Contents
- 1 Introduction and programs
- 2 Accessing the data using sra-toolkit
- 3 Quality control using sickle
- 4 Aligning reads to a genome using HISAT2
- 5 Reference Guided Transcript Assembly
- 6 Transcript quantification with StringTie
- 7 Differential expression analysis using Ballgown
- 8 Gene annotation with BiomaRt
- 9 Topological networking using Cytoscape
- 10 Conclusion
Arabidopsis thaliana is a small flowering plant that is used as a model system in research of plant biology. It helped researchers to build basic understanding around molecular, biochemical and genetics processes in the plants. A wealth of knowledge and information is available around Arabidopsis genomics (genome sequence, transcriptome, genetic markers etc) and hence could be used as ideal system to develop fundamental understanding plant RNA-seq analysis before venturing in the transcriptomics world of non model species. In this tutorial we will be using RNAseq dataset from the flower buds of A. thaliana and the study was published in "Frontiers in Plant Science" (https://www.frontiersin.org/articles/10.3389/fpls.2019.00763/full). This study was aimed at understanding the functional role of Monoacylglycerol lipase (MAGL) hydrolyzes known to produce free fatty acid and glycerol. The enzyme is well studied in animal kingdom but a little is known about its function in plants. This study involves ectopic expression (EE) of BnaC.MAGL8.a in Arabidopsis to explore its potential biological function. They observed that this ectopic expression causes male sterility by affecting the devlopment of pollens. To develop their molecular understanding around the process they carried out RNAseq studies in the flower buds. Total 6 RNAseq datasets representing 3 biological replicates each for WT and BnaC.MAGL8.a were used in this study The RNA profiles are archived in the SRA, and meta Information on each may be viewed through the SRA ID: SRR8428904, SRR8428905, SRR8428906, SRR8428907, SRR8428908, SRR8428909(https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP178230).
The Sequence Read Archive, or SRA, is a publicly available database containing read sequences from a variety of experiments. Scientists who would like their read sequences present on the SRA submit a report containing the read sequences, experimental details, and any other accessory meta-data.
Our data, SRR8428904, SRR8428905, SRR8428906, SRR8428907, SRR8428908, SRR8428909 come from EE1, EE2, EE3, WT1, WT2, and WT3 respectively. Our objective is to identify genes which are differentially expressed between WT and EE sample conditions. We have 3 replicates for each condition WT1, WT2, WT3 and EE1, EE2, EE3 for wildtype and ectopic-expression conditions respectively. Finally we will create a visual topological network of genes with similar expression profiles.
You may connect to Xanadu via SSH, which will direct you in your home directory
-bash-4.2$ cd /home/CAM/$USER
Your home directory contains 2TB of storage and will not pollute the capacities of other users on the cluster.
The workflow may be cloned into the appropriate directory using the terminal command:
git clone https://github.com/CBC-UCONN/RNA-Seq-Model-Organism-Arabidopsis-thaliana.gitThen change the directory to the RNA-Seq-Model-Organism-Arabidopsis-thaliana/ folder, where you can see the following folder structure:
RNA-Seq-Model-Organism-Arabidopsis-thaliana
├── raw_data
├── trimmed_reads
├── trimmed_fastqc
├── mapping
└── ballgown
The tutorial is divided in sections so you can follow it easily.
We know that the SRA contain the read sequences and accessory meta Information from experiments. Rather than downloading experimental data through a browser, we may use the sratoolkit's "fastq-dump" function to directly dump raw read data into the current terminal directory. Let's have a look at this function (it is expected that you have read the Xanadu tutorial, and are familiar with loading modules):
To load the module and to check the options you can simply type fastq-dump once you load the module in the terminal window.
module load sratoolkit
fastq-dumpWhich will show you the following options it has:
fastq-dump [options] <path> [<path>...]
fastq-dump [options] <accession>
Use option --help for more Information
fastq-dump : 2.8.2
For our needs, we will simply be using the accession numbers to download our experimental data into our directory. We know our accession numbers, so let's write a shell script to retrieve our raw reads. There are a variety of text editors available on Xanadu. My preferred text editor is "nano". Therefore, we will be using nano to write our shell script.
nano data_dump.sh
#!/bin/bash
#SBATCH --job-name=data_dump
#SBATCH [email protected]
#SBATCH --mail-type=ALL
#SBATCH -n 1
#SBATCH -N 1
#SBATCH -c 1
#SBATCH --mem=10G
#SBATCH -o %x_%j.out
#SBATCH -e %x_%j.err
#SBATCH --partition=general
#SBATCH --qos=general
mkdir /home/CAM/$USER/tmp/
export TMPDIR=/home/CAM/$USER/tmp/
module load sratoolkit
fasttq-dump --split-files SRR8428909
mv SRR8428909_1.fastq wt_Rep1_R1.fastq
mv SRR8428909_2.fastq wt_Rep1_R2.fastq
fastq-dump --split-files SRR8428908
mv SRR8428908_1.fastq wt_Rep2_R1.fastq
mv SRR8428908_2.fastq wt_Rep2_R2.fastq
fastq-dump --split-files SRR8428907
mv SRR8428907_1.fastq wt_Rep3_R1.fastq
mv SRR8428907_2.fastq wt_Rep3_R2.fastq
fastq-dump --split-files SRR8428906
mv SRR8428906_1.fastq EE_Rep1_R1.fastq
mv SRR8428906_2.fastq EE_Rep1_R2.fastq
fastq-dump --split-files SRR8428905
mv SRR8428905_1.fastq EE_Rep2_R1.fastq
mv SRR8428905_2.fastq EE_Rep2_R2.fastq
fastq-dump --split-files SRR8428904
mv SRR8428904_1.fastq EE_Rep3_R1.fastq
mv SRR8428904_2.fastq EE_Rep3_R2.fastqThe full slurm script is called data_dump.sh can be found in raw_data/ folder.
As a precautionary measure, always include your temporary directory in the environment. While not all programs require a temporary directory to work, it takes far less time to include ours in the environment than it would to wait for an error! After typing our script, we press CTRL + X to exit, 'y', and then enter to save.
Now that we have our script saved, we submit it to the compute nodes with the following command:
-bash-4.2$ sbatch data_dump.sh
Now we wait until we receive an email that our process has finished.
Let's take a look at one of our files:
-bash-4.2$ head EE_Rep1_R1.fastq
@SRR8428906.1 1 length=150
CCTGAACAACTCATCAGCGGTAAAGAAGATGCAGCTAACAATTTCGCCCGTGGTCATTACACCATTGGGAAAGAGATTGTTGACCTGTGCTTAGACCGTATCAGAAAGCTTGCTGATAACTGTACTGGTCTCCAAGGATTCCTCGTCTTC
+SRR8428906.1 1 length=150
AAAFFJFJJ<FJFJJJJJJJJJFJJJJFJAJJJJJJFJJJJJJJJJJJJFJAJJFJJJJJJJJJJJJAAFJFJJJJFJJJJJFFJJFJJJJJJJJJJFJJ7<AFAJJFFJJJJJJJJJJJJJJJJFJFJ7AJFJJ<AJJJJFJ7FFJJJ-
@SRR8428906.2 2 length=150
GGGTCTTGTATGCCTCAGCAGGGATATCAGCGAAGTAGGTCTCGACAAGAACATTCAAACCAGAAAGAGTTGATTCAAGTTCAGCATAGGCACCAGTAAAGGCCTGGAGTTTCTGACCCTCAAGATCCATAACAAGGACAGGCTCGTCAA
+SRR8428906.2 2 length=150
<AFFFJJJJFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ7JJJJJFJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJJJJJJJ<JJ
@SRR8428906.3 3 length=150
AAAATCTGTAGAATCATCTTTCAACAATGGCTTCCACTGCTCTCTCCAGCGCAATCGTAAGCACCTCTTTCCTCCGCCGTCAACAGACACCAATCAGCCTCAGTTCCCTCCCGTTTGCCAACACACAATCTCTCTTCGGCCTCAAATCTTWe see that for our first three runs we have Information about the sampled read including its length followed by the nucleotide read and then a "+" sign. The "+" sign marks the beginning of the corresponding scores for each nucleotide read for the nucleotide sequence preceding the "+" sign.
Sickle performs quality control on illumina paired-end and single-end short read data using a sliding window. As the window slides along the fastq file, the average score of all the reads contained in the window is calculated. Should the average window score fall beneath a set threshold, sickle determines the reads responsible and removes them from the run. After visiting the SRA pages for our data, we see that our data are single end reads. Let's find out what sickle can do with these:
-bash-4.2$ module load sickle -bash-4.2$ sickle Usage: sickle [options] Command: pe paired-end sequence trimming se single-end sequence trimming --help, display this help and exit --version, output version Information and exit
We have single-end sequences.
-bash-4.2$ sickle pe Usage: sickle pe [options] -f -r -t -o -p -s Options: Options: Paired-end separated reads -------------------------- -f, --pe-file1, Input paired-end forward fastq file (Input files must have same number of records) -r, --pe-file2, Input paired-end reverse fastq file -o, --output-pe1, Output trimmed forward fastq file -p, --output-pe2, Output trimmed reverse fastq file. Must use -s option. Paired-end interleaved reads ---------------------------- -c, --pe-combo, Combined (interleaved) input paired-end fastq -m, --output-combo, Output combined (interleaved) paired-end fastq file. Must use -s option. -M, --output-combo-all, Output combined (interleaved) paired-end fastq file with any discarded read written to output file as a single N. Cannot be used with the -s option. Global options -------------- -t, --qual-type, Type of quality values (solexa (CASAVA < 1.3), illumina (CASAVA 1.3 to 1.7), sanger (which is CASAVA >= 1.8)) (required) -s, --output-single, Output trimmed singles fastq file -q, --qual-threshold, Threshold for trimming based on average quality in a window. Default 20. -l, --length-threshold, Threshold to keep a read based on length after trimming. Default 20. -x, --no-fiveprime, Don't do five prime trimming. -n, --truncate-n, Truncate sequences at position of first N. -g, --gzip-output, Output gzipped files. --quiet, do not output trimming info --help, display this help and exit --version, output version information and exit
The quality may be any score from 0 to 40. The default of 20 is much too low for a robust analysis. We want to select only reads with a quality of 25 or better and a minimum acceptible read length of 45 bps post trimming. The 45bp read length will ensure unique mapping of the reads across the genome. Lastly, we must know the scoring type. While the quality type is not listed on the SRA pages, most SRA reads use the "sanger" quality type. Unless explicitly stated, try running sickle using the sanger qualities.
