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Bioarray Inc software environment database base 1.2.17
Software Environment Database Base 1.2.17, supplied by Bioarray Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/software+environment+database/software+environment+database+base+1+2+12/pm22079614-65-13-13
Average 90 stars, based on 1 article reviews
software environment database base 1.2.17 - by Bioz Stars, 2026-09
90/100 stars

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Microarray:

Article Title: Nickel response in function of temperature differences: effects at different levels of biological organization in Daphnia magna.
Article Snippet: a Laboratory for Ecophysiology, Biochemistry and Toxicology, Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium b Plantijn Hogeschool, Kronenburgstraat 47, B-2000 Antwerp, Belgium c CESAM & Department of Biology, University of Aveiro, Campus Universitario de Santioago, 3810-193 Aveiro, Portugal d Apeiron-Team NV, Pluyseghemstraat 69, B-2550 Antwerp, Belgium

Article Title: Histone H3 lysine 4 trimethylation marks meiotic recombination initiation sites
Article Snippet: For transcriptome analyses, these ratios, representing the transcript abundance relative to the reference mix, were normalized using the Lowess algorithm implemented in BASE (BioArray Software Environment) 1.0.7 program ( Saal et al , 2002 ; Yang et al , 2002 ).

Article Title: Nickel and binary metal mixture responses in Daphnia magna: molecular fingerprints and (sub)organismal effects.
Article Snippet: The recent development of a custom cDNA microarray platform for one of thé standard organisms in aquatic toxicology, Daphnia magna, opened up new ways to mechanistic insights of toxicological responses.. In this study, the mRNA expression of several genes and (sub)organismal responses (Cellular Energy Allocation, growth) were assayed after short-term waterborne metal exposure.. Microarray analysis of Ni-exposed daphnids revealed several affected functional gene classes, of which the largest ones were involved in different metabolic processes (mainly protein and chitin related processes), cuticula turnover, transport and signal transduction.

Article Title: Mechanistic profiling of the cAMP-dependent steroidogenic pathway in the H295R endocrine disrupter screening system: new endpoints for toxicity testing.
Article Snippet: The need for implementation of effects on steroid synthesis and hormone processing in screening batteries of endocrine disruptive compounds is widely acknowledged.. In this perspective, hormone profiling in the H295R adrenocortical cell system is extensively examined and recently OECD validated (TG 456) as a replacement of the minced testis assay.. To further elucidate the complete mechanisms and endocrine responsiveness of this cell system, microarray-based gene expression profiling of the cAMP response pathway, one of the major pathways in steroidogenesis regulation, was examined in H295R cells.

Article Title: Development of a microarray for Enchytraeus albidus (Oligochaeta): preliminary tool with diverse applications.
Article Snippet: Standard bioassays allow hazard assessment at the population level, but much remains to be learned about the molecular level response of organisms to stressors.. The main aim of this study was the development of a DNA microarray for Enchytraeus albidus, a common soil worm species.. Further, this microarray was tested using worms exposed to Cu, phenmedipham, and different soil types.

Article Title: Gene transcription profiles, global DNA methylation and potential transgenerational epigenetic effects related to Zn exposure history in Daphnia magna.
Article Snippet: Subsequently, datawere evaluated with the MIAME platform based Bioarray Software Environment (BASE 1.2.17, http://www.islab.ua.ac. be/base/), using local background subtraction, variance stabilization normalization (Huber et al., 2002) and Limma (linear models for microarray data) (Smyth, 2004; Smyth et al., 2005).

Article Title: Identification of fibroblast growth factor-8b target genes associated with early and late cell cycle events in breast cancer cells.
Article Snippet: Primary hybridization data was collected using GenePix Pro 4.0 software (Axon Instruments, Foster City, CA) and raw data files were uploaded into BioArray Software Environment (BASE) version 1.2.17 (Saal et al., 2002).

Article Title: High expression of cholesterol biosynthesis genes is associated with resistance to statin treatment and inferior survival in breast cancer
Article Snippet: Microarray data were pre-processed and quantile-normalized using the GenomeStudio Software V2011.1 and BioArray Software Environment (BASE) V3.4.1 respectively.

