scverse · abearab · Apr 13, 2024 · Apr 14, 2024 · Apr 14, 2024 · Apr 14, 2024
diff --git a/docs/assets/website_v2_gget_alphafold.png b/docs/assets/website_v2_gget_alphafold.png
diff --git a/docs/assets/website_v2_gget_alphafold_es.png b/docs/assets/website_v2_gget_alphafold_es.png
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diff --git a/docs/assets/website_v2_gget_blat_es.png b/docs/assets/website_v2_gget_blat_es.png
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diff --git a/docs/assets/website_v2_gget_cellxgene_es.png b/docs/assets/website_v2_gget_cellxgene_es.png
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diff --git a/docs/src/SUMMARY.md b/docs/src/SUMMARY.md
@@ -2,6 +2,7 @@
 
 [Introduction](en/introduction.md)  
 [What's New?](en/updates.md)  
+[Dependents & News](en/dependents.md)  
 
 # User Guide
 * [Installation](en/installation.md)  
@@ -41,6 +42,7 @@
 # Español
 * [Introdución](es/introduction.md)  
 * [¡Lo más reciente!](es/updates.md)
+* [Dependientes y Noticias](es/dependents.md)   
 
 # Guía del usario
 * [Instalación](es/installation.md)  
@@ -49,8 +51,10 @@
 # Manuál
 * [gget alphafold](es/alphafold.md)  
 * [gget archs4](es/archs4.md)  
+* [gget bgee](es/bgee.md)  
 * [gget blast](es/blast.md)  
 * [gget blat](es/blat.md)  
+* [gget cbio](es/cbio.md)  
 * [gget cellxgene](es/cellxgene.md)
 * [gget cosmic](es/cosmic.md)  
 * [gget diamond](es/diamond.md)  
@@ -60,6 +64,7 @@
 * [gget info](es/info.md)  
 * [gget muscle](es/muscle.md)
 * [gget mutate](es/mutate.md)  
+* [gget opentargets](es/opentargets.md)   
 * [gget pdb](es/pdb.md)  
 * [gget ref](es/ref.md)  
 * [gget search](es/search.md)  

diff --git a/docs/src/en/alphafold.md b/docs/src/en/alphafold.md
@@ -1,5 +1,5 @@
 > Python arguments are equivalent to long-option arguments (`--arg`), unless otherwise specified. Flags are True/False arguments in Python. The manual for any gget tool can be called from the command-line using the `-h` `--help` flag.  
-## gget alphafold 🪢
+# gget alphafold 🪢
 Predict the 3D structure of a protein from its amino acid sequence using a simplified version of [DeepMind](https://www.deepmind.com/)’s [AlphaFold2](https://github.com/deepmind/alphafold) originally released and benchmarked for [AlphaFold Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb).  
 Returns: Predicted structure (PDB) and alignment error (json).  
 
@@ -67,5 +67,19 @@ gget.pdb("2K42", save=True)
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/4qxGF1tbZ3I?si=mEqQ5oSnDYtg2OP7" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
 
-### [Example in Google Colab](https://github.com/pachterlab/gget_examples/blob/main/gget_alphafold.ipynb)
-### [gget alphafold FAQ](https://github.com/pachterlab/gget/discussions/39)
+# Tutorials
+### [🔗 Google Colab tutorial](https://github.com/pachterlab/gget_examples/blob/main/gget_alphafold.ipynb)  
+
+### [🔗 Protein structure prediction with comparison to related crystal structures](https://github.com/pachterlab/gget_examples/blob/main/protein_structure_prediction_comparison.ipynb)
+
+### [🔗 gget alphafold FAQ](https://github.com/pachterlab/gget/discussions/39)
+
+# References
+If you use `gget alphafold` in a publication, please cite the following articles:   
+
+- Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. [https://doi.org/10.1093/bioinformatics/btac836](https://doi.org/10.1093/bioinformatics/btac836)
+
+- Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). [https://doi.org/10.1038/s41586-021-03819-2](https://doi.org/10.1038/s41586-021-03819-2)
+
+And, if applicable:  
+- Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv 2021.10.04.463034; [https://doi.org/10.1101/2021.10.04.463034](https://doi.org/10.1101/2021.10.04.463034)
diff --git a/docs/src/en/archs4.md b/docs/src/en/archs4.md
@@ -1,5 +1,5 @@
 > Python arguments are equivalent to long-option arguments (`--arg`), unless otherwise specified. Flags are True/False arguments in Python. The manual for any gget tool can be called from the command-line using the `-h` `--help` flag.  
-## gget archs4 🐁
+# gget archs4 🐁
 Find the most correlated genes to a gene of interest or find the gene's tissue expression atlas using [ARCHS4](https://maayanlab.cloud/archs4/).  
 Return format: JSON (command-line) or data frame/CSV (Python).
 