Let's put all of this together for our sickle script using our downloaded fastq files:
-bash-4.2$ nano sickle_run.sh #!/bin/bash #SBATCH --job-name=sickle_run #SBATCH --mail-user= #SBATCH --mail-type=ALL #SBATCH -n 1 #SBATCH -N 1 #SBATCH -c 1 #SBATCH --mem=10G #SBATCH -o sickle_run_%j.out #SBATCH -e sickle_run_%j.err #SBATCH --partition=general #SBATCH --qos=general export TMPDIR=/home/CAM/$USER/tmp/ module load sickle sickle pe -t sanger -f ../raw_data/wt_Rep1_R1.fastq -r ../raw_data/wt_Rep1_R2.fastq -o trimmed_wt_Rep1_R1.fastq -p trimmed_wt_Rep1_R2.fastq -l 45 -q 25 -s singles_wt_Rep1_R1.fastq sickle pe -t sanger -f ../raw_data/wt_Rep2_R1.fastq -r ../raw_data/wt_Rep2_R2.fastq -o trimmed_wt_Rep2_R1.fastq -p trimmed_wt_Rep2_R2.fastq -l 45 -q 25 -s singles_wt_Rep2_R1.fastq sickle pe -t sanger -f ../raw_data/wt_Rep3_R1.fastq -r ../raw_data/wt_Rep3_R2.fastq -o trimmed_wt_Rep3_R1.fastq -p trimmed_wt_Rep3_R2.fastq -l 4 5-q 25 -s singles_wt_Rep3_R1.fastq sickle pe -t sanger -f ../raw_data/EE_Rep1_R1.fastq -r ../raw_data/EE_Rep1_R2.fastq -o trimmed_EE_Rep1_R1.fastq -p trimmed_EE_Rep1_R2.fastq -l 45 -q 25 -s singles_EE_Rep1_R1.fastq sickle pe -t sanger -f ../raw_data/EE_Rep2_R1.fastq -r ../raw_data/EE_Rep2_R2.fastq -o trimmed_EE_Rep2_R1.fastq -p trimmed_EE_Rep2_R2.fastq -l 45 -q 25 -s singles_EE_Rep2_R1.fastq sickle pe -t sanger -f ../raw_data/EE_Rep3_R1.fastq -r ../raw_data/EE_Rep3_R2.fastq -o trimmed_EE_Rep3_R1.fastq -p trimmed_EE_Rep3_R2.fastq -l 45 -q 25 -s singles_EE_Rep3_R1.fastq
The full slurm script is called sickle_run.sh can be found in trimmed_reads/ folder.
-bash-4.2$ sbatch sickle_run.sh
It is helpful to see how the quality of the data has changed after using sickle. To do this, we will be using the commandline versions of fastqc and MultiQC. These two programs simply create reports of the average quality of our trimmed reads, with some graphs. There is no way to view a --help menu for these programs in the command-line. However, their use is quite simple, we simply run "fastqc <trimmed_fastq>" or "multiqc -f -n trimmed trimmed". Do not worry too much about the options for MultiQC! Let's write our script:
-bash-4.2$ nano quality_control.sh #!/bin/bash #SBATCH --job-name=quality_control #SBATCH --mail-user= #SBATCH --mail-type=ALL #SBATCH -n 1 #SBATCH -N 1 #SBATCH -c 4 #SBATCH --mem=10G #SBATCH -o %x_%j.out #SBATCH -e %x_%j.err #SBATCH --partition=general #SBATCH --qos=general export TMPDIR=/home/CAM/$USER/tmp/ module load fastqc module load MultiQC #Running in #trimmed_fastqc fastqc -t 4 ../trimmed_reads/trimmed_wt_Rep1_R1.fastq ../trimmed_reads/trimmed_wt_Rep1_R2.fastq fastqc -t 4 ../trimmed_reads/trimmed_wt_Rep2_R1.fastq ../trimmed_reads/trimmed_wt_Rep2_R2.fastq fastqc -t 4 ../trimmed_reads/trimmed_wt_Rep3_R1.fastq ../trimmed_reads/trimmed_wt_Rep3_R2.fastq fastqc -t 4 ../trimmed_reads/trimmed_EE_Rep1_R1.fastq ../trimmed_reads/trimmed_EE_Rep1_R2.fastq fastqc -t 4 ../trimmed_reads/trimmed_EE_Rep2_R1.fastq ../trimmed_reads/trimmed_EE_Rep2_R2.fastq fastqc -t 4 ../trimmed_reads/trimmed_EE_Rep3_R1.fastq ../trimmed_reads/trimmed_EE_Rep3_R2.fastq multiqc -n trimmed_fastqc .
The full slurm script trimmed_fastqc.sh can be found in rimmed_fastqc/ folder.
-bash-4.2$ sbatch quality_control.sh
fastqc will create the files "trimmed_file_fastqc.html". To have a look at one, we need to move all of our "trimmed_file_fastqc.html" files into a single directory, and then secure copy that folder to our local directory. Then, we may open our files!
This script will also create a directory "trimmed_data". Let's look inside of that directory:
-bash-4.2$ cd trimmed_data< -bash-4.2$ ls multiqc_fastqc.txt multiqc.log multiqc_general_stats.txt multiqc_sources.txt
Let's have a look at the file format from fastqc and multiqc. When loading the fastqc file, you will be greeted with this screen:
There are some basic statistics which are all pretty self-explanatory.
This screen is simply a box-and-whiskers plot of our quality scores per base pair.
Our next index is the per sequence quality scores:
This index is simply the total number of base pairs (y-axis) which have a given quality score (x-axis). This plot is discontinuous and discrete, and should you calculate the Riemann sum the result is the total number of base pairs present across all reads.
The last index at which we are going to look is the "Overrepresented Sequences" index:
This is simply a list of sequences which appear disproportionately in our reads file. The reads file actually includes the primer sequences for this exact reason. When fastqc calculates a sequence which appears many times beyond the expected distribution, it may check the primer sequences in the reads file to determine if the sequence is a primer. If the sequence is not a primer, the result will be returned as "No Hit". Sequences which are returned as "No Hit" are most likely highly expressed genes.
We see that our multiqc file has the same indices as our fastqc files, but is simply the mean of all the statistics across our fastqc files:
-bash-4.2$ module load hisat2 -bash-4.2$ hisat2-build No input sequence or sequence file specified! HISAT2 version 2.1.0 by Daehwan Kim ( [email protected], http://www.ccb.jhu.edu/people/ Infphilo) Usage: hisat2-build [options] reference_in comma-separated list of files with ref sequences hisat2_index_base write ht2 data to files with this dir/basename
As you can see, we simply enter our reference genome files and the desired prefix for our .ht2 files. Now, fortunately for us, Xanadu has many indexed genomes which we may use. To see if there is a hisat2 Arabidopsis thaliana indexed genome we need to look at the Xanadu databases page. We see that our desired indexed genome is in the location /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/Athaliana_HISAT2/. Now we are ready to align our reads using hisat2 (for hisat2, the script is going to be written first with an explanation of the options after).
nano hisat2_run.sh#!/bin/bash
#SBATCH --job-name=hisat2_run
#SBATCH --mail-user=
#SBATCH --mail-type=ALL
#SBATCH -n 1
#SBATCH -N 1
#SBATCH -c 8
#SBATCH --mem=120G
#SBATCH -o hisat2_run_%j.out
#SBATCH -e hisat2_run_%j.err
#SBATCH --partition=general
#SBATCH --qos=general
export TMPDIR=/home/CAM/$USER/tmp/
module load hisat2
mkdir -p ../mapping
hisat2 -p 8 --dta -x /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/athaliana10/athaliana10 -1 ../trimmed_reads/trimmed_wt_Rep1_R1.fastq -2 ../trimmed_reads/trimmed_wt_Rep1_R2.fastq -S ../mapping/wt_Rep1.sam
hisat2 -p 8 --dta -x /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/athaliana10/athaliana10 -1 ../trimmed_reads/trimmed_wt_Rep2_R1.fastq -2 ../trimmed_reads/trimmed_wt_Rep2_R2.fastq -S ../mapping/wt_Rep2.sam
hisat2 -p 8 --dta -x /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/athaliana10/athaliana10 -1 ../trimmed_reads/trimmed_wt_Rep3_R1.fastq -2 ../trimmed_reads/trimmed_wt_Rep3_R2.fastq -S ../mapping/wt_Rep3.sam
hisat2 -p 8 --dta -x /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/athaliana10/athaliana10 -1 ../trimmed_reads/trimmed_EE_Rep1_R1.fastq -2 ../trimmed_reads/trimmed_EE_Rep1_R2.fastq -S ../mapping/_EE_Rep1.sam
hisat2 -p 8 --dta -x /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/athaliana10/athaliana10 -1 ../trimmed_reads/trimmed_EE_Rep2_R1.fastq -2 ../trimmed_reads/trimmed_EE_Rep2_R2.fastq -S ../mapping/_EE_Rep2.sam
hisat2 -p 8 --dta -x /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/athaliana10/athaliana10 -1 ../trimmed_reads/trimmed_EE_Rep3_R1.fastq -2 ../trimmed_reads/trimmed_EE_Rep3_R2.fastq -S ../mapping/_EE_Rep3.sam
Command
-p : number of processors been used
--dta: report alignments tailored for transcript assemblers
-x: path to index generated from previous step
-q: query input files in fastq format
-S: output SAM file
The full slurm script hisat2_run.sh can be found in the mapping/ directory.
