IDeRare

IDeRare or "Indonesia Exome Rare Disease Variant Discovery Pipeline" is simple and ready to use variant discovery pipeline to discover rare disease variants from exome sequencing data.

Authored by

Ivan William Harsono^a, Yulia Ariani^b, Beben Benyamin^c,d,e, Fadilah Fadilah^f,g, Dwi Ari Pujianto^b, Cut Nurul Hafifah^h

^aDoctoral Program in Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia.
^bDepartment of Medical Biology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia.
^cAustralian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
^dUniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
^eSouth Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
^fDepartment of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 4, Jakarta, 10430, Indonesia.
^gBioinformatics Core Facilities - IMERI, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 6, Jakarta, 10430, Indonesia .
^hDepartment of Child Health, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia.

How to Cite

Please kindly cite the main paper titled "IDeRare: a lightweight and extensible open-source phenotype and exome analysis pipeline for germline rare disease diagnosis" available at https://doi.org/10.1093/jamiaopen/ooae052

Example :

Ivan William Harsono, Yulia Ariani, Beben Benyamin, Fadilah Fadilah, Dwi Ari Pujianto, Cut Nurul Hafifah, IDeRare: a lightweight and extensible open-source phenotype and exome analysis pipeline for germline rare disease diagnosis, JAMIA Open, Volume 7, Issue 2, July 2024, ooae052, https://doi.org/10.1093/jamiaopen/ooae052

Description

• IDeRare split into 2 parts, namely phenotype and genotype part.
• This pipeline is designed to be used in Linux environment
• Original paper may used different version of tools, and the prerequisite used in this pipeline is the latest version of the tools
• This pipeline is designed and tested with Indonesia rare disease trio patient, but it should be also usable for general cases of rare disease variant discovery from Exome Sequences data given paired end .fq.gz file and HPO data(s)
• Ensure you have at least 250GB free for database and application setup, and 100GB free for each Trio family exome set
• The .yaml file path are assuming all the folder are stored in Downloads folder with subfolder of Database (for RefSeq, dbNSFP, dbSNP, ClinVar), Sandbox (for application and its database), IDeRare (git cloned folder)

Data Example

• Explanation how to write the entry available at Clinical Information Example section and file example at example/clinical_data_example.txt
• Genotype data accessible from the SRR of Bioproject database 1077459 and SRA database: with accession number SRR27997290-SRR27997292. Data paper submission of this samples without Author's permission is strictly prohibited.

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Quick Installation

1. Clone this repository

git clone https://github.com/ivanwilliammd/IDeRare

2. Have a Linux environment (Ubuntu or Ubuntu-like 22.04 LTS distro is recommended)
3. Install Docker and Anaconda - optional- see Prerequisite.md for more details
4. Run dependency installation script and database script

cd installation
source install_dependencies.sh
source download_database.sh
cd ../

5. Optional : if you have difficulty in downoading the database and installing the executable dependencies, you could download the data from here and extract it to the Downloads folder, the run the script below to execute install_dependencies.sh from step 7

source install_dependencies.sh --skip-executable

Tips :

To log all bash script (.sh) run, you could use script command to log the terminal output to a file. Example : script -a install.log

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IDeRare Phenotype Pipeline - Phenotype Translation, Linkage Analysis, Phenotype Similarity Scoring, Gene-disease recommendation (iderare_phenomizing.py)

Interactive Webapps Implementation of iderare_phenomizing.py hosted at Streamlit and based on our homemade Python library iderare-pheno Python library

IDeRare-Pheno stat :

1. This script is recommended if you would like to do conversion, linkage analysis, similarity scoring, and gene-disease recommendation based on the phenotype data provided at clinical_data.txt. Full feature :
   1. Convert the phenotype data to HPO code (accept mixed SNOMED, LOINC, and HPO code)
   2. Similarity scoring of differential diagnosis
   3. Linkage analysis of differential diagnosis (accept mixed SNOMED, ICD-10, ORPHA, OMIM code), include dendrogram tree visualization.
      • This should help clinician to systematically doing work-up and excluding similar diagnosis together based on the patient's phenotype.
   4. Gene and disease recommendation based on the phenotype data similarity scoring between phenotype and OMIM gene and disease databank.
   5. Linkage analysis of recommended causative gene and disease based on phenotype data (include dendrogram tree visualization).
      • This should help clinician to explore / enrich their differential diagnosis based on the patient's phenotype.
   6. Example of the clinical data provided at Clinical Information Example section
2. Run iderare_phenomizing.sh
3. Advance usage : you could add extra parameter of threshold, top-n differential diagnoses gene, and number of gene / diagnoses recommendation by using the

# Threshold : float 0 to 1 - default : 0.4 . Description : Threshold of similarity score to be considered as similar
# Differential : int - default : 10 . Description : Top-n differential diagnosis to be furtherly focused on linkage analysis / dendrogram plot, used only if there are no diagnoses passing the minimum threshold
# Recommendation : int - default : 20 . Description : Top-n gene / disease recommendation to be furtherly focused on linkage analysis / dendrogram plot, used only if there are no diagnoses passing the minimum threshold
### Note : all tsv file in output contained all differential diagnoses, and all gene/disease recommendation with their respective similarity score

source iderare_phenomizing.sh --threshold 0.5 --differential 5 --recommendation 5

For more detail, please refer to iderare-pheno Playbook for demonstration of phenotype conversion and analysis.

