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About FindPlantNLRs

We developed a comprehensive pipeline for annotating predicted NLR genes from a non-masked genome fasta file input. We identify loci using NLR-annotator software (Steuernagel et al. 2020), tblastn (Altschul et al. 1990) and Hidden Markov Model (HMM) (Eddy 2010). The unmasked loci identified through these methods, and including 20 kb flanking regions, are then annotated with Braker2 software (Hoff et al. 2019) using experimentally validated resistance genes as reference (Kourelis et al. 2021). Annotated amino acid fasta files are screened for domains using Interproscan (Jones et al. 2014) and the predicted coding and amino acid sequences containing both NB-ARC and LRR domains are located back to scaffolds/chromosomes and extracted in fasta and gff3 format.

Overview of the whole pipeline

Dependencies

NLR_annotator: https://doi.org/10.1104/pp.19.01273 Steuernagel, B., Witek, K., Krattinger, S.G., Ramirez-Gonzalez, R.H., Schoonbeek, H.J., Yu, G., Baggs, E., Witek, A.I., Yadav, I., Krasileva, K.V. and Jones, J.D., 2020. The NLR-Annotator tool enables annotation of the intracellular immune receptor repertoire. Plant Physiology, 183(2), pp.468-482.

BRAKER2: https://github.com/Gaius-Augustus/BRAKER

Interproscan: https://interproscan-docs.readthedocs.io/en/latest/UserDocs.html#obtaining-a-copy-of-interproscan Jones, P., Binns, D., Chang, H.Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G. and Pesseat, S., 2014. InterProScan 5: genome-scale protein function classification. Bioinformatics, 30(9), pp.1236-1240.

Blast: https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download

hmmsearch: http://hmmer.org/download.html

Meme: https://meme-suite.org/meme/meme-software/4.9.1/readme.html

BRAKER2: https://github.com/Gaius-Augustus/BRAKER

dependencies:
name: NLR
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_gnu
  - argtable2=2.13=h14c3975_1001
  - bedtools=2.30.0=h468198e_3
  - blast=2.5.0=hc0b0e79_3
  - boost=1.80.0=py37h48bf904_1
  - boost-cpp=1.80.0=h75c5d50_0
  - bzip2=1.0.8=h7f98852_4
  - c-ares=1.18.1=h7f98852_0
  - ca-certificates=2022.9.24=ha878542_0
  - clustalo=1.2.4=h87f3376_5
  - htslib=1.11=hd3b49d5_2
  - icu=70.1=h27087fc_0
  - keyutils=1.6.1=h166bdaf_0
  - krb5=1.19.3=h3790be6_0
  - ld_impl_linux-64=2.39=hcc3a1bd_1
  - libblas=3.9.0=16_linux64_openblas
  - libcblas=3.9.0=16_linux64_openblas
  - libcurl=7.86.0=h7bff187_1
  - libdeflate=1.7=h7f98852_5
  - libedit=3.1.20191231=he28a2e2_2
  - libev=4.33=h516909a_1
  - libffi=3.4.2=h7f98852_5
  - libgcc=7.2.0=h69d50b8_2
  - libgcc-ng=12.2.0=h65d4601_19
  - libgfortran-ng=12.2.0=h69a702a_19
  - libgfortran5=12.2.0=h337968e_19
  - libgomp=12.2.0=h65d4601_19
  - liblapack=3.9.0=16_linux64_openblas
  - libnghttp2=1.47.0=hdcd2b5c_1
  - libnsl=2.0.0=h7f98852_0
  - libopenblas=0.3.21=pthreads_h78a6416_3
  - libssh2=1.10.0=haa6b8db_3
  - libstdcxx-ng=12.2.0=h46fd767_19
  - libzlib=1.2.13=h166bdaf_4
  - ncurses=6.2=h58526e2_4
  - numpy=1.21.6=py37h976b520_0
  - openssl=1.1.1s=h166bdaf_0
  - perl=5.22.2.1=0
  - perl-bioperl=1.6.924=2
  - perl-threaded=5.32.1=hdfd78af_1
  - pip=22.3.1=pyhd8ed1ab_0
  - python=3.7.12=hb7a2778_100_cpython
  - python_abi=3.7=3_cp37m
  - readline=8.1=h46c0cb4_0
  - samtools=1.11=h6270b1f_0
  - seqkit=2.3.1=h9ee0642_0
  - setuptools=65.5.1=pyhd8ed1ab_0
  - sqlite=3.37.0=h9cd32fc_0
  - tk=8.6.12=h27826a3_0
  - wheel=0.38.4=pyhd8ed1ab_0
  - xz=5.2.6=h166bdaf_0
  - zlib=1.2.13=h166bdaf_4
  - zstd=1.5.2=h6239696_4
  - pip:
    - appdirs==1.4.4
    - attrs==22.1.0
    - certifi==2022.9.24
    - charset-normalizer==2.1.1
    - configargparse==1.5.3
    - connection-pool==0.0.3
    - datrie==0.8.2
    - docutils==0.19
    - dpath==2.1.1
    - fastjsonschema==2.16.2
    - gitdb==4.0.10
    - gitpython==3.1.29
    - idna==3.4
    - importlib-metadata==5.1.0
    - importlib-resources==5.10.0
    - jinja2==3.1.2
    - jsonschema==4.17.3
    - jupyter-core==4.12.0
    - markupsafe==2.1.1
    - nbformat==5.7.0
    - pkgutil-resolve-name==1.3.10
    - plac==1.3.5
    - psutil==5.9.4
    - pulp==2.7.0
    - pyrsistent==0.19.2
    - pyyaml==6.0
    - requests==2.28.1
    - reretry==0.11.1
    - smart-open==6.2.0
    - smmap==5.0.0
    - snakemake==7.18.2
    - stopit==1.1.2
    - tabulate==0.9.0
    - throttler==1.2.2
    - toposort==1.7
    - traitlets==5.6.0
    - typing-extensions==4.4.0
    - urllib3==1.26.13
    - wrapt==1.14.1
    - yte==1.5.1
    - zipp==3.11.0