You can run this using sbatch hisat2_run.sh
Once the mapping have been completed, the file structure is as follows:
bash-4.2$ ls wt_Rep1.sam wt_Rep2.sam wt_Rep3.sam EE_Rep1.sam EE_Rep2.sam EE_Rep3.sam
When HISAT2 completes its run, it will summarize each of it’s alignments, and it is written to the standard error file, which can be found in the same folder once the run is completed.
bash-4.2$ less hisat2_run*err
6723160 reads; of these:
6723160 (100.00%) were paired; of these:
138497 (2.06%) aligned concordantly 0 times
6459952 (96.09%) aligned concordantly exactly 1 time
124711 (1.85%) aligned concordantly >1 times
----
138497 pairs aligned concordantly 0 times; of these:
24883 (17.97%) aligned discordantly 1 time
----
113614 pairs aligned 0 times concordantly or discordantly; of these:
227228 mates make up the pairs; of these:
134333 (59.12%) aligned 0 times
91008 (40.05%) aligned exactly 1 time
1887 (0.83%) aligned >1 times
99.00% overall alignment rate
6948614 reads; of these:
6948614 (100.00%) were paired; of these:
145561 (2.09%) aligned concordantly 0 times
6657086 (95.80%) aligned concordantly exactly 1 time
145967 (2.10%) aligned concordantly >1 times
----
145561 pairs aligned concordantly 0 times; of these:
25976 (17.85%) aligned discordantly 1 time
----
119585 pairs aligned 0 times concordantly or discordantly; of these:
239170 mates make up the pairs; of these:
138700 (57.99%) aligned 0 times
97982 (40.97%) aligned exactly 1 time
2488 (1.04%) aligned >1 times
99.00% overall alignment rate
7781644 reads; of these:
7781644 (100.00%) were paired; of these:
288062 (3.70%) aligned concordantly 0 times
7336202 (94.28%) aligned concordantly exactly 1 time
157380 (2.02%) aligned concordantly >1 times
----
288062 pairs aligned concordantly 0 times; of these:
25771 (8.95%) aligned discordantly 1 time
----
262291 pairs aligned 0 times concordantly or discordantly; of these:
524582 mates make up the pairs; of these:
311994 (59.47%) aligned 0 times
207748 (39.60%) aligned exactly 1 time
4840 (0.92%) aligned >1 times
98.00% overall alignment rate
6952854 reads; of these:
6952854 (100.00%) were paired; of these:
347150 (4.99%) aligned concordantly 0 times
6469572 (93.05%) aligned concordantly exactly 1 time
136132 (1.96%) aligned concordantly >1 times
----
347150 pairs aligned concordantly 0 times; of these:
25321 (7.29%) aligned discordantly 1 time
----
321829 pairs aligned 0 times concordantly or discordantly; of these:
643658 mates make up the pairs; of these:
550852 (85.58%) aligned 0 times
90597 (14.08%) aligned exactly 1 time
2209 (0.34%) aligned >1 times
96.04% overall alignment rate
7113688 reads; of these:
7113688 (100.00%) were paired; of these:
208961 (2.94%) aligned concordantly 0 times
6747265 (94.85%) aligned concordantly exactly 1 time
157462 (2.21%) aligned concordantly >1 times
----
208961 pairs aligned concordantly 0 times; of these:
23050 (11.03%) aligned discordantly 1 time
----
185911 pairs aligned 0 times concordantly or discordantly; of these:
371822 mates make up the pairs; of these:
264942 (71.26%) aligned 0 times
104137 (28.01%) aligned exactly 1 time
2743 (0.74%) aligned >1 times
98.14% overall alignment rate
6753777 reads; of these:
6753777 (100.00%) were paired; of these:
189669 (2.81%) aligned concordantly 0 times
6420786 (95.07%) aligned concordantly exactly 1 time
143322 (2.12%) aligned concordantly >1 times
----
189669 pairs aligned concordantly 0 times; of these:
30470 (16.06%) aligned discordantly 1 time
----
159199 pairs aligned 0 times concordantly or discordantly; of these:
318398 mates make up the pairs; of these:
222385 (69.84%) aligned 0 times
93444 (29.35%) aligned exactly 1 time
2569 (0.81%) aligned >1 times
98.35% overall alignment rate
Let's have a look at a SAM file:
-bash-4.2$ head -n 20 EE_Rep1.sam
@HD VN:1.0 SO:unsorted
@SQ SN:1 LN:30427671
@SQ SN:2 LN:19698289
@SQ SN:3 LN:23459830
@SQ SN:4 LN:18585056
@SQ SN:5 LN:26975502
@SQ SN:mitochondria LN:366924
@SQ SN:chloroplast LN:154478
SRR8428906.4 137 1 5041549 60 150M = 5041549 AAGAAGTTGAACTTGTGAGCGTGCAAAACAGAGAGCTGGATTGAGAAAACCGCAAGTTTTTGAGGCAATGTTTTGCAGAGAGAAGCCATGGATCTAACAAGTTCAACAAGAGAAAGAGTATCAAGATGATGAGTAGTCCGATTGAGAAGA AAA<AJFFFJ7JJJJJFJJ7F7FJJ7FJ<JJJFFJFJJJJJ-JFF<AAJJJJA<A<<FJJJJJJJJ<AJJJJ-FF7JFJ7FJFF7F7JJAJFF<AJJF<-AJJJJJJAJJJJ<-F7-FFFJJFJFJJJJJJ7F--7AFAFFF7F<F-777 AS:i:-10 XN:i:0 XM:i:3 XO:i:0 XG:i:0 NM:i:3 MD:Z:21A19A30C77 YT:Z:UP NH:i:1
SRR8428906.4 69 1 5041549 0 * = 5041549 0 TTTTGGTAATTATTATTAATATGAATGAGCAGTTCTGCCAATCTAAACAATAGCCAGAACTGCTCATTCATATTCATAATAATTACCAAAAGAGATTCAAACAAAACCACTCATCAGCATTGGATCTAAACTTCATCAATCGATAAATTC AAFFFJJJFFJJJFJJ-FJJJJJJJJJ-JFJJJJJJFJJFJJJJJJJFJJJJJFJJJJJFJJJFJJJJJJJJJJJJJJJJJJJJJFFJFJJJJJJFFJJJJJJJFJJ<F7A<FJJJFJJFJ<JJJJJJF-FJJJFJJJFFAF-AJFFJJJ YT:Z:UP
SRR8428906.5 99 2 19199346 60 150M = 19199425 229 ATCGTAAACCGGTACAAGCTTAAAACCGACGTCAAAACTTACAATCTCTCCGGTATGGGATGCAGCGCCGGCGCAATCTCCGTCGACCTCGCCACAAATCTACTAAAGGCAAACCCTAATACCTACGCAGTTATTGTTAGTACGGAGAAC AAFFFJFJJ<JJJFJJFFJJJJJJJJJJJJJJFFFJJJJJJJJJJJJJJ<JFJJFJJJJJJJFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJFJJJFJJFAFJJJJJJJJJJJJJJJJJJJ AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:150 YS:i:0 YT:Z:CP NH:i:1
SRR8428906.5 147 2 19199425 60 150M = 19199346 -229 CCGTCGACCTCGCCACAAATCTACTAAAGGCAAACCCTAATACCTACGCAGTTATTGTTAGTACGGAGAACATGACCCTAAGCATGTATCGAGGCAACGACCGGTCAATGTTAGTCCCTAACTGTCTATTCCGAGTAGGTGGCGCTGCGG A<F7JJFFJJJJA7AJJJF<F-FJJFJJJJJJJJJJJFJJJFFFFJJJJAJJFJJFJJFFJJJJF7JJJJFJJAF<J7-JAJJJJJFJFJJFAJFFJJJJJJJJJJJJFJJJJJFJJJJJJJJJJJJJJJJFJAJJJJJ<FJJJJFAFAA AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:150 YS:i:0 YT:Z:CP NH:i:1
SRR8428906.1 83 1 1357443 60 150M = 1357430 -163 GAAGACGAGGAATCCTTGGAGACCAGTACAGTTATCAGCAAGCTTTCTGATACGGTCTAAGCACAGGTCAACAATCTCTTTCCCAATGGTGTAATGACCACGGGCGAAATTGTTAGCTGCATCTTCTTTACCGCTGATGAGTTGTTCAGG -JJJFF7JFJJJJA<JJFJA7JFJFJJJJJJJJJJJJJJJJFFJJAFA<7JJFJJJJJJJJJJFJJFFJJJJJFJJJJFJFAAJJJJJJJJJJJJFJJAJFJJJJJJJJJJJJFJJJJJJAJFJJJJFJJJJJJJJJFJF<JJFJFFAAA AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:150 YS:i:-6 YT:Z:CP NH:i:1
SRR8428906.1 163 1 1357430 60 134M = 1357443 163 CACCAACAGCGTTGAAGACGAGGAATTCTTGGAGACCAGTACAGTTATCAGCAAGCTTTCTGATACGGTCTAAGCTCAGGTCAACAATCTCTTTCCCAATGGTGTAATGACCACGGGCGAAATTGTTAGCTGCA AAFF7F7-F7FJFAJJJJFFJFJFJJ-AFJJJFJJ<AJJJFAAFFFJFJJFJ7AFFFFJJJJFJAJJFFFJJJJJ-A-FJJFJJFJ7FJA<JJJJFJ-7FF-FJFJFFAJJJJJF7777-<F7AFFJFFJJJJJ AS:i:-6 XN:i:0 XM:i:2 XO:i:0 XG:i:0 NM:i:2 MD:Z:26C48A58 YS:i:0 YT:Z:CP NH:i:1
SRR8428906.14 77 * 0 0 * * 0 0 CTGATGCCGCCGTGTTTCGGCTGTCAGCGCAGGGGCGCCCGGTTCTTTTTGTCAAGACCGACCTGTCCGGTGCCCTGAATGAACTCCAGGACGAGGCAGCGCGGCTATCGTGGCTGGCCACGACGGGCGTTCCTTGCGCAGCTGTGCTCG AAFFFJJJJAJJJJJJJ-FJJJJJJJJJJJJJFJFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJFJJJJJJFFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ<JJJ-FJJJJFJFFJJJJJJJJJJJ YT:Z:UP
SRR8428906.14 141 * 0 0 * * 0 0 GTCACGACGAGATCCTCGCCGTCGGGCATGCGCGCCTTGAGCCTGGCGAACAGTTCGGCTGGCGCGAGCCCCTGATGCTCTTCGTCCAGATCATTCTGATCGACAAGACCGGCTTCCATCCGAGTACGTGCTCGTTCGATGCGATGTTTC AAAAFJJAJJJJJJJJJJAJJ7FJJJ<JJJJJJJJJJJJJJ7JFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJJJJF-<FJJJFJJJFJJJJ<FJJJJFJJFJJJJJJJFFJFJJJJ)AFJFJJ<JJJJJJFJ YT:Z:UP
SRR8428906.15 77 * 0 0 * * 0 0 GACCAGTATAAATCTTTGCCGCAACAGCTCTTCTCCGCCGCTCTCTTCTCCGATTGTTCTCCCTCTCTCTCCACGACGGTTTCCTCCTCGTCGCCATCGCTGCTGCTGCAGCTGCAGCGCTGGCGCCGAGGAGGTAACCGTCGTGGAGAG <AFFFFF<AFJJJJJJ-FFJJJJJJJJJJJJJJJFJA7AFJJJJJJJJJ<J<JFFFJJJJJJJJJJFJJJJJJJFFJ<AJJ<FJJFJFJJJJFFJJJJ<FJ<<<<JJFF-FJJFJ<AFJ-77AJJ7JAA7A<AJ-7JAAFFJ7AAF<--A YT:Z:UP
