The bioinformatics pipeline leverages BWA/Minimap2 to identify ‘where’ reads belong to provides taxonomy identification specific to strain-level. Other than community profiling, PanGIA uses two approaches to obtain a metric of confidence, one that relies on uniqueness of sequences and the other one that relies on comparing test samples with control samples (organism-basis).
The software associated a web-based user interface for job submission in docker and interactive result visualization for providing pathogenic information and real-time filtering results. The pipeline was tested and validated using many synthetic datasets ranging in community composition and complexity, and was successfully applied to spiked clinical samples.
The docker version is also available at Docker hub. The docker container runs PanGIA-UI that provides a web-based GUI to facilitate users to analyze their datasets through PanGIA and access to results.
Third-party softwares:
- Python >= 3.4
- BWA >= v0.7
- Minimap2 >= 2.1
- samtools >= 1.8
- GNU parallel
PanGIA requires following Python dependencies:
- Pandas >= 0.22
- SciPy >= 0.14
- Bokeh >= 0.13 (optional)
PanGIA Database can be downloaded from LANL:
https://edge-dl.lanl.gov/PanGIA/database/
-
Download taxonomy and pathogen metadata:
-
Download BWA index(es) for reference genomes:
-
(Optional) Download BWA indexes for host genomes:
-
(Optional) Original sequences databases in FASTA format:
- All raw sequences can be found here.
-
Make sure you have requirements and dependencies installed properly. Conda is quick way.
-
Retrieving PanGIA:
git clone https://github.com/poeli/pangia.git && cd pangia
- Download databases:
curl -O https://edge-dl.lanl.gov/PanGIA/database/PanGIA_20180915_taxonomy.tar.gz
curl -O https://edge-dl.lanl.gov/PanGIA/database/PanGIA_20180915_NCBI_genomes_refseq89_BAV.fa.tar
curl -O https://edge-dl.lanl.gov/PanGIA/database/PanGIA_20180915_NCBI_genomes_refseq89_adds.fa.tar
curl -O https://edge-dl.lanl.gov/PanGIA/database/PanGIA_20180915_NCBI_genomes_refseq89_Human_GRCh38.p12.fa.tar
- Decompress databases. All files will be decompressed to "pangia/database" directory.
tar -xzf PanGIA_20180915_taxonomy.tar.gz
tar -xzf PanGIA_20180915_NCBI_genomes_refseq89_BAV.fa.tar
tar -xzf PanGIA_20180915_NCBI_genomes_refseq89_adds.fa.tar
tar -xzf PanGIA_20180915_NCBI_genomes_refseq89_Human_GRCh38.p12.fa.tar
- Enjoy.
./pangia.py \
-i test.1.fastq test.2.fastq\
-db database/NCBI_genomes_refseq89_*.fa \
-t 24
Run dataset HMP Mock Community even sample (SRR172902) against PanGIA NCBI refseq89 BAV and adds database with 24 threads, save mapping information to JSON file for use as a background later.
./pangia.py \
-i SRR172902.fastq \
-db database/NCBI_genomes_refseq89_BAV.fa database/NCBI_genomes_refseq89_adds.fa \
-sb \
-t 24
Run dataset "test.fq" against all PanGIA databases with 24 threads, load QCB_background_REP1 as background and report a "combined" score.
./pangia.py \
-i HPV_test.fq \
-db database/NCBI_genomes_*.fa \
-lb background/QCB_background_REP1_allQC.pHostDB_NoHost.pangia.json.gz \
-st combined \
-t 24
-
PanGIA will cleanup the temp directory after the job is done. Run pangia.py with
--keepTempif you want PanGIA-VIS to display genome coverage plot. -
Install Bokeh >= v1.0.
conda install -c bokeh bokeh
- Run
pangia-vis.plwith PanGIA result file (*.result.tsv). For example:
pangia-vis.pl pangia_vis/data/test.tsv
- Enjoy!
