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Journal articles on the topic 'Illumina array'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Smith, Mike L., Keith A. Baggerly, Henrik Bengtsson, Matthew E. Ritchie, and Kasper D. Hansen. "illuminaio: An open source IDAT parsing tool for Illumina microarrays." F1000Research 2 (December 4, 2013): 264. http://dx.doi.org/10.12688/f1000research.2-264.v1.

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The IDAT file format is used to store BeadArray data from the myriad of genomewide profiling platforms on offer from Illumina Inc. This proprietary format is output directly from the scanner and stores summary intensities for each probe-type on an array in a compact manner. A lack of open source tools to process IDAT files has hampered their uptake by the research community beyond the standard step of using the vendor’s software to extract the data they contain in a human readable text format. To fill this void, we have developed the illuminaio package that parses IDAT files from any BeadArray

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Noble, Alexandra J., John F. Pearson, Joseph M. Boden, et al. "A validation of Illumina EPIC array system with bisulfite-based amplicon sequencing." PeerJ 9 (February 10, 2021): e10762. http://dx.doi.org/10.7717/peerj.10762.

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The Illumina Infinium® MethylationEPIC BeadChip system (hereafter EPIC array) is considered to be the current gold standard detection method for assessing DNA methylation at the genome-wide level. EPIC arrays are often used for hypothesis generation or pilot studies, the natural conclusion to which is to validate methylation candidates and expand these in a larger cohort, in a targeted manner. As such, an accurate smaller-scale, targeted technique, that generates data at the individual CpG level that is equivalent to the EPIC array, is needed. Here, we tested an alternative DNA methylation det

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Tait, Rich, Ryan Ferretti, Barry Simpson, et al. "34 Present and future of genomic test reporting in the cattle industry." Journal of Animal Science 97, Supplement_2 (2019): 19–20. http://dx.doi.org/10.1093/jas/skz122.036.

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Abstract A series of custom low density (LD) SNP genotyping platforms have been created over the years. Recognized by the GeneSeek Genomic Profiler (GGP) nomenclature, these SNP arrays have increased in size as new versions were created, such as: GGP-LD-v1 (n = 8,762), GGP-LD-v2 (n = 20,057), GGP-LD-v3 (n = 26,151), GGP-LD-v4 (n = 30,108), and GGP Bovine 50K (n = 47,843), all of which contained a base of the Illumina Bovine LD array (n = 7,931) and then added SNPs to provide maximum information content (Shannon Entropy) and optimal genomic coverage into target populations without specific rest

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4

Mah, Clarence K., Jill P. Mesirov, and Lukas Chavez. "An accessible GenePattern notebook for the copy number variation analysis of Illumina Infinium DNA methylation arrays." F1000Research 7 (December 5, 2018): 1897. http://dx.doi.org/10.12688/f1000research.16338.1.

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Illumina Infinium DNA methylation arrays are a cost-effective technology to measure DNA methylation at CpG sites genome-wide and across cohorts of normal and cancer samples. While copy number alterations are commonly inferred from array-CGH, SNP arrays, or whole-genome DNA sequencing, Illumina Infinium DNA methylation arrays have been shown to detect copy number alterations at comparable sensitivity. Here we present an accessible, interactive GenePattern notebook for the analysis of copy number variation using Illumina Infinium DNA methylation arrays. The notebook provides a graphical user int

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Guo, Yan, Jing He, Shilin Zhao, et al. "Illumina human exome genotyping array clustering and quality control." Nature Protocols 9, no. 11 (2014): 2643–62. http://dx.doi.org/10.1038/nprot.2014.174.

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6

Zhang, Pan, David C. Samuels, Shilin Zhao, Jing Wang, Yu Shyr, and Yan Guo. "Practicability of mitochondrial heteroplasmy detection through an Illumina genotyping array." Mitochondrion 31 (November 2016): 75–78. http://dx.doi.org/10.1016/j.mito.2016.08.018.

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7

Wang, Jing, David C. Samuels, Yu Shyr, and Yan Guo. "StrandScript: evaluation of Illumina genotyping array design and strand correction." Bioinformatics 33, no. 15 (2017): 2399–401. http://dx.doi.org/10.1093/bioinformatics/btx186.

