MoNaS

An automated pipeline to genotype mosquito's voltage-gated sodium channel genes using NGS data

About

MoNaS (Mosquito Na⁺ channel mutation Search) is an automated pipeline conducting genotyping of voltage-gated sodium channel (VGSC) in mosquitos from NGS reads. Basically, MoNaS is designed to utilize NGS data from genomic DNA such as targeted captured library (SureSelect, xGen probes for instance) or RNA/cDNA such as shotgun library of PCR amplified VGSC cDNA.

How To Use

Currently, we have confirmed MoNaS works in Linux OSs of both Ubuntu18 and CentOS6 & 7. Although we have not confirmed yet, this program may work in other Linux and Mac OSs.

Installation

We recommend creating a conda environment for MoNaS as follows

git clone  https://github.com/ItokawaK/MoNaS.git

cd MoNaS
conda create -n monas --file requirements.txt

conda activate monas
python setup.py install

MoNaS depends some third-party softwares below:

• bwa

• Hisat2

• samtools

• bcftools

• freebayes

• Optionally MUSCLE v3.8.31*

  *Versions are those we are currently working on.

For manual installation, those softwares need to be callable through $PATH environment variable.

Additionally, you need biopython package installed in your python if you utilize some helper tools which automatically construct reference files or run genotype_sanger.py.

Genome references

MoNaS requires a reference genomic sequence in FASTA, and annotation for CDSs in BED and GFF3 files for VGSC gene of your species. Accurate genome and annotation information, of course, is the most vital part of this pipeline. By default, MoNas includes references of three species of mosquitos, Aedes aegypti (-s Aaeg), Aedes albopictus (-s Aalb) and Culex quinquefasciatus (-s Cpip). Because of the file size limitation hosted in GitHub, those references only include a part of the entire genome of each species which contain VGSC gene.

Each file should have name with prefix ref (eg. ref.ga) and be organized under a directory as:

  your_species_dir/           # Arbitrary name of directory. Will be recognized as a species name (-s).
       ├- ref.fa         # Reference FASTA.
       ├- ref.fa.fai*    # FASTA index.
       ├- ref.gff3*      # GFF3 annotation file for VGSC gene.
       ├- ref.bed        # BED annotation file for VGSC CDSs.
       ├- ref.dict*      # Genome dictionary used by GATK.
       ├- ref.mdom.fa*   # Pair-wise aligned VGSC amino-acid sequences.
       ├- bwadb*/        # BWA indices
       │     ├- ref.amb
       │     ├- ref.ann
       │     ├- ref.bwt
       │     ├- ....
       │     
       └- hisatdb*/       # HISAT2 indices
             ├- ref.1.ht2
             ├- ref.2.ht2
             ├- .....
 *: Will be created by MoNaS automatically if absent

Practically, many of those files (with asterisk*) will be created automatically by MoNaS if absent. You will need only re.fa and ref.bed files, at least, to start analysis. By default, MoNaS expect your_species_dir/ locates under in the references directory of MoNaS. You can explicitly specify the location of your_species_dir/ by

-r your_reference_dir -s your_species_dir

in which reference files will be searched from your_reference_dir/your_species_dir.

• ref.bed annotation file.

  The 4th column (name) should be in a string with format as ExonXX(c/d/k/l). The 5th column (score) is not used and is always 0.

  example

3:315930000-316250000	879	1875	Exon32	0	-
3:315930000-316250000	1941	2246	Exon31	0	-
3:315930000-316250000	8708	8979	Exon30	0	-
3:315930000-316250000	9046	9292	Exon29	0	-
3:315930000-316250000	9361	9556	Exon28	0	-
3:315930000-316250000	9627	9750	Exon27	0	-
3:315930000-316250000	17891	18014	Exon26l	0	-
3:315930000-316250000	20113	20236	Exon26k	0	-
3:315930000-316250000	29958	30132	Exon25	0	-
...
3:315930000-316250000	68369	68532	Exon19d	0	-
3:315930000-316250000	69185	69348	Exon19c	0	-
3:315930000-316250000	70427	70601	Exon18	0	-
...

