Each SNP typed by this array is at a bi-allelic locus, as determined from extensive sequencing of global P. falciparum isolates [8 (link)]. The heuristic algorithm therefore focuses on the two intensities of each possible allele. This algorithm then identifies the global mean intensity for every probe with the identical center base and adjusts each individual intensity by the difference in intensity between the global means of the two bases being interrogated. Intensities are evaluated and adjusted independently for the sense and anti-sense direction. After adjusting the intensity, a SNP is called after fulfilling the following criteria: 1) the contrast of intensities is greater than or equal to 0.98, 2) the forward and reverse SNP calls are concordant, and 3) all intensities are above global background levels determined by the average value of the random probes. Multiple thresholds were tested for background levels up to average random plus two standard deviations (data not shown). SNP calling accuracy changed minimally when thresholds were raised, however SNP call rate dropped more significantly. This algorithm was written in the PERL programming language and uses standard outputs from the Roche NimbleScan (v2.6) software. Given the discontinuation of this microarray the algorithm will be made publically available when the probe set is validated on a new platform.
Nimblescan v2
Manufactured by Roche
Sourced in United States
NimbleScan v2.5 is a versatile and high-performance lab equipment designed for efficient data analysis. It features advanced scanning capabilities and a user-friendly interface, enabling researchers to streamline their workflow.
Automatically generated - may contain errors
Lab products found in correlation
Nanodrop 1000 spectrophotometer, by Thermo Fisher Scientific (2 mentions)
Nanodrop 2000 spectrophotometer, by Thermo Fisher Scientific (2 mentions)
Genespring v11, by Agilent Technologies (2 mentions)
Nanodrop, by Thermo Fisher Scientific (2 mentions)
Sas software, by SAS Institute (2 mentions)
Spss version 17, by IBM (1 mentions)
Rneasy kit, by Roche (1 mentions)
Nimblegen ms200 scanner, by Roche (1 mentions)
Agilent s 2100 bioanalyzer, by Agilent Technologies (1 mentions)
Nimblegen microarray 090818 vitis exp hx12, by Roche (1 mentions)
Spectrum plant total rna kit, by Merck Group (1 mentions)
Rnase free dnase set, by Qiagen (1 mentions)
Genepix 4000b, by Molecular Devices (1 mentions)
Axon genepix 4000b microarray scanner, by Molecular Devices (1 mentions)
Human lncrna array, by Arraystar (1 mentions)
Feature extraction software, by Agilent Technologies (1 mentions)
Nimblescan software, by Roche (1 mentions)
Qubit dsdna br kit, by Thermo Fisher Scientific (1 mentions)
Signalmap v 1, by Roche (1 mentions)
Nimblegen human cgh 12 135 k whole genome tiling v3.1 array, by Agilent Technologies (1 mentions)
31 protocols using nimblescan v2
1
SNP Calling from Microarray Data
2
Gene Expression Analysis for Clinical Outcomes
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Scanned images were gridded by using the NimbleScan v2.6 Software (Nimblegen Roche). Then, robust multi-array analysis background normalization and quantile normalization were performed for intra-array and inter-array normalization, respectively. Genes with signal intensities above a 95% random threshold were chosen [18] (link). Differential expression between clinical outcomes was assessed by moderated t tests and significance statistics for each gene were obtained by the empirical Bayes method implemented in limma package from Bioconductor [19] (link). Global differential expression was also examined by random sampling of class labels. We selected gene subsets on the basis of classifier optimal performance ranking, as in previous approaches [20,21] . A nearest mean classifier was trained for feature selection in a leave-one-out cross-validation process and feature selection was further tested by another leave-one-out cross-validation procedure to select the profile with the strongest association with clinical response. Graphics were generated using Genesis 2.1 software [22] (link). The total microarray raw and normalized data of this study are public available at the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE56303.
3
Comprehensive Genomic Profiling Using CGH
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Comparative genomic hybridization (CGH) on daughters and their parents were performed in CapitalBio Cooperation (Beijing, China) to discover large deletions and copy number variations (CNVs). Human CGH 3×1.4M Whole-Genome Exon-Focused Array (Roche Diagnostics, Mannheim, Germany) was used with a median spacing of 7 kb and 5 probes/exon. Raw CGH data were extracted and analyzed using Nimble Scan v2.6 software (Roche Diagnostics) and CNVs were identified by filtering more than 5 consecutive probe segments with [log2 ratio]>0.25.
4
Transcriptomic Analysis of O. oeni
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O. oeni cells were harvested by centrifugation, frozen in liquid nitrogen and kept at -80ºC until RNA extraction. Total RNA extractions were performed using the Roche RNeasy kit following the manufacturer's instructions (Mannheim, Germany). RNA concentrations were calculated by measuring absorbance at 260 nm using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific SL, Alcobendas, Spain). cDNA was subsequently prepared. Samples were hybridized to the arrays for 18 hours at 42ºC. After washing, the arrays were scanned in a Roche Nimblegen MS 200 scanner. Raw data files (Pair and XYS files) were obtained from images using Nimblescan v2.6 software (Roche Nimblegen). Normalized gene expression values were obtained with Nimblescan software using the robust multichip average (RMA) algorithm as described by Irizarry et al. (2003a; 2003b) . Data univariate (ANOVA) analyses of transcriptomic data were conducted using SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Variable means showing statistical significance were compared using Bonferroni post-test comparisons at a significance level of 0.05, after testing the homogeneity of variance assumption between the various groups. The results were submitted to GEO (Gene Expression Omnibus Database, NCBI) under accession number GSE62036.