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8

Pidsley, Ruth, Chloe C. Y Wong, Manuela Volta, Katie Lunnon, Jonathan Mill, and Leonard C. Schalkwyk. "A data-driven approach to preprocessing Illumina 450K methylation array data." BMC Genomics 14, no. 1 (2013): 293. http://dx.doi.org/10.1186/1471-2164-14-293.

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9

Maksimovic, Jovana, Lavinia Gordon, and Alicia Oshlack. "SWAN: Subset-quantile Within Array Normalization for Illumina Infinium HumanMethylation450 BeadChips." Genome Biology 13, no. 6 (2012): R44. http://dx.doi.org/10.1186/gb-2012-13-6-r44.

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10

Maksimovic, Jovana, Belinda Phipson, and Alicia Oshlack. "A cross-package Bioconductor workflow for analysing methylation array data." F1000Research 5 (June 8, 2016): 1281. http://dx.doi.org/10.12688/f1000research.8839.1.

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Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses su

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Maksimovic, Jovana, Belinda Phipson, and Alicia Oshlack. "A cross-package Bioconductor workflow for analysing methylation array data." F1000Research 5 (July 26, 2016): 1281. http://dx.doi.org/10.12688/f1000research.8839.2.

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Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses su

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Maksimovic, Jovana, Belinda Phipson, and Alicia Oshlack. "A cross-package Bioconductor workflow for analysing methylation array data." F1000Research 5 (April 5, 2017): 1281. http://dx.doi.org/10.12688/f1000research.8839.3.

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Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses su

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13

Jiao, Chuan, Chunling Zhang, Rujia Dai, et al. "Positional effects revealed in Illumina methylation array and the impact on analysis." Epigenomics 10, no. 5 (2018): 643–59. http://dx.doi.org/10.2217/epi-2017-0105.

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14

Turuspekov, Yerlan, Joerg Plieske, Martin Ganal, Eduard Akhunov, and Saule Abugalieva. "Phylogenetic analysis of wheat cultivars in Kazakhstan based on the wheat 90 K single nucleotide polymorphism array." Plant Genetic Resources 15, no. 1 (2015): 29–35. http://dx.doi.org/10.1017/s1479262115000325.

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The recent introduction of Illumina single nucleotide polymorphism (SNP) arrays is an important step towards comprehensive genome-wide studies of genetic diversity in wheat. In this study, 90 cultivars of hexaploid spring wheat growing in Kazakhstan were genotyped using the high-density wheat 90 K Illumina SNP array. The analysis allowed the identification of 30,288 polymorphic SNPs. A subset of 3541 high-quality SNPs were used for a comparison of 690 wheat accessions representing landraces and varieties, including those from Asia, Australia, Canada, Europe, Kazakhstan, USA and other parts of

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15

Hernandez Mora, Jose R., Chiharu Tayama, Marta Sánchez-Delgado, et al. "Characterization of parent-of-origin methylation using the Illumina Infinium MethylationEPIC array platform." Epigenomics 10, no. 7 (2018): 941–54. http://dx.doi.org/10.2217/epi-2017-0172.

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16

Maksimovic, Jovana, Johann A. Gagnon-Bartsch, Terence P. Speed, and Alicia Oshlack. "Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data." Nucleic Acids Research 43, no. 16 (2015): e106-e106. http://dx.doi.org/10.1093/nar/gkv526.

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17

Willet, Cali E., and Bianca Haase. "An updated felCat5 SNP manifest for the Illumina Feline 63k SNP genotyping array." Animal Genetics 45, no. 4 (2014): 614–15. http://dx.doi.org/10.1111/age.12169.

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18

Gondro, Cedric, Laercio R. Porto-Neto, and Seung Hwan Lee. "snpqc- an R pipeline for quality control of Illumina SNP genotyping array data." Animal Genetics 45, no. 5 (2014): 758–61. http://dx.doi.org/10.1111/age.12198.

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19

Mason, Annaliese S., Erin E. Higgins, Rod J. Snowdon, et al. "A user guide to the Brassica 60K Illumina Infinium™ SNP genotyping array." Theoretical and Applied Genetics 130, no. 4 (2017): 621–33. http://dx.doi.org/10.1007/s00122-016-2849-1.

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20

D. Allen, Jeffrey, Min Chen, and Yang Xie. "Model-Based Background Correction (MBCB): R Methods and GUI for Illumina Bead-array Data." Journal of Cancer Science & Therapy 01, no. 01 (2009): 025–27. http://dx.doi.org/10.4172/1948-5956.1000004.

