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.

Comprehensive mutation analysis of 17 Y-chromosomal short tandem repeat polymorphisms included in the AmpFlSTR® Yfiler® PCR amplification kit

Abstract: The Y-chromosomal short tandem repeat (Y- STR) polymorphisms included in the AmpFlSTR® Yfiler® polymerase chain reaction amplification kit have become widely used for forensic and evolutionary applications where a reliable knowledge on mutation properties is necessary for correct data interpretation. Therefore, we investigated the 17 Yfiler Y-STRs in 1,730-1,764 DNA- confirmed father-son pairs per locus and found 84 sequence-confirmed mutations among the 29,792 meiotic transfers covered. Of the 84 mutations, 83 (98.8%) were single-repeat changes and one (1.2%) was a double-repeat change (ratio, 1:0.01), as well as 43 (51.2%) were repeat gains and 41 (48.8%) repeat losses (ratio, 1:0.95). Medians from Bayesian estimation of locus-specific mutation rates

Coalescent estimates of HIV-1 generation time in vivo

Abstract: The generation time of HIV Type 1 (HIV-1) in vivo has previously been estimated using a mathematical model of viral dynamics and was found to be on the order of one to two days per generation. Here, we describe a new method based on coalescence theory that allows the estimate of generation times to be derived by using nucleotide sequence data and a reconstructed genealogy of sequences obtained over time. The method is applied to sequences obtained from a long-term nonprogressing individual at five sampling occasions. The estimate of viral generation time using the coalescent method is 1.2 days per generation and is close to that obtained by mathematical modeling (1.8 days per generation), thus strengthening confidence in estimates of a short viral generation time. Apart from the estimation of relevant parameters relating to viral dynamics, coalescent modeling also allows us to simulate the evolutionary behavior of samples of sequences obtained over time.

Microfluidic size-exclusion devices, systems, and methods

Abstract: Microfluidic devices, assemblies, and systems are provided, as are methods of manipulating micro-sized samples of fluids. Microfluidic devices having a plurality of specialized processing features are also provided.

Accurate Prediction of the Functional Significance of Single Nucleotide Polymorphisms and Mutations in the ABCA1 Gene

Abstract: The human genome contains an estimated 100,000 to 300,000 DNA variants that alter an amino acid in an encoded protein. However, our ability to predict which of these variants are functionally significant is limited. We used a bioinformatics approach to define the functional significance of genetic variation in the ABCA1 gene, a cholesterol transporter crucial for the metabolism of high density lipoprotein cholesterol. To predict the functional consequence of each coding single nucleotide polymorphism and mutation in this gene, we calculated a substitution position-specific evolutionary conservation score for each variant, which considers site-specific variation among evolutionarily related proteins. To test the bioinformatics predictions experimentally, we evaluated the biochemical consequence of these sequence variants by examining the ability of cell lines stably transfected with the ABCA1 alleles to elicit cholesterol efflux. Our bioinformatics approach correctly predicted the functional impact of greater than 94% of the naturally occurring variants we assessed. The bioinformatics predictions were significantly correlated with the degree of functional impairment of ABCA1 mutations (r2 = 0.62, p = 0.0008). These results have allowed us to define the impact of genetic variation on ABCA1 function and to suggest that the in silico evolutionary approach we used may be a useful tool in general for predicting the effects of DNA variation on gene function. In addition, our data suggest that considering patterns of positive selection, along with patterns of negative selection such as evolutionary conservation, may improve our ability to predict the functional effects of amino acid variation.