SRR8428906.15 141 * 0 0 * * 0 0 GTTCTCCCTCTCTCTCCACGACGGTCTCCTCCTCGTCGCCATCGCTGCAGCTGCAGCAGCAGCGATGGCGACGAGGAGGAAACCGTCGTGGAGAGAGAGGGAGAACAATCGGAGAAGAGAGCGGCGGAGAAGAGCTGTTGCGGCGAAGAT AAAFFAFJJJ7-FJJFJJJFA-FJJ-AFJJAFFJJ<JJJJ-AJFJJJA-FJJ7<-AJ--<AJFJ7JAJFJ--7AJFJJJ-FJFJAJFJ<FA77-77A-AF<7<FJJJJJ<AF<F<-7AFFJJJJJJJFAFFAFF-FAJFAF<FFJA<J-A YT:Z:UP
SRR8428906.10 83 4 12885525 60 150M = 12885314 -275 CATTGTTGTAAGTGCTGTGGATAATGTGCTCTAAGGGCTTGCCTTCAAGCTCGGTTCCAAGAACCTGTTTCTTGAGGTTGTCCACGTAAGCTCTGTGATGTTTTCCCCAGTGAAACTCCAGAGTTTGTTTGCTCATATGCGGCTCCAAAG FJJJJJJJJJJJFJJJJJFFFFJF<AJJF<FJFJJJJJFJ7JAFJAA<JJJA-JJJJJJJJJFFJJJJJFFFJA<JJJJJJF<FJFJ7JJ<-JFFFFJJJJJJJJJJJJJJAFFFJJJJJFJJJJJJJJJJA<FJJJJJF7JFJFFAAAA AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:150 YS:i:-3 YT:Z:CP NH:i:1
All of the lines starting with an "@" symbol tell us something about the chromosomes or our input. For instance "@SQ SN:Chr1 LN:30427671" tells us that we have a sequence (@SQ) whose sequence name is Chr1 (SN:Chr1), lastly the sequence has a length of 30427671bp (LN:30427671). You may be wondering what the first line means. It is quite straightfoward! The first line is simply the header (@HD) stating that the file is unsorted (SO:unsorted). The second column in the first line is somewhat of a dummy variable, but stands for "version number". Lastly we have the "@PG" line, which, in order, keeps track of the software used to write the file (ID:hisat2), the program name used to align the reads (PN:hisat2), the version of the program used (VN:2.1.0), and lastly the user input which started the process (written in the form that the program reads, not in which we wrote it).
The alignment portion of the SAM file is much more straight-forward and may be understood by reading the SAM output formatting guide linked in the beginning of this tutorial.
Because of the density of the sam file, it is compressed to binary to create a more easily tractable file for manipulation by future programs. We convert the sam file to bam with the following command and sort it such that the alignments are listed in the order the genes appear in the genome. To do this we use the software samtools:
-bash-4.2$ module load samtools
bash-4.2$ samtools
Usage: samtools [options]
Commands:
-- Indexing
dict create a sequence dictionary file
faidx index/extract FASTA
index index alignment
-- Editing
calmd recalculate MD/NM tags and '=' bases
fixmate fix mate Information
reheader replace BAM header
targetcut cut fosmid regions (for fosmid pool only)
addreplacerg adds or replaces RG tags
markdup mark duplicates
-- File operations
collate shuffle and group alignments by name
cat concatenate BAMs
merge merge sorted alignments
mpileup multi-way pileup
sort sort alignment file
split splits a file by read group
quickcheck quickly check if SAM/BAM/CRAM file appears intact
fastq converts a BAM to a FASTQ
fasta converts a BAM to a FASTA
-- Statistics
bedcov read depth per BED region
depth compute the depth
flagstat simple stats
idxstats BAM index stats
phase phase heterozygotes
stats generate stats (former bamcheck)
-- Viewing
flags explain BAM flags
tview text alignment viewer
view SAM<->BAM<->CRAM conversion
depad convert padded BAM to unpadded BAM
We are truly only interested in sorting our SAM files.
-bash-4.2$ samtools sort
Usage: samtools sort [options...] [in.bam]
Options:
-l INT Set compression level, from 0 (uncompressed) to 9 (best)
-m INT Set maximum memory per thread; suffix K/M/G recognized [768M]
-n Sort by read name
-t TAG Sort by value of TAG. Uses position as secondary index (or read name if -n is set)
-o FILE Write final output to FILE rather than standard output
-T PREFIX Write temporary files to PREFIX.nnnn.bam
--input-fmt-option OPT[=VAL]
Specify a single input file format option in the form
of OPTION or OPTION=VALUE
-O, --output-fmt FORMAT[,OPT[=VAL]]...
Specify output format (SAM, BAM, CRAM)
--output-fmt-option OPT[=VAL]
Specify a single output file format option in the form
of OPTION or OPTION=VALUE
--reference FILE
Reference sequence FASTA FILE [null]
-@, --threads INT
Number of additional threads to use [0]
The sort function converts SAM files to BAM automatically. Therefore, we can cut through most of these options and do a simple "samtools sort -o <output.bam> <inupt.sam>. Let's write our script:
bash-4.2$ #!/bin/bash #SBATCH --job-name=sam_sort_bam #SBATCH --mail-user= #SBATCH --mail-type=ALL #SBATCH -n 1 #SBATCH -N 1 #SBATCH -c 8 #SBATCH --mem=20G #SBATCH -o %x_%j.out #SBATCH -e %x_%j.err #SBATCH --partition=general #SBATCH --qos=general export TMPDIR=/home/CAM/$USER/tmp/ module load samtools samtools view -@ 8 -bhS wt_Rep1.sam -o wt_Rep1.bam samtools sort -@ 8 wt_Rep1.bam -o wt_Rep1_sort.bam samtools view -@ 8 -bhS wt_Rep2.sam -o wt_Rep2.bam samtools sort -@ 8 wt_Rep2.bam -o wt_Rep2_sort.bam samtools view -@ 8 -bhS wt_Rep3.sam -o wt_Rep3.bam samtools sort -@ 8 wt_Rep3.bam -o wt_Rep3_sort.bam samtools view -@ 8 -bhS EE_Rep1.sam -o EE_Rep1.bam samtools sort -@ 8 EE_Rep1.bam -o EE_Rep1_sort.bam samtools view -@ 8 -bhS EE_Rep2.sam -o EE_Rep2.bam samtools sort -@ 8 EE_Rep2.bam -o EE_Rep2_sort.bam samtools view -@ 8 -bhS EE_Rep3.sam -o EE_Rep3.bam samtools sort -@ 8 EE_Rep3.bam -o EE_Rep3_sort.bam
The full slurm script sam_sort_bam.sh can be found in mapping/ directory.
bash-4.2$ sbatch sam_sort_bam.sh
Stringtie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It can be executed in 3 different modes
- Exclusively reference guided : In this mode stringtie quantify the expression of known transcripts only.
- Reference guided transcript discovery mode : Quantify known transcripts and detect novel ones.
- De-novo mode : Detect and assemble transcripts.
We will be running stringtie using the option 2 that includes step 7, 8 and 9 of the workflow. In the first step of this process stringtie along with sample bam file and reference gtf file generate a gtf file corresponding to the sample. This gtf file has information on expression levels of transcripts, exons and other features along with any novel transcripts. The syntax of the command is
stringtie -p 4 -l label -G Reference.gtf -o sample.gtf sample.bam
In this command
-p specifies the number of threads to use.
-l label used in gtf file
-G Reference GTF available from public domain databases
-o output gtf corresponding to expression levels of features of the sample
Once we have run this command through all our six samples (WT1, WT2, WT3, EE1,EE2 and EE3) we will have 6 gtf files each corresponding to one of the sample containing feature expression values. Having 6 different gtf files is of no advantage as each may contain the same novel transcript but labelled differently. Ideally we would like to merge these 6 gtf files along with the reference GTF to achieve following goals
- Redundant transcripts across the samples should be represented once
- Known transcripts should hold their stable gene ID's (assigned in Ensembl)
The command we will use to achieve this is stringtie --merge and the syntax is
stringtie --merge -p 4 -o stringtie_merged.gtf -G Reference.gtf listOfSampleGTFs.txt
options used:
-p specifies the number of threads to use
-G Reference GTF available from public domain databases
-o output merged gtf file
listOfSampleGTFs.txt : This is a text file with list of gtfs generated freom the samples in previous step.
ls -1 ath*/*.gtf >> sample_assembly_gtf_list.txt
The command above is to generate listOfSampleGTFs.txt that will be used in stringtie --merge command.