Software:

Article Title: Nickel response in function of temperature differences: effects at different levels of biological organization in Daphnia magna.
Article Snippet: a Laboratory for Ecophysiology, Biochemistry and Toxicology, Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium b Plantijn Hogeschool, Kronenburgstraat 47, B-2000 Antwerp, Belgium c CESAM & Department of Biology, University of Aveiro, Campus Universitario de Santioago, 3810-193 Aveiro, Portugal d Apeiron-Team NV, Pluyseghemstraat 69, B-2550 Antwerp, Belgium

Article Title: Histone H3 lysine 4 trimethylation marks meiotic recombination initiation sites
Article Snippet: For transcriptome analyses, these ratios, representing the transcript abundance relative to the reference mix, were normalized using the Lowess algorithm implemented in BASE (BioArray Software Environment) 1.0.7 program ( Saal et al , 2002 ; Yang et al , 2002 ).

Article Title: Nickel and binary metal mixture responses in Daphnia magna: molecular fingerprints and (sub)organismal effects.
Article Snippet: The recent development of a custom cDNA microarray platform for one of thé standard organisms in aquatic toxicology, Daphnia magna, opened up new ways to mechanistic insights of toxicological responses.. In this study, the mRNA expression of several genes and (sub)organismal responses (Cellular Energy Allocation, growth) were assayed after short-term waterborne metal exposure.. Microarray analysis of Ni-exposed daphnids revealed several affected functional gene classes, of which the largest ones were involved in different metabolic processes (mainly protein and chitin related processes), cuticula turnover, transport and signal transduction.

Article Title: Mechanistic profiling of the cAMP-dependent steroidogenic pathway in the H295R endocrine disrupter screening system: new endpoints for toxicity testing.
Article Snippet: The need for implementation of effects on steroid synthesis and hormone processing in screening batteries of endocrine disruptive compounds is widely acknowledged.. In this perspective, hormone profiling in the H295R adrenocortical cell system is extensively examined and recently OECD validated (TG 456) as a replacement of the minced testis assay.. To further elucidate the complete mechanisms and endocrine responsiveness of this cell system, microarray-based gene expression profiling of the cAMP response pathway, one of the major pathways in steroidogenesis regulation, was examined in H295R cells.

Article Title: Development of a microarray for Enchytraeus albidus (Oligochaeta): preliminary tool with diverse applications.
Article Snippet: Standard bioassays allow hazard assessment at the population level, but much remains to be learned about the molecular level response of organisms to stressors.. The main aim of this study was the development of a DNA microarray for Enchytraeus albidus, a common soil worm species.. Further, this microarray was tested using worms exposed to Cu, phenmedipham, and different soil types.

Article Title: Gene transcription profiles, global DNA methylation and potential transgenerational epigenetic effects related to Zn exposure history in Daphnia magna.
Article Snippet: Subsequently, datawere evaluated with the MIAME platform based Bioarray Software Environment (BASE 1.2.17, http://www.islab.ua.ac. be/base/), using local background subtraction, variance stabilization normalization (Huber et al., 2002) and Limma (linear models for microarray data) (Smyth, 2004; Smyth et al., 2005).

Article Title: Identification of fibroblast growth factor-8b target genes associated with early and late cell cycle events in breast cancer cells.
Article Snippet: Primary hybridization data was collected using GenePix Pro 4.0 software (Axon Instruments, Foster City, CA) and raw data files were uploaded into BioArray Software Environment (BASE) version 1.2.17 (Saal et al., 2002).

Article Title: High expression of cholesterol biosynthesis genes is associated with resistance to statin treatment and inferior survival in breast cancer
Article Snippet: Microarray data were pre-processed and quantile-normalized using the GenomeStudio Software V2011.1 and BioArray Software Environment (BASE) V3.4.1 respectively.

