scholarly journals RNA expression microarrays (REMs), a high-throughput method to measure differences in gene expression in diverse biological samples

2004 ◽  
Vol 32 (15) ◽  
pp. e120-e120 ◽  
Author(s):  
C. E. Rogler
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Biological Samples ◽  
Rna Expression ◽  
High Throughput Method ◽  
Expression Microarrays

SNARE-seq2 v2

2021 ◽  
Author(s):  
Nongluk Plongthongkum ◽  
Dinh H Diep ◽  
Song Chen ◽  
Blue Lake ◽  
Kun Zhang
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Chromatin Accessibility ◽  
Single Experiment ◽  
Data Set ◽  
Whole Cells ◽  
High Throughput Method ◽  
Single Nucleus ◽  
Accessible Chromatin ◽  
Entire Procedure

To study the heterogeneity of complex tissues by joint profiling of gene expression and its regulation, we require an accurate and high-throughput method. Here we described improved high-throughput combinatorial indexing-based single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-Seq2) co-assay. This protocol involves fixing and permeabilizing the nucleus followed by tagmentation, chromatin barcode ligation, reverse transcription, pooling and splitting for the next rounds of cell barcode ligation into cDNA and accessible chromatin (AC) on the same nucleus. The captured cDNA and AC are co-amplified before splitting and enrichment into single-nucleus RNA and single-nucleus AC sequencing libraries. The protocol can also be applied to both nuclei and whole cells to capture mRNA in the cytoplasm. This improvement allows us to generate hundreds of thousands of data set of each assay and can be scaled up to half a million cells from a single experiment. The entire procedure can be complete in 3.5 d for generating joint single-nucleus RNA and single-nucleus ATAC sequencing libraries.

A high-throughput method for quantifying gene expression data from early Drosophila embryos

2005 ◽  
Vol 215 (7) ◽  
pp. 374-381 ◽  
Author(s):  
Hilde Janssens ◽  
Dave Kosman ◽  
Carlos E. Vanario-Alonso ◽  
Johannes Jaeger ◽  
Maria Samsonova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
High Throughput Method ◽  
Drosophila Embryos

A high-throughput method for the determination of Os concentrations and isotope ratio measurements in small-size biological samples

2013 ◽  
Vol 28 (10) ◽  
pp. 1591 ◽  
Author(s):  
Nicola Pallavicini ◽  
Frauke Ecke ◽  
Emma Engström ◽  
Douglas C. Baxter ◽  
Ilia Rodushkin
Keyword(s):  
High Throughput ◽  
Biological Samples ◽  
Isotope Ratio ◽  
High Throughput Method ◽  
Isotope Ratio Measurements

SNARE-seq2 v2

2021 ◽  
Author(s):  
Nongluk Plongthongkum ◽  
Dinh H Diep ◽  
Song Chen ◽  
Blue Lake ◽  
Kun Zhang
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Chromatin Accessibility ◽  
Single Experiment ◽  
Data Set ◽  
Whole Cells ◽  
High Throughput Method ◽  
Single Nucleus ◽  
Accessible Chromatin ◽  
Entire Procedure

To study the heterogeneity of complex tissues by joint profiling of gene expression and its regulation, we require an accurate and high-throughput method. Here we described improved high-throughput combinatorial indexing-based single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-Seq2) co-assay. This protocol involves fixing and permeabilizing the nucleus followed by tagmentation, chromatin barcode ligation, reverse transcription, pooling and splitting for the next rounds of cell barcode ligation into cDNA and accessible chromatin (AC) on the same nucleus. The captured cDNA and AC are co-amplified before splitting and enrichment into single-nucleus RNA and single-nucleus AC sequencing libraries. The protocol can also be applied to both nuclei and whole cells to capture mRNA in the cytoplasm. This improvement allows us to generate hundreds of thousands of data set of each assay and can be scaled up to half a million cells from a single experiment. The entire procedure can be complete in 3.5 d for generating joint single-nucleus RNA and single-nucleus ATAC sequencing libraries.

scholarly journals A user-friendly, high-throughput tool for the precise fluorescent quantification of deoxyribonucleoside triphosphates from biological samples

