Large-Scale Multiplexed Quantitative Discovery Proteomics Enabled by the Use of an18O-Labeled “Universal” Reference Sample

Serpentine heat treatment at temperatures of 650–750 °C yields magnesium–silicate reagent with high chemical activity. Precise and express control of roasting conditions in laboratory kilns and industrial aggregates is needed to derive thermally activated serpentines on a large scale. Color change in serpentines with a high iron content during roasting might be used to indicate the changes in chemical activity in the technological process. This study gives a scientific basis for the express control of roasting of such serpentines by comparing the colors of the obtained material and the reference sample. Serpentines with different chemical activity were studied by X-ray diffraction, Mössbauer spectroscopy, and optical spectroscopy. The color parameters were determined using RGB (red, green, blue), CIELAB (International Commission on Illumination 1976 L*a*b), and HSB (hue, brightness, saturation) color models. The color of heat-treated samples was found to be affected by changes in the crystallochemical characteristics of iron included in the structure of the serpentine minerals. The color characteristics given by the CIELAB model were in good coherence with the acid-neutralizing ability and optical spectra of heat-treated serpentines. Thus, in contrast to the long-term analysis by these methods, the control by color palette provides an express assessment of the quality of the resulting product.

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Towards Optimal Microarray Universal Reference Sample Designs

International Journal of Systems Biology and Biomedical Technologies ◽

10.4018/ijsbbt.2012040105 ◽

2012 ◽

Vol 1 (2) ◽

pp. 66-77

Author(s):

George Potamias ◽

Sofia Kaforou ◽

Dimitris Kafetzopoulos

Keyword(s):

Microarray Data ◽

In Silico ◽

Reference Sample ◽

Threshold Value ◽

Experimental Results ◽

Color Channel ◽

Microarray Experiments ◽

Microarray Datasets ◽

Universal Reference ◽

Critical Challenge

In this paper, the authors present an assessment of the reliability of microarray experiments as well as their cross-laboratory/platform reproducibility rise as the major need. A critical challenge concerns the design of optimal universal reference rna (urr) samples to maximize detectable spots in two-color/channel microarray experiments, decrease the variability of microarray data, and finally ease the comparison between heterogeneous microarray datasets. Toward this target, the authors present an in-silico (binary) optimization process the solutions of which present optimal urr sample designs. Setting a cut-off threshold value over which a gene is considered as detectably expressed enables the process. Experimental results are quite encouraging and the related discussion highlights the suitability and flexibility of the approach.

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Ethnically relevant consensus Korean reference genome towards personal reference genomes

10.1101/070805 ◽

2016 ◽

Author(s):

Yun Sung Cho ◽

Hyunho Kim ◽

Hak-Min Kim ◽

Sungwoong Jho ◽

JeHoon Jun ◽

...

Keyword(s):

Large Scale ◽

New Technologies ◽

Reference Genome ◽

Genomic Structure ◽

Personal Genomic ◽

Personal Reference ◽

Human Genomes ◽

Scale Population ◽

Genome Assemblies ◽

Universal Reference

AbstractHuman genomes are routinely compared against a universal reference. However, this strategy could miss population-specific or personal genomic variations, which may be detected more efficiently using an ethnically-relevant and/or a personal reference. Here we report a hybrid assembly of Korean reference (KOREF) as a pilot case for constructing personal and ethnic references by combining sequencing and mapping methods. KOREF is also the first consensus variome reference, providing information on millions of variants from additional ethnically homogeneous personal genomes. We found that this ethnically-relevant consensus reference was beneficial for efficiently detecting variants. Systematic comparison of KOREF with previously established human assemblies showed the importance of assembly quality, suggesting the necessity of using new technologies to comprehensively map ethnic and personal genomic structure variations. In the era of large-scale population genome projects, the leveraging of ethnicity-specific genome assemblies as well as the human reference genome will accelerate mapping all human genome diversity.

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Blind estimation and correction of microarray batch effect

10.1101/202952 ◽

2017 ◽

Author(s):

Sudhir Varma

Keyword(s):

Large Scale ◽

Reference Sample ◽

R Package ◽

Batch Effect ◽

Data Repository ◽

Blind Estimation ◽

Reference Set ◽

Shared Information ◽

Scale Integration ◽

Biological Differences

AbstractMicroarray batch effect (BE) has been the primary bottleneck for large-scale integration of data from multiple experiments. Current BE correction methods either need known batch identities (ComBat)or have the potential to overcorrect, by removing true but unknown biological differences (SVA).Even though the effects of technical differences on measured expression have been published, there are no BE correction algorithms that take the approach of predicting technical effects from parameters computed from a fixed reference sample set. We show that a set of signatures, each of which is a vector the length of the number of probes, calculated on a Reference set of microarray samples can predict much of the batch effect in other Validation sets. We present a rationale of selecting a Reference set of samples designed to estimate technical differences without removing biological differences. Putting both together, we introduce the Batch Effect Signature Correction (BESC) algorithm that uses the BES calculated on the Reference set to efficiently predict and remove BE. Using two independent Validation sets, we show that BESC is capable of removing batch effect without removing unknown but true biological differences. Much of the variations due to batch effect is shared between different microarray datasets. That shared information can be used to predict signatures (i.e. directions of perturbation) due to batch effect in new datasets. The correction is blind (without needing to re-compute the parameters on new samples to be corrected), single sample, (each sample is corrected independently of each other) and conservative (only those perturbations known to be likely to be due to technical differences are removed ensuring that unknown but important biological differences are maintained). Those three characteristics make it ideal for high-throughput correction of samples for a microarray data repository. An R Package besc implementing the algorithm is available from http://explainbio.com.

