- New Chlamydomonas reinhardtii chromatin states
- Additional down-stream analysis for differential proteins
- Epigenome-browser
Chlamytina is a small project focused in the well known green-algae model Chlamydomonas reinhardtii trying to answer a common question in some proteomic/transcriptomic studies:
Are my molecules of interest epigenetically regulated?
To fill this gap, we collected all epigenectic files published until the date (Fu et al., 2015, Lopez et al., 2015, Ngan et al., 2015) and developed a new chromatin states model (ChromHMM) including 6mA, 5mC and nucleosome-profile for the first time. Additionally,
an epigenome-browser was conducted focusing on the site-specific approach. This tool engage the link-up between proteomic/transcriptomic changes and epigenetic patterns, thus displaying the
Chlamydomonas reinhardtii epi-proteogenomic landscape.
The data has to be input as a table (first column = Phytozome accessions, other columns = different treatment abundance/expression) in .xlsx, .xlx or tab-separated .txt format.
At least a Linux Ubuntu 19.04 OS is required. Download this repository as zip or clone with the command line
git clone https://github.com/RocesV/Chlamytina
You need a user with root permissions. Change directory to Chlamytina and run 0_ChlamytinaEssential.sh to install all dependecies and software needed.
cd Chlamytina/ # IMPORTANT: all processes need to be done inside Chlamytina directory NOT from Chlamytina subdirectories
bash Code/0_ChlamytinaEssential.sh
Using this via the following steps (1_DataPrepare and 2_EnrichmentsLOLA) will require more time (~ 1.5 h) for installing the R packages needed.
Install Docker or Docker Desktop as follow https://docs.docker.com/ . This is the only way to get access to epigenome-browser. Pull rocesv/chlamytina image from dockerhub
docker pull rocesv/chlamytina
Build the container using the image pulled. Because the jbrowse inside the container is running in apache2 server, an empty port from the host (8080) need to be connected to container's 80 port. In order to share data between host and the container it is advisable to define a volume (-v) linking a host directory to /home/rocesv/Documents/Transfer folder.
docker run -t -i -d --name chlamytina_rocesv -p 8080:80 -v <ABSOLUTE PATH TO HOST SHARED DIRECTORY>:/home/rocesv/Documents/Transfer rocesv/chamytina bash
Check docker container is running
docker ps -a
Get inside the container
docker exec -i -t chlamytina_rocesv bash
Now you should see your container user like root@6a32e10fc951:/home#. Change directory to Chlamytina
cd Chlamytina/
Brief docker tutorial:
docker stop chlamytina_rocesv # Stop the container
docker start chlamytina_rocesv # Start the container
exit # Get outside the container
Using this via the time required for the following steps is minimum.
1_DataPrepare.R script performs differential expression analysis (limma), unwanted variation correction (sva), Phytozome Accession liftover to the last version (5.5), intersection (nVennR) between proteins of different treatments and bed data generation (possible backgrounds and inputs for 4. LOLA Enrichment).
Rscript --vanilla Code/1_DataPrepare.R -h
Usage: 1_DataPrepare.R [file] [condition] [file] [condition] ... [options]
Options:
-A CHARACTER, --file1=CHARACTER
Dataset1 file path. First column CreIDs. Other columns quantification data.
--condition1=CHARACTER
Dataset1 Condition vector. It representes replicates for each treatment, separated by -
Condition vector must contain all your replicates. For example (9 samples): 1) 3-6 will set a contrast between the first three replicates and the last six
2) 3-3-3 will set all possible two by two contrasts between the three treatments
-B CHARACTER, --file2=CHARACTER
Dataset2 file path
--condition2=CHARACTER
Dataset2 Condition vector
-C CHARACTER, --file3=CHARACTER
Dataset3 file path
--condition3=CHARACTER
Dataset3 Condition vector
-D CHARACTER, --file4=CHARACTER
Dataset4 file path
--condition4=CHARACTER
Dataset4 Condition vector
-E CHARACTER, --file5=CHARACTER
Dataset5 file path
--condition5=CHARACTER
Dataset5 Condition vector
-d DIFFERENTIAL, --differential=DIFFERENTIAL
If true, differential expression limma based test is performed [default = TRUE]
-s SVA, --sva=SVA
If true, sva removing unwanted variation is performed. Only for n>10-15 samples datasets. [default = FALSE]
-i CHARACTER, --intersect=CHARACTER
CreIDs intra-inter group specific discrimination [default = TRUE]
-o CHARACTER, --out=CHARACTER
Output directory [default = ./Outputs/]
-c CHARACTER, --chromosome=CHARACTER
If true, non-chromosome mapped (scaffolds ...) proteins are not taked into account [default = TRUE]
-n CHARACTER, --normalization=CHARACTER
