Applied Biosystems

About: Applied Biosystems is a based out in . It is known for research contribution in the topics: Mass spectrometry & Nucleic acid. The organization has 1521 authors who have published 1579 publications receiving 285423 citations.

Evaluation of candidate control genes for diagnosis and residual disease detection in leukemic patients using 'real-time' quantitative reverse-transcriptase polymerase chain reaction (RQ-PCR) - a Europe against cancer program

Abstract: Real-time quantitative RT-PCR (RQ-PCR) is a sensitive tool to monitor minimal residual disease (MRD) in leukemic patients through the amplification of a fusion gene (FG) transcript. In order to correct variations in RNA quality and quantity and to calculate the sensitivity of each measurement, a control gene (CG) transcript should be amplified in parallel to the FG transcript. To identify suitable CGs, a study group within the Europe Against Cancer (EAC) program initially focused on 14 potential CGs using a standardized RQ-PCR protocol. Based on the absence of pseudogenes and the level and stability of the CG expression, three genes were finally selected: Abelson (ABL), beta-2-microglobulin (B2M), and beta-glucuronidase (GUS). A multicenter prospective study on normal (n=126) and diagnostic leukemic (n=184) samples processed the same day has established reference values for the CG expression. A multicenter retrospective study on over 250 acute and chronic leukemia samples obtained at diagnosis and with an identified FG transcript confirmed that the three CGs had a stable expression in the different types of samples. However, only ABL gene transcript expression did not differ significantly between normal and leukemic samples at diagnosis. We therefore propose to use the ABL gene as CG for RQ-PCR-based diagnosis and MRD detection in leukemic patients. Overall, these data are not only eligible for quantification of fusion gene transcripts, but also for the quantification of aberrantly expressed genes.

Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes.

Abstract: background Several gene-expression signatures can be used to predict the prognosis in diffuse large-B-cell lymphoma, but the lack of practical tests for a genome-scale analysis has restricted the use of this method. methods We studied 36 genes whose expression had been reported to predict survival in diffuse large-B-cell lymphoma. We measured the expression of each of these genes in independent samples of lymphoma from 66 patients by quantitative real-time polymerasechain-reaction analyses and related the results to overall survival. results In a univariate analysis, genes were ranked on the basis of their ability to predict survival. The genes that were the strongest predictors were LMO2, BCL6, FN1, CCND2, SCYA3, and BCL2. We developed a multivariate model that was based on the expression of these six genes, and we validated the model in two independent microarray data sets. The model was independent of the International Prognostic Index and added to its predictive power. conclusions Measurement of the expression of six genes is sufficient to predict overall survival in diffuse large-B-cell lymphoma.

Identification of miRNA Changes in Alzheimer's Disease Brain and CSF Yields Putative Biomarkers and Insights into Disease Pathways

Abstract: MicroRNAs have essential functional roles in brain development and neuronal specification but their roles in neurode- generative diseases such as Alzheimer's disease (AD) is unknown. Using a sensitive qRT-PCR platform we identified regional and stage-specific deregulation of miRNA expression in AD patient brains. We used experimental validation in addition to literature to reveal how the deregulated brain microRNAs are biomarkers for known and novel pathways in AD pathogenesis related to amyloid processing, neurogenesis, insulin resistance, and innate immunity. We additionally recovered miRNAs from cerebrospinal fluid and discovered AD-specific miRNA changes consistent with their role as potential biomarkers of disease.

An evaluation of the utility of SSR loci as molecular markers in maize (Zea mays L.): comparisons with data from RFLPS and pedigree

Abstract: The utility of 131 simple sequence repeat (SSR) loci to characterize and identify maize inbred lines, validate pedigree, and show associations among inbred lines was evaluated using a set of 58 inbred lines and four hybrids. Thirteen sets of inbred parent-progeny triplet pedigrees together with four hybrids and their parental lines were used to quantify incidences of scoring that departed from expectations based upon simple Mendelian inheritance. Results were compared to those obtained using 80 restriction fragment length polymorphism (RFLP) probes. Over all inbred triplets, 2.2% of SSRs and 3.6% of RFLP loci resulted in profiles that were scored as having segregated in a non-Mendelian fashion. Polymorphic index content (PIC, a measure of discrimination ability) values ranged from 0.06 to 0.91 for SSRs and from 0.10 to 0.84 for RFLPs. Mean values for PIC for SSRs and RFLPs were similar, approximately 0.62. However, PIC values for nine SSRs exceeded the maximum PIC for RFLPs. Di-repeats gave the highest mean PIC scores for SSRs but this class of repeats can result in “stutter” bands that complicate accurate genotyping. Associations among inbreds were similar for SSR and RFLP data, closely approximating expectations from known pedigrees. SSR technology presents the potential advantages of reliability, reproducibility, discrimination, standardization and cost effectiveness over RFLPs. SSR profiles can be readily interpreted in terms of alleles at mapped loci across a broad range of maize germ plasm. Consequently, SSRs represent the optimum approach for the identification and pedigree validation of maize genotypes compared to other currently available methods.

The PANTHER database of protein families, subfamilies, functions and pathways.

Abstract: PANTHER is a large collection of protein families that have been subdivided into functionally related subfamilies, using human expertise. These subfamilies model the divergence of specific functions within protein families, allowing more accurate association with function (ontology terms and pathways), as well as inference of amino acids important for functional specificity. Hidden Markov models (HMMs) are built for each family and subfamily for classifying additional protein sequences. The latest version, 5.0, contains 6683 protein families, divided into 31,705 subfamilies, covering approximately 90% of mammalian protein-coding genes. PANTHER 5.0 includes a number of significant improvements over previous versions, most notably (i) representation of pathways (primarily signaling pathways) and association with subfamilies and individual protein sequences; (ii) an improved methodology for defining the PANTHER families and subfamilies, and for building the HMMs; (iii) resources for scoring sequences against PANTHER HMMs both over the web and locally; and (iv) a number of new web resources to facilitate analysis of large gene lists, including data generated from high-throughput expression experiments. Efforts are underway to add PANTHER to the InterPro suite of databases, and to make PANTHER consistent with the PIRSF database. PANTHER is now publicly available without restriction at http://panther.appliedbiosystems.com.