RESUMEN
Objective: Substantial effort has been put into designing DNA-based biosensors, which are commonly used to detect presence of known sequences including the quantification of gene expression. Porous silicon [PSi], as a nanostructured base, has been commonly used in the fabrication of optimally transducing biosensors. Given that the function of any PSi-based biosensor is highly dependent on its nanomorphology, we systematically optimized a PSi biosensor based on reflectometric interference spectroscopy [RIS] detecting the high penetrance breast cancer susceptibility gene, BRCA1
Materials and Methods: In this experimental study, PSi pore sizes on the PSi surface were controlled for optimum filling with DNA oligonucleotides and surface roughness was optimized for obtaining higher resolution RIS patterns. In addition, the influence of two different organic electrolyte mixtures on the formation and morphology of the pores, based on various current densities and etching times on doped p-type silicon, were examined. Moreover, we introduce two cleaning processes which can efficiently remove the undesirable outer parasitic layer created during PSi formation. Results of all the optimization steps were observed by field emission scanning electron microscopy [FE-SEM]
Results: DNA sensing reached its optimum when PSi was formed in a two-step process in the ethanol electrolyte accompanied by removal of the parasitic layer in NaOH solution. These optimal conditions, which result in pore sizes of approximately 20 nm as well as a low surface roughness, provide a considerable RIS shift upon complementary sequence hybridization, suggesting efficient detectability
Conclusion: We demonstrate that the optimal conditions identified here makes PSi an attractive solid-phase DNA-based biosensing method and may be used to not only detect full complementary DNA sequences, but it may also be used for detecting point mutations such as single nucleotide substitutions and indels
RESUMEN
Currently, numerous papers are published reporting analysis of biological data at different omics levels by making statistical inferences. Of note, many studies, as those published in this Journal, report association of gene[s] at the genomic and transcriptomic levels by undertaking appropriate statistical tests. For instance, genotype, allele or haplotype frequencies at the genomic level or normalized expression levels at the transcriptomic level are compared between the case and control groups using the Chi-square/Fisher's exact test or independent [i.e. two-sampled] t-test respectively, with this culminating into a single numeric, namely the P value [or the degree of the false positive rate], which is used to make or break the outcome of the association test. This approach has flaws but nevertheless remains a standard and convenient approach in association studies. However, what becomes a critical issue is that the same cut-off is used when multiple' tests are undertaken on the same case-control [or any pairwise] comparison. Here, in brevity, we present what the P value represents, and why and when it should be adjusted. We also show, with worked examples, how to adjust P values for multiple testing in the R environment for statistical computing [http://www.R-project.org]
RESUMEN
Population-based genetic association studies have proven to be a powerful tool in identifying genes implicated in many complex human diseases that have a huge impact on public health. An essential quality control step in such studies is to undertake Hardy-Weinberg equilibrium [HWE] calculations. Deviations from HWE in the control group may reflect important problems including selection bias, population stratification and genotyping errors. If HWE is violated, the inferences of these studies may thus be biased. We therefore aimed to examine the extent to which HWE calculations are reported in genetic association studies published in Cell Journal[Yakhteh] [Cell J]. Using keywords pertaining to genetic association studies, eleven relevant articles were identified of which ten provided full genotypic data. The genotype distribution of 16 single nucleotide polymorphisms [SNPs] was re-analyzed for HWE by using three different methods where appropriate. HWE was not reported in 60% of all articles investigated. Among those reporting, only one article provided calculations correctly and in detail. Therefore, 90% of articles analyzed failed to provide sufficient HWE data. Interestingly, three articles had significant HWE deviation in their control groups of which one highly deviated from HWE expectations [P= 9.8×10[-12]]. We thus show that HWE calculations are under-reported in genetic association studies published in this journal. Furthermore, the conclusions of the three studies showing significant HWE in their control groups should be treated cautiously as they may be potentially misleading. We therefore recommend that reporting of detailed HWE calculations should become mandatory for such studies in the future