The progression of gliomas has been extensively studied at the genomic level using cDNA microarrays. However, systematic examinations at the protein translational and post-translational levels are far more limited. We constructed a glioma protein lysate array from 82 different primary glioma tissues, and surveyed the expression and phosphorylation of 46 different proteins involved in signaling pathways of cell proliferation, cell survival, apoptosis, angiogenesis, and cell invasion. An analysis algorithm was employed to robustly estimate the protein expressions in these samples. When ranked by their discriminating power to separate 37 glioblastomas (high-grade gliomas) from 45 lower-grade gliomas, the following 12 proteins were identified as the most powerful discriminators: IBalpha, EGFRpTyr845, AKTpThr308, phosphatidylinositol 3-kinase (PI3K), BadpSer136, insulin-like growth factor binding protein (IGFBP) 2, IGFBP5, matrix metalloproteinase 9 (MMP9), vascular endothelial growth factor (VEGF), phosphorylated retinoblastoma protein (pRB), Bcl-2, and c-Abl. Clustering analysis showed a close link between PI3K and AKTpThr308, IGFBP5 and IGFBP2, and IBalpha and EGFRpTyr845. Another cluster includes MMP9, Bcl-2, VEGF, and pRB. These clustering patterns may suggest functional relationships, which warrant further investigation. The marked association of phosphorylation of AKT at Thr308, but not Ser473, with glioblastoma suggests a specific event of PI3K pathway activation in glioma progression.
Genomic sequencing techniques introduce experimental errors into reads which can mislead sequence assembly efforts and complicate the diagnostic process. Here we present a method for detecting and removing sequencing errors from reads generated in genomic shotgun sequencing projects prior to sequence assembly. For each input read, the set of all length k substrings (k-mers) it contains are calculated. The read is evaluated based on the frequency with which each k-mer occurs in the complete data set (k-count). For each read, k-mers are clustered using the variable-bandwidth mean-shift algorithm. Based on the k-count of the cluster center, clusters are classified as error regions or non-error regions. For the 23 real and simulated data sets tested (454 and Solexa), our algorithm detected error regions that cover 99% of all errors. A heuristic algorithm is then applied to detect the location of errors in each putative error region. A read is corrected by removing the errors, thereby creating two or more smaller, error-free read fragments. After performing error removal, the error-rate for all data sets tested decreased (∼35-fold reduction, on average). EDAR has comparable accuracy to methods that correct rather than remove errors and when the error rate is greater than 3% for simulated data sets, it performs better. The performance of the Velvet assembler is generally better with error-removed data. However, for short reads, splitting at the location of errors can be problematic. Following error detection with error correction, rather than removal, may improve the assembly results.
This paper addresses the problem of estimating the expressions or concentrations of proteins from measurements obtained from protein arrays and illustrates the methodology on lysate microarray data. With several families of parametric models we design a number of algorithms for the estimation of a highly nonlinear calibration curve as well as the concentrations themselves. The model families include polynomial and sigmoidal nonlinearities for the calibration curve and homoscedastic or heteroscedastic models for the noise. The accuracy of the estimation methods is tested on simulated data and applied to real lysate array data. The results are generally very good, provided that strongly nonlinear models are used.
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