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
Mitochondria play key roles in many cell functions including energy production, fatty acid metabolism, pyrimidine biosynthesis, calcium homeostasis, and aging. They also regulate crucial signaling cascades such as apoptosis and oxidative stress. The proteome is often used to investigate the functional correlations on protein levels. Based upon the human, genome there is estimated 2000 to 2500 associated mitochondrial proteins, however, just over 600-800 have been identified at the protein level. For this reason, mitochondria contain a great number of proteins that have yet to be identified and characterized. The identification of these proteins can help in discovery of biological process. This protocol focuses on step-by-step procedure of mitochondrial proteome extraction from peripheral blood mononuclear cell [PBMC] mitochondria. The isolation and preparation procedures described here require 6 hours approximately
RESUMEN
Information avalanche [overload or expansion] in various scientific fields is a novel issue turned out by a number of factors considered necessary to facilitate their record and registration. Though, the biological science and its diverse fields like proteomics are not immune of this event and even may be as the event's herald. On the other hand, time as the most valued anxiety of human has encountered a huge mass of information. Therefore, in order to maintain access and ease the understanding of information in several fields some emprises have been prepared. Bioinformatics is an upshot of this anxiety and emprise. Interestingly, proteomics through studying proteins collection in alive things has covered a great portion of bioinformatics. Consequently, a noteworthy outlook on proteomics related databases [DBs] and websites not only can help investigators to face the upcoming archive of databases but also estimate the volume of the needed facilitates. Furthermore, enrichment of the DBs or related websites must be the priority of researchers. Herein, by covering the major proteomics related databases and websites, we have presented a comprehensive classification to simplify and clarify their understanding and applications