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Grebnev PA, Meshkov IO, Ershov PV, Makhotenko AV, Azarian VB, Erokhina MV, Galeta AA, Zakubanskiy AV, Shingalieva OS, Tregubova AV, Asaturova AV, Yudin VS, Yudin SM, Makarov VV, Keskinov AA, Makarova AS, Snigir EA, Skvortsova VI. Benchmarking of Approaches for Gene Copy-Number Variation Analysis and Its Utility for Genetic Aberration Detection in High-Grade Serous Ovarian Carcinomas. Cancers (Basel) 2024; 16:3252. [PMID: 39409874 PMCID: PMC11475927 DOI: 10.3390/cancers16193252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 09/20/2024] [Accepted: 09/21/2024] [Indexed: 10/20/2024] Open
Abstract
Objective: The goal of this study was to compare the results of CNV detection by three different methods using 13 paired carcinoma samples, as well as to perform a statistical analysis of the agreement. Methods: CNV was studied using NanoString nCounter v2 Cancer CN Assay (Nanostring), Illumina Infinium CoreExome microarrays (CoreExome microarrays) and digital droplet PCR (ddPCR). Results: There was a good level of agreement (PABAK score > 0.6) between the CoreExome microarrays and the ddPCR results for finding CNVs. There was a moderate level of agreement (PABAK values ≈ 0.3-0.6) between the NanoString Assay results and microarrays or ddPCR. For 83 out of 87 target genes studied (95%), the agreement between the CoreExome microarrays and NanoString nCounter was characterized by PABAK values < 0.75, except for MAGI3, PDGFRA, NKX2-1 and KDR genes (>0.75). The MET, HMGA2, KDR, C8orf4, PAX9, CDK6, and CCND2 genes had the highest agreement among all three approaches. Conclusions: Therefore, to get a better idea of how to genotype an unknown CNV spectrum in tumor or normal tissue samples that are very different molecularly, it makes sense to use at least two CNV detection methods. One of them, like ddPCR, should be able to quantitatively confirm the results of the other.
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Affiliation(s)
- Pavel Alekseevich Grebnev
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Ivan Olegovich Meshkov
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Pavel Viktorovich Ershov
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Antonida Viktorovna Makhotenko
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Valentina Bogdanovna Azarian
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Marina Vyacheslavovna Erokhina
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Anastasiya Aleksandrovna Galeta
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Aleksandr Vladimirovich Zakubanskiy
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Olga Sergeevna Shingalieva
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Anna Vasilevna Tregubova
- Federal State Budgetary Institution “National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov”, Ministry of Healthcare of The Russian Federation, Oparina Street, Bld. 4, 117997 Moscow, Russia; (A.V.T.); (A.V.A.)
| | - Aleksandra Vyacheslavovna Asaturova
- Federal State Budgetary Institution “National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov”, Ministry of Healthcare of The Russian Federation, Oparina Street, Bld. 4, 117997 Moscow, Russia; (A.V.T.); (A.V.A.)
| | - Vladimir Sergeevich Yudin
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Sergey Mihaylovich Yudin
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Valentin Vladimirovich Makarov
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Anton Arturovich Keskinov
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Anna Sergeevna Makarova
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
| | - Ekaterina Andreevna Snigir
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia; (P.A.G.); (I.O.M.); (P.V.E.); (A.V.M.); (V.B.A.); (M.V.E.); (A.A.G.); (A.V.Z.); (O.S.S.); (V.S.Y.); (S.M.Y.); (V.V.M.); (A.S.M.)
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Cabello-Aguilar S, Vendrell JA, Solassol J. A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology. Curr Issues Mol Biol 2023; 45:9737-9752. [PMID: 38132454 PMCID: PMC10741970 DOI: 10.3390/cimb45120608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.
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Affiliation(s)
- Simon Cabello-Aguilar
- Montpellier BioInformatics for Clinical Diagnosis (MOBIDIC), Molecular Medicine and Genomics Platform (PMMG), CHU Montpellier, 34295 Montpellier, France
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
| | - Julie A. Vendrell
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
| | - Jérôme Solassol
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
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Ding Y, Liao Y, He J, Ma J, Wei X, Liu X, Zhang G, Wang J. Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity. Front Genet 2023; 14:1213907. [PMID: 37323665 PMCID: PMC10267386 DOI: 10.3389/fgene.2023.1213907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 05/24/2023] [Indexed: 06/17/2023] Open
Abstract
Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms. Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated. Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different. Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.