Normalization metric used. Options: normalizeQuantiles (limma), none
It is advisable to set this argument as none and preprocess the data with other pkgs like Processomics [default = none]
-h, --help
Show this help message and exit
Example:
Rscript --vanilla Code/1_DataPrepare.R -A <PATH TO DATASET>/Dataset1.xlsx --condition1 3-15 -B <PATH TO DATASET>/Dataset2.xlsx --condition2 4-20 -C <PATH TO DATASET>/Dataset3.xlsx --condition3 3-6
-s TRUE -n normalizeQuantiles
To decipher the potential epigenetic regulation of the dataset, 2_EnrichmentsLOLA.R script performs enrichment analysis based on genomic regions overlap using LOLA package and plots a heatmap. This analysis needs three components:
a) Query set or input, as genomic regions (.bed outputs from 1_DataPrepare.R)
b) Universe or background, set of regions that could potentially have been included in the query set. This depends on the biological question, see FAQ (.bed outputs from 1_DataPrepare.R or Data/DB/Universe.bed)
c) region data base sets that are to be tested for overlap with the input (Data/regionDB/Chlamytina)
Rscript --vanilla Code/2_EnrichmentsLOLA -h
Usage: 2_EnrichmentsLOLA.R [file] [file] [file] [background] [database] ... [options]
Options:
-A CHARACTER, --file1=CHARACTER
First BED file path. Any BED file with chr, start and end or DataPrepare output
-B CHARACTER, --file2=CHARACTER
Second BED file path
-C CHARACTER, --file3=CHARACTER
Third BED file path
-D CHARACTER, --file4=CHARACTER
Fourth BED file path
-E CHARACTER, --file5=CHARACTER
Fifth file path
-F CHARACTER, --file6=CHARACTER
Sixth file path
-G CHARACTER, --file7=CHARACTER
Seventh file path
-H CHARACTER, --file8=CHARACTER
Eighth file path
-I CHARACTER, --file9=CHARACTER
Nineth file path
-J CHARACTER, --file10=CHARACTER
Tenth file path
-b CHARACTER, --background=CHARACTER
Background BED file path. The set of regions tested for enrichments
-l CHARACTER, --list=CHARACTER
If true, the rest of args are ignored and list all the files for one regionDB
-o CHARACTER, --out=CHARACTER
Output directory [default = ./Outputs/]
-r CHARACTER, --database=CHARACTER
regionDB used. Options: Marks (epigenetic marks by original conditions), MMarks (merged Marks wo conditions) or CS_Control, CS_N, CS_S (Ngan et al., Nat.Plants 2015, Chromatin States !Nitrogen !Sulfur) or CS_Chlamytina (Updated Chromatin states with 5mC, 6mA and MNase) [default = MMarks]
-c CHARACTER, --cores=CHARACTER
Number of cores [default = 1]
-h, --help
Show this help message and exit
Example:
Rscript --vanilla Code/2_EnrichmentsLOLA.R -A Query1.bed -B Query2.bed -C Query3.bed -D Query4.bed -b Data/DB/Universe.bed -r CS_Chlamytina
Once you got into the docker container (2. Installation - Via Dockerhub) you need to start the apache2 server
service apache2 start
Now you can enjoy the epigenome browser at http://localhost:8080/jbrowse . The browser will be available while the container is running so as long as docker stop chlamytina_rocesv is not executed you
can acces to jbrowse. We recommend to always select refseq track and one of the .Genes tracks (Nuclear, Mitochondrion, Chloroplast). The epigenomic tracks can be displayed by condition (Control, light ...)
or merged (M-).
(Q) What type of outputs can I obtain from Chlamytina?
- It depends on the arguments selected. For a more detailed description, see Outputs/README.md
(Q) What type of inputs can I use?
- Both transcripts and proteins can be used as long as the accessions comes from Phytozome
(Q) Which background/universe should I use?
- Using the same subset of proteins/transcripts as query you can obtain different results depending on the universe/background selected. Enrichments are computed using the background/universe as reference so all potential enrichments already present in your background will be deprecated. To obtain a general view of your data we recommend use first whole proteome/coding-transcriptome background (Data/DB/Universe.bed) because your global background may have some enrichments. Possible background in Chlamytina: Universe.bed, Global_background.bed (total set of proteins), Differential_background.bed (total set of differential proteins), file1.bed (single file set of proteins). Any .bed file that include your query can be used as background but you have to be careful with query:background size.
(Q) Why are there different regionDBs?
- To be able to elucidate the epigenetic regulation at different levels (marks with conditions, merged marks, chromatin states) we decided to maintain several regionDBs so you can choose the one that best suits your questions. Data/DB/CS_Chlamytina_interpretation heatmaps would make easier the biological interpretation of CS_Chlamytina regionDB results