The merged GTF can be compared with Reference GTF to get some stats on the stringtie_merged.gtf. The above set of commands can be put together in a script as shown below,
#!/bin/bash
#SBATCH --job-name=stringtie
#SBATCH --mail-user=
#SBATCH --mail-type=ALL
#SBATCH -n 1
#SBATCH -N 1
#SBATCH -c 8
#SBATCH --mem=40G
#SBATCH -o %x_%j.out
#SBATCH -e %x_%j.err
#SBATCH --partition=himem
#SBATCH --qos=himem
export TMPDIR=/home/CAM/$USER/tmp/
#mkdir -p {athaliana_wt_Rep1,athaliana_wt_Rep2,athaliana_wt_Rep3,athaliana_EE_Rep1,athaliana_EE_Rep2,athaliana_EE_Rep3}
module load stringtie
stringtie -p 8 -l wT1 -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o athaliana_wt_Rep1/transcripts.gtf ../mapping/wt_Rep1_sort.bam
stringtie -p 8 -l wT2 -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o athaliana_wt_Rep2/transcripts.gtf ../mapping/wt_Rep2_sort.bam
stringtie -p 8 -l wT3 -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o athaliana_wt_Rep3/transcripts.gtf ../mapping/wt_Rep3_sort.bam
stringtie -p 8 -l EE1 -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o athaliana_EE_Rep1/transcripts.gtf ../mapping/EE_Rep1_sort.bam
stringtie -p 8 -l EE2 -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o athaliana_EE_Rep2/transcripts.gtf ../mapping/EE_Rep2_sort.bam
stringtie -p 8 -l EE3 -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o athaliana_EE_Rep3/transcripts.gtf ../mapping/EE_Rep3_sort.bam
ls -1 ath*/*.gtf >> sample_assembly_gtf_list.txt
stringtie --merge -p 8 -o stringtie_merged.gtf -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf sample_assembly_gtf_list.txt
module load gffcompare/0.10.4
gffcompare -r /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o gffcompare stringtie_merged.gtf
The full script is called stringtie_gft.sh and can be found in the ballgown/ folder.
Now lets examine the outputs generated from this script. As discussed above in first step stringtie genrates a gtf file for each sample with details of covrage, FPKM, TPM and other information on the transcripts based on sample bam file.
# StringTie version 2.0.3
1 StringTie transcript 3631 5899 1000 + . gene_id "EE1.1"; transcript_id "EE1.1.1"; reference_id "AT1G01010.1"; ref_gene_id "AT1G01010"; ref_gene_name "AT1G01010"; cov "2.338863"; FPKM "1.194506"; TPM "1.609814";
1 StringTie exon 3631 3913 1000 + . gene_id "EE1.1"; transcript_id "EE1.1.1"; exon_number "1"; reference_id "AT1G01010.1"; ref_gene_id "AT1G01010"; ref_gene_name "AT1G01010"; cov "3.505300";
1 StringTie exon 3996 4276 1000 + . gene_id "EE1.1"; transcript_id "EE1.1.1"; exon_number "2"; reference_id "AT1G01010.1"; ref_gene_id "AT1G01010"; ref_gene_name "AT1G01010"; cov "3.217082";
1 StringTie exon 4486 4605 1000 + . gene_id "EE1.1"; transcript_id "EE1.1.1"; exon_number "3"; reference_id "AT1G01010.1"; ref_gene_id "AT1G01010"; ref_gene_name "AT1G01010"; cov "0.866667";
1 StringTie exon 4706 5095 1000 + . gene_id "EE1.1"; transcript_id "EE1.1.1"; exon_number "4"; reference_id "AT1G01010.1"; ref_gene_id "AT1G01010"; ref_gene_name "AT1G01010"; cov "2.884615";Now lets have a look at out merged GTF file stringtie_merged.gtf from the previous step:
# stringtie --merge -p 8 -o stringtie_merged.gtf -G /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf sample_assembly_gtf_list.txt
# StringTie version 2.0.3
1 StringTie transcript 3631 5899 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; gene_name "AT1G01010"; ref_gene_id "AT1G01010";
1 StringTie exon 3631 3913 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "1"; gene_name "AT1G01010"; ref_gene_id "AT1G01010";
1 StringTie exon 3996 4276 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "2"; gene_name "AT1G01010"; ref_gene_id "AT1G01010";
1 StringTie exon 4486 4605 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "3"; gene_name "AT1G01010"; ref_gene_id "AT1G01010";
1 StringTie exon 4706 5095 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "4"; gene_name "AT1G01010"; ref_gene_id "AT1G01010";
1 StringTie exon 5174 5326 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "5"; gene_name "AT1G01010"; ref_gene_id "AT1G01010";
1 StringTie exon 5439 5899 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "6"; gene_name "AT1G01010"; ref_gene_id "AT1G01010";
1 StringTie transcript 11649 13714 1000 - . gene_id "MSTRG.2"; transcript_id "AT1G01030.1"; gene_name "AT1G01030"; ref_gene_id "AT1G01030";
1 StringTie exon 11649 13173 1000 - . gene_id "MSTRG.2"; transcript_id "AT1G01030.1"; exon_number "1"; gene_name "AT1G01030"; ref_gene_id "AT1G01030";
1 StringTie exon 13335 13714 1000 - . gene_id "MSTRG.2"; transcript_id "AT1G01030.1"; exon_number "2"; gene_name "AT1G01030"; ref_gene_id "AT1G01030";
1 StringTie transcript 11676 13714 1000 - . gene_id "MSTRG.2"; transcript_id "MSTRG.2.2";
1 StringTie exon 11676 12354 1000 - . gene_id "MSTRG.2"; transcript_id "MSTRG.2.2"; exon_number "1";
1 StringTie exon 12424 13173 1000 - . gene_id "MSTRG.2"; transcript_id "MSTRG.2.2"; exon_number "2";
1 StringTie exon 13335 13714 1000 - . gene_id "MSTRG.2"; transcript_id "MSTRG.2.2"; exon_number "3";This is our new reference GTF file we will be using to quantify the expression of dfferent genes and transcripts. If we look closer we can see that the file have information different features but exclude coverage, TPM and FPKM information. Thats how we want it to be for use as reference in subsequent analysis. Also note that the first two transcripts have known ENSEMBL transcrip-id,gene_name and ref_gene_id, however it is missing in transcript 3. This is because it represents a novel transcript identified in the study.
Before we go ahead lets have look at the GFF compare stats. The file we are looking for is gffcompare.stats, and the contents are self explanatory. One can explore other files gffcompare.annotated.gtf, gffcompare.loci ,gffcompare.stats,gffcompare.stringtie_merged.gtf.refmap , gffcompare.stringtie_merged.gtf.tmap , gffcompare.tracking of the comparison to have a deeper understanding of the differences.
# gffcompare v0.10.4 | Command line was:
#gffcompare -r /isg/shared/databases/alignerIndex/plant/Arabidopsis/thaliana/TAIR10_GFF3_genes.gtf -o gffcompare stringtie_merged.gtf .
#
#= Summary for dataset: stringtie_merged.gtf
# Query mRNAs : 49714 in 33302 loci (38049 multi-exon transcripts)
# (9420 multi-transcript loci, ~1.5 transcripts per locus)
# Reference mRNAs : 41607 in 33350 loci (30127 multi-exon)
# Super-loci w/ reference transcripts: 32738
#-----------------| Sensitivity | Precision |
Base level: 100.0 | 97.9 |
Exon level: 99.0 | 92.9 |
Intron level: 100.0 | 94.6 |
Intron chain level: 100.0 | 79.2 |
Transcript level: 99.8 | 83.5 |
Locus level: 99.8 | 98.6 |
Matching intron chains: 30127
Matching transcripts: 41529
Matching loci: 33295
Missed exons: 0/169264 ( 0.0%)
Novel exons: 2325/181918 ( 1.3%)
Missed introns: 0/127896 ( 0.0%)
Novel introns: 2451/135134 ( 1.8%)
Missed loci: 0/33350 ( 0.0%)
Novel loci: 424/33302 ( 1.3%)
Total union super-loci across all input datasets: 33302
49714 out of 49714 consensus transcripts written in gffcompare.annotated.gtf (0 discarded as redundant)
Now lets go ahead and do the transcript quantification using stringtie.
In this step we will use the stringtie_merged.gtf file as reference and measure the expression of exons, transcripts and other features present in the gtf file. The syntax of command we will be executing is,
stringtie -e -B -p 4 sample.bam -G stringtie_merged.gtf -o output.count -A gene_abundance.out
Where:
-B returns a Ballgown input table file
-e only estimate the abundance of given reference transcripts
-o output path/file name
-A gene abundance estimation output file
We can compose a script based on the above command to run all our samples.
