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San Bruno, CA, United States

Martins V.D.P.,University of Georgia | Okura M.,University of Illinois at Urbana - Champaign | Okura M.,Real Time Genomics | Maric D.,Northwestern University | And 6 more authors.
Journal of Biological Chemistry | Year: 2010

Phosphoinositide phospholipase C (PI-PLC) plays an essential role in cell signaling. A unique Trypanosoma cruzi PI-PLC (TcPI-PLC) is lipid-modified in its N terminus and localizes to the plasma membrane of amastigotes. Here, we show that TcPI-PLC is located onto the extracellular phase of the plasma membrane of amastigotes and that its N-terminal 20 amino acids are necessary and sufficient to target the fused GFP to the outer surface of the parasite. Mutagenesis of the predicted acylated residues confirmed that myristoylation of a glycine residue in the 2nd position and acyl modification of a cysteine in the 4th but not in the 8th or 15th position of the coding sequence are required for correct plasma membrane localization in T. cruzi epimastigotes or amastigotes. Interestingly, mutagenesis of the cysteine at the 8th position increased its flagellar localization. When expressed as fusion constructs with GFP, the N-terminal 6 and 10 amino acids fused to GFP are predominantly located in the cytosol and concentrated in a compartment that co-localizes with a Golgi complex marker. The N-terminal 20 amino acids of TcPI-PLC associate with lipid rafts when dually acylated. Taken together, these results indicate that N-terminal acyl modifications serve as a molecular addressing system for sending TcPI-PLC to the outer surface of the cell. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc. Source


Patent
Real Time Genomics | Date: 2013-08-20

Methods and systems for simultaneously evaluating biological sequences across multiple population members, and methods and systems for simultaneously calling normal and cancerous biological sequences from a mixed sample containing normal and cancerous material are disclosed. This may be achieved by evaluating the probability of one or more hypothesis being correct for a plurality of population members based on biological sequence information for the population. For related family members, Mendelian inheritance may be integrated into the method. For populations, information from members under evaluation may be used to refine priors to more accurately call population members. Copy number variation, de novo mutations, and phenotypic traits and their genetic explanations may also be accommodated in the methods. Specific systems for implementing the methods are also disclosed.


Trademark
Real Time Genomics | Date: 2012-07-10

Computer software, namely, executable computer software for analysis and management of genetic sequence data within variant detection, metagenomics, transcriptomics and molecular diagnostics applications in the fields of personalized medicine, healthcare, agbio, bio-fuels and environmental security.


A computer implemented method for characterizing one or more sequences by generating index values representing portions of the sequences and finding characterizing index values based on a comparison of the index values. The index values may be obtained by applying one or more mask over each sequence. The modified masks may have associated weightings and index values obtained using modified masks may be retained in the index only if the weightings are above a threshold value. Characterising index values may also be assessed for for their degree of uniqueness. Characterizing indexes may be used for predicting correlation between a sample sequence and one or more reference sequences. Biological monitoring systems utilising the characterizing index values are also disclosed. A biological indicator may be generatgenerated using one or more characterizing index values obtained by the above method and be used to produce an indicator that undergoes a property change in the presence of the one or more sequence.


Patent
Real Time Genomics | Date: 2013-06-24

Methods and systems for evaluating genomic sequences are described. The methods include approaches for evaluating the prevalence of genomes in a sample based on the prevalence of segments in the sample, and may additionally rely on the prevalence of segments in reference genomes and an estimated genome population distribution of the sample.

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