GeneParliamentID:
A pipeline for multi-gene species identification

GeneParliamentID by Benedikt Kuhnhäuser, Royal Botanic Gardens, Kew
Current version: 1.1.2 (June 2026)

Citation
Kuhnhäuser, B.G., Quintero-Berns, C., Schley, R., Stevenson, J., Ndiade Bourobou, D., Cziba, L., Deklerck, V., Gallego, B., Lisingo, J., Baker, W.J. & Bellot, S. GeneParliamentID: A pipeline for multi-gene species identification. (In review)

Overview

GeneParliamentID (GPID) is a pipeline for identification of biological samples to the species level using hundreds or thousands of genes, such as those generated using targeted sequence capture.

GPID integrates species identifications inferred from individual genes to provide an overall identification that reflects the relative support for each alternative identification. We conceptualise this process as a “Gene Parliament” in which each gene represents one part of the genomic identity of an individual, and where the overall species identity is established through consideration of the number of genes supporting each different identification. This approach allows explicit assessment of congruence and discordance among multiple genes in species identification. Besides sample identification, the pipeline includes reference directory preparation, method calibration and method validation.

The pipeline is structured into four main commands that are explained in detail in the Wiki:

1. gpid reference: Prepare a reference directory. See 1. Reference construction.
2. gpid calibrate: Run the calibration workflow to identify the optimal pipeline settings. See 2. Method calibration.
3. gpid validate: Run validation analyses on samples with known identity to test the accuracy of identification. See 3. Method validation.
4. gpid identify: Run the identification workflow for sample identification using optimal pipeline settings. See 4. Sample identification.

The Wiki also contains guidance on the Interpretation of the identification results and a hands-on Tutorial using example data.

Pipeline summary

The key steps of the GeneParliamentID pipeline are:

1. Select genes with performance of species identification above user-defined threshold
2. Align each gene against reference, select top match based on highest Bit-score
3. Remove low-confidence matches that don't meet the user-defined alignment filtering thresholds
4. Summarise the number of genes supporting each identification to the Gene Parliament
5. Flag the sample as data-deficient if the number of genes (parliament size) is below the user-defined threshold
6. Select identification with most support as the top identification
7. Evaluate confidence in the top identification based on the percentage of genes supporting this identification

Setup

System requirements

GeneParliamentID is a command-line tool written for Unix-operating systems such as Linux.

The minimum requirements for running GPID are 1 CPU and 1 GB memory. Depending on the size of the dataset analysed, more processing power or memory may be needed.

Install GPID

We recommend installation of GPID including all dependencies using conda with a new environment:
conda create --name gpid gpid

Activate GPID environment

To activate the GPID conda environment, use:
conda activate gpid

Confirm successful installation

To confirm that the installation has worked and show a help message on how to use GPID, simply run:
gpid

Quick start

To give you a first taste of the capabilities of GPID, this is a minimal example that only covers sample identification.

Reference construction, Method calibration and Method validation have already been performed. Note that these steps only need to be conducted once for a given lineage and set of genes.

For a full worked example including reference construction to method calibration and validation, see the Tutorial.

Download data

To download the folder, run:
wget https://github.com/BenKuhnhaeuser/GPID/blob/main/quickstart.tar.gz

Then, extract the files in the folder using:
tar -zxvf example_data.tar.gz

The extracted directory contains the following files and folders:

• reference: folder containing BLAST reference databases for all genes. See 1. Reference construction.
• calibration_gene_performance.csv: file listing performance of each gene, i.e. the percentage of samples correctly identified to species, estimated using method calibration. See 2. Method calibration.
• calibration_filtering_thresholds.csv: file with optimal filtering thresholds selected using method calibration. See 2. Method calibration.
• validation_confidence_support.csv: file listing the probability of the top identification being correct, close or wrong depending on the percentage of genes supporting the identification; produced during method validation. See 3. Method validation.
• species_groups.csv: optional file specifying for each species a user-defined group of closely related species. See See 3. Method validation.
• samples: folder containing a sub-folder for each sample to be identified. Each sub-folder contains one fasta file per gene for the sample to be identified, and each file contains a single corresponding gene sequence for the sample. See 4. Sample identification.

Sample identification

Sample identification is conducted using the following command:
gpid identify -i samples/CQL_2/ -r reference/ -g calibration_gene_performance.csv -t calibration_filtering_thresholds.csv -c calibration_confidence_support.csv -s species_groups.csv

The required input arguments are:
-i: Sample directory containing one FASTA file per gene for the sample to identify
-r: Reference directory containing one FASTA file per gene and the corresponding BLAST databases

For method calibration and validation, the standard file names and locations produced during method calibration and validation are used by default. This can be specified using:
-g: Gene performance file
-t: Filtering thresholds file
-c: Confidence estimates file

Optionally, manually defined groups of closely related species can be included in the results:
-s: Species groups file

See 4. Sample identification for a full list of arguments and detailed instructions on the requirements for each argument.

Pipeline outputs

GPID summarises all individual gene identifications in a Gene Parliament, which represents the percentage of genes supporting all competing identifications. The Gene Parliament is presented both as a table and as a figure.

For a detailed description of the Gene Parliament figure and table and their interpretation, see Interpretation.

Gene Parliament figure

The Gene Parliament figure gives a quick overview of the top 10 identifications that were retrieved and their relative support.

Gene Parliament table

To see all identifications that were retrieved, we can have a look at the Gene Parliament table. Importantly, the table contains for the top identification information on the probability of the identification being correct (correct to species level), close (correct to species group) or wrong (neither correct to species nor to species group).

Sample	Rank	Identification	Species_group	Support_pct	Support_count	Parliament_size	Data_checks	ID_correct_pct	ID_close_pct	ID_wrong_pct
CQL_2	1	Entandrophragma_angolense	Entandrophragma	45.08	55	122	PASSED	82.14	17.86	0
CQL_2	2	Entandrophragma_excelsum	Entandrophragma	18.85	23
CQL_2	3	Entandrophragma_congoense	Entandrophragma	16.39	20
...

And the identification is...?

Inspection of the Gene Parliament figure and table shows that a clear majority of genes (45.1%) support the identification as Entandrophragma angolense, whilst Entandrophragma excelsum (18.85%) and Entandrophragma congoense (16.39%) also get sizeable support. Other species have almost negligible support, but all belong to the same genus Entandrophragma.

The table indicates a probability of 82.14% that the identification is correct to species level, and a further 17.86% (thus totaling 100%) that the identification is close, i.e. correct to species group (in this case genus). The probability that the identification is wrong, i.e. neither correct nor close, is estimated to be 0. Overall, we can be fully confident that the sample belongs to the genus Entandrophragma, and have high confidence that it was taken from the species Entandrophragma_angolense.

Next steps

To work through the full GPID workflow using example data, explore the Tutorial.