Hybridization:

Article Title: Nickel response in function of temperature differences: effects at different levels of biological organization in Daphnia magna.
Article Snippet: a Laboratory for Ecophysiology, Biochemistry and Toxicology, Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium b Plantijn Hogeschool, Kronenburgstraat 47, B-2000 Antwerp, Belgium c CESAM & Department of Biology, University of Aveiro, Campus Universitario de Santioago, 3810-193 Aveiro, Portugal d Apeiron-Team NV, Pluyseghemstraat 69, B-2550 Antwerp, Belgium

Article Title: Histone H3 lysine 4 trimethylation marks meiotic recombination initiation sites
Article Snippet: For transcriptome analyses, these ratios, representing the transcript abundance relative to the reference mix, were normalized using the Lowess algorithm implemented in BASE (BioArray Software Environment) 1.0.7 program ( Saal et al , 2002 ; Yang et al , 2002 ).

Article Title: Nickel and binary metal mixture responses in Daphnia magna: molecular fingerprints and (sub)organismal effects.
Article Snippet: The recent development of a custom cDNA microarray platform for one of thé standard organisms in aquatic toxicology, Daphnia magna, opened up new ways to mechanistic insights of toxicological responses.. In this study, the mRNA expression of several genes and (sub)organismal responses (Cellular Energy Allocation, growth) were assayed after short-term waterborne metal exposure.. Microarray analysis of Ni-exposed daphnids revealed several affected functional gene classes, of which the largest ones were involved in different metabolic processes (mainly protein and chitin related processes), cuticula turnover, transport and signal transduction.

Article Title: Mechanistic profiling of the cAMP-dependent steroidogenic pathway in the H295R endocrine disrupter screening system: new endpoints for toxicity testing.
Article Snippet: The need for implementation of effects on steroid synthesis and hormone processing in screening batteries of endocrine disruptive compounds is widely acknowledged.. In this perspective, hormone profiling in the H295R adrenocortical cell system is extensively examined and recently OECD validated (TG 456) as a replacement of the minced testis assay.. To further elucidate the complete mechanisms and endocrine responsiveness of this cell system, microarray-based gene expression profiling of the cAMP response pathway, one of the major pathways in steroidogenesis regulation, was examined in H295R cells.

Article Title: Development of a microarray for Enchytraeus albidus (Oligochaeta): preliminary tool with diverse applications.
Article Snippet: Standard bioassays allow hazard assessment at the population level, but much remains to be learned about the molecular level response of organisms to stressors.. The main aim of this study was the development of a DNA microarray for Enchytraeus albidus, a common soil worm species.. Further, this microarray was tested using worms exposed to Cu, phenmedipham, and different soil types.

Article Title: Gene transcription profiles, global DNA methylation and potential transgenerational epigenetic effects related to Zn exposure history in Daphnia magna.
Article Snippet: Subsequently, datawere evaluated with the MIAME platform based Bioarray Software Environment (BASE 1.2.17, http://www.islab.ua.ac. be/base/), using local background subtraction, variance stabilization normalization (Huber et al., 2002) and Limma (linear models for microarray data) (Smyth, 2004; Smyth et al., 2005).

Article Title: Identification of fibroblast growth factor-8b target genes associated with early and late cell cycle events in breast cancer cells.
Article Snippet: Primary hybridization data was collected using GenePix Pro 4.0 software (Axon Instruments, Foster City, CA) and raw data files were uploaded into BioArray Software Environment (BASE) version 1.2.17 (Saal et al., 2002).

Article Title: High expression of cholesterol biosynthesis genes is associated with resistance to statin treatment and inferior survival in breast cancer
Article Snippet: Microarray data were pre-processed and quantile-normalized using the GenomeStudio Software V2011.1 and BioArray Software Environment (BASE) V3.4.1 respectively.



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Differing reproducibility of  microarray  FC values. (Correlations between FC values (QUI/PRO) are shown for each pair of microarrays. The values in the upper diagonal contain the Pearson correlations, while those in the lower diagonal contain the Spearman correlations. Values not in parentheses represent correlations between untransformed FC values, while those in parentheses represent correlations between log-transformed FC values. As log transformation does not change the rank order, only one number is shown for the Spearman correlation for each pair. Correlations varied substantially depending on the pair of microarrays and the correlation metric used, ranging from −0.55 to 0.74.)