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21

Roessler, Jessica, Ole Ammerpohl, Jana Gutwein, et al. "Quantitative cross-validation and content analysis of the 450k DNA methylation array from Illumina, Inc." BMC Research Notes 5, no. 1 (2012): 210. http://dx.doi.org/10.1186/1756-0500-5-210.

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22

Slieker, Roderick C., Steffan D. Bos, Jelle J. Goeman, et al. "Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array." Epigenetics & Chromatin 6, no. 1 (2013): 26. http://dx.doi.org/10.1186/1756-8935-6-26.

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23

Price, Magda E., Allison M. Cotton, Lucia L. Lam, et al. "Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array." Epigenetics & Chromatin 6, no. 1 (2013): 4. http://dx.doi.org/10.1186/1756-8935-6-4.

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24

Forest, Marie, Kieran J. O'Donnell, Greg Voisin, et al. "Agreement in DNA methylation levels from the Illumina 450K array across batches, tissues, and time." Epigenetics 13, no. 1 (2018): 19–32. http://dx.doi.org/10.1080/15592294.2017.1411443.

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25

Argos, Maria. "Arsenic Exposure and Epigenetic Alterations: Recent Findings Based on the Illumina 450K DNA Methylation Array." Current Environmental Health Reports 2, no. 2 (2015): 137–44. http://dx.doi.org/10.1007/s40572-015-0052-1.

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26

Breeze, Charles E., Alex P. Reynolds, Jenny van Dongen, et al. "eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data." Bioinformatics 35, no. 22 (2019): 4767–69. http://dx.doi.org/10.1093/bioinformatics/btz456.

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Abstract Summary The Illumina Infinium EPIC BeadChip is a new high-throughput array for DNA methylation analysis, extending the earlier 450k array by over 400 000 new sites. Previously, a method named eFORGE was developed to provide insights into cell type-specific and cell-composition effects for 450k data. Here, we present a significantly updated and improved version of eFORGE that can analyze both EPIC and 450k array data. New features include analysis of chromatin states, transcription factor motifs and DNase I footprints, providing tools for epigenome-wide association study interpretation

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27

Akond, Masum, Shiming Liu, Lauren Schoener, et al. "A SNP-Based Genetic Linkage Map of Soybean Using the SoySNP6K Illumina Infinium BeadChip Genotyping Array." Plant Genetics, Genomics, and Biotechnology 1, no. 3 (2017): 80–89. http://dx.doi.org/10.5147/pggb.v1i3.154.

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This study reports a high density genetic linkage map based on the ‘Maryland 96-5722’ by ‘Spencer’ recombinant inbred line (RIL) population of soybean [Glycine max (L.) Merr.] and constructed exclusively with single nucleotide polymorphism (SNP) markers. The Illumina Infinium SoySNP6K BeadChip genotyping array produced 5,376 SNPs in the mapping population, with a 96.75% success rate. Significant level of goodness-of-fit for each locus was tested based on the observed vs. expected ratio (1:1). Out of 5,376 markers, 1,465 SNPs fit the 1:1 segregation rate having ≤20% missing data plus heterozygo

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28

Chen, Z., Q. Liu, and S. Nadarajah. "A new statistical approach to detecting differentially methylated loci for case control Illumina array methylation data." Bioinformatics 28, no. 8 (2012): 1109–13. http://dx.doi.org/10.1093/bioinformatics/bts093.

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29

Kissel, Heather D., Thomas G. Paulson, Karen Liu, et al. "Feasibility of RNA and DNA Extraction from Fresh Pipelle and Archival Endometrial Tissues for Use in Gene Expression and SNP Arrays." Obstetrics and Gynecology International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/576842.

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Identifying molecular markers of endometrial hyperplasia (neoplasia) progression is critical to cancer prevention. To assess RNA and DNA quantity and quality from routinely collected endometrial samples and evaluate the performance of RNA- and DNA-based arrays across endometrial tissue types, we collected fresh frozen (FF) Pipelle, FF curettage, and formalin-fixed paraffin-embedded (FFPE) hysterectomy specimens (benign indications) from eight women. Additionally, neoplastic and uninvolved tissues from 24 FFPE archival hysterectomy specimens with endometrial hyperplasias and carcinomas were ass

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30

Park, Tae-Joon, Lyong Heo, Sanghoon Moon, et al. "Practical Calling Approach for Exome Array-Based Genome-Wide Association Studies in Korean Population." International Journal of Genomics 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/421715.