| COLUMN | NAME | DESCRIPTION |
|---|---|---|
| 1 | LEVEL | Taxonomic rank |
| 2 | NAME | Taxonomic name |
| 3 | TAXID | Taxonomic ID |
| 4 | READ_COUNT | Number of raw mapped reads |
| 5 | READ_COUNT_RNR | Number of mapped reads normalized by shared reference |
| 6 | READ_COUNT_RSNB | Number of rank-specific mapped reads normalized by identity and # of shared reference |
| 7 | LINEAR_COV | Proportion of covered signatures to total signatures of mapped organism(s) |
| 8 | DEPTH_COV | Depth of coverage |
| 9 | DEPTH_COV_NR | Depth of coverage normalized by # of shared reference |
| 10 | RS_DEPTH_COV_NR | Depth of coverage calculated by rank-specific reads normalized by # of shared reference at this rank |
| 11 | PATHOGEN | Pathogen or not |
| 12 | SCORE | Confidence score |
| 13 | REL_ABUNDANCE | Relative abundance |
| 14 | ABUNDANCE | Abundance |
| 15 | TOTAL_BP_MISMATCH | Total number of mismatch base-pairs |
| 16 | NOTE | Note |
| 17 | RPKM | Reads Per Kilobase Million |
| 18 | PRI_READ_COUNT | Number of reads mapped to this organism as a primary alignment |
| 19 | TOL_RS_READ_CNT | Total rank specific read count |
| 20 | TOL_NS_READ_CNT | Total rank non-specific read count |
| 21 | TOL_RS_RNR | Total rank specific read count |
| 22 | TOL_NS_RNR | Total rank non-specific read count |
| 23 | TOL_GENOME_SIZE | Total size of genome(s) belong to this taxa |
| 24 | LINEAR_LENGTH | Number of non-overlapping bases covering the signatures |
| 25 | TOTAL_BP_MAPPED | Total bases of mapped reads |
| 26 | RS_DEPTH_COV | Depth of coverage calculated by rank-specific reads |
| 27 | FLAG | Superkingdom flag |
| 38-36 | STR - ROOT | Number of READ_COUNT at each rank (strain to root) |
| 37-45 | STR_rnb - ROOT_rnb | Number of READ_COUNT_RSNB at each rank (strain to root) |
| 46-54 | STR_rnr - ROOT_rnr | Number of READ_COUNT_RNR at each rank (strain to root) |
| 55-63 | STR_ri - ROOT_ri | read-mapping identity at each rank (strain to root) |
| 64 | SOURCE | Pathogenic - sample sources |
| 65 | LOCATION | Pathogenic - sample locations |
| 66 | HOST | Pathogenic - sample hosts |
| 67 | DISEASE | Pathogenic - diseases |
| 68 | SCORE_UNIQ | Score based on uniqueness information among genomes (overall) |
| 69 | SCORE_BG | Score based on comparing input dataset with input background |
| 70 | SCORE_UNIQ_CUR_LVL | Score based on uniqueness information among genomes (rank) |
usage: pangia.py [-h] (-i [FASTQ] [[FASTQ] ...] | -s [SAMFILE])
[-d [[BWA_INDEX] [[BWA_INDEX] ...]]] [-dp [PATH]]
[-asl <INT>] [-ams <INT>] [-ao <STR>] [-se]
[-st {bg,standalone,combined}]
[-m {report,class,extract,lineage}]
[-rf {basic,r,rnb,rnr,ri,patho,score,ref,full,all} [{basic,r,rnb,rnr,ri,patho,score,ref,full,all} ...]]
[-da] [-par <INT>] [-xnm <INT>] [-x [TAXID]] [-r [FIELD]]
[-t <INT>] [-o [DIR]] [-td [DIR]] [-kt] [-p <STR>] [-ps]
[-sb] [-lb [<FILE> [<FILE> ...]]] [-ms <FLOAT>] [-mr <INT>]
[-mb <INT>] [-ml <INT>] [-mc <FLOAT>] [-md <FLOAT>]
[-mrd <FLOAT>] [-np] [-pd] [-if <STR>] [-nc] [-c] [--silent]
[--verbose] [--version]
PanGIA Bioinformatics 1.0.0
optional arguments:
-h, --help show this help message and exit
-i [FASTQ] [[FASTQ] ...], --input [FASTQ] [[FASTQ] ...]
Input one or multiple FASTQ file(s). Use space to
separate multiple input files.
-s [SAMFILE], --sam [SAMFILE]
Specify the input SAM file. Use '-' for standard
input.
-d [[BWA_INDEX] [[BWA_INDEX] ...]], --database [[BWA_INDEX] [[BWA_INDEX] ...]]