Journal: Royal Society Open Science

Article Title: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

doi: 10.1098/rsos.150402

Figure Lengend Snippet: Differing reproducibility of microarray FC values. (Correlations between FC values (QUI/PRO) are shown for each pair of microarrays. The values in the upper diagonal contain the Pearson correlations, while those in the lower diagonal contain the Spearman correlations. Values not in parentheses represent correlations between untransformed FC values, while those in parentheses represent correlations between log-transformed FC values. As log transformation does not change the rank order, only one number is shown for the Spearman correlation for each pair. Correlations varied substantially depending on the pair of microarrays and the correlation metric used, ranging from −0.55 to 0.74.)

Article Snippet: Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) [ ], with which cross-channel correction and LOWESS normalization were performed.

Techniques: Microarray, Transformation Assay

Differing reproducibility of microarray FC values. The log-transformed FC values from some pairs of microarrays were consistent with one another, while negative correlations were observed for other pairs. Panel ( a ) shows the relationship between the log-transformed FC values from microarray QP2 and those from microarray QP4, which exhibited a moderate to strong correlation ( r =0.74). By contrast, panel ( b ) shows the relationship between the log-transformed FC values from microarray QP1 and those from microarray QP4, which had a negative correlation ( r =−0.41).

Journal: Royal Society Open Science

Article Title: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

doi: 10.1098/rsos.150402

Figure Lengend Snippet: Differing reproducibility of microarray FC values. The log-transformed FC values from some pairs of microarrays were consistent with one another, while negative correlations were observed for other pairs. Panel ( a ) shows the relationship between the log-transformed FC values from microarray QP2 and those from microarray QP4, which exhibited a moderate to strong correlation ( r =0.74). By contrast, panel ( b ) shows the relationship between the log-transformed FC values from microarray QP1 and those from microarray QP4, which had a negative correlation ( r =−0.41).

Article Snippet: Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) [ ], with which cross-channel correction and LOWESS normalization were performed.

Techniques: Microarray, Transformation Assay

High reproducibility of RNA-seq read counts, and moderate reproducibility of RNA-seq FC values. (The correlations between read counts (PRO1 versus PRO2 and QUI1 versus QUI2) and FC values ( QUI 1/ PRO 1 versus QUI 2/ PRO 2) are shown. Except for the Pearson correlations between non-log-transformed values, correlations between read counts were similar in magnitude to the correlations observed between  microarray  intensity values (electronic supplementary material, table S1). Correlations between FC values were close to those observed in the most highly correlated pairs of microarrays.)

Journal: Royal Society Open Science

Article Title: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

doi: 10.1098/rsos.150402

Figure Lengend Snippet: High reproducibility of RNA-seq read counts, and moderate reproducibility of RNA-seq FC values. (The correlations between read counts (PRO1 versus PRO2 and QUI1 versus QUI2) and FC values ( QUI 1/ PRO 1 versus QUI 2/ PRO 2) are shown. Except for the Pearson correlations between non-log-transformed values, correlations between read counts were similar in magnitude to the correlations observed between microarray intensity values (electronic supplementary material, table S1). Correlations between FC values were close to those observed in the most highly correlated pairs of microarrays.)

Article Snippet: Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) [ ], with which cross-channel correction and LOWESS normalization were performed.

Techniques: Microarray

Low concordance between RNA-seq data and DNA  microarray  data. (For each cell state (PRO and QUI), reads from the two RNA-seq replicates were pooled to give a single read count for each probe. Concordance was determined using both correlation between reads counts (for the RNA-seq data) and intensity values (for the  microarray  data), and between FC values (QUI/PRO). Correlations between read counts and intensity values were low, ranging from 0.18 to 0.41, as were correlations between FC values, which ranged from 0.02 to 0.23. ‘All’ represents the geometric mean of the FC values of the four microarrays. The correlations between the RNA-seq data and the mean of the four microarrays was better than between the RNA-seq data and any of the individual microarrays.)

Journal: Royal Society Open Science

Article Title: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

doi: 10.1098/rsos.150402

Figure Lengend Snippet: Low concordance between RNA-seq data and DNA microarray data. (For each cell state (PRO and QUI), reads from the two RNA-seq replicates were pooled to give a single read count for each probe. Concordance was determined using both correlation between reads counts (for the RNA-seq data) and intensity values (for the microarray data), and between FC values (QUI/PRO). Correlations between read counts and intensity values were low, ranging from 0.18 to 0.41, as were correlations between FC values, which ranged from 0.02 to 0.23. ‘All’ represents the geometric mean of the FC values of the four microarrays. The correlations between the RNA-seq data and the mean of the four microarrays was better than between the RNA-seq data and any of the individual microarrays.)