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Exome-based genotyping arrays are cost-effective and have recently been used as alternative platforms to whole-exome sequencing. However, the automated clustering algorithm in an exome array has a genotype calling problem in accuracy for identifying rare and low-frequency variants. To address these shortcomings, we present a practical approach for accurate genotype calling using the Illumina Infinium HumanExome BeadChip. We present comparison results and a statistical summary of our genotype data sets. Our data set comprises 14,647 Korean samples. To solve the limitation of automated clusterin

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Rounge, Trine B., Marianne Lauritzen, Sten Even Erlandsen, Hilde Langseth, Oddgeir Lingaas Holmen, and Randi E. Gislefoss. "Ultralow amounts of DNA from long-term archived serum samples produce quality genotypes." European Journal of Human Genetics 28, no. 4 (2019): 521–24. http://dx.doi.org/10.1038/s41431-019-0543-x.

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Abstract While genotyping studies are scavenging for suitable samples to analyze, large serum collections are currently left unused as they are assumed to provide insufficient amounts of DNA for array-based genotyping. Long-term stored serum is considered to be difficult to genotype since preanalytical treatments and storage effects on DNA yields are not well understood. Successful genotyping of such samples has the potential to activate large biobanks for future genome-wide association studies (GWAS). We aimed to evaluate genotyping of ultralow amounts of DNA from samples stored up to 45 year

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Akond, Masum, Shiming Liu, Lauren Schoener, et al. "A SNP-Based Genetic Linkage Map of Soybean Using the SoyS - NP6K Illumina Infinium BeadChip Genotyping Array." Plant Genetics, Genomics, and Biotechnology 1, no. 3 (2013): 80–89. http://dx.doi.org/10.5147/jpgs.2013.0090.

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33

Eckel-Passow, Jeanette, Paul Decker, Edward Hughes, et al. "PATH-47. CLINICAL SENSITIVITY AND SPECIFICITY OF ILLUMINA METHYLATION ARRAY FOR CLASSIFYING ADULT GLIOMAS INTO WHO GROUPS." Neuro-Oncology 19, suppl_6 (2017): vi181. http://dx.doi.org/10.1093/neuonc/nox168.737.

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34

Nakachi, Yutaka, Kazuhiro Ishii, Miki Bundo, Tomoyuki Masuda, and Kazuya Iwamoto. "Use of the Illumina EPIC methylation array for epigenomic research in the crab‐eating macaque ( Macaca fascicularis )." Neuropsychopharmacology Reports 40, no. 4 (2020): 423–26. http://dx.doi.org/10.1002/npr2.12145.

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35

Zaimi, Ina, Dong Pei, Devin C. Koestler, et al. "Variation in DNA methylation of human blood over a 1-year period using the Illumina MethylationEPIC array." Epigenetics 13, no. 10-11 (2018): 1056–71. http://dx.doi.org/10.1080/15592294.2018.1530008.

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36

Zhou, Jin, Erwin Tantoso, Lai-Ping Wong, et al. "iCall: a genotype-calling algorithm for rare, low-frequency and common variants on the Illumina exome array." Bioinformatics 30, no. 12 (2014): 1714–20. http://dx.doi.org/10.1093/bioinformatics/btu107.

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37

Knoll, Maximilian, Jürgen Debus, and Amir Abdollahi. "cnAnalysis450k: an R package for comparative analysis of 450k/EPIC Illumina methylation array derived copy number data." Bioinformatics 33, no. 15 (2017): 2266–72. http://dx.doi.org/10.1093/bioinformatics/btx156.

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38

Sissung, Tristan M., Roberto H. Barbier, Douglas K. Price, et al. "Comparison of Eight Technologies to Determine Genotype at the UGT1A1 (TA)n Repeat Polymorphism: Potential Clinical Consequences of Genotyping Errors?" International Journal of Molecular Sciences 21, no. 3 (2020): 896. http://dx.doi.org/10.3390/ijms21030896.

