Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study

Foox, Jonathan; Tighe, Scott; Nicolet, Charles M.; Zook, Justin M.; Byrska-Bishop, Marta; Clarke, Wayne E.; Khayat, Michael M.; Mahmoud, Medhat; Laaguiby, Phoebe K.; Herbert, Zachary T.; Warner, Derek; Grills, George S.; Jen, Jin; Levy, Shawn; Xiang, Jenny; Alonso, Alicia; Zhao, Xing‐Ming; Zhang, Wenwei; Teng, Fei; Zhao, Yongjiu; Lu, Haorong; Schroth, Gary P.; Narzisi, Giuseppe; Farmerie, William G.; Sedlazeck, Fritz J.; Baldwin, Don A.; Mason, Christopher E.

doi:10.1038/s41587-021-01049-5

Cited by 77 publications

(60 citation statements)

References 55 publications

(43 reference statements)

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“…However, this can be overcome with documentation and careful analysis. What remains challenging, especially for SV identification, seems to be the heterogeneous study design especially across sequencing centers [ 30 ]. This is important for consideration of small to midsize projects, but unavoidable for large scale studies such as TOPMed etc.…”

Section: Discussionmentioning

confidence: 99%

Hidden biases in germline structural variant detection

et al. 2021

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Background Genomic structural variations (SV) are important determinants of genotypic and phenotypic changes in many organisms. However, the detection of SV from next-generation sequencing data remains challenging. Results In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches. Conclusions This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.

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Section: Discussionmentioning

confidence: 99%

Hidden biases in germline structural variant detection

et al. 2021

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“…DNA nanoballs (DNBs) were generated with the ssDNA circle by rolling circle replication (RCR) to enlarge the fluorescent signals at the sequencing process. The DNBs were loaded into the patterned nanoarrays and single-end read of 50 bp were read through on the BGISEQ-500 platform (BGI, Shenzhen, China) [14].…”

Section: Elevated Zero Maze Testmentioning

confidence: 99%

Histological, functional and transcriptomic alterations in the juvenile hippocampus in a mouse model of thyroid hormone resistance

Fang

Dang

Liang

et al. 2022

European Thyroid Journal

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Background: Proper thyroid hormone signaling via the TRα1 nuclear receptor is required for normal neurodevelopmental processes. The specific downstream mechanisms mediated by TRα1 that impact brain development remain to be investigated. Methods: In this study, the structure, function, and transcriptome of hippocampal tissue in a mouse model expressing the first RTHα mutation discovered in a patient, THRAE403X, were analyzed. RNAscope was used to visualize the spatial and temporal expression of Thra1 mRNA in the hippocampus of wild-type mice, which corresponds to THRA1 mRNA in humans. The morphological structure was analyzed by Nissl staining, and the synaptic transmission was analyzed on the basis of long-term potentiation. The Morris water maze test and the zero maze test were used to evaluate the behavior. RNA-seq and quantitative real-time PCR were used to analyze the differentially expressed genes (DEGs) of the hippocampal tissues in the mouse model expressing the ThraE403X mutation. Results: The juvenile mutant ThraE403X mice presented with delayed neuronal migration, disordered neuronal distribution, and decreased synaptic plasticity. A total of 754 DEGs, including 361 upregulated genes and 393 downregulated genes, were identified by RNA-seq. DEG-enriched Gene Ontology (GO) and KEGG pathways were associated with PI3K−Akt signaling, ECM−receptor interaction, neuroactive ligand−receptor interaction, and a range of immune-related pathways. 25 DEGs were validated by qPCR. Conclusions: The ThraE403X mutation results in histological and functional abnormalities, as well as transcriptomic alterations in the juvenile mouse hippocampus. This study of the ThraE403X mutant offers new insights into the biological cause of RTHα-associated neurological diseases.

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“…The SEQC2 project had three specific aims: (i) develop reference materials that could be shared by laboratories for standardized evaluation of NGS technologies, (ii) benchmark the impact of experimental and bioinformatic variables on the generation and analysis of NGS data and, (iii) evaluate inter- and intra-lab reproducibility of NGS technologies across different laboratories [ 8 ].…”

Section: The Sequencing Quality Control Phase 2 (Seqc2) Consortiummentioning

confidence: 99%

“…Performance was evaluated according to read alignment and coverage, error rates, and the sensitivity and specificity for correctly detecting known germline and structural variants in the reference genomes. These metrics were then stratified across genome, regions including repeats, transposons, duplicated, and challenging regions of the human genome [ 8 ].…”

Section: Germline Variantsmentioning

confidence: 99%

The Sequencing Quality Control 2 study: establishing community standards for sequencing in precision medicine

Mercer

Mason

et al. 2021

Genome Biol

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Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study

Abstract: Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

Cited by 77 publications

References 55 publications

Hidden biases in germline structural variant detection

Hidden biases in germline structural variant detection

Histological, functional and transcriptomic alterations in the juvenile hippocampus in a mouse model of thyroid hormone resistance

The Sequencing Quality Control 2 study: establishing community standards for sequencing in precision medicine

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