Article Snippet: Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) [ ], with which cross-channel correction and LOWESS normalization were performed.

Techniques: Microarray

Moderate concordance between the log-transformed RNA-seq FC values and the log-transformed geometric mean of the microarray FC values. The scatterplot shows that there was a moderate linear relationship between these two variables ( r =0.42).

Journal: Royal Society Open Science

Article Title: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

doi: 10.1098/rsos.150402

Figure Lengend Snippet: Moderate concordance between the log-transformed RNA-seq FC values and the log-transformed geometric mean of the microarray FC values. The scatterplot shows that there was a moderate linear relationship between these two variables ( r =0.42).

Article Snippet: Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) [ ], with which cross-channel correction and LOWESS normalization were performed.

Techniques: Transformation Assay, RNA Sequencing, Microarray

Moderate overlap between the probes with the highest FC values in the RNA-seq data and those with the highest FC values in the DNA microarray data. ( k represents the size of a given list (the 10, 50, 100, 500 or 1000 probes with the highest FC values), while n represents the number of probes in common between a list from the RNA-seq data and the corresponding list from the DNA  microarray.  The p -value represents the proportion of 10 000 random trials that had an equal or greater level of overlap than that actually observed. Thus, if none of the random trials had a greater level of overlap, then the p -value is 0. More overlapping probes than would be expected by chance were observed for all microarrays for k =100, 500 and 1000, while some arrays had statistically significant p -values for k =10 and k =50. ‘All’ represents the geometric mean of the FC values of the four microarrays.)

Journal: Royal Society Open Science

Article Title: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

doi: 10.1098/rsos.150402

Figure Lengend Snippet: Moderate overlap between the probes with the highest FC values in the RNA-seq data and those with the highest FC values in the DNA microarray data. ( k represents the size of a given list (the 10, 50, 100, 500 or 1000 probes with the highest FC values), while n represents the number of probes in common between a list from the RNA-seq data and the corresponding list from the DNA microarray. The p -value represents the proportion of 10 000 random trials that had an equal or greater level of overlap than that actually observed. Thus, if none of the random trials had a greater level of overlap, then the p -value is 0. More overlapping probes than would be expected by chance were observed for all microarrays for k =100, 500 and 1000, while some arrays had statistically significant p -values for k =10 and k =50. ‘All’ represents the geometric mean of the FC values of the four microarrays.)

Article Snippet: Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) [ ], with which cross-channel correction and LOWESS normalization were performed.

Techniques: Microarray

RNA-seq FC values correlate better with qRT-PCR FC values than do  microarray  FC values, although not to a statistically significant degree. Correlation coefficients are shown between the qRT-PCR FC values for 76 genes, and the FC values for corresponding probes in each individual  microarray  or in the combined RNA-seq replicates. ‘All’ represents the geometric mean of the FC values of the four microarrays. For all three correlation measures, the RNA-seq correlation was not significantly different ( p -value >0.05) from the correlation of any of the microarrays (Fisher's z -transformation).

Journal: Royal Society Open Science

Article Title: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

doi: 10.1098/rsos.150402

Figure Lengend Snippet: RNA-seq FC values correlate better with qRT-PCR FC values than do microarray FC values, although not to a statistically significant degree. Correlation coefficients are shown between the qRT-PCR FC values for 76 genes, and the FC values for corresponding probes in each individual microarray or in the combined RNA-seq replicates. ‘All’ represents the geometric mean of the FC values of the four microarrays. For all three correlation measures, the RNA-seq correlation was not significantly different ( p -value >0.05) from the correlation of any of the microarrays (Fisher's z -transformation).

Article Snippet: Analysis of raw datasets was performed using the online microarray database software BioArray Software Environment (BASE) [ ], with which cross-channel correction and LOWESS normalization were performed.

Techniques: Microarray, Transformation Assay