About

MITE Tracker: an accurate method for identifying miniature inverted-repeat transposable elements in large genomes.

An efficient and easy to run tool for discovering Miniature Inverted repeats Transposable Elements (MITEs) in genomic sequences. It is written in python 3 and uses ncbi's blast+ for finding inverted repeats and cdhit to do the clustering.

Large genomes can be processed in desktop computers.

Requirements

• tested in macOS 10.13.1, Debian 7.6, Ubuntu 16.04, Windows 7
• ncbi blast+ (Nucleotide-Nucleotide BLAST 2.6.0+)
• python requirements are in requirements.txt file (bipython and pandas)

Installation and running

# clone repo
git clone https://github.com/INTABiotechMJ/MITE-Tracker.git
cd MITE-Tracker

# blast
sudo apt-get install ncbi-blast+ virtualenv
# in macOS: brew install ncbi-blast+ virtualenv


#vsearch
wget https://github.com/torognes/vsearch/archive/v2.7.1.tar.gz
tar xzf v2.7.1.tar.gz
cd vsearch-2.7.1
#might need: sudo apt-get install autoconf
sh autogen.sh
./configure
make

#python dependencies
cd ..
virtualenv -p python3 venv
source venv/bin/activate
#might need: sudo apt-get install python3.6-dev
#if pandas failed to install, run: pip3 install cython
pip3 install -r requirements.txt

# running

python3 -m MITETracker -g /path/to/your/genome.fasta -w 3 -j jobname

# or to run in background

nohup python3 -m MITETracker -g /path/to/your/genome.fasta -w 3 -j jobname &

In order to check the output and progress you can use these command (ctrl+c to exit)

#nohup will have the program output as well as the output from cdhit execution
tail -f nohup.out
#out.log contaings a log file with timing information
tail -f results/[jobname]/out.log

Command line options

Argument	Description	Data type	Required or default
-g	Genome file in fasta format	string	required
-j	Jobname. Result files will be created in results/jobname	string	required
-w	Max number of processes to use simultaneously	int	1
-tsd_min_len	TSD min lenght	int	2
-tsd_max_len	TSD max lenght	int	10
-mite_min_len	MITE min lenght	int	50
-mite_max_len	MITE max lenght	int	650
--task	cluster or candidates	string

Results

All the results are placed in results/[yourjobname]/. Here you will find: families.fasta all the MITEs sequences divided by families (custom format) families_nr.fasta with one MITE per family in fasta format all.fasta all MITEs in fasta format all.gff3 a gff file describing all MITEs found

Troubleshooting

If getting any error while running the BLASTn searches please check you blast+ version

Running large genomes in different computers

This is an example of how we run wheat genome. Each chromosome can be run separately (--task candidates) in a different computers. Results should be merged together using cat and then run the cluster command (--task cluster). Files required for clustering are candidates.csv and candidates.fasta.

21 wheat chromosomes were downloaded in different files.

python3 -m MITETracker -g /media/chr1A.fasta -w 2 -j IWGSC_1A --task candidates
python3 -m MITETracker -g /media/chr1B.fasta -w 2 -j IWGSC_1B --task candidates
python3 -m MITETracker -g /media/chr1D.fasta -w 2 -j IWGSC_1D --task candidates
python3 -m MITETracker -g /media/chr2A.fasta -w 2 -j IWGSC_2A --task candidates
python3 -m MITETracker -g /media/chr2B.fasta -w 2 -j IWGSC_2B --task candidates
python3 -m MITETracker -g /media/chr2D.fasta -w 2 -j IWGSC_2D --task candidates
python3 -m MITETracker -g /media/chr3A.fasta -w 2 -j IWGSC_3A --task candidates
python3 -m MITETracker -g /media/chr3B.fasta -w 2 -j IWGSC_3B --task candidates
python3 -m MITETracker -g /media/chr3D.fasta -w 2 -j IWGSC_3D --task candidates
python3 -m MITETracker -g /media/chr4A.fasta -w 2 -j IWGSC_4A --task candidates
python3 -m MITETracker -g /media/chr4B.fasta -w 2 -j IWGSC_4B --task candidates
...
mkdir results/IWGSC
cat results/IWGSC_*/candidates.csv > results/IWGSC/candidates.csv
cat results/IWGSC_*/candidates.fasta > results/IWGSC/candidates.fasta
python3 -m MITETracker -g none -w 3 -j IWGSC --task cluster --min_copy_number 4

Publication and citing

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2376-y

Please cite with:

Crescente, Juan Manuel, et al. "MITE Tracker: an accurate approach to identify miniature inverted-repeat transposable elements in large genomes." BMC Bioinformatics 19.1 (2018): 348.

Or for bibtex users:

@article{crescente2018mite, title={MITE Tracker: an accurate approach to identify miniature inverted-repeat transposable elements in large genomes}, author={Crescente, Juan Manuel and Zavallo, Diego and Helguera, Marcelo and Vanzetti, Leonardo Sebasti{\'a}n}, journal={BMC Bioinformatics}, volume={19}, number={1}, pages={348}, year={2018}, publisher={Springer} }

Note:

Due to a problem with additionals files in the publication we have added those files in this repository under supplementary_materials/

rice_mites.fasta: Database of non-redundant MITE family database obtained from the rice genome

wheat_mites.fasta: Database of non-redundant MITE family database obtained from the wheat genome

tools_comparison.csv: Execution summary of MITE Tracker and other tools using several genomes

wheat_genes.csv: Wheat genes containing MITEs within its coding region.

Additional notes

Annotating all arabidopsis MITEs as an example

#### Clone MITETracker and install dependencies

#clone
git clone git@github.com:INTABiotechMJ/MITE-Tracker.git
#enter program directory
cd MITE-Tracker
#create virtual enviornment with python3
virtualenv -p python3 venv
#activate virtual environment
source venv/bin/activate
#install requirements
pip3 install -r requirements.txt
#run MITE Tracker
python3 MITETracker.py  -g TAIR10_chr_all.fas -j ata

With this version of TAIR genome we get a total of 38 distinct MITE families.

I'm gonna use the all.fasta file to map MITEs genome-wide because it contains all found elements.

blastn -task blastn -query results/ata/all.fasta  -subject ../data/tair10/TAIR10_chr_all.fas -outfmt "6 qseqid sseqid qstart qend sstart send score length mismatch gaps gapopen nident pident evalue qlen slen qcovs" > results/ata/blast_families_ata.csv

Let's run out notebook for filtering blast results, run till the end. This will explain at each step how filtering is done and what are the results.

jupyter lab

Ultimately, convert the blast filtered output to gff

python blast2gff.py -i results/ata/blast_families_ata.filtered.csv  -o results/ata/mitesInGenome.gff3 -n MITE_TRACKER

This is our resulting annotated file

results/ata/mitesInGenome.gff3