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To ensure accuracy of UGT1A1 (TA)n (rs3064744) genotyping for use in pharmacogenomics-based irinotecan dosing, we tested the concordance of several commonly used genotyping technologies. Heuristic genotype groupings and principal component analysis demonstrated concordance for Illumina sequencing, fragment analysis, and fluorescent PCR. However, Illumina sequencing and fragment analysis returned a range of fragment sizes, likely arising due to PCR “slippage”. Direct sequencing was accurate, but this method led to ambiguous electrophoregrams, hampering interpretation of heterozygotes. Gel sizin

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Cargo, Catherine, Nicola Rowbotham, Paul Evans, Sharon Barrans, Simon Crouch, and Andrew Jack. "Early Diagnosis of Myelodysplastic Syndromes Can be Improved By Deep Sequencing and Array Based Cytogenetics." Blood 124, no. 21 (2014): 167. http://dx.doi.org/10.1182/blood.v124.21.167.167.

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Abstract Background Identifying myelodysplasia (MDS) by morphological assessment, particularly in patients with <5% blasts, is problematic. There is poor inter-observer concordance in recognising dysplasia and difficulty distinguishing MDS from non-malignant conditions. This method is also limited by sample quality. To date cytogenetics has provided the only clonal marker of disease, however, recent reports of frequent driver mutations and/or structural variants detected by single nucleotide polymorphism (SNP) arrays have provided potential targets for assessment. To determine whether deep

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40

Kimani, Jane W., Koh-ichiro Yoshiura, Min Shi, et al. "Search for Genomic Alterations in Monozygotic Twins Discordant for Cleft Lip and/or Palate." Twin Research and Human Genetics 12, no. 5 (2009): 462–68. http://dx.doi.org/10.1375/twin.12.5.462.

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AbstractPhenotypically discordant monozygotic twins offer the possibility of gene discovery through delineation of molecular abnormalities in one member of the twin pair. One proposed mechanism of discordance is postzygotically occurring genomic alterations resulting from mitotic recombination and other somatic changes. Detection of altered genomic fragments can reveal candidate gene loci that can be verified through additional analyses. We investigated this hypothesis using array comparative genomic hybridization; the 50K and 250K Affymetrix GeneChip® SNP arrays and an Illumina custom array c

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41

Dias, Renuka P., Irina Bogdarina, Jean-Baptiste Cazier, et al. "Multiple Segmental Uniparental Disomy Associated with Abnormal DNA Methylation of Imprinted Loci in Silver-Russell Syndrome." Journal of Clinical Endocrinology & Metabolism 97, no. 11 (2012): E2188—E2193. http://dx.doi.org/10.1210/jc.2012-1980.

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Background: Silver-Russell syndrome (SRS; online inheritance in man 180860) is a low-birth-weight syndrome characterized by postnatal growth restriction and variable dysmorphic features. Although maternal uniparental disomy (UPD) of chromosome 7 and hypomethylation of H19 have been reported in up to 50% of all cases, no unifying mechanism is apparent. Subjects and Methods: Ten patients and their parents were studied using the Illumina GoldenGate methylation array and the Illumina 370K HumHap single-nucleotide polymorphism array to identify aberrations in DNA methylation as well as genomic chan

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42

Almeida, Diogo, Ida Skov, Jesper Lund, et al. "Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data processing." Journal of Integrative Bioinformatics 13, no. 4 (2016): 24–32. http://dx.doi.org/10.1515/jib-2016-294.

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Summary Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software lib

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Shinozaki, Gen, Patricia Braun, Benjamin Hing, et al. "57. Genome-Wide DNA Methylation Comparison by Illumina Epic Array between Live Human Brain and Peripheral Tissues within Individuals." Biological Psychiatry 81, no. 10 (2017): S24. http://dx.doi.org/10.1016/j.biopsych.2017.02.068.

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44

Gatev, Evan, Nicole Gladish, Sara Mostafavi, and Michael S. Kobor. "CoMeBack: DNA methylation array data analysis for co-methylated regions." Bioinformatics 36, no. 9 (2020): 2675–83. http://dx.doi.org/10.1093/bioinformatics/btaa049.

