Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ivanwilliammd/iderare
Indonesia Exome Rare Disease Variant Discovery Pipeline. Phenotype analysis part available on Streamlit and PyPi
https://github.com/ivanwilliammd/iderare
exome-sequencing-analysis gene-prioritization phenotype-exchange-format phenotype-genotype-information phenotype-ontologies phenotype-prediction rare-disease rare-variant-analysis
Last synced: about 1 month ago
JSON representation
Indonesia Exome Rare Disease Variant Discovery Pipeline. Phenotype analysis part available on Streamlit and PyPi
- Host: GitHub
- URL: https://github.com/ivanwilliammd/iderare
- Owner: ivanwilliammd
- License: bsd-3-clause-clear
- Created: 2024-02-04T12:36:28.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-08-06T14:27:26.000Z (5 months ago)
- Last Synced: 2024-08-07T15:30:02.976Z (5 months ago)
- Topics: exome-sequencing-analysis, gene-prioritization, phenotype-exchange-format, phenotype-genotype-information, phenotype-ontologies, phenotype-prediction, rare-disease, rare-variant-analysis
- Language: Jupyter Notebook
- Homepage: https://bioinformatics-ivanwilliamharsono.streamlit.app/IDeRare_Pheno
- Size: 39 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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 Harsonoa, Yulia Arianib, Beben Benyaminc,d,e, Fadilah Fadilahf,g, Dwi Ari Pujiantob, Cut Nurul Hafifahh
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](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](#clinical-information-example) and file example at [example/clinical_data_example.txt](example/clinical_data_example.txt)
- Genotype data accessible from the SRR of Bioproject database [1077459](https://www.ncbi.nlm.nih.gov/bioproject/1077459) and SRA database: with accession number [SRR27997290-SRR27997292](https://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=1077459). **Data paper submission of this samples without Author's permission is strictly prohibited.**
___
### Quick Installation
1. Clone this repository
```bash
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](https://docs.docker.com/engine/install/ubuntu/) and [Anaconda - optional](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html)- see [Prerequisite.md](installation/Prerequisite.md) for more details
4. Run dependency installation script and database script
```bash
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](https://urlqr.xyz/IDeRare-SuppDL) and extract it to the ```Downloads``` folder, the run the script below to execute ```install_dependencies.sh``` from step 7
```bash
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```
___
### 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](https://bioinformatics-ivanwilliamharsono.streamlitapp.com/iderare_phenomizing) and based on our homemade Python library [iderare-pheno](https://github.com/ivanwilliammd/iderare-pheno) Python library
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](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](#clinical-information-example)
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
```bash
# 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](https://github.com/ivanwilliammd/iderare-pheno/blob/main/Playbook.ipynb) 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 |
___
### 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](example/iderare_example.yml)
1. Run the bash script
```bash
# 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
```
___
## Appendix
Clinical Information Example
- Coded clinical information example in txt format provided at [example/clinical_data_example.txt](example/clinical_data_example.txt).
- This clinical information is the patient phenotype and differential diagnoses complementing trio exome data provided at Bioproject database [1077459](https://www.ncbi.nlm.nih.gov/bioproject/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|L |
| Low HDL Level | Clinical Pathology (Lab) | LOINC | 2085-9 | L | LOINC:2085-9|L |
| Low Platelet Count | Clinical Pathology (Lab) | LOINC | 777-3 | L | LOINC:777-3|L |
| Increased Lactate Level | Clinical Pathology (Lab) | LOINC | 2519-7 | H | LOINC:2519-7|H |
| Increased ALT Level | Clinical Pathology (Lab) | LOINC | 1742-6 | H | LOINC:1742-6|H |
| Increased AST Level | Clinical Pathology (Lab) | LOINC | 1920-8 | H | LOINC:1920-8|H |
| 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 |
