The pipeline generates the most optimal single or multiple barcodes that could reach the highest resolution of taxonomy identification, based on provided amplicon sequences.
If you use this pipeline in your projects, please cite:
- Zhu, T., & Iwasaki, W. (2023). MultiBarcodeTools: Easy selection of optimal primers for eDNA multi-metabarcoding. Environmental DNA, 5, 1793-1808. https://doi.org/10.1002/edn3.499
MAFFT (v7.505 or compatible). After installation, make sure the following command works with no errors.
mafft
It should output something like this:
------------------------------------------------------------------------------
MAFFT v7.505 (2022/Apr/10)
https://mafft.cbrc.jp/alignment/software/
MBE 30:772-780 (2013), NAR 30:3059-3066 (2002)
------------------------------------------------------------------------------
High speed:
% mafft in > out
% mafft --retree 1 in > out (fast)
High accuracy (for <~200 sequences x <~2,000 aa/nt):
% mafft --maxiterate 1000 --localpair in > out (% linsi in > out is also ok)
% mafft --maxiterate 1000 --genafpair in > out (% einsi in > out)
% mafft --maxiterate 1000 --globalpair in > out (% ginsi in > out)
If unsure which option to use:
% mafft --auto in > out
--op # : Gap opening penalty, default: 1.53
--ep # : Offset (works like gap extension penalty), default: 0.0
--maxiterate # : Maximum number of iterative refinement, default: 0
--clustalout : Output: clustal format, default: fasta
--reorder : Outorder: aligned, default: input order
--quiet : Do not report progress
--thread # : Number of threads (if unsure, --thread -1)
--dash : Add structural information (Rozewicki et al, submitted)
Then you can install MultiBarcodePipeline into a new conda environment.
conda create -n MultiBarcode python=3.9
conda activate MultiBarcode
git clone https://github.com/billzt/MultiBarcodePipeline.git
cd MultiBarcodePipeline
python3 setup.py develop
multi-barcode -h
If you do not have a device running Linux OS, (i.e., macOS or Windows users), or you just want to have a quick look, you can try the Docker version
cd test
multi-barcode amplicon_fish85_primers34.tsv
The result is:
# Finished
# ###########
# Number of Species: 85
# ###########
# Selected Barcodes
# 1: West_FishF1_COX1, resolve 85 species
# ###########
# Number of Unresolved Species under diff=1: 0
# ###########
# See MultiBarcodeResult/matrix.xlsx for details
The result indicates that the West_FishF1_COX1 barcode is best for the inputted 85 fishes, as all of them demonstrate amplicons with at least 1bp variation under this barcode.
multi-barcode amplicon_fish85_primers34.tsv -d 8
The result is:
# Finished
# ###########
# Number of Species: 85
# ###########
# Selected Barcodes
# 1: Weigt_FISHCOI, resolve 83 species
# ###########
# Number of Unresolved Species under diff=8: 2
# ###########
# See MultiBarcodeResult/matrix.xlsx for details
Tips: Since we have not changed the default output directory after the previous run, it would reuse the previous alignment results.
The result indicates that the Weigt_FISHCOI barcode is best for the inputted 85 fishes, as 83 of them demonstrate amplicons with at least 8bp variation under this barcode. No additional barcodes could increase the number of distinguished species.
multi-barcode amplicon_fish85_primers34.tsv -p Miya_MiFish_U_12S
The result is:
# Finished
# ###########
# Number of Species: 85
# ###########
# Selected Barcodes
# 1: Miya_MiFish_U_12S, resolve 79 species
# 2: West_FishF2_COX1, resolve additional 6 species
# ###########
# Number of Unresolved Species under diff=1: 0
# ###########
# See MultiBarcodeResult/matrix.xlsx for details
The result indicates that the best combination is Miya_MiFish_U_12S + West_FishF2_COX1.
rm -rf MultiBarcodeResult/
multi-barcode amplicon_fish85_primers34.tsv -p Miya_MiFish_U_12S,Valentini_Teleo_12S -n 2
The result is:
# Sequence alignment: 0/2 barcodes finished
# Sequence alignment: 2/2 barcodes finished
# Step 2: Find Siblings for Each Target Species
# Tree analysis: 0/2 barcodes finished
# Tree analysis: 1/2 barcodes finished
# Tree analysis: 2/2 barcodes finished
# Step 3: Find Best Combination
# Finished
# ###########
# Number of Species: 85
# ###########
# Selected Barcodes
# 1: Miya_MiFish_U_12S, resolve 79 species
# 2: Valentini_Teleo_12S, resolve additional 4 species
# ###########
# Number of Unresolved Species under diff=1: 2
# ###########
# See MultiBarcodeResult/matrix.xlsx for details
The result indicates that Miya_MiFish_U_12S + Valentini_Teleo_12S can distinguish 83 (79+4) out of the 85 species. The remaining 2 species cannot be distinguished by them. Other barcodes were not calculated.
a four-column-file in TSV format for amplicon sequences. Each line stands for an amplicon. Lines starting with # are ignored.
- Sequence Name or ID (only use alphabets, numbers and underscores)
- Barcode Name or ID (only use alphabets, numbers and underscores)
- Species Name or ID (only use alphabets, numbers and underscores)
- Amplicon-region DNA sequence
Multiple sequences from the same species are acceptable to deal with intra-species variations and polymorphisms.
An example
| Sequence Name | Barcode Name | Species Name | Amplicon Seq. under this Barcode |
|---|---|---|---|
| seqid_1 | p1 | tax1 | ACAAAGTTTAACCATGTTAAACAACTTATTAAAGA |
| seqid_1b | p1 | tax1 | ACAAAGTTTAACCATGCTAAACAACTTATTAAAGA |
| seqid_2 | p1 | tax2 | ACCCAGTTTAACCATGCTAAACAACTTATTAAAGA |
| seqid_1 | p2 | tax1 | CGCCTCTTGCATTCTACGTATAAGAGGTCCCGCCTG |
| seqid_2 | p2 | tax2 | CGCCTCTTGCATTCTACGTATAAGATGTCCCGCCTG |
-h, --help show this help message and exit
-d THRESHOLD_DIFF, --threshold-diff THRESHOLD_DIFF
minimum DNA difference required for most closely related species under the same barcode (default: 1)
-p PREFER, --prefer PREFER
preferred barcodes, in order, separated by comma. eg. barcode1,barcode2 (default: any)
-n THRESHOLD_NUM, --threshold-num THRESHOLD_NUM
maximum number of selected barcodes. (default: 5)
-t THREADS, --threads THREADS
number of CPUs (default: 4)
-o OUTPUT_DIR, --output-dir OUTPUT_DIR
directory for output (default: MultiBarcodeResult)