• ref.gff3 file

  The GFF3 file interpretable by BCFtools csq. This file can be created from ref.bed using monas gff3.

  example.

• ref.mdom.fa file

  A multi alignment FASTA file of VGSC amino-acid (aa) sequences. The first entry should be house fly (M. domestica) VGSC. The second and third entries are aa sequences of ck and dl transcript variants of your species, respectively. This data will be used to convert aa position coordinate of your species into M. domestica universal coordinates. This file can also be created from ref.fa and ref.bed using monas aln (MUSCLE and biopython are required).

  example

Testing

If you have cloned MoNaS from GitHub, example fastq.gz files to test scripts are included. To conduct a test run cd MoNaS/monas/misc/example, and then, execute

monas run -l list.txt -s Aalb -o out

Check out/table_with_Mdomcoord.tsv to confirm if your test run finished properly.

Usage

$ monas -h
usage: monas [-h] {version,run,table,aln,gff3} ...

MoNaS (version 2.0) - A program genotyping VGSC genes from NGS reads.

positional arguments:
  {version,run,table,aln,gff3}
                        MoNaS command options
    version             Show version and exit
    run                 Run all processes
    table               Convert a csq VCF to a table
    aln                 Make AA aligment with M. domestica VGSC from DNA fasta and BED
    gff3                Create MoNaS compartible gff3 from BED file

options:
  -h, --help            show this help message and exit

There are several subcommands to choose

• monas run will execute all pipeline processes from raw fastq(.gz).

monas run -h
usage: monas run [-h] [-s SPECIES] [-l SAMPLE_LIST] [-t NUM_CPU] [-b NUM_THREADS] [-o OUT_DIR] [-r REF_ROOT]
                 [-m {ngs_dna,ngs_rna}] [-c {freebayes,gatk}] [-n] [--resume]

options:
  -h, --help            show this help message and exit
  -s SPECIES, --species SPECIES
                        Species name. It should be same to the dirname of references.
  -l SAMPLE_LIST, --sample_list SAMPLE_LIST
                        Path for a list file decribing name of sampeles and fastq files.
  -t NUM_CPU, --max_cpu NUM_CPU
                        Maximum number of threads. [4]
  -b NUM_THREADS, --bwa_treads NUM_THREADS
                        Number of treads per bwa process. [4]
  -o OUT_DIR, --out_dir OUT_DIR
                        Name of out directly. Should be new.
  -r REF_ROOT, --ref_root REF_ROOT
                        Root directly of references. Deault = MoNaS/references
  -m {ngs_dna,ngs_rna}, --mode {ngs_dna,ngs_rna}
                        Analysis mode. [ngs_dna]
  -c {freebayes,gatk}, --variant_caller {freebayes,gatk}
                        Variant caller to be used. Default is freebayes.
  -n, --no_clean        Do not clean old BAM files after rmdup. Off by default.
  --resume              Resume from existing run

Examples

MoNaS/genotype.py  -s Aalb  -l sample_list.txt  -t n_threads  -o out_dir

Mandatory options

• -s, --species

  This option specifies the name of directory storing reference files of each species. By default, this directory will be searched in the built-in reference folder MoNaS/monas/references/. In stead, you can explicitly specify another directory using -r, --ref_root option (see below).

• -l, --sample_list

  Specify a path of space-delimited text file describing sample names and FASTQ paths (paried-ends or single-end) in each single row.

  sample1 pe_1F.fq[.gz] pe_1R.fq[.gz]
  sample2 pe_2F.fq[.gz] pe_2R.fq[.gz]
  sample3 se_1.fq[.gz]
  sample4 pe_3F.fq[.gz] pe_3R.fq[.gz]
  ...            

sample1, sample2, ... are arbitrary unique strings identifying each your sample. Do not include a space or characters such as /, *, , etc. because MoNaS will use these values for file names. The FASTQ paths can be either absolute or relative from where you execute monas run.

Other options

• -m, --mode

  Choose your sample type either from -m ngs_dna or -m ngs_rna:

  ngs_dna: NGS reads from genomic DNA (i.e. including intron) [default]. ngs_rna: NGS reads from RNA/cDNA (i.e. introns are spliced).

