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.gitignore

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README.md

-# ricinus_proteome_arc
+# Wrobel-2023-CastorBeanEndospermProteome
 
-Legacy data on Ricinus proteome from Nicole Linka and Thomas Wrobel
+## Publication
+
+Wrobel TJ, Brilhaus D, Stefanski A, Stühler K, Weber APM, Linka N. Mapping the castor bean endosperm proteome revealed a metabolic interaction between plastid, mitochondria, and peroxisomes to optimize seedling growth. Front Plant Sci. 2023 Oct 6;14:1182105. doi: 10.3389/fpls.2023.1182105. PMID: 37868318; PMCID: PMC10588648.
+
+## License
+
+Copyright © 2023 Wrobel, Brilhaus, Stefanski, Stühler, Weber and Linka
+
+This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
+
+## Data availability
+
+The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD040932.
\ No newline at end of file

_DominikNotes/2022-04-12_anja_stefanski.md

-
-# Meeting Anja Stefanski
-
-> 12.04.2022 - 10:30 - Webex
-
-- sample quality not soo good
-- data analysis with proteome discoverer (thermo fisher)
-  - maxquant is good, but tricky for newbies
-- identification or quantification
-  - quantification possible with triplicates
-
-## to dos
-
-### Dominik
-
-- [x] send back sample list
-  - indicate order of fractions (F1-F4)
-  - species used
-
-### Anja
-
-- runs DA in standard pipeline (proteome discoverer)
-  - ricinus proteome / reference from uniprot? If available
-  - t-test, fold changes, ratios, normalised abundances for every peptide => plenty of excel tables
-  - possible output fasta of identified peptides / proteins?
\ No newline at end of file

_DominikNotes/2022-04-13_data_notes.md

-
-# questions to Anja 
-
-- what's the ricinus reference?
-
-- analysis protocols?
-  - what statistical tests?
-  - why membrane vs. lumen?
-    - can we also get ER vs. mito etc.?
-
-1. Hast du eine spezifische Referenz für Ricinus genommen (welche?) oder vergleicht das komplett gegen UniProt (und daher die gefundenen Contaminants)?
-
-2. Bezieht sich die "Protein FDR Confidence: Combined"? auf den Match zwischen identifiziertem Protein und der Accession?
-
-3. Es gibt insgesamt 31 Contaminant=TRUE. Sollten wir die herausfiltern? Und mal aus Neugierde: Das ist überwiegend Keratin - ist das üblich oder unsaubere Arbeit?
-
-4. Zu den abundances:
-   - Wie werden die Ratios berechnet? fold changes, log2 fold changes? Und wie werden dann NAs behandelt?
-   - Welche Statistik (t test, Anova?) und Methode für adjusted p-value?
-   - Wie werden die normalisiert und gruppiert?
-
-5. Gibt's dazu vielleicht einen standard Report oder andere Beschreibung von Proteome Discoverer? Ich versuche für unsere Daten immer so eine Art Data Dictionary anzulegen (siehe Anhang) - vielleicht gibt Proteome Discoverer auch standardisierte Definitionen für die einzelnen Spalten raus. 
-
-6. Komplette ANOVA, die dann auch z.B. ER-Membrane gegen Mito-Lumen vergleicht wäre vermutlich overkill. Aber könntest du mit wenig Aufwand auch jeweils die Membrane Samples und die Lumen Samples gegeneinander testen (Also Mito-Mem vs. ER-Mem vs. Peroxisome-Mem, ...)?
-
-7. Dürfen NAs bei den abundances durch 0 ersetzt werden?
-
-8. Gibt es Insgesamt für die Datenanalyse eine Art Protokoll?  
-
-Auf den ersten Blick (schnelle PCA) sehen die Ergebnisse nicht so verkehrt aus bzw. Trennen zumindest schön nach Organelle und compartment. Lediglich die ER Proben schlagen etwas raus.

_DominikNotes/README.md

-
-# setting up this arc
-
-> using arcCommander v0.3.1
-
-```bash
-arc init
-arc i create -i "Ricinus proteome"
-mkdir _NicolesData; touch _NicolesData/.gitkeep
-```
-
-## add raw dataset as retrieved from Anja Stefanski via Sciebo (06.04.2022)
-
-### setup assay structure
-
-```bash
-arc assay add -s "2012_ricinus_proteome_wrobel" -a "2012_proteome_data_bmfz_stefanski"
-arc assay add -s "2012_ricinus_proteome_wrobel" -a "2022-04-13_proteome_discoverer"
-```
-
-### run md5s on downloaded data
-
-```bash
-md5 ~/Downloads/Daten\ Linka/*.raw > _NicolesData/raw_data_md5s.txt
-```
-
-### Add large data to gitignore for now...
-
-```bash
-printf "\n*.raw" >> .gitignore
-```
-
-### add downloaded data to arc
-
-```bash
-mv ~/Downloads/Daten\ Linka/Messliste_Wrobel.xlsx _NicolesData/
-mv ~/Downloads/Daten\ Linka/*.raw assays/2012_proteome_data_bmfz_stefanski/dataset
-```
-
-### sync arc with GitLab
-
-```bash
-arc sync -m "add raw data from bmfz"
-```

