Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data

Wang, Ning; Lysenkov, Vladislav; Orte, Katri; Kairisto, Veli; Aakko, Juhani; Khan, Sofia; Elo, Laura L.

doi:10.1371/journal.pcbi.1009269

Cited by 17 publications

(13 citation statements)

References 58 publications

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“…Despite their effect on phenotype was not well studied. Approaches to detect genomics deletion might fail for some deletions based on their length and the amount of supporting evidence [39]. By consequence, current genome-wide deletion detection combines several tools that induce a large effort in terms of installing but also running time.…”

Section: Discussionmentioning

confidence: 99%

RF4Del: A Random Forest approach for accurate deletion detection

Xavier

Alves

Cherif³

2022

Preprint

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Efficiently detecting genomic structural variants (SVs) is a key step to grasp the "missing heritability" underlying complex traits involved in major evolutionary processes such as speciation, phenotypic plasticity, and adaptive responses. Yet, the SV-based genotype/trait association studies are still largely overlooked mainly due to the lack of reliable detection methods. Here, we present a random forest (RF) method for accurate deletion identification: RF4Del. By relying on the analysis of the mapping profiles, data already available in most sequencing projects, RF4Del can easily and quickly call deletions. Several classic and ensemble learning strategies were carefully evaluated using proper benchmark data. To detect deletions, RF4Del was trained and tested on simulated data from the model species Drosophila melanogaster. The model consists of 13 features extracted from a mapping file. We show that RF4Del outperforms established SV callers (DELLY, Pindel) with higher overall performance (F1-score > 0.75; 6x-12x sequence coverage) and is less affected by low sequencing coverage and deletion size variations. RF4Del could learn from a compilation of sequence patterns linked to a given SV. Such models can then be combined to form a learning system able to detect all types of SVs in a given genome, beyond the one used in our study.

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Section: Discussionmentioning

confidence: 99%

RF4Del: A Random Forest approach for accurate deletion detection

Xavier

Alves

Cherif³

2022

Preprint

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“…While there is little correlation between motifs and enhancer activity, these methods could be potentially incorporated to characterize the mechanism of variants identified from snpSTARRseq ( 41 ). Lastly, we did not characterize 36 INDELS as our snpSTARRseq computational pipeline exclusively focused on SNPs due to the ambiguity in INDELs calling ( 92 ).…”

Section: Discussionmentioning

confidence: 99%

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

Morova¹,

Ding

Huang³

et al. 2022

Nucleic Acids Research

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The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

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“…The emergence of next‐generation sequencing (NGS) technology has enabled the large‐scale identification of personalised genetic characteristics of colorectal cancer (CRC). 1 However, the accuracy may be influenced by certain sample factors, such as sampling methods, biospecimen type (fresh vs. formalin‐fixed paraffin‐embedded) and input DNA amount. 2 , 3 , 4 We creatively performed a contrastive analysis based on homogenous paired real‐world surgical tumour specimens to comprehensively assess the impact of low tumour cell fraction on the authenticity of somatic mutation calling.…”

Section: Dear Editormentioning

confidence: 99%