PreP+07: improvements of a user friendly tool to preprocess and analyse microarray data

<p>Abstract</p> <p>Background</p> <p>Nowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way.</p> <p>Results</p> <p>PreP+07 is a standalone Windows XP application that presents a friendly interface for spot filtration, inter- and intra-slide normalization, duplicate resolution, dye-swapping, error removal and statistical analyses. Additionally, it contains two unique implementation of the procedures – double scan and Supervised Lowess-, a complete set of graphical representations – MA plot, RG plot, QQ plot, PP plot, PN plot – and can deal with many data formats, such as tabulated text, GenePix GPR and ArrayPRO. PreP+07 performance has been compared with the equivalent functions in Bioconductor using a tomato chip with 13056 spots. The number of differentially expressed genes considering p-values coming from the PreP+07 and Bioconductor Limma packages were statistically identical when the data set was only normalized; however, a slight variability was appreciated when the data was both normalized and scaled.</p> <p>Conclusion</p> <p>PreP+07 implementation provides a high degree of freedom in selecting and organizing a small set of widely used data processing protocols, and can handle many data formats. Its reliability has been proven so that a laboratory researcher can afford a statistical pre-processing of his/her microarray results and obtain a list of differentially expressed genes using PreP+07 without any programming skills. All of this gives support to scientists that have been using previous PreP releases since its first version in 2003.</p

Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons

Background: Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. Since prokaryotes in general have less conserved genome sequences than eukaryotes, sequence divergences between the genes in the genomes used for an aCGH experiment obstruct determination of genome variations (e.g. deletions). Current normalization methods do not take into consideration sequence divergence between target and microarray features and therefore cannot distinguish a difference in signal due to systematic errors in the data or due to sequence divergence. Results: We present supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method. By using a predicted subset of array features with minimal sequence divergence between the analyzed strains for the normalization procedure we remove systematic errors from dual-dye aCGH data in two steps: (1) determination of a subset of conserved genes (i.e. likely conserved genes, LCG); and (2) using the LCG for subset Lowess normalization. Subset Lowess determines the correction factors for systematic errors in the subset of array features and normalizes all array features using these correction factors. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 DNA microarrays. Since both genomes are sequenced and gene deletions identified, the success rate of different aCGH normalization methods in detecting these deletions in the MG1363 genome were determined. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. Conclusion: S-Lowess is implemented in a user-friendly web-tool. We demonstrate that it outperforms existing normalization methods and maximizes detection of genomic variation (e.g. deletions) from microbial aCGH data.

Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons-1

1363 signals) and red (positive M values; IL1403 signals) channels. A: non-normalized data. B: grid-based Lowess normalization. C: S-Lowess normalization based on the LCG set obtained from the comparison of IL1403 amplicon sequences to the ORFs of three strains. D: S-Lowess normalization with a stringent LCG set (99% identity over 100 bp).<p><b>Copyright information:</b></p><p>Taken from "Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons"</p><p>http://www.biomedcentral.com/1471-2105/9/93</p><p>BMC Bioinformatics 2008;9():93-93.</p><p>Published online 11 Feb 2008</p><p>PMCID:PMC2275246.</p><p></p

Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons-4

Array dataset with the LCGs. In case that for phase 1 prediction of LCGs is selected, the user has to upload microarray feature sequences and select (multiple) genomes (in this study 3 genomes). The optimal parameters for selection of LCGs from a sequence comparison using BLAT of array features versus multiple reporter genomes are difficult to predict. Therefore, selection of a LCG set is facilitated by cycling through a maximum of 2 parameters. These parameters are (a combination of two): (i) alignment length cutoff, (ii) E-value cutoff, (iii) percentage nucleotide identity cutoff, (iv) maximum number of hits within the same genome (to account for paralogous genes or duplicated genome fragments), (v) minimum number of hits across genomes (to account for gene conservation in multiple genome sequences). Those array feature sequences meeting the criteria (here in at least 2 out of three genomes a significant BLAT hit; one hit over at least 100 bp with at least 80% nucleotide identity) are marked as LCG and added to the conserved array feature list. In phase 2, the LCGs are used to normalize an uploaded aCGH microarray dataset. The result of phase 2 is a normalized dataset and diagnostic plots.<p><b>Copyright information:</b></p><p>Taken from "Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons"</p><p>http://www.biomedcentral.com/1471-2105/9/93</p><p>BMC Bioinformatics 2008;9():93-93.</p><p>Published online 11 Feb 2008</p><p>PMCID:PMC2275246.</p><p></p

Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons-3

Ree strains. The Rvalues indicate the quality of the regression curve fit (where higher is better).<p><b>Copyright information:</b></p><p>Taken from "Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons"</p><p>http://www.biomedcentral.com/1471-2105/9/93</p><p>BMC Bioinformatics 2008;9():93-93.</p><p>Published online 11 Feb 2008</p><p>PMCID:PMC2275246.</p><p></p

Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons-0

Array dataset with the LCGs. In case that for phase 1 prediction of LCGs is selected, the user has to upload microarray feature sequences and select (multiple) genomes (in this study 3 genomes). The optimal parameters for selection of LCGs from a sequence comparison using BLAT of array features versus multiple reporter genomes are difficult to predict. Therefore, selection of a LCG set is facilitated by cycling through a maximum of 2 parameters. These parameters are (a combination of two): (i) alignment length cutoff, (ii) E-value cutoff, (iii) percentage nucleotide identity cutoff, (iv) maximum number of hits within the same genome (to account for paralogous genes or duplicated genome fragments), (v) minimum number of hits across genomes (to account for gene conservation in multiple genome sequences). Those array feature sequences meeting the criteria (here in at least 2 out of three genomes a significant BLAT hit; one hit over at least 100 bp with at least 80% nucleotide identity) are marked as LCG and added to the conserved array feature list. In phase 2, the LCGs are used to normalize an uploaded aCGH microarray dataset. The result of phase 2 is a normalized dataset and diagnostic plots.<p><b>Copyright information:</b></p><p>Taken from "Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons"</p><p>http://www.biomedcentral.com/1471-2105/9/93</p><p>BMC Bioinformatics 2008;9():93-93.</p><p>Published online 11 Feb 2008</p><p>PMCID:PMC2275246.</p><p></p