Name/path of BWA-MEM index(es). [default: None]
-dp [PATH], --dbPath [PATH]
Path of databases. If this option isn't specified but
a path is provided in "--database" option, this path
of database will also be used in dbPath. Otherwise,
the program will search "database/" in program
directory. [default: database/]
-asl <INT>, --alignSeedLength <INT>
Minimum seed length uses in BWA-MEM [default: 40]
-ams <INT>, --alignMinScore <INT>
Minimum alignment score (AS:i tag) for BWA-MEM
[default: 60]
-ao <STR>, --addOptions <STR>
Additional options for BWA-MEM (no need to add -t)
[default: '-h150 -B2']
-se, --singleEnd Input single-end reads or treat paired-end reads as
single-end [default: False]
-st {bg,standalone,combined}, --scoreMethod {bg,standalone,combined}
You can specify one of the following scoring method:
"bg" : compare mapping results with the background;
"standalone" : score based on uniqueness;
"combined" : bg * standalone;
[default: 'standalone']
-m {report,class,extract,lineage}, --mode {report,class,extract,lineage}
You can specify one of the following output modes:
"report" : report a summary of profiling result;
"class" : output results of classified reads;
"extract" : extract mapped reads;
"lineage" : output abundance and lineage in a line;
Note that only results/reads belongs to descendants of TAXID will be reported/extracted if option [--taxonomy TAXID] is specified. [default: 'report']
-rf {basic,r,rnb,rnr,ri,patho,score,ref,full,all} [{basic,r,rnb,rnr,ri,patho,score,ref,full,all} ...], --reportFields {basic,r,rnb,rnr,ri,patho,score,ref,full,all} [{basic,r,rnb,rnr,ri,patho,score,ref,full,all} ...]
You can specify following set of fields to display in the report:
"basic" : essential fields that will display in the reports;
"r" : rank specific read count;
"rnb" : rank specific read count normalized by
both identity and # of ref (1*identity/num_refs);
"rnr" : rank specific read count normalized by
the number of references (1/num_refs);
"ri" : rank specific read identity
(mapped_length-nm)/read_length;
"patho" : metadata of pathogen;
"score" : detail score information;
"ref" : mapped reference(s) and their locations
"full" : display additional information
"all" : display all of above;
[default: 'all']
-da, --displayAll Display all taxonomies including being filtered out
[default: None]
-par <INT>, --procAltRefs <INT>
Process the number of different references in
alternative alignments [default: 30]
-xnm <INT>, --extraNM <INT>
Process alternative alignments with extra number of
mismatches than primary alignment [default: 1]
-x [TAXID], --taxonomy [TAXID]
Specify a NCBI taxonomy ID. The program will only
report/extract the taxonomy you specified.
-r [FIELD], --relAbu [FIELD]
The field will be used to calculate relative
abundance. [default: DEPTH_COV]
-t <INT>, --threads <INT>
Number of threads [default: 1]
-o [DIR], --outdir [DIR]
Output directory [default: .]
-td [DIR], --tempdir [DIR]
Default temporary directory [default:
<OUTDIR>/<PREFIX>_tmp]
-kt, --keepTemp Keep temporary directory after finishing the pipeline.
-p <STR>, --prefix <STR>
Prefix of the output file [default:
<INPUT_FILE_PREFIX>]
-ps, --pathoScoreOnly
Only calculate score for pathogen under '--scoreMethod
bg'
-sb, --saveBg Save current readmapping result in JSON to
<PREFIX>.json
-lb [<FILE> [<FILE> ...]], --loadBg [<FILE> [<FILE> ...]]
Load one or more background JSON gzip file(s)
[default: None
-ms <FLOAT>, --minScore <FLOAT>
Minimum score to be considered valid [default: 0]
-mr <INT>, --minReads <INT>
Minimum number of reads to be considered valid
[default: 10]
-mb <INT>, --minRsnb <INT>
Minimum number of reads to be considered valid
[default: 2.5]
-ml <INT>, --minLen <INT>
Minimum linear length to be considered valid [default:
200]
-mc <FLOAT>, --minCov <FLOAT>
Minimum linear coverage to be considered a valid
strain [default: 0.004]
-md <FLOAT>, --minDc <FLOAT>
Minimum depth of coverage to be considered a valid
strain [default: 0.01]
-mrd <FLOAT>, --minRsdcnr <FLOAT>
Minimum rank specific depth of coverage normalized by
the number of mapped references to be considered a
valid strain [default: 0.0009]
-np, --nanopore Input reads is nanopore data. This option is
equivalent to use [-oa='-h 150 -x ont2d' -ms 0 -mr 1
-mb 3 -ml 50 -asl 24 -ams 70]. [default: FALSE]
-pd, --pathogenDiscovery
Adjust options for pathogen discovery. This option is
equivalent to use [-ms 0 -mr 3 -mb 1 -ml 50 -asl 24
-ams 50 -mc 0 -md 0 -mrd 0]. [default: FALSE]
-if <STR>, --ignoreFlag <STR>
Ignore reads that mapped to the references that have
the flag(s) [default: None]
-nc, --noCutoff Remove all cutoffs. This option is equivalent to use
[-ms 0 -mr 0 -mb 0 -ml 0 -mc 0 -md 0 -mrd 0].
-c, --stdout Write on standard output.
--silent Disable all messages.
--verbose Provide verbose running messages and keep all
temporary files.
--version Print version number.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent