Bayesian LCA-based Taxonomic Classification Method (BLCA) is a Bayesian-based method that provides a solid probabilistic basis for evaluating the taxonomic assignments for the query sequences with bootstrap confidence scores, which is based on Bayesian posterior probability that quantitatively weigh each database hit sequence according to its similarity to the query sequence - the more similar database hit sequence to the query, the more its contribution to the taxonomic assignment of the query.
We implemented the above algorithm as a simple python script here.
- August 29 2023 Update of 1.subset_db_acc.py to handle multiple taxID assocated with the same organisms (NCBI taxonomy database may have multiple different IDs associated with the same organisms).
- Sep 16 2019 Minor update of 2.blca_main.py to fix minus strand range from Blastn output (credit to Carter Hoffman).
- Jul 8 2019 Minor update of 2.blca_main.py to fix clustalo's compatibility issue with blast 2.9.0.
- Jun 3 2019 Minor update of 2.blca_main.py to fix the hidden 100% confidence score bug.
- May 9 2019 Minor update to 1.subset_db_gg.py to include a new function to extract only sequences with full taxonomy information.
- Feb 26 2019 update One utility script (generate_abundance_table.py) for merging multiple BLCA output is available in the utils folder.
- Feb 21 2019 update The entire package has been updated to python 3.
- Nov 15 2018 update Thanks to Kristjan's contribution, now we incorporated the use of clustalo as alignment software. Also, now BLCA main program is based on python 3.
- May 11 2017 update to be compatible for the latest blastn v2.5 and added a new parameter -j to limit the accepted hits number to 50. After another round of testing, we've decided to change the default value of coverage and identify filter to 0.80 and 90 respectively.
- BLCA has migrated to Python 3. If you'd like to use python2.7, please install from release (https://github.com/qunfengdong/BLCA/releases).
- BLCA currently is compatible with blast 2.9.0+, please make sure you have the latest blast: version 2.9.0 or above.
- There should NOT be any "|" (pipe) present in the sequence ID of input fasta, database fasta and taxonomy files.
-
What threshold should I use for the confidence score?
0.8 -
What should I do if I see "Error while loading shared libraries: ..." when running BLCA?
This error is caused because your system lack the specified shared library. There are two things you can do:
Ask your system administrator to install or provide access to the specified shared library. Then add its path to your $PATH, or copy it to your $PATH. (recommended)
Or reinstall blca, make sure all modules and dependencies are installed correctly. -
What should I do if I see something like "ValueError: max() arg is an empty sequence"?
This error is caused because the format of your database is wrong. If you are using custom database, please refer to "Custom database" section for the correct format of the database files.
- Python 3
- Linux
- Biopython
- BLAST binary (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.9.0/ or above)
- clustalo (http://www.clustal.org/omega/)
- MUSCLE (http://www.drive5.com/muscle/downloads.htm)
A Bayesian Taxonomic Classification Method for 16S rRNA Gene Sequences with Improved Species-level Accuracy. Xiang Gao; Huaiying Lin; Kashi Revanna; Qunfeng Dong BMC Bioinformatics 2017 May 10;18(1):247.
To check out the source code, go to https://github.com/qunfengdong/BLCA. To obtain the scripts and example fasta files, do the following:
$ git clone https://github.com/qunfengdong/BLCA.git
After the github repository is cloned, you will find a folder named BLCA. All the scripts and example data files will be included in it. It is highly recommended to run your own analysis inside this directory (BLCA), meaning you should have your fasta files moved to here, so you don't have to change the default database directory.
We do not include a pre-compiled database with this release, so the first step is to build a taxonomy database from the NCBI 16S microbial database. We achieve this by using script 1.subset_db_acc.py (or 1.subset_db_gg.py). After the database is built and stored on your local machine, you will supply the location of the taxonomy output file (16SMicrobial.taxID.taxonomy) from the last step along with your input fasta file (test.fasta) to 2.blca_main.py, then you will get a blca output as test.fasta.blca.out.
- To use the standard NCBI 16S microbial database, please go to Step 1.
- To use GreenGenes database, please go to Alternative Step 1.
- To use pre-compiled SILVA LSU database, please go to "SILVA LSU database".
- To use a custom database, please go to "Custom database".
