CLEVER: clique-enumerating variant finder.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) enables large-scale human genetic variation analysis.
• Computational discovery of structural variants (SVs) remains a challenge, with many variants potentially undiscovered.
• Accurate identification of small insertions and deletions (indels) is crucial for understanding genetic variation.

Purpose of the Study:

• To present a novel computational approach for structural variant discovery.
• To improve the detection of insertions and deletions (indels) in human genomes.
• To benchmark the performance of the new method against existing state-of-the-art approaches.

Main Methods:

• Development of an internal segment size-based approach organizing reads into a read alignment graph.
• Utilizing a novel algorithm to enumerate max-cliques and statistically evaluate them for indel detection.
• Comparison of the novel method with existing approaches using simulated Illumina reads from an annotated genome.

Main Results:

• The novel method demonstrates superior performance in detecting deletions or insertions (indels) of length 20-100 nt.
• Outperforms existing insert size-based approaches and split-read aligners for indels in the 20-100 nt range.
• Achieves competitive and complementary results on biological data, identifying unique, correct predictions.

Conclusions:

• The presented method offers a significant advancement in the computational discovery of structural variants, particularly small indels.
• This approach addresses a key challenge in genomic variation analysis, improving the completeness of variant detection.
• The tool provides a valuable addition to the bioinformatics toolkit for human genetic variation studies.