_DominikNotes/notes_data.md

-
-
-- input
-  - FASTA database: ricinus genome, proteom, transcriptome?
\ No newline at end of file

_DominikNotes/notes_focus_issue.md

-
-
-https://www.frontiersin.org/research-topics/26431/photosynthetic-and-photorespiratory-organelles-metabolism-dynamics-and-signaling
-
-## area: "Interaction and crosstalk between these organelles" ?
-
-## Submission deadline: 14 June 2022
\ No newline at end of file

_DominikNotes/notes_proteomics.md

-
-# proteomics notes
-
-> adapted from: https://training.galaxyproject.org/training-material/topics/proteomics/slides/introduction
-
-- [glossary](#glossary)
-- [sample-prep](#sample-prep)
-- [LC](#lc)
-- [LC-MS/MS - liquid chromatography tandem mass spectrometry](#lc-msms---liquid-chromatography-tandem-mass-spectrometry)
-- [data analysis](#data-analysis)
-
-## glossary
-
-- LFQ = label-free quantification
-
-- explorative / shotgun proteomics (untargeted)
-  - DDA: data dependent acquisition
-  - DIA: data independent acquisition
-
-## sample-prep
-
-1. protein extraction
-2. reduction (of disulfide bridges) and alkylation (of cysteins)
-   - ensures that tryptic peptides are separated from each other and allows their mass based identification
-3. tryptic digestion: Trypsin cleaves the amino acid sequences C-terminal of arginin and lysine
-4. desalting: clean up step to protect the instrument from contamination and clogging
-
-## LC
-
-- separates peptide mixture according to hydrophobicity
-- reduces sample complexity and gives the mass spectrometer time for the measurement
-- acidic LC buffer charges the peptides positively at their N-terminus and the basic lysin or arginin amino acids on the C-terminus
-- electrospray ionization:
-  - LC column is directly connected with ion source needle
-  - high voltage and heat are applied to evaporate the ionized peptides into the gas phase
-  - mass spectrometer mass analyzer separates peptides based on their mass-to-charge ratio
-
-## LC-MS/MS - liquid chromatography tandem mass spectrometry
-
-- While sample elutes from the LC column, thousands of mass spectra are acquired
-- First, a mass spectrum of all peptides: **MS1 spectra**
-- From MS1 spectra the N most abundant peptide peaks are determined. These topN peptides get fragmented. N is typically between 3 and 20
-  - filter unit of the mass spectrometer (a quadrupole) allows only these peptides to pass
-  - One after the other is selected in the filter unit and then fragmented by collision with neutral gas molecules. This fragmentation breaks the peptide bonds and generates peptide fragments
-  - The peptide fragments are measured again via the mass analyzer and detector: **MS2 spectra**.
-  - After all topN peptides were fragmented and measured, another full MS1 mass spectra is acquired.
-  - MS1 and MS2 spectra are acquired in this way during the elution of the sample from the LC.
-
-## data analysis
-
-1. peptides are identified via their MS2 fragmentation spectra
-2. From these peptide identities the corresponding proteins are assembled
-3. The MS1 spectra are used for peptide quantification
-4. Peptide quantities are summarized into protein quantities
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_DominikNotes/notes_software.md

-
-# Notes on proteomics software
-
-- [MaxQuant](#maxquant)
-  - [installation](#installation)
-  - [tutorial](#tutorial)
-  - [galaxy](#galaxy)
-- [rawrr package](#rawrr-package)
-- [pyteomics](#pyteomics)
-
-## MaxQuant
-
-### installation
-
-### tutorial
-
-- hot to run maxquant on linux: https://youtu.be/KHdvO1M85VM
-- requires windows...
-
-### galaxy 
-- https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/maxquant-label-free/tutorial.html
-
-
-## rawrr package
-
-- The rawrr R Package: Direct Access to Orbitrap Data and Beyond
-- https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00866
-
-
-## pyteomics 
-
-- https://github.com/levitsky/pyteomics
\ No newline at end of file