@@ -76,3 +76,12 @@ Check out [this tutorial](https://davetang.org/muse/2023/05/16/check-where-a-gen
 
 
 #### [More examples](https://github.com/pachterlab/gget_examples)
+
+# References
+If you use `gget archs4` in a publication, please cite the following articles:   
+
+- Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. [https://doi.org/10.1093/bioinformatics/btac836](https://doi.org/10.1093/bioinformatics/btac836)
+
+- Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, Ma’ayan A. Massive mining of publicly available RNA-seq data from human and mouse. Nature Communications 9. Article number: 1366 (2018), doi:10.1038/s41467-018-03751-6
+
+- Bray NL, Pimentel H, Melsted P and Pachter L, Near optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, p 525--527 (2016). [https://doi.org/10.1038/nbt.3519](https://doi.org/10.1038/nbt.3519)
diff --git a/docs/src/en/bgee.md b/docs/src/en/bgee.md
@@ -1,22 +1,23 @@
 > Python arguments are equivalent to long-option arguments (`--arg`), unless otherwise specified. Flags are True/False arguments in Python. The manual for any gget tool can be called from the command-line using the `-h` `--help` flag.  
-## gget bgee 🐝
+# gget bgee 🐝
 Fetch orthology and gene expression data from [Bgee](https://www.bgee.org/) using Ensembl IDs.   
 Return format: JSON/CSV (command-line) or data frame (Python).
 
-> If you are specifically interested in human gene expression data, consider using [gget opentargets](./opentargets.md) instead.
+> If you are specifically interested in human gene expression data, consider using [gget opentargets](./opentargets.md) or [gget archs4](./archs4.md) instead.
 > **gget bgee** has less data, but supports more species.
 
 This module was written by [Sam Wagenaar](https://github.com/techno-sam).
 
 **Positional argument**  
 `ens_id`  
-Ensembl gene ID, e.g. ENSG00000169194 or ENSSSCG00000014725.
+Ensembl gene ID, e.g. ENSG00000169194 or ENSSSCG00000014725.  
 
-**Required arguments**  
-`-t` `--type`  
-Type of data to fetch. Options: `orthologs`, `expression`.  
+NOTE: Some of the species in [Bgee](https://www.bgee.org/) are not in Ensembl or Ensembl metazoa, and for those you can use NCBI gene IDs, e.g. 118215821 (a gene in _Anguilla anguilla_).
 
 **Optional arguments**  
+`-t` `--type`  
+Type of data to fetch. Options: `orthologs` (default), `expression`.  
+
 `-o` `--out`    
 Path to the JSON file the results will be saved in, e.g. path/to/directory/results.json. Default: Standard out.
 
@@ -35,12 +36,12 @@ Python: Use `verbose=False` to prevent progress information from being displayed
 **Get orthologs for a gene**
 
 ```bash
-gget bgee ENSSSCG00000014725 -t orthologs
+gget bgee ENSSSCG00000014725
 ```
 
 ```python
 import gget
-gget.bgee("ENSSSCG00000014725", type="orthologs")
+gget.bgee("ENSSSCG00000014725")
 ```
 
 &rarr; Returns orthologs for the gene with Ensembl ID ENSSSCG00000014725.
@@ -79,3 +80,10 @@ gget.bgee("ENSSSCG00000014725", type="expression")
 
 
 #### [More examples](https://github.com/pachterlab/gget_examples)
+
+# References
+If you use `gget bgee` in a publication, please cite the following articles:   
+
+- Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. [https://doi.org/10.1093/bioinformatics/btac836](https://doi.org/10.1093/bioinformatics/btac836)
+
+- Frederic B Bastian, Julien Roux, Anne Niknejad, Aurélie Comte, Sara S Fonseca Costa, Tarcisio Mendes de Farias, Sébastien Moretti, Gilles Parmentier, Valentine Rech de Laval, Marta Rosikiewicz, Julien Wollbrett, Amina Echchiki, Angélique Escoriza, Walid H Gharib, Mar Gonzales-Porta, Yohan Jarosz, Balazs Laurenczy, Philippe Moret, Emilie Person, Patrick Roelli, Komal Sanjeev, Mathieu Seppey, Marc Robinson-Rechavi (2021). The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals. Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D831–D847, [https://doi.org/10.1093/nar/gkaa793](https://doi.org/10.1093/nar/gkaa793)
diff --git a/docs/src/en/blast.md b/docs/src/en/blast.md
@@ -1,5 +1,5 @@
 > Python arguments are equivalent to long-option arguments (`--arg`), unless otherwise specified. Flags are True/False arguments in Python.  The manual for any gget tool can be called from the command-line using the `-h` `--help` flag.  
-## gget blast 💥
+# gget blast 💥
 BLAST a nucleotide or amino acid sequence to any [BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi) database.  
 Return format: JSON (command-line) or data frame/CSV (Python).
 