5
Transcriptome Analysis of Grapevine using NimbleGen Microarrays
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Total RNA was extracted from samples using the Spectrum plant total RNA kit (Sigma, www.sigmaaldrich.com ) as recommended by manufacturer. DNase I digestion was carried out with the RNase-Free DNase Set (QIAGEN). RNA integrity and quantity were assessed with a Nanodrop 2000 spectrophotometer (Thermo Scientific) and an Agilent's Bioanalyzer 2100. Microarray hybridizations were performed at the Genomics Unit of the National Centre for Biotechnology (CNB-CSIC, Madrid).
Synthesis of cDNA, labeling, hybridization, and washing steps were performed according to the NimbleGen arrays user's guide. Each sample was hybridized to a NimbleGen microarray 090818 Vitis exp HX12 (Roche, NimbleGen), which contains probes targeted to 29,549 predicted grapevine genes and 19,091 random probes as negative controls. Images were analyzed using NimbleScan v2.6 software (Roche), which produces.xys files containing the raw signal intensity data for each.
Synthesis of cDNA, labeling, hybridization, and washing steps were performed according to the NimbleGen arrays user's guide. Each sample was hybridized to a NimbleGen microarray 090818 Vitis exp HX12 (Roche, NimbleGen), which contains probes targeted to 29,549 predicted grapevine genes and 19,091 random probes as negative controls. Images were analyzed using NimbleScan v2.6 software (Roche), which produces.xys files containing the raw signal intensity data for each.
6
Differential Gene Expression Analysis
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Digitalized images were gridded by using the NimbleScan v2.6 Software (Nimblegen Roche). Then, robust multiarray analysis background normalization and quantile normalization were performed for intra-and inter-array normalization, respectively. Genes with signal intensities above 95% of arbitrary threshold were filtered. 19 Differential expression between normal and tumor tissues and between clinical outcomes was assessed by analysis of variance (ANOVA) tests and significance statistics for each gene were obtained by Genesis software (ver. 1.7.7). 20 Using the same software, graphics were generated.
7
Gene Expression Profiling of Pigs
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cDNA was prepared from total RNA by standard methods. Gene expression experiments were conducted on custom Sus Scrofa 12-plex microarrays containing 142 073 probes targeting 17 261 genes. Probe selection and array design is described elsewhere.59 (link) In all, 200 ng double-stranded cDNA was Cy3-labelled using the 'One colour DNA Labelling Kit' (Roche NimbleGen, Madison, WI, USA). A minimum of three technical replicates of each sample were loaded, and raw data intensities were collected using a NimbleGen MS200 scanner. Background corrected, quantile normalised and RMA (Robust Multi-Array Analysis) normalised probe intensities were generated using the NimbleScan v2.6 software (Roche NimbleGen). Average signal intensities were calculated for each probe set and log2 transformed. Data were analysed using the Partek Genomics Suite software, v6.6 (Partek, St Louis, MO, USA). Significant differentially expressed genes were identified for a false-discovery rate P-value of 0.05 and twofold change.
8
Genome-wide DNA Methylation Profiling
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Raw data was extracted as pair files by NimbleScan software. We performed median-centering, quantile normalization, and linear smoothing using NimbleScan by Nimblegen and R Bioconductor packages (Ringo, limma, and MEDME) [17 (link)]. The enrichment peaks and differentially methylated peaks were analyzed and annotated by NimbleScan software. The user guide and result data formats can be found at http://www.nimblegen.com/downloads/support/NimbleScan_v2p6_UsersGuide.pdf . After normalization, a normalized log2-ratio data (∗_ratio.gff file) was created for each sample. From the normalized log2-ratio data, a sliding-window peak-finding algorithm provided by NimbleScan v2.5 (Roche-NimbleGen) was applied to find the enriched peaks with specified parameters (sliding-window width: 750 bp; miniprobes per peak: 2; p-value minimum cutoff: 2; maximum spacing between nearby probes within peak: 500 bp). After obtaining the ∗_peaks.gff files, the identified peaks were mapped to genomic features: transcripts and CpG Islands.
9
ChIP-seq Data Normalization and Analysis
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Raw data was extracted as pair files by NimbleScan software. We ArrayStar performed Median-centering, quantile normalization, and linear smoothing by Bioconductor packages Ringo, limma, and MEDME. After normalization, a normalized log2-ratio data (*_ratio.gff file) was created for each sample. From the normalized log2-ratio data, a sliding-window peak-finding algorithm provided by NimbleScan v2.5 (Roche-NimbleGen) was applied to find the enriched peaks with specified parameters (sliding window width: 750bp; mini probes per peak: 2; p-value minimum cut-off: 2; maximum spacing between nearby probes within peak: 500bp). Raw and normalized data files were uploaded to GEO.
10
Mouse Tumor DNA Microarray Analysis
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Genomic DNA from mouse tumors was analyzed versus reference normal DNA. The samples were labeled and co-hybridized to the NimbleGen Mouse CGH 3×720K WG-T arrays according to the manufacturer's protocol. Arrays were washed and then scanned on a GenePix 4000B Scanner using GenePix 5.0 software. Raw data were normalized using NimbleScan v2.5 software (Roche NimbleGen). The normalized data were processed using NimbleScan softwares.
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