- To compile, subset the 16S Microbial database. Please run:
$ python 1.subset_db_acc.py
More options available:
$ python 1.subset_db_acc.py -h
usage: 1.subset_db_acc.py [--dir DIR] [-d DATABASE] [--taxdmp TAXDMP]
[-h]
<< Bayesian-based LCA taxonomic classification method >>
Please make sure the following softwares are in your PATH:
1.muscle (http://www.drive5.com/muscle/downloads.htm), muscle should be the program's name.
2.ncbi-blast suite (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
3.clustalo (http://www.clustal.org/omega/), clustalo should be the program's name.
4.Biopython should be installed locally.
optional arguments:
--dir DIR The local directory name where you want to store the formatted database. Default: db
-d DATABASE, --database DATABASE
The database link that you want to download from and format. Default: https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz
--taxdmp TAXDMP The taxonomy database dmp link from NCBI. Default: ftp://ftp.ncbi.nih.gov/pub/taxonomy/taxdmp.zip
-h, --help show this help message and exit
No warrenty comes with this script. Author: hlin2@luc.edu.
Any suggestions or bugs report are welcomed.
During the process of setting up the database, NCBI's 16S_ribosomal_RNA.tar.gz file, and taxdmp.zip will be downloaded into a default folder: ./db/, and uncompressed. 16S_ribosomal_RNA.ACC.taxonomy under the ./db directory is the taxonomy file should be supplied to the 2.blca_main.py as the database.
- To format GreenGenes database, first you have to download the Greengenes fasta and taxonomy files from https://greengenes.secondgenome.com/?prefix=downloads/greengenes_database/gg_13_5/. The files you need are gg_13_5.fasta.gz and gg_13_5_taxonomy.txt.gz. After you make sure you download the targeted two files under BLCA folder, please run:
$ python 1.subset_db_gg.py
This script will unzip the downloaded files and create a new folder called "gg" to store all needed information.
More options available:
$ python 1.subset_db_gg.py -h
usage: 1.subset_db_gg.py [--dir DIR] [--ggfasta GGFASTA] [--ggtax GGTAX] [-t]
[-h]
<< Bayesian-based LCA taxonomic classification method >>
Please make sure the following softwares are in your PATH:
1.muscle (http://www.drive5.com/muscle/downloads.htm), muscle should be the program's name.
2.ncbi-blast suite (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
3.clustalo (http://www.clustal.org/omega/), clustalo should be the program's name.
4.Biopython should be installed locally.
This is the utility script to format Greengene Database before running the BLCA taxonomy profiling.
>> Please first download the Greengenes fasta and taxonomy files from https://greengenes.secondgenome.com/?prefix=downloads/greengenes_database/gg_13_5/.
optional arguments:
--dir DIR The local directory name where you want to store the formatted database. Default: gg
--ggfasta GGFASTA The GreenGene database fasta file. Default: gg_13_5.fasta.gz
--ggtax GGTAX The GreenGene database taxonomy file. Default: gg_13_5_taxonomy.txt.gz
-t, --fulltax Extract a subset of GreenGene with only reads with full taxonomy information. This could take a while.
-h, --help show this help message and exit
No warrenty comes with this script. Author: hlin2@luc.edu.
Any suggestions or bugs report are welcomed.
To use a custom database, a taxonomy file in the following format and a blastn database need to be generated.
- Taxonomy file: The format must be ID column + taxonomy column (in the format of "species:xxx;genus:xxx;...") separated by tab. For the taxonomy column, the taxonomy rank must be present and followed by ":", and followed by the name of that taxonomy rank, and followed by ";".
NR_170391.1 species:Azospirillum ramasamyi;genus:Azospirillum;family:Azospirillaceae;order:Rhodospirillales;class:Alphaproteobacteria;phylum:Proteobacteria;superkingdom:Bacteria;
NR_170392.1 species:Erysipelothrix piscisicarius;genus:Erysipelothrix;family:Erysipelotrichaceae;order:Erysipelotrichales;class:Erysipelotrichia;phylum:Firmicutes;superkingdom:Bacteria;
NR_170393.1 species:Erysipelothrix piscisicarius;genus:Erysipelothrix;family:Erysipelotrichaceae;order:Erysipelotrichales;class:Erysipelotrichia;phylum:Firmicutes;superkingdom:Bacteria;
NR_170394.1 species:Erysipelothrix piscisicarius;genus:Erysipelothrix;family:Erysipelotrichaceae;order:Erysipelotrichales;class:Erysipelotrichia;phylum:Firmicutes;superkingdom:Bacteria;
NR_170395.1 species:Sphingorhabdus lacus;genus:Sphingorhabdus;family:Sphingomonadaceae;order:Sphingomonadales;class:Alphaproteobacteria;phylum:Proteobacteria;superkingdom:Bacteria;
- Blastn database file: This file can be generated by fasta file with record IDs the same as the taxonomy file. Use the following command:
$ makeblastdb -in <input_fasta> -parse_seqids -blastdb_version 5 -title "custom_db_title" -dbtype nucl
- If you have a big fasta file, and you want to run BLCA in "parallel", you can use this python package to split fasta sequences into multiple parts, then run BLCA on each individual part.
Run your analysis with the compiled database.
- For default database in default directory, please run:
$ python 2.blca_main.py -i test.fasta
- If you are running your analysis somewhere else other than in the BLCA directory, please do the following:
$ python /location/to/2.blca_main.py -i test.fasta -r /location/to/your/database/16S_ribosomal_RNA.ACC.taxonomy -q /location/to/your/database/16S_ribosomal_RNA
- If you are using the Greengene database as your reference, please do the following:
$ python /location/to/2.blca_main.py -i test.fasta -r gg/gg_13_5_taxonomy.taxonomy -q gg/gg_13_5
- If you are using custom database, please do the following:
$ python /location/to/2.blca_main.py -i test.fasta -r /location/to/your/database/your_taxonomy_file -q /location/to/your/database/database_name
More options are the following:
$ python 2.blca_main.py -h
usage: 2.blca_main.py -i FSA [-x] [-n NPER] [-j NSUB] [-d TOPPER] [-e ESET]
[-b BSET] [-c CVRSET] [--iset ISET] [-a ALIGN]
[-m MATCH] [-f MISMATCH] [-g NGAP] [-r TAX] [-q DB]
[-t GAP] [-o OUTFILE] [-p PROC] [-h]
<< Bayesian-based LCA taxonomic classification method >>
Please make sure the following softwares are in your PATH:
1.muscle (http://www.drive5.com/muscle/downloads.htm), muscle should be the program's name.
2.ncbi-blast suite (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
3.clustalo (http://www.clustal.org/omega/), clustalo should be the program's name.
4.Biopython should be installed locally.
required arguments:
-i FSA, --fsa FSA Input fasta file
taxonomy profiling options [filtering of hits]:
-x, --skipblast skip blastn. Default: blastn is not skipped
-n NPER, --nper NPER number of times to bootstrap. Default: 100
-j NSUB, --nsub NSUB maximum number of subjects to include for each query reads. Default: 50
-d TOPPER, --topper TOPPER
proportion of hits to include from top hit. Default: 0.1 [0-1]
-e ESET, --eset ESET minimum evalue to include for blastn. Default: 0.1
-b BSET, --bset BSET minimum bitscore to include for blastn hits. Default: 100
-c CVRSET, --cvrset CVRSET
minimum coverage to include. Default: 0.85 [0-1]
--iset ISET minimum identity score to include. Default: 90 [0-100]
alignment control arguments:
-a ALIGN, --align ALIGN
alignment tool: clustal omega or muscle. Default: clustalo
-m MATCH, --match MATCH
alignment match score. Default: 1
-f MISMATCH, --mismatch MISMATCH
alignment mismatch penalty. Default: -2.5
-g NGAP, --ngap NGAP alignment gap penalty. Default: -2
other arguments:
-r TAX, --tax TAX reference taxonomy file for the Database. Default: db/16SMicrobial.ACC.taxonomy
-q DB, --db DB refernece blast database. Default: db/16SMicrobial
-t GAP, --gap GAP extra number of nucleotides to include at the beginning and end of the hits. Default: 10
-o OUTFILE, --outfile OUTFILE
output file name. Default: <fasta>.blca.out
-p PROC, --proc PROC how many processors are used in blastn step. Default: 2 processors
-h, --help show this help message and exit
No warrenty comes with this script. Author: hlin2@luc.edu.
Any suggestions or bugs report are welcomed.
- A text file with sequence id in the first column, and taxonomy annotation with confidence scores after each level of annotaion (superkingdom, phylum, class, order, family, genus, species).
- Any reads that do not have a classification will be recorded as "Unclassified".
