DEPRECATED NinjaMap

NinjaMap is no longer supported, please consider using nf-ninjamap (https://github.com/FischbachLab/nf-ninjamap) instead.

NinjaIndex is no longer supported, please consider using nf-ninjaindex (https://github.com/FischbachLab/nf-ninjaindex) instead.

ninjaMap is a software tool to calculate strain abundance for a given microbial database.

This tool runs in two steps, ninjaIndex and ninjaMap. It will accept a directory of your reference genomes (one genome per file). It calculate the uniqueness of the genome in the database along with other contigs related metadata, and return a binmap file along with a concatenated fasta file of your references.

Requirements

1. Nextflow (https://www.nextflow.io/)
2. Python 3.7
3. numpy
4. scipy
5. numba
6. pysam
7. SeqIO
8. samtools
9. bowtie2
10. bbmap

Pipeline overview

Step 1. The ninjaIndex pipeline is built using Nextflow and processes data using the following steps:

• [ART] - Generate synthetic short reads for each genome
• [Bowtie2] - align reads to all reference genomes
• [ninjaIndex] - generate ninja index for a given synthetic community

Step 2. The ninjaMap pipeline accurately quantify a strain with abundance.

Quick usage

1. Generate a ninjaIndex file on aws

The input of the ninjaIndex is a list of genome files in fasta format.

nextflow run ./nf-core-ninjaindex/main.nf --genomes 's3://bucket/input/*.fna' --outdir 's3://bucket/output/' -profile aws

OR

aws batch submit-job \
    --profile maf \
    --job-name nf-ninjaindex \
    --job-queue default-maf-pipelines \
    --job-definition nextflow-production \
    --container-overrides command=s3://nextflow-pipelines/ninjaindex,\
"--genomes","'s3://dev-scratch/ReferenceDBs/NinjaMap/Index/12Com/fasta/*.fna'",\
"--outdir","s3://genomics-workflow-core/Results/NinjaIndex/12Com/db"

2. Run ninjaMap with an existing ninjaIndex using 16 threads:

The main input of the ninjaMap is a binmap file generated from the 1st step, and a sorted BAM file and its indexed bam.bai file must be present in same directory.

./ninjaMap/scripts/ninjaMap.py -threads 16 -bam sample_sorted.bam -bin binmap.tsv -prefix mycommunity

A wrapper script ninjaMap/ninjaMap_index.sh is provided to run ninjaMap on aws via batch.

Docker

The generic command to run a ninjaMap docker container:

docker container run \
    -v /host/path/to/indata/:/input_data/ \
    -v /host/path/to/outdata/:/output_data/ \
    fischbachlab/ninjamap \
    python ./scripts/ninjaMap.py \
    -bin /input_data/binmap.tsv \
    -bam /input_data/input.sorted.bam \
    -outdir /output_data/summary \
    -prefix mycommunity

ninjaMap Full usage

python  ./ninjaMap/scripts/ninjaMap.py --help
Description:
This script will calculate the abundance of a strain in a defined microbial community.
Usage: ninjaMap.py -bam sorted.bam -bin binmap.tsv -prefix my_community

optional arguments:
  -h, --help          show this help message and exit
  -bam BAMFILE        sorted bam file and its indexed bam.bai file must be present in same directory.
  -bin BINMAP         tab-delimited file with Col1= contig name and Col2=Bin/Strain name
  -outdir OUTDIR      output directory
  -prefix PREFIX      output prefix
  -threads THREADS    number of threads available for this job and subprocesses
  -debug              save intermediate false positives bam file
  -truth TRUTH        If using debug, please provide one strain name that you would like to track.
  -mbq MIN_BASE_QUAL  minimum read base quality to consider for coverage calculations.

Output files for each sample

The output files are organized into 4 folders.

bowtie2 folder

The alignment file of all input reads aligned the defined community database in the bam format

Logs folder

The running logs of various scripts

ninjaMap folder

1. *.ninjaMap.abundance.csv: this file shows the statistics of the abundance, coverage and depth of each strain in the defined community

• Strain_Name: strain name
  
• Read_Fraction: the abundance in the defined community in percentage
  
• Percent_Coverage: the average coverage per strain in percentage
  
• Coverage_Depth: the average coverage depth
  

2. *.ninjaMap.read_stats.csv: this file shows the statistics of input reads

• File_Name: sample name
  
• Reads_Aligned: the number of aligned reads
  
• Reads_wPerfect_Aln: the number of perfectly aligned reads
  
• Reads_wSingular_Votes: the number of reads voted as singular
  
• Reads_wEscrowed_Votes: the number of reads voted as escrow
  
• Discarded_Reads_w_Perfect_Aln: the number of discarded perfectly aligned reads

3. *.ninjaMap.strain_stats.csv: this file shows the various statistics of each strains

4. *.ninjaMap.votes.csv.gz: the statistics of reads voting (singular or escrow)

Stats folder

1. adapter_trimming_stats_per_ref.txt: this file shows the statistics of adapter trimming

2. read_accounting.csv: this file shows the statistics shows the total number of reads, the number of reads after trimming and the number of aligned reads

Aggregated output files for each study

The aggregated output files are organized into 6 files.

1. *.covDepth.csv: this file shows the average coverage depth per strain by samples
2. *.host_contaminants.csv: this file shows the detected host contaminants (Human or Mouse) by samples if the unalignment rate is over 5%
3. *.long.csv: this is the long format of three files (*.readFraction.csv, *.covDepth.csv and *.percCoverage.csv)
4. *.percCoverage.csv: this file shows the average coverage per strain in percentage by samples
5. *.reads_stats.csv: this file shows the reads statistics in read numbers by samples
6. *.readFraction.csv: this file shows the abundance in the defined community in percentage by samples

Citation

To be added

License

[GNU GPL] (https://www.gnu.org/licenses/gpl-3.0.html)

Questions / Concerns

• Sunit Jain
• Xiandong Meng (xdmeng at stanford.edu)
• PI: Michael Fischbach ( fischbach at fischbachgroup.org )