Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.
The concept of sequencing by hybridization (SBH) makes use of an array of all possible n-nucleotide oligomers (n-mers) to identify n-mers present in an unknown DNA sequence. Computational approaches can then be used to assemble the complete sequence. As a validation of this concept, the sequences of three DNA fragments, 343 base pairs in length, were determined with octamer oligonucleotides. Possible applications of SBH include physical mapping (ordering) of overlapping DNA clones, sequence checking, DNA fingerprinting comparisons of normal and disease-causing genes, and the identification of DNA fragments with particular sequence motifs in complementary DNA and genomic libraries. The SBH techniques may accelerate the mapping and sequencing phases of the human genome project.
Medical DNA diagnostics will increasingly rely on an accurate and inexpensive identification of mutations that affect the function of a gene. To validate diagnostic sequencing by hybridization (SBH), a number of p53 samples were analyzed with the complete set of 8192 noncomplementary 7-mer oligonucleotides. In four repeated, blind experiments we accurately sequenced 1.1 kb per each of 12 homozygote and heterozygote samples possessing base substitutions, insertions, and deletions. This SBH variant offers a high throughput platform to inexpensively sequence individual gene or pathogen genome samples within the clinical laboratory setting.
Although there are many new applications for hybridizing short, synthetic oligonucleotide probes to DNA, such applications have not included determining unknown sequences of DNA. The lack of clear discrimination in hybridization of oligo probes shorter than 11 nucleotides and the lack of a theoretical understanding of factors influencing hybridization of short oligos have hampered the development of their use. We have found conditions for reliable hybridization of oligonucleotides as short as seven nucleotides to cloned DNA or to oligonucleotides attached to filters. Low-temperature hybridization and washing conditions, in contrast to the high stringency conditions currently used in hybridization experiments, have the potential for allowing the simple use of all oligos of six nucleotides or longer in meaningful hybridizations. We also present the hybridization discrimination theory that provides the conceptual framework for understanding these results.
Here we present the application of deep neural network (DNN) ensembles trained on transcriptomic data to identify the novel markers associated with the mammalian embryonic-fetal transition (EFT). Molecular markers of this process could provide important insights into regulatory mechanisms of normal development, epimorphic tissue regeneration and cancer. Subsequent analysis of the most significant genes behind the DNNs classifier on an independent dataset of adult-derived and human embryonic stem cell (hESC)-derived progenitor cell lines led to the identification of COX7A1 gene as a potential EFT marker. COX7A1, encoding a cytochrome C oxidase subunit, was up-regulated in post-EFT murine and human cells including adult stem cells, but was not expressed in pre-EFT pluripotent embryonic stem cells or their in vitro-derived progeny. COX7A1 expression level was observed to be undetectable or low in multiple sarcoma and carcinoma cell lines as compared to normal controls. The knockout of the gene in mice led to a marked glycolytic shift reminiscent of the Warburg effect that occurs in cancer cells. The DNN approach facilitated the elucidation of a potentially new biomarker of cancer and pre-EFT cells, the embryo-onco phenotype, which may potentially be used as a target for controlling the embryonic-fetal transition.
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