#!/bin/bash
#SBATCH --job-name=stringtie
#SBATCH --mail-user=
#SBATCH --mail-type=ALL
#SBATCH -n 1
#SBATCH -N 1
#SBATCH -c 4
#SBATCH --mem=40G
#SBATCH -o %x_%j.out
#SBATCH -e %x_%j.err
#SBATCH --partition=himem
#SBATCH --qos=himem
export TMPDIR=/home/CAM/$USER/tmp/
mkdir -p {athaliana_wt_Rep1,athaliana_wt_Rep2,athaliana_wt_Rep3,athaliana_EE_Rep1,athaliana_EE_Rep2,athaliana_EE_Rep3}
module load stringtie
stringtie -e -B -p 4 ../mapping/wt_Rep1_sort.bam -G stringtie_merged.gtf -o athaliana_wt_Rep1/athaliana_wt_Rep1.count -A athaliana_wt_Rep1/wt_Rep1_gene_abun.out
stringtie -e -B -p 4 ../mapping/wt_Rep2_sort.bam -G stringtie_merged.gtf -o athaliana_wt_Rep2/athaliana_wt_Rep2.count -A athaliana_wt_Rep2/wt_Rep2_gene_abun.out
stringtie -e -B -p 4 ../mapping/wt_Rep3_sort.bam -G stringtie_merged.gtf -o athaliana_wt_Rep3/athaliana_wt_Rep3.count -A athaliana_wt_Rep3/wt_Rep3_gene_abun.out
stringtie -e -B -p 4 ../mapping/EE_Rep1_sort.bam -G stringtie_merged.gtf -o athaliana_EE_Rep1/athaliana_EE_Rep1.count -A athaliana_EE_Rep1/EE_Rep1_gene_abun.out
stringtie -e -B -p 4 ../mapping/EE_Rep2_sort.bam -G stringtie_merged.gtf -o athaliana_EE_Rep2/athaliana_EE_Rep2.count -A athaliana_EE_Rep2/EE_Rep2_gene_abun.out
stringtie -e -B -p 4 ../mapping/EE_Rep3_sort.bam -G stringtie_merged.gtf -o athaliana_EE_Rep3/athaliana_EE_Rep3.count -A athaliana_EE_Rep3/EE_Rep3_gene_abun.out
The full slurm script is stringtie.sh can be found in ballgown/ folder.
The following files will be genrated from the above command
bash-4.2$ e2t.ctab e_data.ctab i2t.ctab i_data.ctab t_data.ctab wt_Rep1_gene_abun.out athaliana_wt_Rep1.count
Description of above files
e_data.ctab: exon-level expression measurements. One row per exon. Columns are e_id (numeric exon id), chr, strand, start, end (genomic location of the exon), and the following expression measurements for each sample:
rcount: reads overlapping the exon
ucount: uniquely mapped reads overlapping the exon
mrcount: multi-map-corrected number of reads overlapping the exon
cov average per-base read coverage
cov_sd: standard deviation of per-base read coverage
mcov: multi-map-corrected average per-base read coverage
mcov_sd: standard deviation of multi-map-corrected per-base coverage
i_data.ctab: intron- (i.e., junction-) level expression measurements. One row per intron. Columns are i_id (numeric intron id), chr, st rand, start, end (genomic location of the intron), and the following expression measurements for each sample:
rcount: number of reads supporting the intron
ucount: number of uniquely mapped reads supporting the intron
mrcount: multi-map-corrected number of reads supporting the intron
t_data.ctab: transcript-level expression measurements. One row per transcript. Columns are:
t_id: numeric transcript id
chr, strand, start, end: genomic location of the transcript
t_name: Cufflinks-generated transcript id
num_exons: number of exons comprising the transcript
length: transcript length, including both exons and introns
gene_id: gene the transcript belongs to
gene_name: HUGO gene name for the transcript, if known
cov: per-base coverage for the transcript (available for each sample)
FPKM: Cufflinks-estimated FPKM for the transcript (available for each sample)
e2t.ctab: table with two columns, e_id and t_id, denoting which exons belong to which transcripts. These ids match the ids in the e_data and t_data tables.
i2t.ctab: table with two columns, i_id and t_id, denoting which introns belong to which transcripts. These ids match the ids in the i_data and t_data tables.
Let's have a look at the stringtie output .counts file which we will be using in Ballgown:
# # stringtie -e -B -p 8 ../mapping/EE_Rep1_sort.bam -G stringtie_merged.gtf -o athaliana_EE_Rep1/athaliana_EE_Rep1.count -A athaliana_EE_Rep1/EE_Rep1_gene_abun.out # StringTie version 2.0.3 1 StringTie transcript 3631 5899 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; ref_gene_name "AT1G01010"; cov "2.338863"; FPKM "1.197653"; TPM "1.634538"; 1 StringTie exon 3631 3913 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "1"; ref_gene_name "AT1G01010"; cov "3.505300"; 1 StringTie exon 3996 4276 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "2"; ref_gene_name "AT1G01010"; cov "3.217082"; 1 StringTie exon 4486 4605 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "3"; ref_gene_name "AT1G01010"; cov "0.866667"; 1 StringTie exon 4706 5095 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "4"; ref_gene_name "AT1G01010"; cov "2.884615"; 1 StringTie exon 5174 5326 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "5"; ref_gene_name "AT1G01010"; cov "2.980392"; 1 StringTie exon 5439 5899 1000 + . gene_id "MSTRG.1"; transcript_id "AT1G01010.1"; exon_number "6"; ref_gene_name "AT1G01010"; cov "0.796095"; 1 StringTie transcript 6908 8737 . - . gene_id "MSTRG.3"; transcript_id "MSTRG.3.4"; cov "0.0"; FPKM "0.000000"; TPM "0.000000"; 1 StringTie exon 6908 7069 . - . gene_id "MSTRG.3"; transcript_id "MSTRG.3.4"; exon_number "1"; cov "0.0";
For many organisms, many of the same genes are expressed in separate cell types, with a variety of phenotype differences a result of the specific isoforms a cell will use. Therefore, when performing a differential expression analysis from different parts of one organism (not one species, but a singular organism), it is wise to perform an isoform expression analysis alongside a standard differential expression analysis and combine the results (as we are doing here). We will only be performing the isoform expresion analysis. Ballgown is a differential expression package for R via Bioconductor ideal for isoform expression analyses. Before beginning, you need to secure copy our ballgown/ directory from Xanadu to your local machine:
-bash-4.2$ exit Connection to transfer.cam.uchc.edu closed. user:~$ scp -r [email protected]:/home/CAM/$USER/rnaseq_for_model_plant/ballgown .
Now we load RStudio with administrator privileges (otherwise you cannot install packages!).
To begin we must download and load the proper packages:
install.packages("devtools")
install.packages("RFLPtools")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("alyssafrazee/RSkittleBrewer","ballgown", "genefilter", "dplyr", "devtools"))
library(ballgown)
library(RSkittleBrewer)
library(genefilter)
library(dplyr)
library(ggplot2)
library(gplots)
library(devtools)
library(RFLPtools)
Now we need to set our working directory to the directory which contains our "ballgown" folder. For me, this is:
setwd("/Users/vijendersingh/Documents/workshop_2019/")
list.files()
You should see the "ballgown" folder after the list.files() command.
Let's have a look at the ballgown function:
help("ballgown")
constructor function for ballgown objects
Description
constructor function for ballgown objects
Usage
ballgown(samples = NULL, dataDir = NULL, samplePattern = NULL,
bamfiles = NULL, pData = NULL, verbose = TRUE, meas = "all")
Arguments
samples vector of file paths to folders containing sample-specific ballgown data (generated by tablemaker). If samples
is provided, dataDir and samplePattern are not used.
dataDir file path to top-level directory containing sample-specific folders with ballgown data in them. Only used if
samples is NULL.
samplePattern regular expression identifying the subdirectories of\ dataDir containing data to be loaded into the ballgown
object (and only those subdirectories). Only used if samples is NULL.
bamfiles optional vector of file paths to read alignment files for each sample. If provided, make sure to sort properly
(e.g., in the same order as samples). Default NULL.
pData optional data.frame with rows corresponding to samples and columns corresponding to phenotypic variables.
verbose if TRUE, print status messages and timing Information as the object is constructed.
meas character vector containing either "all" or one or more of: "rcount", "ucount", "mrcount", "cov", "cov_sd",
"mcov", "mcov_sd", or "FPKM". The resulting ballgown object will only contain the specified expression
measurements, for the appropriate features. See vignette for which expression measurements are available for
which features. "all" creates the full object.
Because of the structure of our ballgown directory, we may use dataDir = "ballgown", samplePattern = "athaliana", measure = "FPKM", and pData = some_type_of_phenotype_matrix.
We want all of the objects in our arguments to be in the same order as they are present in the ballgown directory. Therefore, we want our pheno_df matrix to have two columns -- the first column being the samples as they appear in the ballgown directory, and the second being the condition of each sample in the column before it (EE or WT). Let's see the order of our sample files:
list.files("ballgown/")
[1] "athaliana_EE_Rep1" "athaliana_EE_Rep2" "athaliana_EE_Rep3"
[4] "athaliana_wt_Rep1" "athaliana_wt_Rep2" "athaliana_wt_Rep3"
Now we construct a 6x2 phenotype matrix with the first column being our samples in order and the second each sample's phenotype:
sample<-c("athaliana_EE_Rep1","athaliana_EE_Rep2", "athaliana_EE_Rep3", "athaliana_wt_Rep1", "athaliana_wt_Rep2", "athaliana_wt_Rep3" )
type<-c(rep("EE",3),rep("wt",3))
pheno_df<-data.frame("sample"=sample,"type"=type)
rownames(pheno_df)<-pheno_df[,1]
pheno_df
sample type
athaliana_EE_Rep1 athaliana_EE_Rep1 EE
athaliana_EE_Rep2 athaliana_EE_Rep2 EE
athaliana_EE_Rep3 athaliana_EE_Rep3 EE
athaliana_wt_Rep1 athaliana_wt_Rep1 wt
athaliana_wt_Rep2 athaliana_wt_Rep2 wt
athaliana_wt_Rep3 athaliana_wt_Rep3 wt
We may now create our ballgown object:
bg <- ballgown(dataDir = ".", pData=pheno_df, samplePattern = "athaliana") Thu May 2 22:33:46 2019 Thu May 2 22:33:46 2019: Reading linking tables Thu May 2 22:33:46 2019: Reading intron data files Thu May 2 22:33:49 2019: Merging intron data Thu May 2 22:33:50 2019: Reading exon data files Thu May 2 22:33:54 2019: Merging exon data Thu May 2 22:33:55 2019: Reading transcript data files Thu May 2 22:33:56 2019: Merging transcript data Wrapping up the results Thu May 2 22:33:56 2019
The ballgown object bg stores the fpkm values corresponding to genes. Before calculating the fold changes in gene expression we can explore the expression of genes across the samples and also check for the variance in the dataset and try to identify any confounding factors.