• -t, --max_cpu and -b, --bwa_treads

  MoNaS uses -t number of threads at the same time in maximum. In the BWA or HISAT2 stage, several samples are processed parallelly using -b threads for each sample. Thus, number of BWA or HISAT2 processes running at simultaneously will be -t / -b. Basically, running many BWA or HISAT2 processes with small number of thread/process may increase speed of analysis but increases memory usage especially if you use large genome reference (e.g. full genome).

• -r, --ref_root

Specify a path of root directory where the species directory will be searched.

• -c, --variant_caller

  You can select variant caller program to be used (freebayes or gatk). Currently, we have tested MoNaS extensively with freebayes more than gatk.

Pipeline detail

1. MoNaS maps NGS reads to reference genome of each mosquito species with bwa mem for DNA data or hisat2 for RNA data.

2. The resulted bam files are sorted, mark PCR duplicates, and indexed with samtools sort, markdup -S and index, respectively.

3. Each indexed bam file are processed with freebayes or gatk HaplotypeCaller. ref.bed will be used to restrict regions to be analyzed. freebayes processes all bam files as single run, but multiprocessed by dividing the region of interest into many sub-regions (exons) which will be integrated in single out.vcf file at the end.

4. Annotates the out.vcf for amino acid changes by bcftools csq -p a -l using information in ref.gff3 resulting in out_csq.vcf.

5. Finally, human-friendly table table_with_Mdomcoord.tsv describing amino acid changes and its corresponding AA positions in Musca domestica will be generated from out_csq.vcf files.

Output

The output directry will look like:

  out_dir/ # your specified name
       ├- BAMs/　# Indexed bam files after marking PCR duplicates.
       │    ├- sample1.bam
       │    ├- sample1.bam.csi
       │    ├- sample2.bam
       │    ├- ...
       │       
       ├- out.vcf # vcf file for multiple samples by freebayes
       ├- out_csq.vcf # vcf file for multiple samples with csq tag
       ├- stats.exon_cov.tsv # Stats about exon coverage. PCR duplicate are not counted.
       ├- stats.mapping.tsv # Stats about on target read rates
       └- table_with_Mdomcoord.tsv # list of mutations and AA changes with M. domestica AA number

table_with_Mdomcoord.tsv

The final output table, table_with_Mdomcoord.tsv, will look like as below.

#ID     CHROM   POS     REF_ALLELE      ALT_ALLELE      QUAL    GENOTYPE        AA_CHANGE       AA_CHANGE_MDOM  KDR_EVIDENCE    AD      EXON
Aalb-Yona-02    MNAF02001058.1  1930245 T       C       6445.79 T/C     wild/14S        wild/synonymous NA/NA   287,249 Exon1
Aalb-Okayama-02 MNAF02001058.1  1978801 G       T       10826.2 G/T     wild/74P        wild/synonymous NA/NA   163,172 Exon3
Aalb-Yona-04    MNAF02001058.1  1978801 G       T       10826.2 G/T     wild/74P        wild/synonymous NA/NA   263,239 Exon3
Aalb-Okayama-02 MNAF02001058.1  1978810 A       G       10877.2 A/G     wild/77T        wild/synonymous NA/NA   163,173 Exon3
...
Aalb-Yona-08    MNAF02001058.1  2179316 CATC    TATT    507016.0        TATT/TATT       1335VI/1335VI   synonymous/synonymous   NA/NA   0,286   Exon24
Aalb-SP-02      MNAF02001058.1  2179316 CATC    TATT    507016.0        TATT/TATT       1335VI/1335VI   synonymous/synonymous   NA/NA   0,1307  Exon24
Aalb-SP-04      MNAF02001058.1  2179316 CATC    TATT    507016.0        TATT/TATT       1335VI/1335VI   synonymous/synonymous   NA/NA   0,1008  Exon24
Aalb-SP-07      MNAF02001058.1  2179316 CATC    TATT    507016.0        TATT/TATT       1335VI/1335VI   synonymous/synonymous   NA/NA   0,1272  Exon24
Aalb-SP-06      MNAF02001058.1  2179316 CATC    TATT    507016.0        TATT/TATT       1335VI/1335VI   synonymous/synonymous   NA/NA   0,1104  Exon24
...
Aalb-SP-03      MNAF02001058.1  2207911 T       G       231649.0        G/G     F1574C/F1574C   F1534C/F1534C   Strong/Strong   0,906   Exon29
Aalb-SP-01      MNAF02001058.1  2207911 T       G       231649.0        G/G     F1574C/F1574C   F1534C/F1534C   Strong/Strong   1,934   Exon29
Aalb-SP-02      MNAF02001058.1  2207911 T       G       231649.0        G/G     F1574C/F1574C   F1534C/F1534C   Strong/Strong   1,990   Exon29