2020 ◽  
Vol 48 (8) ◽  
pp. e45-e45 ◽  
Author(s):  
Judit Eszter Szabó ◽  
Éva Viola Surányi ◽  
Bence Sándor Mébold ◽  
Tamás Trombitás ◽  
Mihály Cserepes ◽  
...  
Keyword(s):  
High Throughput ◽  
Biological Samples ◽  
Cell Cycle Control ◽  
Taq Polymerase ◽  
Replication Arrest ◽  
Physiological Processes ◽  
Analysis Process ◽  
High Throughput Method ◽  
Inexperienced User ◽  
User Friendly

Abstract Cells maintain a fine-tuned, dynamic concentration balance in the pool of deoxyribonucleoside 5′-triphosphates (dNTPs). This balance is essential for physiological processes including cell cycle control or antiviral defense. Its perturbation results in increased mutation frequencies, replication arrest and may promote cancer development. An easily accessible and relatively high-throughput method would greatly accelerate the exploration of the diversified consequences of dNTP imbalances. The dNTP incorporation based, fluorescent TaqMan-like assay published by Wilson et al. has the aforementioned advantages over mass spectrometry, radioactive or chromatography based dNTP quantification methods. Nevertheless, the assay failed to produce reliable data in several biological samples. Therefore, we applied enzyme kinetics analysis on the fluorescent dNTP incorporation curves and found that the Taq polymerase exhibits a dNTP independent exonuclease activity that decouples signal generation from dNTP incorporation. Furthermore, we found that both polymerization and exonuclease activities are unpredictably inhibited by the sample matrix. To resolve these issues, we established a kinetics based data analysis method which identifies the signal generated by dNTP incorporation. We automated the analysis process in the nucleoTIDY software which enables even the inexperienced user to calculate the final and accurate dNTP amounts in a 96-well-plate setup within minutes.

SNARE-seq2 v2

2021 ◽  
Author(s):  
Nongluk Plongthongkum ◽  
Dinh H Diep ◽  
Song Chen ◽  
Blue Lake ◽  
Kun Zhang
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Chromatin Accessibility ◽  
Single Experiment ◽  
Data Set ◽  
Whole Cells ◽  
High Throughput Method ◽  
Single Nucleus ◽  
Accessible Chromatin ◽  
Entire Procedure

To study the heterogeneity of complex tissues by joint profiling of gene expression and its regulation, we require an accurate and high-throughput method. Here we described improved high-throughput combinatorial indexing-based single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-Seq2) co-assay. This protocol involves fixing and permeabilizing the nucleus followed by tagmentation, chromatin barcode ligation, reverse transcription, pooling and splitting for the next rounds of cell barcode ligation into cDNA and accessible chromatin (AC) on the same nucleus. The captured cDNA and AC are co-amplified before splitting and enrichment into single-nucleus RNA and single-nucleus AC sequencing libraries. The protocol can also be applied to both nuclei and whole cells to capture mRNA in the cytoplasm. This improvement allows us to generate hundreds of thousands of data set of each assay and can be scaled up to half a million cells from a single experiment. The entire procedure can be complete in 3.5 d for generating joint single-nucleus RNA and single-nucleus ATAC sequencing libraries.

scholarly journals Re-Annotator: Annotation Pipeline for Microarrays

2015 ◽  
Author(s):  
Janine Arloth ◽  
Daniel Magnus Bader ◽  
Simone Röh ◽  
Andre Altmann
Keyword(s):  
Gene Expression ◽  
Microarray Analysis ◽  
High Throughput ◽  
Gene Expression Array ◽  
Expression Array ◽  
Annotation Pipeline ◽  
Gene Expression Microarrays ◽  
Array Probe ◽  
Expression Microarrays ◽  
High Throughput Gene Expression

Background: Microarray technologies are established approaches for high throughput gene expression, methylation and genotyping analysis. An accurate mapping of the array probes is essential to generate reliable biological findings. Manufacturers typically provide incomplete and outdated annotation tables, which often rely on older genome and transcriptome versions differing substantially from up-to-date sequence databases. Results: Here, we present the Re-Annotator, a re-annotation pipeline for microarrays. It is primarily designed for gene expression microarrays but can be adapted to other types of microarrays. The Re-Annotator is based on a custom-built mRNA reference, used to identify the positions of gene expression array probe sequences. A comparison of our re-annotation of the Human-HT12-v4 microarray to the manufacturer's annotation led to over 25% differently interpreted probes. Conclusions: A thorough re-annotation of probe information is crucial to any microarray analysis. The Re-Annotator pipeline consists of Perl and Shell scripts, freely available at (http://sourceforge.net/projects/reannotator). Re-annotation files for Illumina microarrays Human HT-12 v3/v4 and MouseRef-8 v2 are available as well.