In the first step will create a gene_expressionvariable holding the fpkm value of genes and then plot a boxplot of fpkm values from all the samples.
gene_expression = gexpr(bg) #extract fpkm values of genes
head(gene_expression)
boxplot(log10(gene_expression+1),names=c("EE1","EE2","EE3","WT1","WT2","WT3"),col=c("red", "red","red","blue", "blue","blue"))
The boxplot below gives an overview of expression of fpkm values of different genes across different samples. We have log10 transformed the values to visualise it better and added 1 gene_expression+1 to avoid errors if the fpkm values are 0.
In the next step we will perform principal component analysis (PCA) with top 500 genes with highest variance. The PCA is a good way of identifying the factors that are responsible for variance in your sample. In an ideal situation we would like to see that PC1 (Principal component 1 or the component with highest variance) cluster our samples(replicates) based on treatment. If the clustering is because of any other factor like source of material, day of experiemnt etc then it will indicate that there are confounding factors affecting the gene expression. We have to model these factors in our analysis.
samples<-c("EE1","EE2","EE3","WT1","WT2","WT3")
colnames(gene_expression)<-samples
library("genefilter")
library("ggplot2")
library("grDevices")
# identify the variance in the genes
rv <- rowVars(gene_expression)
select <- order(rv, decreasing=T)[seq_len(min(500,length(rv)))]
pc<-prcomp(t(gene_expression[select,]),scale=TRUE)
pc$x
scores <- data.frame(pc$x, samples)
scores
pcpcnt<-round(100*pc$sdev^2/sum(pc$sdev^2),1)
names(pcpcnt)<-c("PC1","PC2","PC3","PC4","PC5","PC6")
# Lets see how much variance is associate with each p[rincipal component.
pcpcnt
point_colors = c("red", "red","red","blue", "blue", "blue")
plot(pc$x[,1],pc$x[,2], xlab="", ylab="", main="PCA plot for all libraries",xlim=c(min(pc$x[,1])-2,max(pc$x[,1])+2),ylim=c(min(pc$x[,2])-2,max(pc$x[,2])+2),col=point_colors)
text(pc$x[,1],pc$x[,2],pos=2,rownames(pc$x), col=c("red", "red","red","blue", "blue", "blue"))
plot(pc$x[,1],pc$x[,2], xlab="PC1", ylab="PC2", main="PCA plot for all libraries",col=point_colors)
PC1 (x-axis) of the plot do not clearly seperates samples from (WT and EE ) condition but PC2 (Y-axis) does. This means the second most source of variance in our samples is the treatment. As a researcher if we encounter such a situation we will be trying to identify the factor(s) that is responsible for the PC1.
To perform the differential expression analysis we use ballgown's "stattest" function. Let's have a look at it:
??ballgown::stattest
statistical tests for differential expression in ballgown
Description
Test each transcript, gene, exon, or intron in a ballgown object for differential expression, using
comparisons of linear models.
Usage
stattest(gown = NULL, gowntable = NULL, pData = NULL, mod = NULL,
mod0 = NULL, feature = c("gene", "exon", "intron", "transcript"),
meas = c("cov", "FPKM", "rcount", "ucount", "mrcount", "mcov"),
timecourse = FALSE, covariate = NULL, adjustvars = NULL, gexpr = NULL,
df = 4, getFC = FALSE, libadjust = NULL, log = TRUE)
Arguments
gown name of an object of class ballgown
gowntable matrix or matrix-like object with rownames representing feature IDs and columns representing samples, with expression
estimates in the cells. Provide the feature name with feature. You must provide exactly one of gown or gowntable. NB:
gowntable is log-transformed within stattest if log is TRUE, so provide un-logged expression values in gowntable.
pData Required if gowntable is provided: data frame giving phenotype data for the samples in the columns of gowntable. (Rows
of pData correspond to columns of gowntable). If gown is used instead, it must have a non-null, valid pData slot (and
the pData argument to stattest should be left NULL).
mod object of class model.matrix representing the design matrix for the linear regression model including covariates of
interest
mod0 object of class model.matrix representing the design matrix for the linear regression model without the covariates of
interest.
feature the type of genomic feature to be tested for differential expression. If gown is used, must be one of "gene",
"transcript", "exon", or "intron". If gowntable is used, this is just used for labeling and can be whatever the rows of
gowntable represent.
meas the expression measurement to use for statistical tests. Must be one of "cov", "FPKM", "rcount", "ucount", "mrcount",
or "mcov". Not all expression measurements are available for all features. Leave as default if gowntable is provided.
timecourse if TRUE, tests whether or not the expression profiles of genomic features vary over time (or another continuous
covariate) in the study. Default FALSE. Natural splines are used to fit time profiles, so you must have more timepoints
than degrees of freedom used to fit the splines. The default df is 4.
covariate string representing the name of the covariate of interest for the differential expression tests. Must correspond to the
name of a column of pData(gown). If timecourse=TRUE, this should be the study's time variable.
adjustvars optional vector of strings representing the names of potential confounders. Must correspond to names of columns of
pData(gown).
gexpr optional data frame that is the result of calling gexpr(gown)). (You can speed this function up by pre-creating
gexpr(gown).)
df degrees of freedom used for modeling expression over time with natural cubic splines. Default 4. Only used if
timecourse=TRUE.
getFC if TRUE, also return estimated fold changes (adjusted for library size and confounders) between populations. Only
available for 2-group comparisons at the moment. Default FALSE.
libadjust library-size adjustment to use in linear models. By default, the adjustment is defined as the sum of the sample's log
expression measurements below the 75th percentile of those measurements. To use a different library-size adjustment,
provide a numeric vector of each sample's adjustment value. Entries of this vector correspond to samples in in rows of
pData. If no library size adjustment is desired, set to FALSE.
log if TRUE, outcome variable in linear models is log(expression+1), otherwise it's expression. Default TRUE.
We see we can determine which transcripts and genes are differentially expressed between the conditions, alongside the fold changes ofeach differentially expressed gene as measured in FPKM with the following code:
results_transcripts = stattest(bg, feature="transcript" , covariate = "type" , getFC = TRUE, meas = "FPKM") results_genes = stattest(bg, feature="gene" , covariate = "type" , getFC = TRUE, meas = "FPKM")
Let's take a look at this object. The changes in expression in all genes is listed, alongside its ID, fold-change (percent increase), p-value, and q-value. It is a good idea to have foldchange (fc) log2 transformed as it is easy to understand the fold change values. A negative value will indicate downregulation and positive value as upregulation of genes between the conditions.
results_genes["log2fc"]<-log2(results_genes$fc) head(results_genes) feature id fc pval qval log2fc 1 gene AT1G01010 0.8087079 0.38793773 0.9981114 -0.30630933 2 gene AT1G01020 0.9421945 0.64827785 0.9981114 -0.08590316 3 gene AT1G01030 0.8025982 0.31221966 0.9981114 -0.31725025 4 gene AT1G01040 1.0508155 0.78611712 0.9981114 0.07150939 5 gene AT1G01046 2.0228448 0.07153084 0.9981114 1.01638560 6 gene AT1G01050 0.7928742 0.01064636 0.9981114 -0.33483603
Now lets filter differential expression result based on p-value and fold change. For this study we will consider genes with p<0.1 and showing more than 1.5 fold changes in expression as gene of interest to us. On log2 scale this is 0.584 log2(1.5)=0.584. So any gene with p<0.1 and log2fc less than -0.584 and more than 0.584 are significant. We will write this result to a .csv file for our records.
g_sign<-subset(results_genes,pval<0.1 & abs(logfc)>0.584)
head(g_sign)
feature id fc pval qval log2fc
5 gene AT1G01046 2.0228448 0.07153084 0.9981114 1.0163856
152 gene AT1G02360 0.6305038 0.08680302 0.9981114 -0.6654230
171 gene AT1G02520 1.6764465 0.09136064 0.9981114 0.7454064
205 gene AT1G02820 0.3489204 0.02106810 0.9981114 -1.5190299
234 gene AT1G03070 1.5210633 0.09282004 0.9981114 0.6050802
331 gene AT1G03935 0.5610848 0.03224290 0.9981114 -0.8337093
## Number of genes differentialy expressed.
dim(g_sign)
[1] 388 6
write.csv(g_sign, "results_genes.csv", row.names=FALSE)
##we use row.names=FALSE because currently the row names are just the numbers 1, 2, 3. . .
So we have 388 genes which shows differential expression across our sample conditions.
In this section we will aim to perform a functional annotation of differentially expressed genes identified in our analysis. These genes are stored in g_sign object and we will use biomart tool available on public databases to extract information using a R packages. The functionalities demonstrated below are applicable to most public domain databases provided they support Biomart. Before getting into R studio lets understand few key features of Ensembl database, the one we will be using for our annotation. It is important to develop an understanding about databases as this will help in extracting data from correct database. Ensembl has 6 different sub domains
- Bacteria : bacteria.ensembl.org
- Fungi : fungi.ensembl.org
- Metazoa : metazoan.ensembl.org
- Plants : plants.ensembl.org
- Protists : protists.ensembl.org
- Vertebrates :ensembl.org
In order to get data we have to link to appropriate database. In our case we will be using plants.ensembl.org.