@@ -72,3 +72,10 @@ gget.blast("fasta.fa")
 &rarr; Returns the BLAST results of the first sequence contained in the fasta.fa file. 
 
 #### [More examples](https://github.com/pachterlab/gget_examples)
+
+# References
+If you use `gget blast` in a publication, please cite the following articles:   
+
+- Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. [https://doi.org/10.1093/bioinformatics/btac836](https://doi.org/10.1093/bioinformatics/btac836)
+
+- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990 Oct 5;215(3):403-10. doi: 10.1016/S0022-2836(05)80360-2. PMID: 2231712.
diff --git a/docs/src/en/blat.md b/docs/src/en/blat.md
@@ -1,5 +1,5 @@
 > Python arguments are equivalent to long-option arguments (`--arg`), unless otherwise specified. Flags are True/False arguments in Python. The manual for any gget tool can be called from the command-line using the `-h` `--help` flag.  
-## gget blat 🎯
+# gget blat 🎯
 Find the genomic location of a nucleotide or amino acid sequence using [BLAT](https://genome.ucsc.edu/cgi-bin/hgBlat).   
 Return format: JSON (command-line) or data frame/CSV (Python).
 
@@ -45,3 +45,11 @@ gget.blat("MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQ
 | taeGut2| 88 | 	12 | 88 | 77 | 0 | 87.5 | ... |
 
 #### [More examples](https://github.com/pachterlab/gget_examples)
+
+# References
+If you use `gget blat` in a publication, please cite the following articles:   
+
+- Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. [https://doi.org/10.1093/bioinformatics/btac836](https://doi.org/10.1093/bioinformatics/btac836)
+
+- Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. doi: 10.1101/gr.229202. PMID: 11932250; PMCID: PMC187518.
+
diff --git a/docs/src/en/cbio.md b/docs/src/en/cbio.md
@@ -1,5 +1,5 @@
 > Python arguments are equivalent to long-option arguments (`--arg`), unless otherwise specified. Flags are True/False arguments in Python. The manual for any gget tool can be called from the command-line using the `-h` `--help` flag.  
-## gget cbio 📖
+# gget cbio 📖
 Plot cancer genomics heatmaps using data from [cBioPortal](https://www.cbioportal.org/) using Ensembl IDs or gene names.  
 
 This module was written by [Sam Wagenaar](https://github.com/techno-sam). 
@@ -33,20 +33,20 @@ Space-separated list of gene names or Ensembl IDs, e.g. <code>NOTCH3&nbsp;ENSG00
 `-st` `--stratification`    
 Column to stratify the data by. Default: `tissue`.  
 Options:
-- `tissue`
-- `cancer_type`
-- `cancer_type_detailed`
-- `study_id`
-- `sample`
+- tissue
+- cancer_type
+- cancer_type_detailed
+- study_id
+- sample
 
 `-vt` `--variation_type`    
 Type of variation to plot. Default: `mutation_occurrences`.     
 Options:
-- `mutation_occurrences`
-- `cna_nonbinary` Note: `stratification` must be `sample` for this option.
-- `sv_occurrences`
-- `cna_occurrences`
-- `Consequence` Note: `stratification` must be `sample` for this option.
+- mutation_occurrences
+- cna_nonbinary (Note: `stratification` must be 'sample' for this option)
+- sv_occurrences
+- cna_occurrences
+- Consequence (Note: `stratification` must be 'sample' for this option)
 
