A scalable distributed pipeline for reference-free variants calling
Abstract Background Precision medicine pipelines typically begin with variant calling to identify disease-related mutations for optimal treatment selection. Reference-free approaches assess variations in the genetic profiles of distinct individuals through the utilization of a De Bruijn graph. Howev...
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| Language: | English |
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BMC
2025-06-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-025-11722-7 |
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| author | Lorenzo Di Rocco Umberto Ferraro Petrillo |
| author_facet | Lorenzo Di Rocco Umberto Ferraro Petrillo |
| author_sort | Lorenzo Di Rocco |
| collection | DOAJ |
| description | Abstract Background Precision medicine pipelines typically begin with variant calling to identify disease-related mutations for optimal treatment selection. Reference-free approaches assess variations in the genetic profiles of distinct individuals through the utilization of a De Bruijn graph. However, the timely analysis of large-scale sequencing data may be beyond the capabilities of single workstations, requiring alternative computational approaches. Results We introduce the first-known distributed pipeline for detecting isolated SNPs (Single Nucleotide Polymorphisms), by leveraging the computational resources of multiple machines in parallel. Our pipeline efficiently analyzes large datasets thanks to the usage of a distributed De Bruijn graph representation. Furthermore, we introduce a cluster-driven algorithm to partition the De Bruijn graph across multiple independent machines according to the inner structure of the sequences under analysis, thus further improving the scalability of our pipeline. Conclusions The results of our experiments, conducted on real-world datasets, show the good performance of our pipeline in terms of efficiency, output quality and scalability. Moreover, the reported results also confirm that the adoption of a specialized partitioning algorithm for the distributed representation of the De Bruijn graph leads to a relevant performance speed-up compared to using standard partitioning techniques. |
| format | Article |
| id | doaj-art-ea45e51a3bc2417bb74a4a70d24d9061 |
| institution | OA Journals |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj-art-ea45e51a3bc2417bb74a4a70d24d90612025-08-20T02:05:46ZengBMCBMC Genomics1471-21642025-06-0126S111410.1186/s12864-025-11722-7A scalable distributed pipeline for reference-free variants callingLorenzo Di Rocco0Umberto Ferraro Petrillo1Department of Statistical Sciences, Sapienza University of RomeDepartment of Statistical Sciences, Sapienza University of RomeAbstract Background Precision medicine pipelines typically begin with variant calling to identify disease-related mutations for optimal treatment selection. Reference-free approaches assess variations in the genetic profiles of distinct individuals through the utilization of a De Bruijn graph. However, the timely analysis of large-scale sequencing data may be beyond the capabilities of single workstations, requiring alternative computational approaches. Results We introduce the first-known distributed pipeline for detecting isolated SNPs (Single Nucleotide Polymorphisms), by leveraging the computational resources of multiple machines in parallel. Our pipeline efficiently analyzes large datasets thanks to the usage of a distributed De Bruijn graph representation. Furthermore, we introduce a cluster-driven algorithm to partition the De Bruijn graph across multiple independent machines according to the inner structure of the sequences under analysis, thus further improving the scalability of our pipeline. Conclusions The results of our experiments, conducted on real-world datasets, show the good performance of our pipeline in terms of efficiency, output quality and scalability. Moreover, the reported results also confirm that the adoption of a specialized partitioning algorithm for the distributed representation of the De Bruijn graph leads to a relevant performance speed-up compared to using standard partitioning techniques.https://doi.org/10.1186/s12864-025-11722-7Computational genomicsVariants callingDistributed computing |
| spellingShingle | Lorenzo Di Rocco Umberto Ferraro Petrillo A scalable distributed pipeline for reference-free variants calling BMC Genomics Computational genomics Variants calling Distributed computing |
| title | A scalable distributed pipeline for reference-free variants calling |
| title_full | A scalable distributed pipeline for reference-free variants calling |
| title_fullStr | A scalable distributed pipeline for reference-free variants calling |
| title_full_unstemmed | A scalable distributed pipeline for reference-free variants calling |
| title_short | A scalable distributed pipeline for reference-free variants calling |
| title_sort | scalable distributed pipeline for reference free variants calling |
| topic | Computational genomics Variants calling Distributed computing |
| url | https://doi.org/10.1186/s12864-025-11722-7 |
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