Column 1: Sample ID

Column 2: Name of scaffold

Column 3: Nucleotide position of variant

Column 4: Reference allele at this site

Column 5: One or set of alternative alleles at this site

Column 6: Quality score from variant caller

Column 7: Genotype of this sample

Column 8: Annotated AA change in reference AA position

Column 9: Annotated AA change in M. domestica AA position.

Collum 10: Whether AA change is known kdr mutation or not.

• AA variant which is confirmed to decrease pyrethroid susceptibility in electrophysiological assay will be described "Strong". Those with weaker evidences are described as "Supportive". Other unknown AA variants will be described as "Unknown". Known kdr AA varoamts are listed in scripts/kdr_list.json which will be updated as possible as regularly.

Note that the list would not always be complete!.

Column 11: Read depth for each allele

Column 12: Exon where this variant belongs

Aanlyzing Sanger sequence reads (disabled forna)

Although it does not seem the best approach, reads from Sanger sequence technology could be analyzed in MoNaS pipeline by regarding those Sanger reads as NGS reads. genotype_sanger.py is a wrapper script to conduct chopping input Sanger reads into 150 bp short reads (~ x5 coverage) with fake quality values, writing fastq and sample list files, and then executing MoNaS for those data.

As any sequecing errors existing in input Sanger reads will be considered as true variants, it is important to trim low-quality regions in advance. Also, ambiguous nucleotide codes (R, Y, S, etc...) are not supported yet (ToDo).

monas run -s Aalb -t 16 -o out_table.tsv sanger_reads.fa

Other subcommands

MoNaS includes some tools assisting creation of new reference annotation file for species of your interest.

• monas gff3

  This script creates and outputs a gff3 file which is interpretable by bcftools csq from a bed file describing VGSC CDSs to STDOUT.

$ monas gff3 -h
usage: monas gff3 [-h] bed

positional arguments:
  bed         bed file

options:
  -h, --help  show this help message and exit

• monas aln

  This script translates genome to VGSC protein using CDS information described in bed file, then conducts pairwise alignment with VGSC in M. domestica which is usable as ref.mdom.fa. The script also reports mismatched AA between your reference VGSC and M. domestica in VGSC OUT_FASTA_PATH.info with notification for potential kdr(s) listed in scripts/kdr_list.json if found.

$ monas aln -h
usage: monas aln [-h] [-o OUT_FASTA_PATH] [-t TRANSLATE] [-m MDOM_PATH] ref_fa bed

positional arguments:
  ref_fa                Genome reference fasta
  bed                   Reference annotation bed

options:
  -h, --help            show this help message and exit
  -o OUT_FASTA_PATH, --out_aln_fasta OUT_FASTA_PATH
                        Multiple AA aligment fasta file to output.
  -t TRANSLATE, --translate TRANSLATE
                        Output only translation to this file.
  -m MDOM_PATH, --mdom_path MDOM_PATH
                        M. domestica aa fasta path [MoNaS/monas/misc/AAB47604.fa]

Citation:

• Kentaro Itokawa et al. (2019), High-throughput genotyping of a full voltage-gated sodium channel gene via genomic DNA using target capture sequencing and analytical pipeline MoNaS to discover novel insecticide resistance mutations. PLoS Negl Trop Dis 13(11): e0007818.

• BioRxiv