Hip/femur fractures associated with the use of benzodiazepines (anxiolytics, hypnotics and related drugs) : A methodological approach to assess consistencies across databases from the PROTECT-EU project

Background: Results from observational studies may be inconsistent because of variations in methodological and clinical factors that may be intrinsically related to the database (DB) where the study is performed. Objectives: The objectives of this paper were to evaluate the impact of applying a common study protocol to study benzodiazepines (BZDs) (anxiolytics, hypnotics, and related drugs) and the risk of hip/femur fracture (HFF) across three European primary care DBs and to investigate any resulting discrepancies. Methods: To measure the risk of HFF among adult users of BZDs during 2001-2009, three cohort and nested case control (NCC) studies were performed in Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) (Spain), Clinical Practice Research Datalink (CPRD) (UK), and Mondriaan (The Netherlands). Four different models (A-D) with increasing levels of adjustment were analyzed. The risk according to duration and type of BZD was also explored. Adjusted hazard ratios (cohort), odds ratios (NCC), and their 95% confidence intervals were estimated. Results: Adjusted hazard ratios (Model C) were 1.34 (1.23-1.47) in BIFAP, 1.66 (1.54-1.78) in CPRD, and 2.22 (1.55-3.29) in Mondriaan in cohort studies. Adjusted odds ratios (Model C) were 1.28 (1.16-1.42) in BIFAP, 1.60 (1.49-1.72) in CPRD, and 1.48 (0.89-2.48) in Mondriaan in NCC studies. A short-term effect was suggested in Mondriaan, but not in CPRD or BIFAP. All DBs showed an increased risk with the concomitant use of anxiolytic and hypnotic drugs. Conclusions: Applying similar study methods to different populations and DBs showed an increased risk of HFF in BZDs users but differed in the magnitude of the risk, which may be because of inherent differences between DBs

Hip/femur fractures associated with the use of benzodiazepines (anxiolytics, hypnotics and related drugs) : A methodological approach to assess consistencies across databases from the PROTECT-EU project

Background: Results from observational studies may be inconsistent because of variations in methodological and clinical factors that may be intrinsically related to the database (DB) where the study is performed. Objectives: The objectives of this paper were to evaluate the impact of applying a common study protocol to study benzodiazepines (BZDs) (anxiolytics, hypnotics, and related drugs) and the risk of hip/femur fracture (HFF) across three European primary care DBs and to investigate any resulting discrepancies. Methods: To measure the risk of HFF among adult users of BZDs during 2001-2009, three cohort and nested case control (NCC) studies were performed in Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) (Spain), Clinical Practice Research Datalink (CPRD) (UK), and Mondriaan (The Netherlands). Four different models (A-D) with increasing levels of adjustment were analyzed. The risk according to duration and type of BZD was also explored. Adjusted hazard ratios (cohort), odds ratios (NCC), and their 95% confidence intervals were estimated. Results: Adjusted hazard ratios (Model C) were 1.34 (1.23-1.47) in BIFAP, 1.66 (1.54-1.78) in CPRD, and 2.22 (1.55-3.29) in Mondriaan in cohort studies. Adjusted odds ratios (Model C) were 1.28 (1.16-1.42) in BIFAP, 1.60 (1.49-1.72) in CPRD, and 1.48 (0.89-2.48) in Mondriaan in NCC studies. A short-term effect was suggested in Mondriaan, but not in CPRD or BIFAP. All DBs showed an increased risk with the concomitant use of anxiolytic and hypnotic drugs. Conclusions: Applying similar study methods to different populations and DBs showed an increased risk of HFF in BZDs users but differed in the magnitude of the risk, which may be because of inherent differences between DBs

Understanding inconsistency in the results from observational pharmacoepidemiological studies : the case of antidepressant use and risk of hip/femur fractures

PURPOSE: Results from observational studies on the same exposure-outcome association may be inconsistent because of variations in methodological factors, clinical factors or health care systems. We evaluated the consistency of results assessing the association between antidepressant use and the risk of hip/femur fractures in three European primary care databases using two different study designs. METHODS: Cohort and nested case control studies were conducted in three European primary care databases (Spanish BIFAP, Dutch Mondriaan and UK THIN) to assess the association between use of antidepressants and hip/femur fracture. A common protocol and statistical analysis plan was applied to harmonize study design and conduct between data sources. RESULTS: Current use of antidepressants was consistently associated with a 1.5 to 2.5-fold increased risk of hip/femur fractures in all data sources with both designs, with estimates for SSRIs generally higher than those for TCAs. In general, risk estimates in Mondriaan, the smallest data source, were higher compared to the other data sources. This difference may be partially explained by an interaction between SSRI and age in Mondriaan. Adjustment for GP-recorded lifestyle factors and matching on general practice had negligible impact on adjusted relative risk estimates. CONCLUSION: We found a consistent increased risk of hip/femur fracture with current use of antidepressants across different databases and different designs. Applying similar pharmacoepidemiological study methods resulted in similar risks for TCA use and some variation for SSRI use. Some of these differences may express real (or natural) variance in the exposure-outcome co-occurrences. Copyright © 2016 John Wiley & Sons, Ltd