4. The output of this file will be saved on output folder, with the file tree and explanation as following.

.
└── output
    ├── {datetime}_linkage_all_ddx.png
    ├── {datetime}_linkage_filt_ddx.png
    ├── {datetime}_linkage_gene.png
    ├── {datetime}_linkage_disease.png
    ├── {datetime}_differential_diagnosis_similarity.tsv
    ├── {datetime}_recommended_disease_similarity.tsv
    ├── {datetime}_recommended_gene_similarity.tsv
    ├── {datetime}_transformed_hpo_set.tsv
    ├── {datetime}_transformed_omim_set.tsv
    └── {datetime}_transformed_hpo_set.txt

File name	Description
{datetime}_Linkage of DDx.png	dendrogram of the linkage analysis of DDx provided on clinical_data.txt (all)
{datetime}_Linkage of DDx with threshold .png	dendrogram of the linkage analysis of DDx provided on clinical_data.txt (threshold)
{datetime}_Linkage of Causative Gene with.png	dendrogram of potential causative top-n candidate gene related to patient's phenotype (from HPO OMIM database)
{datetime}_Linkage of Causative Disease w.png	dendrogram of potential causative top-n candidate disease related to patient's phenotype (from HPO OMIM database)
{datetime}_differential_diagnosis_similarity.tsv	TSV file of differential diagnosis similarity score
{datetime}_differential_recommended_disease_similarity.tsv	TSV file of all disease similarity score
{datetime}_differential_recommended_gene_similarity.tsv	TSV file of all gene similarity score
{datetime}_transformed_hpo_set.tsv	Converted clinical_data to readily used HPO code
{datetime}_transformed_hpo_set.tsv	Converted clinical_data to readily used HPO list for yml

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IDeRare Genotype Pipeline - Preparing the iderare.yml for exome analysis and phenotype-based variant prioritization

1. Set the data, directory file reference and trio information on iderare.yml.
   
   Note : all exome files should be located in the input/A_FASTQ folder of absolute path setup by data_dir at iderare.yml. Example of filled yml available on example/iderare_example.yml
   
   
2. Run the bash script

# Mode : both / solo / trio - default : both . Both will run the pipeline for solo and trio exome data analysis.
# Trimming : true / false - default : false . True will run the trimming process using fastp

source iderare.sh --mode solo --trimming false

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Appendix

Clinical Information Example

• Coded clinical information example in txt format provided at example/clinical_data_example.txt.
• This clinical information is the patient phenotype and differential diagnoses complementing trio exome data provided at Bioproject database 1077459

Phenotype Data

Clinical Finding	Source of Information	Coded in	EMR Code	Interpretation	Writing format at clinical_data.txt
Autosomal recessive inheritance	Inheritance Pattern	SNOMED-CT	258211005		SNOMEDCT:258211005
Hepatosplenomegaly	Physical Examination	SNOMED-CT	36760000		SNOMEDCT:36760000
Anemia	Physical Examination	SNOMED-CT	271737000		SNOMEDCT:271737000
Ascites	Physical Examination	SNOMED-CT	389026000		SNOMEDCT:389026000
Inadequate RBC production	Problem List	SNOMED-CT	70730006		SNOMEDCT:70730006
Abnormality of bone marrow cell morphology	Problem List	SNOMED-CT	127035006		SNOMEDCT:127035006
Cholestasis	Problem List	SNOMED-CT	33688009		SNOMEDCT:33688009
Abnormal liver function	Problem List	SNOMED-CT	75183008		SNOMEDCT:75183008
Impending hepatic failure	Problem List	SNOMED-CT	59927004		SNOMEDCT:59927004
Osteopenia	Problem List (Radiology Finding)	SNOMED-CT	312894000		SNOMEDCT:312894000
Mitral regurgitation	Problem List (Cardiology Finding)	SNOMED-CT	48724000		SNOMEDCT:48724000
Metabolic alkalosis	Problem List (Blood Gas Analysis)	SNOMED-CT	1388004		SNOMEDCT:1388004
Low Albumin Serum Level	Clinical Pathology (Lab)	LOINC	1751-7	L	LOINC:1751-7
Low HDL Level	Clinical Pathology (Lab)	LOINC	2085-9	L	LOINC:2085-9
Low Platelet Count	Clinical Pathology (Lab)	LOINC	777-3	L	LOINC:777-3
Increased Lactate Level	Clinical Pathology (Lab)	LOINC	2519-7	H	LOINC:2519-7
Increased ALT Level	Clinical Pathology (Lab)	LOINC	1742-6	H	LOINC:1742-6
Increased AST Level	Clinical Pathology (Lab)	LOINC	1920-8	H	LOINC:1920-8
Abnormal lower motor neuron	Disease Spectrum related to EMG result	HPO	0002366		HP:0002366
Increase Hepatic Glycogen Content	Liver Biopsy Pathology Interpretation	HPO	0006568		HP:0006568
Bone-marrow foam cells	Pathology Anatomy Bone Marrow Aspiration	HPO	0004333		HP:0004333
Failure to thrive during infancy	Developmental history	HPO	0001531		HP:0001531

Working diagnosis before Exome Sequencing

Differential Diagnosis	Code Type	EMR Code	Writing format at clinical_data.txt
Beta thalassemia	SNOMED-CT	65959000	SNOMEDCT:65959000
Gaucher Disease	SNOMED-CT	190794006	SNOMEDCT:190794006
Niemann Pick Disease type C	SNOMED-CT	66751000	SNOMEDCT:66751000
Glycogen storage diseases spectrum	ICD10	E74.0	ICD-10:E74.0