Before running the pipeline

Step 1: Install required dependencies before running this pipeline

git clone https://github.com/ZhenyanLuo/FindPlantNLRs
cd FindPlantNLRs

Some packages can be installed by using the environment.yml

Create a new environment using the environment.yml

conda env create --name NLR -f environment.yml

or install/update your environment with the environment.yml file

conda activate {your environment}
conda env update --file environment.yml

Install open-jdk for NLR-Annotator and Interproscan

conda activate {your environment}
wget https://anaconda.org/conda-forge/openjdk/11.0.9.1/download/linux-64/openjdk-11.0.9.1-h5cc2fde_1.tar.bz2
conda install openjdk-11.0.9.1-h5cc2fde_1.tar.bz2
rm openjdk-11.0.9.1-h5cc2fde_1.tar.bz2

Other dependencies need to be install manually to the FindPlantNLRs folder:

NLR_annotator: https://github.com/steuernb/NLR-Annotator Use nlr_parser3 branch only and don't forget to download meme.xml

git clone -b nlr_parser3 https://github.com/steuernb/NLR-Annotator

wget https://github.com/steuernb/NLR-Annotator/releases/download/v0.7-beta/meme.xml

BRAKER: https://github.com/Gaius-Augustus/BRAKER

Interproscan: https://www.ebi.ac.uk/interpro/download/

Download and install meme-4.9.1 manually

meme-4.9.1: https://meme-suite.org/meme/meme-software/4.9.1/readme.html

Install Perl modules for BRAKER2

cpan install Scalar::Util::Numeric Parallel::ForkManager File::HomeDir List::MoreUtils

Step 2: Get reference database

Recommended reference database for tblastn and braker can be downloaded from supplementary file S1 of 'RefPlantNLR is a comprehensive collection of experimentally validated plant disease resistance proteins from the NLR family' https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001124 Place the reference sequence in ref_db folder and add absolute path of the reference in the FindPlantNLRs_config.yaml file

Step 3: Edit the FindPlantNLRs_config.yaml configure file by adding path to dependencies

#Path to NLR-Annotator

NLR-Annotator: "{path/to/your}/NLR-Annotator"

#Path to reference database

ref: "{path/to/your/ref/fasta}"

#Add path to meme

meme: "{path/to/your/meme}/mast"

#Add path to meme.xml from NLR-Annotator

meme_xml: "{path/to/your}/meme.xml"

#Add path to braker.pl

braker: "{path/to/your}/BRAKER"

#Add path to augustus script

augustus: "{path/to/your/augustus}/scripts/gtf2gff.pl

#Add path to Interproscan

interproscan: "{path/to/your/interproscan}/interproscan.sh"

Step 4: Place your samples in the 'genome' folder, make sure their file names only have one dot and ends with 'fa' (e.g. E_globulus.fa)

Chromosome 3 of E_grandis is a good choice https://www.ncbi.nlm.nih.gov/nuccore/NC_052614.1 Click Send to, then choose complete record-> file-> fasta format-> create file save as E_grandis_chr3.fasta, put this file into genome folder as test data

cat E_grandis_chr3.fasta|cut -d' ' -f1 >E_grandis_chr3.fa

It's header should look like this

NC_052614.1

For your own data, make sure sequence headers are short, unique and only have numerics and characters.

Step 5: Test Snakemake pipeline by using testing file in genome/ folder

conda activate NLR
snakemake -s FindPlantNLRs --cores 16
snakemake -s Annotate_NLR --cores 1 --wait-for-files

Step 6: Check your main output

tmp:

Three bed files cover identified loci from tblastn, hmm search and NLR-Annotator:

tmp/E_grandis_chr3.blast.20kbflanking.bed

tmp/E_grandis_chr3_NBARC.20kbflanking.bed

tmp/E_grandis_chr3_parser.20kbflanking.bed

A merged bed file and corresponding fasta file:

tmp/E_grandis_chr3.all_20kbflanking_merged.fasta

tmp/E_grandis_chr3.all_20kbflanking_merged_upper.fasta

Result:

Gene prediction result from Augustus:

result/E_grandis_chr3_augustus_aa.fasta

result/E_grandis_chr3_augustus_aa.fasta.tsv

result/E_grandis_chr3_augustus.gff3

Lists of different classes of R genes and corresponding bed files:

result/E_grandis_chr3_BNL.list

result/E_grandis_chr3_BNL.gff3

result/E_grandis_chr3_CNL.list

result/E_grandis_chr3_CNL.gff3

result/E_grandis_chr3_JNL.list

result/E_grandis_chr3_JNL.gff3

result/E_grandis_chr3_NBARC.list

result/E_grandis_chr3_NBARC.gff3

result/E_grandis_chr3_NLR.list

result/E_grandis_chr3_NLR.gff3

result/E_grandis_chr3_RNL.list

result/E_grandis_chr3_RNL.gff3

result/E_grandis_chr3_RxNL.list

result/E_grandis_chr3_RxNL.gff3

result/E_grandis_chr3_TNL.list

result/E_grandis_chr3_TNL.gff3

A file indicates the completion of classification:

result/E_grandis_chr3_NLRclassification.done

Now is ready to use this pipeline on your own samples.

Contributor

Tamene Tolessa, Peri Tobias, Benjamin Schwessinger, Zhenyan Luo

How to cite this pipeline

Please also remember to cite all dependencies used in this pipeline