- There could be cases having the confidence score showing while there is no taxonomy assignment at genus/species level. It is due to the lack of taxonomy information in the database.
seq94 superkingdom:Bacteria;100.0;phylum:Firmicutes;100.0;class:Clostridia;100.0;order:Clostridiales;100.0;family:Lachnospiraceae;100.0;genus:Lachnoclostridium;100.0;species:[Clostridium] symbiosum;100.0;
seq89 superkingdom:Bacteria;100.0;phylum:Proteobacteria;100.0;class:Gammaproteobacteria;100.0;order:Aeromonadales;57.4166666667;family:Aeromonadaceae;57.4166666667;genus:;57.4166666667;species:;100.0;
seq87 superkingdom:Bacteria;100.0;phylum:Firmicutes;100.0;class:Clostridia;100.0;order:Clostridiales;100.0;family:Ruminococcaceae;100.0;genus:;69.0019047619;species:;100.0;
seq93 superkingdom:Bacteria;100.0;phylum:Actinobacteria;100.0;class:Actinobacteria;100.0;order:Corynebacteriales;100.0;family:Nocardiaceae;100.0;genus:Rhodococcus;100.0;species:Rhodococcus zopfii;99.5;
seq96 Unclassified
- BLCA main script 2.blca_main.py needs
- A BLAST formatted library from a fasta file containing sequences of your interest, using makeblastdb, as the following:
>NR_117221.1
AGTCGATCGATCGATCATCGCTCTCTAGAGAGAAAACCCGATCGATCGA...
>NR_144700.1
CGCGCGACGAGCAAGCGCAAACGGCAACGCGCGAAACCCGCGAGCGAGA...
$ makeblastdb -in YourDatabase.fasta -dbtype nucl -parse_seqids -out YourDatabase
- A taxonomy file with two columns, sequence ID in fasta file, and its taxonomy from superkingdom to species in the following format (The deliminator between the sequence ID and taxonomy information should be a tab [\t]):
NR_117221.1 species:Mycobacterium arosiense;genus:Mycobacterium;family:Mycobacteriaceae;order:Corynebacteriales;class:Actinobacteria;phylum:Actinobacteria;superkingdom:Bacteria;
NR_144700.1 species:Virgibacillus massiliensis;genus:Virgibacillus;family:Bacillaceae;order:Bacillales;class:Bacilli;phylum:Firmicutes;superkingdom:Bacteria;
NR_108831.1 species:Bacillus endoradicis;genus:Bacillus;family:Bacillaceae;order:Bacillales;class:Bacilli;phylum:Firmicutes;superkingdom:Bacteria;
NR_113104.1 species:Prevotella enoeca;genus:Prevotella;family:Prevotellaceae;order:Bacteroidales;class:Bacteroidia;phylum:Bacteroidetes;superkingdom:Bacteria;
NR_027573.1 species:Intestinibacter bartlettii;genus:Intestinibacter;family:Peptostreptococcaceae;order:Clostridiales;class:Clostridia;phylum:Firmicutes;superkingdom:Bacteria;
- Run 2.blca_main.py with the formatted database and taxonomy file.
$ python 2.blca_main.py -i test.fasta -r /location/to/your/database/YourDatabase.taxonomy -q /location/to/your/database/YourDatabase- Version 2.2 An alternative public release
- Dr. Xiang Gao, theoretical conception and algorithm development
- Dr. Qunfeng Dong, algorithm development
- Huaiying Lin, program coding and testing
- Kashi Revanna, program coding and package development
- Dr. Yue Xing, program developer and maintainer
Please report any errors or bugs in "Issues".
GNU
- BLAST program: Camacho C., Coulouris G., Avagyan V., Ma N., Papadopoulos J., Bealer K., & Madden T.L. (2008) "BLAST+: architecture and applications." BMC Bioinformatics 10:421.
- Clustal Omega: Sievers, Fabian, Andreas Wilm, David Dineen, Toby J. Gibson, Kevin Karplus, Weizhong Li, Rodrigo Lopez et al. "Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega." Molecular systems biology 7, no. 1 (2011): 539.
- MUSCLE: Edgar, R.C. (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput.Nucleic Acids Res. 32(5):1792-1797. doi:10.1093/nar/gkh340
- Biopython: Cock PA, Antao T, Chang JT, Bradman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B and de Hoon MJL (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25, 1422-1423