Test development, optimization and validation of a WGS pipeline for genetic disorders

Yang, Ziying; Xu, Yang; Sun, Yan; Wang, Yaoshen; Song, Lijie; Qiao, Zhihong; Fang, Zhonghai; Wang, Zhonghua; Liu, Lipei; Chen, Yunmei; Yan, Saiying; Guo, Xueqin; Zhang, Junqing; Fan, Chunna; Liu, Fengxia; Peng, Zhiyu; Peng, Huanhuan; Sun, Jun; Chen, Wei

doi:10.1186/s12920-023-01495-x

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…For each strain, 300 ng gDNA was used for short-read sequencing library construction according to MGI paired-end libraries construction protocol 16 . Briefly, gDNA was fragmented to 200-300 bp using segmentase followed by fragment selection with VAHTS™ DNA Clean Beads (Vazyme, Nanjing, Jiangsu, China).…”

Section: Methodsmentioning

confidence: 99%

Expanding the genome information onBacillalesfor biosynthetic gene cluster discovery

Song,

Dyrbye Nielsen,

et al. 2024

Preprint

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This study showcases 121 new genomes of spore-formingBacillalesfrom strains collected globally from a variety of habitats, assembled using Oxford Nanopore long-read and MGI short-read sequences.Bacilliare renowned for their capacity to produce diverse secondary metabolites with use in agriculture, biotechnology, and medicine. These secondary metabolites are encoded within biosynthetic gene clusters (smBGCs). smBGCs have significant research interest due to their potential for the discovery of new bioactivate compounds. Our dataset includes 62 complete genomes, 2 at chromosome level, and 57 at contig level, covering a genomic size range from 3.50 Mb to 7.15 Mb. Phylotaxonomic analysis revealed that these genomes span 16 genera, with 69 of them belonging toBacillus. A total of 1,176 predicted BGCs were identified byin silicogenome mining. We anticipate that the open-access data presented here will expand the reported genomic information of spore-formingBacillalesand facilitate a deeper understanding of the genetic basis ofBacillales’ potential for secondary metabolite production.

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Section: Methodsmentioning

confidence: 99%

Expanding the genome information onBacillalesfor biosynthetic gene cluster discovery

Song,

Dyrbye Nielsen,

et al. 2024

Preprint

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“…Short-read sequencing was performed on the DNBSEQ-T7 platform (MGI, Wuhan, China) with 150 bp paired-end reads to obtain 40 × mean coverage. The sequencing reads were aligned to the genome reference (hg19/GRCh37) and single nucleotide variants (SNVs), small insertions and deletions (Indels), copy number variations (CNVs), mitochondrial (MT) variants, structural variants (SVs), repeat expansions, and absence of heterozygosity (AOH) were analyzed using pipelines previously described [17].…”

Section: Wgsmentioning

confidence: 99%

Whole genome and transcriptome sequencing in neuromuscular disorders: a diagnostic and health economic analysis

Yang,

Chen

et al. 2023

Preprint

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BackgroundA considerable number of patients suffering from neuromuscular disorders (NMD) are unable to receive an accurate diagnosis through initial genetic testing. It is imperative to develop a cost-effective diagnostic strategy that incorporates appropriate multi-omics techniques.MethodsThis study included 33 NMD patients with negative results from whole-exome sequencing (WES). Whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) were performed concurrently to evaluate clinical utility. Additionally, eight diagnostic pathways were compared in terms of diagnostic rate, turnaround time, and cost.ResultsOur implementation of parallel WGS and RNA-seq testing successfully validated the clinical utility of this strategy in the cohort of 33 NMD patients initially yielding negative results from WES. The combined utilization of both methods resulted in an additional diagnosis for 42% (15/33) of the patients, with WGS contributing to 36% and RNA-seq contributing to 6% of the diagnoses. The Integration of alternative splicing results derived from RNA-seq data into variant filtering significantly reduced the number of rare intronic variants requiring interpretation and provided compelling evidence to support the classification of variant pathogenicity based on functional impact. Our comprehensive analysis, comparing eight different diagnostic pathways, revealed the cost-effectiveness of parallel WGS and RNA-seq testing as a diagnostic approach for patients. Moreover, the analysis of rare genomic findings within our cases showcased their potential to inform patient care, aid treatment decisions, and expand the range of NMD mutations in diagnosing rare NMD cases.ConclusionThe integration of parallel WGS and RNA-seq testing represents a transformative diagnostic approach for NMD patients. The cost-effectiveness of this approach, coupled with its ability to improve diagnostic yield and interpretation efficiency, makes it a highly recommended strategy for clinical implementation to enhance the management and care of NMD patients.

