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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Genomic data integration and user-defined sample-set extraction for population variant analysis.

Tommaso Alfonsi¹, Anna Bernasconi², Arif Canakoglu^2,3

¹Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy. tommaso.alfonsi@polimi.it.

BMC Bioinformatics

|September 29, 2022

Summary

This summary is machine-generated.

This study introduces a new data integration pipeline and VarSum API for analyzing population genetic variation. It enables faster, flexible selection and summarization of variant data for large-scale genomic studies.

Keywords:

1000 Genomes Data integration Data warehousing Data wrangling Human genetic variation Population variant analysis

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Population variant analysis is crucial for understanding genotype-phenotype links.
Existing data repositories lack full integration and flexible subsetting capabilities.
A need exists for efficient selection of population partitions based on variant and metadata characteristics.

Purpose of the Study:

To develop an interoperable repository for germline and somatic mutation data.
To provide a data summarization service (VarSum) with an API for flexible population subsetting.
To facilitate integrated use of genomic data within bioinformatic workflows.

Main Methods:

Developed an interoperable data integration pipeline.
Created VarSum, a data summarization service with an API.
Designed an optimized computational framework for variant data processing.

Main Results:

Seamless inclusion of germline/somatic mutation data sources.
VarSum enables sub-population selection via metadata and variant filters.
API allows integration into existing pipelines and scripts, demonstrated by use cases.

Conclusions:

The pipeline and API offer robust computational infrastructure for variation data.
Enables scalable analysis of user-defined partitions from large genetic studies.
Addresses the growing need for computational support in large-scale sequencing initiatives.