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Affiliation(s)
- Youde Ding
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Yuan Liao
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China
| | - Ji He
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Jianfeng Ma
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Xu Wei
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Xuemei Liu
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Guiying Zhang
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Jing Wang
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
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Copy Number Variations Contribute to Intramuscular Fat Content Differences by Affecting the Expression of PELP1 Alternative Splices in Pigs. Animals (Basel) 2022; 12:ani12111382. [PMID: 35681846 PMCID: PMC9179479 DOI: 10.3390/ani12111382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Copy number variation (CNV) is a type of variant that may influence meat quality of, for example intramuscular fat (IMF). In this study, a genome-wide association study (GWAS) was then performed between CNVs and IMF in a pig F2 resource population. A total of 19 CNVRs were found to be significantly associated with IMF. RNA-seq and qPCR validation results indicated that CNV150, which is located on the 3′UTR end of the proline, as well as glutamate and the leucine rich protein 1 (PELP1) gene may affect the expression of PELP1 alternative splices. We infer that the CNVR may influence IMF content by regulating the alternative splicing of the PELP1 gene and ultimately affects the structure of the PELP1 protein. These findings suggest a novel mechanistic approach for meat quality improvement in animals and the potential treatment of insulin resistance in human beings. Abstract Intramuscular fat (IMF) is a key meat quality trait. Research on the genetic mechanisms of IMF decomposition is valuable for both pork quality improvement and the treatment of obesity and type 2 diabetes. Copy number variations (CNVs) are a type of variant that may influence meat quality. In this study, a total of 1185 CNV regions (CNVRs) including 393 duplicated CNVRs, 432 deleted CNVRs, and 361 CNVRs with both duplicated and deleted status were identified in a pig F2 resource population using next-generation sequencing data. A genome-wide association study (GWAS) was then performed between CNVs and IMF, and a total of 19 CNVRs were found to be significantly associated with IMF. QTL colocation analysis indicated that 3 of the 19 CNVRs overlapped with known QTLs. RNA-seq and qPCR validation results indicated that CNV150, which is located on the 3′UTR end of the proline, as well as glutamate and the leucine rich protein 1 (PELP1) gene may affect the expression of PELP1 alternative splices. Sequence alignment and Alphafold2 structure prediction results indicated that the two alternative splices of PELP1 have a 23 AA sequence variation and a helix-fold structure variation. This region is located in the region of interaction between PELP1 and other proteins which have been reported to be significantly associated with fat deposition or insulin resistance. We infer that the CNVR may influence IMF content by regulating the alternative splicing of the PELP1 gene and ultimately affects the structure of the PELP1 protein. In conclusion, we found some CNVRs, especially CNV150, located in PELP1 that affect IMF. These findings suggest a novel mechanistic approach for meat quality improvement in animals and the potential treatment of insulin resistance in human beings.
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Pillay NS, Ross OA, Christoffels A, Bardien S. Current Status of Next-Generation Sequencing Approaches for Candidate Gene Discovery in Familial Parkinson´s Disease. Front Genet 2022; 13:781816. [PMID: 35299952 PMCID: PMC8921601 DOI: 10.3389/fgene.2022.781816] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Abstract
Parkinson's disease is a neurodegenerative disorder with a heterogeneous genetic etiology. The advent of next-generation sequencing (NGS) technologies has aided novel gene discovery in several complex diseases, including PD. This Perspective article aimed to explore the use of NGS approaches to identify novel loci in familial PD, and to consider their current relevance. A total of 17 studies, spanning various populations (including Asian, Middle Eastern and European ancestry), were identified. All the studies used whole-exome sequencing (WES), with only one study incorporating both WES and whole-genome sequencing. It is worth noting how additional genetic analyses (including linkage analysis, haplotyping and homozygosity mapping) were incorporated to enhance the efficacy of some studies. Also, the use of consanguineous families and the specific search for de novo mutations appeared to facilitate the finding of causal mutations. Across the studies, similarities and differences in downstream analysis methods and the types of bioinformatic tools used, were observed. Although these studies serve as a practical guide for novel gene discovery in familial PD, these approaches have not significantly resolved the "missing heritability" of PD. We speculate that what is needed is the use of third-generation sequencing technologies to identify complex genomic rearrangements and new sequence variation, missed with existing methods. Additionally, the study of ancestrally diverse populations (in particular those of Black African ancestry), with the concomitant optimization and tailoring of sequencing and analytic workflows to these populations, are critical. Only then, will this pave the way for exciting new discoveries in the field.
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Affiliation(s)
- Nikita Simone Pillay
- South African National Bioinformatics Institute (SANBI), South African Medical Research Council Bioinformatics Unit, University of the Western Cape, Bellville, South Africa
| | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
- Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL, United States
| | - Alan Christoffels
- South African National Bioinformatics Institute (SANBI), South African Medical Research Council Bioinformatics Unit, University of the Western Cape, Bellville, South Africa
- Africa Centres for Disease Control and Prevention, African Union Headquarters, Addis Ababa, Ethiopia
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Cape Town, South Africa
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