Now let’s get some details on the R package biomaRt. There are 3 main functions that are associated with this package are
- listFilters : Lists the available filters
- listAttributes : Lists the available attributes
- getBM : Performs the actual query and returns a data.frame
While using biomaRt we have to make following choices
- Database referred as host (for us it is plants.ensembl.org)
- Biomart
- dataset
- filters (e.g. chromosome, scaffold,Gene type, Transcript type, phenotype etc)
- Attributes (This reflect the attributes of the filter we are interested in e.g, Gene stable ID, Gene start, Gene end, GO terms, protein domain and families etc )
Lets begin, and first we want to identify which Biomart to use from plants.ensembl.org
BiocManager::install(c("biomaRt"))
install.packages("biomartr")
library(biomaRt)
listMarts(host="plants.ensembl.org")
biomart version
1 plants_mart Ensembl Plants Genes 45
2 plants_variations Ensembl Plants Variations 45
## we would like to use the "Ensembl Plants Genes 45" as we are interested in gene information
## so the mart of choice is “plants_mart”
## Next lets get all the datasets that are associated with Arabidopsis thaliana from “plants_mart”
mart=useMart("plants_mart", host="plants.ensembl.org")
head(listDatasets(mart))[grep("thaliana",listDatasets(mart)[,1]),]
dataset description version
5 athaliana_eg_gene Arabidopsis thaliana genes (TAIR10) TAIR10
## There is one dataset"athaliana_eg_gene” holding information about Arabidopsis genes.
## Now we need to identify filters and then the attributes before we extract the data.
thale_mart <- useMart(biomart = "plants_mart", host = "plants.ensembl.org", dataset = "athaliana_eg_gene")
## To get information on filters you can try the command below.
## This will list out 206 filters with their name and description. We have to make a selection of filter from there
listFilters(thale_mart)
## to view first 20 filters in the list
head(listFilters(thale_mart),20)
## we know that our gene list has Ensembl gene ids so lets check for filters having ‘ensembl’ key word in them
listFilters(thale_mart)[grep("ensembl",listFilters(thale_mart)[,1]),]
name description
34 ensembl_gene_id Gene stable ID(s) [e.g. AT1G01010]
35 ensembl_transcript_id Transcript stable ID(s) [e.g. AT1G01010.1]
36 ensembl_peptide_id Protein stable ID(s) [e.g. AT1G01010.1]
37 ensembl_exon_id Exon ID(s) [e.g. AT1G01010.1.exon1]
## Great so have to use ‘ ensembl_gene_id’ as filter, Now we have to find attributes corresponding to ensembl_gene_id .
## Please note that the attributes are sometimes located on different pages ,
## so our first goal is to find the page using searchAttributes(mart, pattern) function
## and then the attributes of that page using listAttributes(mart, page,what = c("name","description","page"))
searchAttributes(mart = thale_mart, pattern = "ensembl_gene_id")
name description page
1 ensembl_gene_id Gene stable ID feature_page
134 ensembl_gene_id Gene stable ID structure
168 ensembl_gene_id Gene stable ID homologs
1251 ensembl_gene_id Gene stable ID snp
1303 ensembl_gene_id Gene stable ID sequences
listAttributes(mart = thale_mart, page="feature_page")
## This will list 133 attributes applicable to the page
1 ensembl_gene_id Gene stable ID feature_page
2 ensembl_transcript_id Transcript stable ID feature_page
3 ensembl_peptide_id Protein stable ID feature_page
4 ensembl_exon_id Exon stable ID feature_page
5 description Gene description feature_page
6 chromosome_name Chromosome/scaffold name feature_page
7 start_position Gene start (bp) feature_page
8 end_position Gene end (bp) feature_page
9 strand Strand feature_page
10 band Karyotype band feature_page
## we can see that the most relevant information could be “description”. So lets extract the information.
## You can explore more in the attributes and can choose as many as attributes you want.
## general syntax would be for first 5 attributes
## getBM(attributes=c("ensembl_gene_id”,”ensembl_transcript_id”,”ensembl_peptide_id”,"ensembl_exon_id" ,”description"),mart=thale_mart)
## here we are picking description and GO term for the gene
thale_data_frame = getBM(attributes=c("ensembl_gene_id","description","name_1006"),mart=thale_mart)
head(thale_data_frame)
ensembl_gene_id description name_1006
1 AT3G11415
2 AT1G31258 other RNA [Source:TAIR;Acc:AT1G31258]
3 AT5G24735 other RNA [Source:TAIR;Acc:AT5G24735]
4 AT2G45780 other RNA [Source:TAIR;Acc:AT2G45780]
5 AT2G42425 Unknown gene [Source:TAIR;Acc:AT2G42425]
6 AT4G01533 other RNA [Source:TAIR;Acc:AT4G01533]
tail(thale_data_frame)
ensembl_gene_id description
208287 AT5G16970 NADPH-dependent oxidoreductase 2-alkenal reductase [Source:UniProtKB/Swiss-Prot;Acc:Q39172]
208288 AT5G16970 NADPH-dependent oxidoreductase 2-alkenal reductase [Source:UniProtKB/Swiss-Prot;Acc:Q39172]
208289 AT5G16970 NADPH-dependent oxidoreductase 2-alkenal reductase [Source:UniProtKB/Swiss-Prot;Acc:Q39172]
208290 AT4G32100 Beta-1,3-N-Acetylglucosaminyltransferase family protein [Source:UniProtKB/TrEMBL;Acc:F4JTI5]
208291 AT4G32100 Beta-1,3-N-Acetylglucosaminyltransferase family protein [Source:UniProtKB/TrEMBL;Acc:F4JTI5]
208292 AT4G32100 Beta-1,3-N-Acetylglucosaminyltransferase family protein [Source:UniProtKB/TrEMBL;Acc:F4JTI5]
name_1006
208287 plastid
208288 cytoplasm
208289 response to oxidative stress
208290 cell fate determination
208291 transferase activity, transferring glycosyl groups
208292 transferase activity
## Now match the genes from our list to this dataset.
annotated_genes = subset(thale_data_frame, ensembl_gene_id %in% g_sign$id)
dim(g_sign)
[1] 388 6
> dim(annotated_genes )
[1] 2378 3
> head(annotated_genes)
ensembl_gene_id description name_1006
61 AT5G22788 other RNA [Source:TAIR;Acc:AT5G22788]
90 AT5G38005 other RNA [Source:TAIR;Acc:AT5G38005]
109 AT1G21529
182 AT2G07042 other RNA [Source:TAIR;Acc:AT2G07042]
220 AT2G05995 other RNA [Source:TAIR;Acc:AT2G05995]
588 AT2G46685 MIR166/MIR166A; miRNA [Source:TAIR;Acc:AT2G46685]
> tail(annotated_genes)
ensembl_gene_id
207261 AT1G24575
207262 AT1G24575
208133 AT4G26490
208134 AT4G26490
208135 AT4G26490
208136 AT4G26490
description
207261 At1g24575 [Source:UniProtKB/TrEMBL;Acc:Q8LAL6]
207262 At1g24575 [Source:UniProtKB/TrEMBL;Acc:Q8LAL6]
208133 Late embryogenesis abundant (LEA) hydroxyproline-rich glycoprotein family [Source:UniProtKB/TrEMBL;Acc:Q56Y59]
208134 Late embryogenesis abundant (LEA) hydroxyproline-rich glycoprotein family [Source:UniProtKB/TrEMBL;Acc:Q56Y59]
208135 Late embryogenesis abundant (LEA) hydroxyproline-rich glycoprotein family [Source:UniProtKB/TrEMBL;Acc:Q56Y59]
208136 Late embryogenesis abundant (LEA) hydroxyproline-rich glycoprotein family [Source:UniProtKB/TrEMBL;Acc:Q56Y59]
name_1006
207261 biological_process
207262 helicase activity
208133 biological_process
208134 membrane
208135 integral component of membrane
208136 plasma membrane
## The same gene can be associated with multiple go terms , as seen for AT4G26490, and that is the reason why the dimensions of the datasets are not matching.
##We successfully completed the annotation of our gene list. Lets write the output to a csv file.
write.csv(file="annotated_genes.csv",annotated_genes,row.names=F)
Cytoscape is a desktop program which creates visual topological networks of data. To visualise our differentially regulated genes in a network on Cytoscape we will follow the following steps
- Load networkfile
- Lay the network out
- Load Expression Data
- Examine node attributes
A detailed slides and tutorial is available at this LINK. Lets start Cytoscape and the application will start with following screen.
Have a look around at different tabs and menu and familiarise yourself with it. A network can be loaded in Cytoscape using the import function located under file menu as shown in the image below. A network can be imported from a file (as we are going to do it), url or from public database.
Import TAIR10 network file TairPP_refined.txt using file>import>Network from FIle option . The following table will be displayed .
The table will display different columns of the network file. In this we have to specify which column represent protein A (source node) and which column represent protein B (target node) of A --> B type protein-protein intercation.
Once imported different layouts can be tried listed under layout tab of Cytoscape. Once you have chosen an appropriate layout and then import differential expression data stored in csv or txt file using icon. 
Once the genes with differential expression is loaded, select the "Style tab and choose the "Fill Color". Under fill color choose Column value to "fc" (fold change from csv file) and set Mapping type to "Continous mapping". You can double click on the color panel to set colors of your choice and can set boudries.
This will highlight the nodes based on the value of fc for protein present in differentially expressed genes and in the network list.
Downloaded experimental data Performed quality control on the experimental data Aligned the experimental data to the reference genome, allowing us to determine which genes are active in each sample Determined and quantified the gene isoforms present in each sample Determined which gene isoforms had different general expression patterns between phenotypes Created visual gene topologial networks Extracted computational Information about those networks
While our work may have seemed completed after creating the visualization, the picture itself is not of much use to other scientists! However, all of the csv files we created are. A scientist may have an interest in one particular gene we studied. With our differential expression results output she will be able to determine how the gene's behavior varied according to phenotype. Perhaps she wants to begin investigating if one gene codes for a transcription factor of another. She can consult our gene complete clusters file and see if the two genes belong to the same cluster (as the activation of one will activate the other!). She may be interested in the total behavior of one cluster in activating or suppressing another cluster. She can determine her base and target clusters by locating the genes in our complete clusters file, extract all genes from those clusters, and then explore how downstream each cluster's effect may be by utilizing the degree of relationship csv. Bio Informatics is a collaborative field. We are always dependent on the work of others to solve the questions about which we are the most passionate. Because of this, it is important to always dig deep in your own analysis, and to create as readable and handy data as you can. Not only because you do not want another scientist to be lost in your files, but they must be readable by a computer! Sometimes, it may have felt like we were going down the rabbit hole in this tutorial. However, the Information we compiled is easy and immediately helpful for fellow scientists to use. Congratulations on finishing this tutorial successfully!
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