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“…Filtering and prioritization strategies aim to balance between maximizing sensitivity and minimizing the number of candidate variants, encompassing both genotype-driven and phenotype-driven analyses [6]. Genotype-driven analysis typically results in several hundred SNV/INDEL variants remaining for consideration [7]. Phenotype-driven analysis often serves as a complement to genotype-driven analysis, further refining the prioritization of remaining variants by integrating phenotypic information, with the aim of elevating genes or variants related to the patient's phenotype for priority review by experts.…”

Section: Introductionmentioning

confidence: 99%

Genetic Transformer: An Innovative Large Language Model Driven Approach for Rapid and Accurate Identification of Causative Variants in Rare Genetic Diseases

Liang,

Chen,

Wang

et al. 2024

Preprint

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BackgroundIdentifying causative variants is crucial for the diagnosis of rare genetic diseases. Over the past two decades, the application of genome sequencing technologies in the field has significantly improved diagnostic outcomes. However, the complexity of data analysis and interpretation continues to limit the efficiency and accuracy of these applications. Various genotype and phenotype-driven filtering and prioritization strategies are used to generate a candidate list of variants for expert curation, with the final report variants determined through knowledge-intensive and labor-intensive expert review. Despite these efforts, the current methods fall short of meeting the growing demand for accurate and efficient diagnosis of rare disease. Recent developments in large language models (LLMs) suggest that LLMs possess the potential to augment or even supplant human labor in this context.MethodsIn this study, we have developed Genetic Transformer (GeneT), an innovative large language model (LLM) driven approach to accelerate identification of candidate causative variants for rare genetic disease. A comprehensive evaluation was conducted between the fine-tuned large language models and four phenotype-driven methods, including Xrare, Exomiser, PhenIX and PHIVE, alongside six pre-trained LLMs (Qwen1.5-0.5B, Qwen1.5-1.8B, Qwen1.5-4B, Mistral-7B, Meta-Llama-3-8B, Meta-Llama-3-70B). This evaluation focused on performance and hallucinations.ResultsGenetic Transformer (GeneT) as an innovative LLM-driven approach demonstrated outstanding performance on identification of candidate causative variants, identified the average number of candidate causative variants reduced from an average of 418 to 8, achieving recall rate of 99% in synthetic datasets. Application in real-world clinical setting demonstrated the potential for a 20-fold increase in processing speed, reducing the time required to analyze each sample from approximately 60 minutes to around 3 minutes. Concurrently, the recall rate has improved from 94.36% to 97.85%. An online analysis platform iGeneT was developed to integrate GeneT into the workflow of rare genetic disease analysis.ConclusionOur study represents the inaugural application of fine-tuned LLMs for identifying candidate causative variants, introducing GeneT as an innovative LLM-driven approach, demonstrating its superiority in both simulated data and real-world clinical setting. The study is unique in that it represents a paradigm shift in addressing the complexity of variant filtering and prioritization of whole exome or genome sequencing data, effectively resolving the challenge akin to finding a needle in a haystack.

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Test development, optimization and validation of a WGS pipeline for genetic disorders

Cited by 6 publications

References 53 publications

Expanding the genome information onBacillalesfor biosynthetic gene cluster discovery

Expanding the genome information onBacillalesfor biosynthetic gene cluster discovery

Whole genome and transcriptome sequencing in neuromuscular disorders: a diagnostic and health economic analysis

Genetic Transformer: An Innovative Large Language Model Driven Approach for Rapid and Accurate Identification of Causative Variants in Rare Genetic Diseases

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