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analysis software bioarray software environment database  (Bioarray Inc)

 
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    Bioarray Inc analysis software bioarray software environment database
    Analysis Software Bioarray Software Environment Database, 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/bioarray+software/us11261492-821-8-13
    Average 90 stars, based on 1 article reviews
    analysis software bioarray software environment database - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Array-based genotype-phenotype correlation in a case of supernumerary ring chromosome 12.
    Article Snippet: Supernumerary ring chromosomes (SRC) account for approximately 10% of prenatal marker chromosomes and 60% of these SRCs are associated with an abnormal phenotype of the patient carrying them.. SRCs have, with few exceptions, not been characterized at the molecular genetic level.. Here, we present the first case of a SRC 12 thoroughly investigated with tiling resolution array-based comparative genomic hybridization (array CGH); multicolor, centromere, subtelomeric and whole chromosome painting fluorescence in situ hybridization.

    Article Title: Array based characterization of a terminal deletion involving chromosome subband 15q26.2: an emerging syndrome associated with growth retardation, cardiac defects and developmental delay
    Article Snippet: All normalizations and analyses were performed in the Bioarray Software Environment database (BASE) [ ].

    Article Title: dup(19)(q12q13.2): array-based genotype-phenotype correlation of a new possibly obesity-related syndrome.
    Article Snippet: All normalizations and analyses were performed in the Bioarray Software Environment Database (17).

    Article Title: Toxicological and gene expression analysis of the impact of aflatoxin B1 on hepatic function of male broiler chicks.
    Article Snippet: The resulting files and images were linked together and stored in the local BioArray Software Environment database (Saal et al., 2002).

    Article Title: Deletion of the SCN gene cluster on 2q24.4 is associated with severe epilepsy: an array-based genotype-phenotype correlation and a comprehensive review of previously published cases.
    Article Snippet: Epilepsy Research (2008) 81, 69—79 journa l homepage: www.e lsev ier .com/ locate /ep i lepsyres Deletion of the SCN gene cluster on 2q24.4 is associated with severe epilepsy: An array-based genotype—phenotype correlation and a comprehensive review of previously published cases Josef Davidssona,∗, Anna Collina, Mia Engman Olssonb, Johan Lundgrenc, Maria Sollera a Department of Clinical Genetics, Lund University Hospital, SE-221 85 Lund, Sweden b Department of Pediatrics, Karlskrona Hospital, Karlskrona, Sweden c Department of Pediatrics, Lund University Hospital, Lund, Sweden Received 17 December 2007; received in revised form 13 March 2008; accepted 22 April 2008 Available online 9 June 2008 KEYWORDS Severe epilepsy; SCN1A; del(2)(q24); Array CGH Summary Purpose: To characterize a deletion of chromosome 2q at the molecular level in a patient suffering from severe epilepsy resembling severe myoclonic epilepsy of infancy/Dravet’s syndrome (SMEI/DS) and to correlate other cases harboring deletions in the same region to morphological and clinical data.. Methods: Array-based comparative genomic hybridization (array CGH) was performed on DNA from the patient.. Forty-three previously published cases reporting deletions within region 2q21q31 were collected and analyzed regarding their cytogenetic and clinical data.

    Article Title: Sublethal effects of waterborne uranium exposures on the zebrafish brain: transcriptional responses and alterations of the olfactory bulb ultrastructure.
    Article Snippet: A D É L A Ï D E L E R E B O U R S , † J E A N P A U L B O U R D I N E A U D , ‡ K A R L I J N V A N D E R V E N , § T I N E V A N D E N B R O U C K , § P A T R I C E G O N Z A L E Z , ‡ V I R G I N I E C A M I L L E R I , † M A G A L I F L O R I A N I , † J A C Q U E L I N E G A R N I E R L A P L A C E , † A N D C H R I S T E L L E A D A M G U I L L E R M I N * , † Laboratoire de Radioécologie et d’Ecotoxicologie, Institut de Radioprotection et de Sûreté Nucléaire, Bât 186, BP 3, 13115 Saint-Paul-Lez-Durance Cedex, France Equipe de Géochimie et Ecotoxicologie des Métaux dans les systèmes Aquatiques (GEMA), UMR 5805 CNRSsUniversité Bordeaux 1, Place du Dr Peyneau, 33120 Arcachon, France Department of Biology, Laboratory of Ecophysiology, Biochemistry and Toxicology, University of Antwerp, Groeneborgerlaan 171, 2020 Antwerp, Belgium

    Article Title: Identification of molecules derived from human fibroblast feeder cells that support the proliferation of human embryonic stem cells
    Article Snippet: All of the normalizations, filtering, merging of technical replicates and analyses were performed in the BioArray Software Environment database [4].

    Filtration:

    Article Title: Tiling resolution array CGH of dic(7;9)(p11 approximately 13;p11 approximately 13) in B-cell precursor acute lymphoblastic leukemia reveals clustered breakpoints at 7p11.2 approximately 12.1 and 9p13.1.
    Article Snippet: a Department of Clinical Genetics and b Department of Oncology, Lund University Hospital, Lund c Lund Strategic Research Center for Stem Cell Biology and Cell Therapy, Lund University, Lund (Sweden) d Department of Medical Genetics and Molecular Medicine, Haukeland University Hospital, Helse-Bergen HF e Department of Medical Genetics, Rikshospitalet-Radiumhospitalet Medical Centre, Oslo f University of Oslo, Oslo (Norway)



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    Differing reproducibility of <t> microarray </t> 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.)
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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