Automated Pipelines for Predicting HRD Scores from Tumor-Only WES Data

do you know any hrd-scores prediction full autmatised pipelines for tumor only wes data?

Yes, there are several tools and pipelines that can predict homologous recombination deficiency (HRD) scores from tumor-only whole exome sequencing (WES) data. These tools typically integrate various bioinformatics methods to analyze genomic alterations such as loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST), which are indicative of HRD.

Here are a few notable tools and pipelines:

1. HRDetect: Originally developed for whole-genome sequencing (WGS) data, HRDetect has been adapted for WES data. It uses a machine learning approach to integrate multiple mutational signatures associated with HRD.

2. ScarHRD: This tool calculates HRD scores based on the presence of LOH, TAI, and LST. It has been widely used for both WGS and WES data. ScarHRD can be integrated into automated pipelines for high-throughput analysis.

3. CHORD: CHORD is a classifier that predicts HRD status using mutational signatures derived from WES data. It employs a machine learning model trained on a large dataset of tumors with known HRD status.

4. HRDetect-WES: This is a specific adaptation of the HRDetect tool for WES data. It uses a combination of mutational signatures and structural variation features to predict HRD status.

5. BRCAness: This tool assesses the "BRCAness" phenotype, which is indicative of HRD, by analyzing specific patterns of mutations and structural variations in WES data.

6. SigMA: SigMA (Signature Mutational Analysis) is a tool that can be used to detect mutational signatures associated with HRD from WES data. It can be integrated into automated pipelines for large-scale analysis.

To set up a fully automated pipeline, you would typically need to:

1. Preprocess the WES data: This includes quality control, alignment to a reference genome, and variant calling.
2. Run the HRD prediction tool: Integrate one of the above tools into your pipeline to calculate the HRD score.
3. Post-process and interpret results: Aggregate the results, generate reports, and interpret the HRD scores in the context of your study.

Many of these tools are available as command-line applications or R packages, making them suitable for integration into automated bioinformatics pipelines. Additionally, some tools may require specific dependencies or configurations, so it's important to consult the documentation for each tool to ensure proper setup and usage.