank">here</a>.
+The MSI classifier was trained using __N = 1,065__ exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a [positive predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Positive_predictive_value) of 97.9% and a [negative predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Negative_predictive_value) of 99.4% on an independent test set of __N = 435 samples__. Details of the MSI classification approach can be found <a href="https://rpubs.com/sigven/msi_classifier" target="_blank">here</a>.
 
 <br>
 

diff --git a/pcgrr/pkgdown/index.md b/pcgrr/pkgdown/index.md
@@ -25,29 +25,29 @@ Example screenshots from the [quarto](https://quarto.org)-based cancer genome re
 ![PCGR screenshot 2](img/sc1.png)
 ![PCGR screenshot 3](img/sc3.png)
 
-PCGR originates from the [Norwegian Cancer Genomics Consortium (NCGC)](http://cancergenomics.no), at the [Institute for Cancer Research, Oslo University Hospital, Norway](http://radium.no).
+PCGR originates from the [Norwegian Cancer Genomics Consortium (NCGC)](https://cancergenomics.no), at the [Institute for Cancer Research, Oslo University Hospital, Norway](https://radium.no).
 
 ### Top News
 
 - *September 29th 2024*: **2.1.0 release**
   - updated bundle, more oncogenic variants, CNA visualization, 
     improved RNA-seq support, bug fixes, and more
-  - [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
 
 - *August 1st 2024*: **2.0.3 release** 
   - patch to fix purity/ploidy propagation, MAF output for tumor-only runs, and other minor issues
-  - [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
 
 - *July 16th 2024*: **2.0.2 release** 
   - patch to ensure correct reference to actionability guidelines
-  - [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
 
 - *July 7th 2024*: **2.0.1 release**
   - patch with bug fix for mitochondrial input variants ([pr245](https://github.com/sigven/pcgr/pull/245))
-  - [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
 
 - *June 2024*: **2.0.0 release**
-  - Details in [CHANGELOG](http://sigven.github.io/pcgr/articles/CHANGELOG.html)
+  - Details in [CHANGELOG](https://sigven.github.io/pcgr/articles/CHANGELOG.html)
   - Massive reference data bundle upgrade, new report layout, oncogenicity classification++
   - Support for Singularity/Apptainer
   - Major data/software updates:

diff --git a/pcgrr/vignettes/CHANGELOG.Rmd b/pcgrr/vignettes/CHANGELOG.Rmd
@@ -63,7 +63,7 @@ excluded if setting `--exclude_nonexonic` is used)
 - Slight change to the default transcript consequence pick order in VEP based on observations of prioritized transcripts (*mane_select > mane_plus_clinical > canonical > biotype > ccds > rank > tsl > appris >length*)
 - Pulled in known oncogenic variants from ClinVar (assessed through ClinGen/CGC/VICC SOP, oncogenic/likely oncogenic) into the variant oncogenicity assessment algorithm
 - Added option `--no_html` to disable HTML report generation
-- Added option `--input_germline` - re-offering the possibility to integrate CPSR-classified germline variants in the PCGR HTML report
+- Added option `--input_cpsr` - re-offering the possibility to integrate CPSR-classified germline variants in the PCGR HTML report
 - Added `HGVSc_RefSeq` as output column in TSV/HTML - using MANE Select RefSeq transcript identifiers (works primarily for grch38)
 - Pulled in coding sequence start annotation for protein-coding transcripts from GENCODE, enabling a more useful annotation of promoter variants (e.g. TERT)
 - Created new column `ALTERATION` in variant tables of HTML report, a combination of `HGVSp`, `HGVSc` (if `HGVSp` not available)
-Original file line number
+Diff line change
@@ Expand Up / @@ -2,7 +2,7 @@ @@
     Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers ([Cortes-Ciriano et al., 2017](https://www.ncbi.nlm.nih.gov/pubmed/28585546)). We have built a statistical MSI classifier that only considers features of the somatic mutation profile (e.g. _fraction of indels_, _load of indels_, _mutations in MMR genes_ etc.) in order to separate _MSI.H_ (MSI-high) from _MSS_ (MS stable) tumors.
-    he MSI classifier was trained using __N = 1,065__ exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a [positive predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Positive_predictive_value) of 97.9% and a [negative predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Negative_predictive_value) of 99.4% on an independent test set of __N = 435 samples__. Details of the MSI classification approach can be found <a href="https://rpubs.com/sigven/msi_classifier" target="_blank">here</a>.
+    The MSI classifier was trained using __N = 1,065__ exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a [positive predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Positive_predictive_value) of 97.9% and a [negative predictive value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Negative_predictive_value) of 99.4% on an independent test set of __N = 435 samples__. Details of the MSI classification approach can be found <a href="https://rpubs.com/sigven/msi_classifier" target="_blank">here</a>.
     <br>
@@ Expand Down @@