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Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach

Engineering Applications of Neural Networks - IFIP Advances in Information and Communication Technology ◽

10.1007/978-3-642-23957-1_49 ◽

2011 ◽

pp. 443-452

Author(s):

George Potamias ◽

Sofia Kaforou ◽

Dimitris Kafetzopoulos

Keyword(s):

In Silico ◽

Reference Sample ◽

Optimization Approach ◽

Universal Reference

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An experimentally-derived measure of inter-replicate variation in reference samples: the same-same permutation methodology

10.1101/797217 ◽

2019 ◽

Cited By ~ 3

Author(s):

David C. Handler ◽

Paul A. Haynes

Keyword(s):

Multiple Testing ◽

Large Scale ◽

Reference Sample ◽

Q Value ◽

Testing Problem ◽

False Discovery ◽

Proteomics Experiment ◽

Multiple Testing Problem ◽

High Throughput Data Analysis ◽

Reference Samples

AbstractThe multiple testing problem is a well-known statistical stumbling block in high-throughput data analysis, where large scale repetition of statistical methods introduces unwanted noise into the results. While approaches exist to overcome the multiple testing problem, these methods focus on theoretical statistical clarification rather than incorporating experimentally-derived measures to ensure appropriately tailored analysis parameters. Here, we introduce a method for estimating inter-replicate variability in reference samples for a quantitative proteomics experiment using permutation analysis. This can function as a modulator to multiple testing corrections such as the Benjamini-Hochberg ordered Q value test. We refer to this as a ‘same-same’ analysis, since this method incorporates the use of six biological replicates of the reference sample and determines, through non-redundant triplet pairwise comparisons, the level of quantitative noise inherent within the system. The method can be used to produce an experiment-specific Q value cut-off that achieves a specified false discovery rate at the quantitation level, such as 1%. The same-same method is applicable to any experimental set that incorporates six replicates of a reference sample. To facilitate access to this approach, we have developed a same-same analysis R module that is freely available and ready to use via the internet.

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A universal reference sample derived from clone vector for improved detection of differential gene expression

BMC Genomics ◽

10.1186/1471-2164-7-109 ◽

2006 ◽

Vol 7 (1) ◽

Cited By ~ 6

Author(s):

Rishi L Khan ◽

Gregory E Gonye ◽

Guang Gao ◽

James S Schwaber

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Reference Sample ◽

Differential Gene ◽

Universal Reference

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Rapid Identification of Commercial Frankincense Products by MALDI-TOF Mass Spectrometry

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210301092111 ◽

2021 ◽

Vol 24 ◽

Author(s):

Shang-Chih Lai ◽

Ren-In You ◽

Tz-Ting Chen ◽

Yu Chang ◽

Chao-Zong Liu ◽

...

Keyword(s):

Mass Spectrometry ◽

High Throughput ◽

High Throughput Screening ◽

Large Scale ◽

Reference Sample ◽

Maldi Tof Ms ◽

Boswellic Acids ◽

Maldi Tof ◽

Tof Ms

Background: Frankincense is a resin secreted by the Boswellia tree. It is used in perfumery, aromatherapy, skincare, and traditional Chinese medicine. However, all Boswellia species are under threat owing to habitat loss and overexploitation. As a result, the market is getting flooded with counterfeit frankincense products. Objective: This study aims to establish a high-throughput method to screen and identify the authenticity of commercial frankincense products. We report, for the first time, a matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based method for rapid and high-throughput screening of frankincense samples. Methods: MALDI-TOF MS, HPLC, thin-layer chromatography (TLC), and in vitro anti-inflammatory activity assay were used to examine the frankincense samples. Results: Well-resolved peaks of frankincense triterpenoids in the spectra were observed in the crude extract of commercial samples, including α-boswellic acids (αBAs), β-boswellic acids (βBAs), 11-keto-β-boswellic acids (KBAs), acetyl-11-keto-β-boswellic acids (AKBAs), and their esters. These compounds can be used as indicators for determining the authenticity of frankincense. Conclusion: Unlike LC–MS, which is a time-consuming and expensive method, and TLC, which requires a reference sample, our inexpensive, rapid high-throughput identification method based on MALDI-TOF MS is ideal for large-scale screening of frankincense samples sold in the market.

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Towards Optimal Microarray Universal Reference Sample Designs

Bioinformatics ◽

10.4018/978-1-4666-3604-0.ch088 ◽

2013 ◽

pp. 1676-1687

Author(s):

George Potamias ◽

Sofia Kaforou ◽

Dimitris Kafetzopoulos

Keyword(s):

Microarray Data ◽

Reference Sample ◽

Threshold Value ◽

Experimental Results ◽

Optimization Process ◽

Color Channel ◽

Microarray Experiments ◽

Microarray Datasets ◽

Universal Reference ◽

Critical Challenge

In this paper, the authors present an assessment of the reliability of microarray experiments as well as their cross-laboratory/platform reproducibility rise as the major need. A critical challenge concerns the design of optimal universal reference rna (urr) samples to maximize detectable spots in two-color/channel microarray experiments, decrease the variability of microarray data, and finally ease the comparison between heterogeneous microarray datasets. Toward this target, the authors present an in-silico (binary) optimization process the solutions of which present optimal urr sample designs. Setting a cut-off threshold value over which a gene is considered as detectably expressed enables the process. Experimental results are quite encouraging and the related discussion highlights the suitability and flexibility of the approach.

Download Full-text