Useful resources
===

:::info
**Four good bioinformatics learning resources:**
- https://hbctraining.github.io/main/
- https://nbis.se/training/
- http://gtpb.igc.gulbenkian.pt/bicourses/Course_materials.html
- https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/training

:::

### Other leardning sources:
***Updated: 07.08.2023***
1. How to make a Volcano plot:
https://www.bioconductor.org/packages/release/bioc/vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html

2.
- String match:
https://stringr.tidyverse.org/articles/regular-expressions.html
- string pattern match: https://rstudio-pubs-static.s3.amazonaws.com/74603_76cd14d5983f47408fdf0b323550b846.html
- cheet sheet: https://rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf

3. Color paletter:
http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette

4. Graphs and apps
- use R for basic graphs: http://rstudio-pubs-static.s3.amazonaws.com/7953_4e3efd5b9415444ca065b1167862c349.html
- all graphs: (1) https://www.data-to-viz.com/ (2)https://ying-ge.github.io/FigureYa/
- grphas + apps: https://plotly.com/
- draw figures: GraphPad; Adobe Photoshop; Adobe Illustrator
- Source for icons and images: Biorender; Smart servier

5. Learning RNA seq:
- mapping: https://hbctraining.github.io/Intro-to-rnaseq-hpc-salmon/ (2020 version, more on Salmon) + https://hbctraining.github.io/Intro-to-rnaseq-hpc-O2/ (2019, more details on STAR and QC).
- DEG analysis: https://hbctraining.github.io/DGE_workshop_salmon_online/
- RNA seq: https://uppsala.instructure.com/courses/76870/pages/contents
- RNA-seq+scRNA: https://gtpb.github.io/ADER19F/
- RNA-seq and pathway analysis: https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2021/

6. scRNA sequencing
- watch videos and practise the code: https://uppsala.instructure.com/courses/52011
- Single cell RNA-seq: https://hbctraining.github.io/scRNA-seq_online/schedule/links-to-lessons.html

7. Epigenomics: https://nbis-workshop-epigenomics.readthedocs.io/en/latest/content/tutorials.html

8. Omics Integration and Genome-scale Metabolic model: https://uppsala.instructure.com/courses/75208

9. Neural Nets and Deep learning: https://uppsala.instructure.com/courses/47910/pages

8. Computational quantification immune cells and deconvolution: https://gtpb.github.io/IO17/

9. Tools for Reproducible Research: https://uppsala.instructure.com/courses/51980

10. How to use supercomputer YZ: https://supercomputingwales.github.io/SCW-tutorial/

11. PhD scholarship/travel grants: https://www.bifonds.de/fellowships-grants/fellowships-grants.html

:::success
**How to give a good talk**
- I suggest you read three below papers before preparing your tak:
--[How to tell a compelling story in scientific presentations](https://www.nature.com/articles/d41586-021-03603-2)
--[How to give a great scientific talk](https://www.nature.com/articles/d41586-018-07780-5)
--[Prioritize the needs of the audience when giving a presentation](https://www.nature.com/articles/d41586-018-06021-z)

- You can also fine others' experience via:
--https://www.nature.com/articles/d41586-019-01574-
--https://threadreaderapp.com/thread/1597793126454001664.html
:::

### Grant/Scholarship resources:
#### -- PhD scholarship/visiting/training travel grants: https://www.bifonds.de/fellowships-grants/fellowships-grants.html
#### -- Travel grants for meetings/conferences: https://www.biologists.com/grants/dmm-conference-travel-grants/
#### https://nnukrf.org.uk/how-to-apply