An Introduction to Text Analysis in Genomics

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Computational Methods ◽  
High Throughput ◽  
Data Interpretation ◽  
Genome Project ◽  
Biological Data ◽  
Data Sets ◽  
Gene Expression Microarrays ◽  
Small Collection ◽  
Expression Microarrays

The February 16th, 2001 issue of Science magazine announced the completion of the human genome project—making the entire nucleotide sequence of the genome available (Venter, Adams et al. 2001). For the first time a comprehensive data set was available with nucleotide sequences for every gene. This marked the beginning of a new era, the ‘‘genomics’’ era, where molecular biological science began a shift from the investigation of single genes towards the investigation of all genes in an organism simultaneously. Alongside the completion of the genome project came the introduction of new high throughput experimental approaches such as gene expression microarrays, rapid single nucleotide polymorphism detection, and proteomics methods such as yeast two hybrid screens (Brown and Botstein 1999; Kwok and Chen 2003; Sharff and Jhoti 2003; Zhu, Bilgin et al. 2003). These methods permitted the investigation of hundreds if not thousands of genes simultaneously. With these high throughput methods, the limiting step in the study of biology began shifting from data collection to data interpretation. To interpret traditional experimental results that addressed the function of only a single or handful of genes, investigators needed to understand only those few genes addressed in the study in detail and perhaps a handful of other related genes. These investigators needed to be familiar with a comparatively small collection of peer-reviewed publications and prior results. Today, new genomics experimental assays, such as gene expression microarrays, are generating data for thousands of genes simultaneously. The increasing complexity and sophistication of these methods makes them extremely unwieldy for manual analysis since the number and diversity of genes involved exceed the expertise of any single investigator. The only practical solution to analyzing these types of data sets is using computational methods that are unhindered by the volume of modern data. Bioinformatics is a new field that emphasizes computational methods to analyze such data sets (Lesk 2002). Bioinformatics combines the algorithms and approaches employed in computer science and statistics to analyze, understand, and hypothesize about the large repositories of collected biological data and knowledge.

scholarly journals DNA Copy Number Analysis in Gastrointestinal Stromal Tumors Using Gene Expression Microarrays

2008 ◽  
Vol 6 ◽  
pp. CIN.S387 ◽  
Author(s):  
Cristina R. Antonescu ◽  
Kai Wu ◽  
Guoliang Leon Xing ◽  
Manqiu Cao ◽  
Yaron Turpaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Copy Number ◽  
Gastrointestinal Stromal Tumors ◽  
Rna Expression ◽  
Copy Number Analysis ◽  
Stromal Tumors ◽  
Gene Expression Microarrays ◽  
Expression Microarrays ◽  
Copy Number Changes

We report a method, Expression-Microarray Copy Number Analysis (ECNA) for the detection of copy number changes using Affymetrix Human Genome U133 Plus 2.0 arrays, starting with as little as 5 ng input genomic DNA. An analytical approach was developed using DNA isolated from cell lines containing various X-chromosome numbers, and validated with DNA from cell lines with defined deletions and amplifications in other chromosomal locations. We applied this method to examine the copy number changes in DNA from 5 frozen gastrointestinal stromal tumors (GIST). We detected known copy number aberrations consistent with previously published results using conventional or BAC-array CGH, as well as novel changes in GIST tumors. These changes were concordant with results from Affymetrix 100K human SNP mapping arrays. Gene expression data for these GIST samples had previously been generated on U133A arrays, allowing us to explore correlations between chromosomal copy number and RNA expression levels. One of the novel aberrations identified in the GIST samples, a previously unreported gain on 1q21.1 containing the PEX11B gene, was confirmed in this study by FISH and was also shown to have significant differences in expression pattern when compared to a control sample. In summary, we have demonstrated the use of gene expression microarrays for the detection of genomic copy number aberrations in tumor samples. This method may be used to study copy number changes in other species for which RNA expression arrays are available, e.g. other mammals, plants, etc., and for which SNPs have not yet been mapped.

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