 `-f` `--filter`     
 Filter the data by a specific value in a specific column, e.g. `study_id:msk_impact_2017`   
@@ -109,8 +109,7 @@ gget cbio plot \
     -g AKT1 ALK FLT4 MAP3K1 MLL2 MLL3 NOTCH3 NOTCH4 PDCD1 RNF43 \
     -st tissue \
     -vt mutation_occurrences \
-    -dpi 200 \
-    -y
+    -dpi 200
 ```
 ```python
 # Python
@@ -137,8 +136,7 @@ gget cbio plot \
     -g AKT1 ALK FLT4 MAP3K1 MLL2 MLL3 NOTCH3 NOTCH4 PDCD1 RNF43 \
     -st sample \
     -vt Consequence \
-    -dpi 200 \
-    -y
+    -dpi 200
 ```
 ```python
 # Python
@@ -166,8 +164,7 @@ gget cbio plot \
     -st sample \
     -vt Consequence \
     -f tissue:intestine \
-    -dpi 200 \
-    -y
+    -dpi 200
 ```
 ```python
 # Python
@@ -197,7 +194,6 @@ gget cbio plot \
     -vt Consequence \
     -f tissue:intestine \
     -dpi 200 \
-    -y \
     -t "Intestinal Mutations" \
     -fn intestinal_mutations.png
 ```
@@ -221,3 +217,17 @@ gget.cbio_plot(
 ![Heatmap](https://raw.githubusercontent.com/pachterlab/gget/b32c01efefd55d37c19034ce96a86826e30ae3e5/docs/assets/gget_cbio_figure_4.png)
 
 #### [More examples](https://github.com/pachterlab/gget_examples)
+
+# References
+If you use `gget cbio` in a publication, please cite the following articles:   
+
+- Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. [https://doi.org/10.1093/bioinformatics/btac836](https://doi.org/10.1093/bioinformatics/btac836)
+
+- Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012 May;2(5):401-4. doi: [10.1158/2159-8290.CD-12-0095](https://doi.org/10.1158/2159-8290.cd-12-0095). Erratum in: Cancer Discov. 2012 Oct;2(10):960. PMID: 22588877; PMCID: PMC3956037.
+
+- Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013 Apr 2;6(269):pl1. doi: [10.1126/scisignal.2004088](https://doi.org/10.1126/scisignal.2004088). PMID: 23550210; PMCID: PMC4160307.
+
+- de Bruijn I, Kundra R, Mastrogiacomo B, Tran TN, Sikina L, Mazor T, Li X, Ochoa A, Zhao G, Lai B, Abeshouse A, Baiceanu D, Ciftci E, Dogrusoz U, Dufilie A, Erkoc Z, Garcia Lara E, Fu Z, Gross B, Haynes C, Heath A, Higgins D, Jagannathan P, Kalletla K, Kumari P, Lindsay J, Lisman A, Leenknegt B, Lukasse P, Madela D, Madupuri R, van Nierop P, Plantalech O, Quach J, Resnick AC, Rodenburg SYA, Satravada BA, Schaeffer F, Sheridan R, Singh J, Sirohi R, Sumer SO, van Hagen S, Wang A, Wilson M, Zhang H, Zhu K, Rusk N, Brown S, Lavery JA, Panageas KS, Rudolph JE, LeNoue-Newton ML, Warner JL, Guo X, Hunter-Zinck H, Yu TV, Pilai S, Nichols C, Gardos SM, Philip J; AACR Project GENIE BPC Core Team, AACR Project GENIE Consortium; Kehl KL, Riely GJ, Schrag D, Lee J, Fiandalo MV, Sweeney SM, Pugh TJ, Sander C, Cerami E, Gao J, Schultz N. Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res. 2023 Dec 1;83(23):3861-3867. doi: [10.1158/0008-5472.CAN-23-0816](https://doi.org/10.1158/0008-5472.CAN-23-0816). PMID: 37668528; PMCID: PMC10690089.
+
+- Please also cite the source of the data if you are using a publicly available dataset.
+
diff --git a/docs/src/en/cellxgene.md b/docs/src/en/cellxgene.md
@@ -1,6 +1,6 @@
 > Python arguments are equivalent to long-option arguments (`--arg`), unless otherwise specified. Flags are True/False arguments in Python.  The manual for any gget tool can be called from the command-line using the `-h` `--help` flag.  
-## gget cellxgene 🍱  
-Query data from [CZ CELLxGENE Discover](https://cellxgene.cziscience.com/) using the [CZ CELLxGENE Discover Census](https://github.com/chanzuckerberg/cellxgene-census).  
+# gget cellxgene 🍱  
+Query data from [CZ CELLxGENE Discover](https://cellxgene.cziscience.com/) using the [CZ CELLxGENE Discover Census](https://github.com/chanzuckerberg/cellxgene-census). [CZ CELLxGENE Discover](https://cellxgene.cziscience.com/) provides ready-to-use single-cell RNA sequencing count matrices for certain tissues/diseases/genes/etc.     
 
 Returns: An AnnData object containing the count matrix and metadata of single-cell RNA sequencing data from the defined tissues/genes/etc.  
 
@@ -136,3 +136,11 @@ df
 &rarr; Returns only the metadata from ENSMUSG00000015405 (ACE2) expression datasets corresponding to mouse lung cells.  
 
 Also see: [https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_gget_demo.html](https://chanzuckerberg.github.io/cellxgene-census/notebooks/api_demo/census_gget_demo.html)
+
+# References
+If you use `gget cellxgene` in a publication, please cite the following articles:   
+
+- Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. [https://doi.org/10.1093/bioinformatics/btac836](https://doi.org/10.1093/bioinformatics/btac836)
+
+- Chanzuckerberg Initiative. (n.d.). CZ CELLxGENE Discover. Retrieved [insert date here], from [https://cellxgene.cziscience.com/](https://cellxgene.cziscience.com/)
+