Applied Biosystems

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

The synthesis of oligonucleotides containing an aliphatic amino group at the 5′ terminus: synthesis of fluorescent DNA primers for use in DNA sequence analysis

Abstract: A rapid and versatile method has been developed for the synthesis of oligonucleotides which contain an aliphatic amino group at their 5' terminus. This amino group reacts specifically with a variety of electrophiles, thereby allowing other chemical species to be attached to the oligonucleotide. This chemistry has been utilized to synthesize several fluorescent derivatives of an oligonucleotide primer used in DNA sequence analysis by the dideoxy (enzymatic) method. The modified primers are highly fluorescent and retain their ability to specifically prime DNA synthesis. The use of these fluorescent primers in DNA sequence analysis will enable DNA sequence analysis to be automated.

Identification of a neuropeptide hormone that regulates sex pheromone production in female moths.

Abstract: A pheromone biosynthesis activating neuropeptide (PBAN) hormone that controls sex pheromone production in female moths was identified from the brain-subesophageal ganglion complexes of the adult corn earworm, Heliothis zea. PBAN has 33 amino acid residues and a molecular weight of 3900. Its amino acid sequence has no significant homology with any of the fully characterized peptide hormones. The synthetic peptide, at a dose of between 2 and 4 picomoles, induced production of a normal quantity of sex pheromone in ligated H. zea females. The peptide also induced pheromone production in six other species of moths, thus indicating that this or similar peptides may be responsible for the regulation of pheromone production in moths.

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Abstract: Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists.

Rat toxicogenomic study reveals analytical consistency across microarray platforms

Abstract: To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed

Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms

Abstract: Gene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms. Expression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan® Gene Expression assays. We have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.