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Abstract Motivation High-dimensional DNA methylation (DNAm) array coverage, while sparse in the context of the entire DNA methylome, still constitutes a very large number of CpG probes. The ensuing multiple-test corrections affect the statistical power to detect associations, likely contributing to prevalent limited reproducibility. Array probes measuring proximal CpG sites often have correlated levels of DNAm that may not only be biologically meaningful but also imply statistical dependence and redundancy. New methods that account for such correlations between adjacent probes may enable impro

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45

Ignatius, Erika, Pirjo Isohanni, Max Pohjanpelto, et al. "Genetic background of ataxia in children younger than 5 years in Finland." Neurology Genetics 6, no. 4 (2020): e444. http://dx.doi.org/10.1212/nxg.0000000000000444.

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ObjectiveTo characterize the genetic background of molecularly undefined childhood-onset ataxias in Finland.MethodsThis study examined a cohort of patients from 50 families with onset of an ataxia syndrome before the age of 5 years collected from a single tertiary center, drawing on the advantages offered by next generation sequencing. A genome-wide genotyping array (Illumina Infinium Global Screening Array MD-24 v.2.0) was used to search for copy number variation undetectable by exome sequencing.ResultsExome sequencing led to a molecular diagnosis for 20 probands (40%). In the 23 patients exa

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Phipson, Belinda, Jovana Maksimovic, and Alicia Oshlack. "missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform." Bioinformatics 32, no. 2 (2015): 286–88. http://dx.doi.org/10.1093/bioinformatics/btv560.

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Abstract Summary: DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a cost-effective way to profile &gt;450 000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array. Availability and implementation: mi

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47

Gonzalez, Alberto, Ahmed Idbaih, Blandine Boisselier, et al. "Recurrent genetic alterations in primary central nervous system lymphoma of immunocompetent patients." Journal of Clinical Oncology 30, no. 15_suppl (2012): 2023. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.2023.

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2023 Background: Little is known about the molecular pathogenesis of primary central nervous system lymphoma (PCNSL) in immunocompetent patients. Our objective was to identify the genetic changes involved in PCNSL oncogenesis and evaluate their clinical relevance. Methods: Twenty nine and four newly diagnosed, HIV-negative PCNSL patients were investigated using high-resolution single nucleotide polymorphism (SNPa) arrays (Infinium Illumina Human 610-Quad SNP array-Illumina; validated by real-time quantitative polymerase chain reaction) and whole-exome sequencing respectively. Molecular results

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48

Zapotocky, Michal, Ales Vicha, Lenka Krskova, et al. "PATH-20. METHYLATION ARRAY PROFILING OF PEDIATRIC BRAIN TUMORS; SINGLE CENTRE EXPERIENCE." Neuro-Oncology 22, Supplement_3 (2020): iii428. http://dx.doi.org/10.1093/neuonc/noaa222.655.

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Abstract BACKGROUND Significant heterogeneity of pediatric brain tumors poses major challenge on diagnostics. Therefore, we aimed to evaluate feasibility of methylation array in the diagnostic process. METHODS Methylation array (Infinium MethylationEPIC, Illumina) was performed on DNA extracted from fresh frozen tissue from prospective newly diagnosed and selected retrospective patients. Results from Heidelberg classifier (www.molecularneuropathology.org) were compared to the histological diagnosis and further genetic testing was performed to establish integrated morphological/molecular diagno

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Hayes, J. L., A. Tzika, H. Thygesen, et al. "Diagnosis of copy number variation by Illumina next generation sequencing is comparable in performance to oligonucleotide array comparative genomic hybridisation." Genomics 102, no. 3 (2013): 174–81. http://dx.doi.org/10.1016/j.ygeno.2013.04.006.

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Rossi, Michael R., Scott Newman, Ajay K. Nooka, et al. "Using RNA-Seq, SNP-CN and Targeted Deep Sequencing To Improve The Diagnostic Paradigm In Multiple Myeloma." Blood 122, no. 21 (2013): 1856. http://dx.doi.org/10.1182/blood.v122.21.1856.1856.

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Abstract:

Abstract Background and Aim The use of G-banded karyotype and FISH have been standard diagnostic tools in monitoring response to treatment and disease progression in hematological disorders, including multiple myeloma. However, with the availability of array and NGS technologies in most clinical diagnostic laboratory settings, it is time to consider evaluating the use of more modern methods in diagnosing plasma cell dyscrasias. To this end, we have consented over 20 patients, most of which have evidence of disease progression to a preliminary study comparing data from RNA-Seq, SNP-CN arrays an

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