Publications by authors named "Natalie E Castellana"

New technologies in genomics and proteomics have influenced the emergence of proteogenomics, a field at the confluence of genomics, transcriptomics, and proteomics. First generation proteogenomic toolkits employ peptide mass spectrometry to identify novel protein coding regions. We extend first generation proteogenomic tools to achieve greater accuracy and enable the analysis of large, complex genomes.

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Background: Proteins are known to be dynamic in nature, changing from one conformation to another while performing vital cellular tasks. It is important to understand these movements in order to better understand protein function. At the same time, experimental techniques provide us with only single snapshots of the whole ensemble of available conformations.

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A mouse hybridoma antibody directed against a member of the tumour necrosis factor (TNF)-superfamily, lymphotoxin-alpha (LT-α), was isolated from stored mouse ascites and purified to homogeneity. After more than a decade of storage the genetic material was not available for cloning; however, biochemical assays with the ascites showed this antibody against LT-α (LT-3F12) to be a preclinical candidate for the treatment of several inflammatory pathologies. We have successfully rescued the LT-3F12 antibody by performing MS analysis, primary amino acid sequence determination by template proteogenomics, and synthesis of the corresponding recombinant DNA by reverse engineering.

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Database search algorithms are the primary workhorses for the identification of tandem mass spectra. However, these methods are limited to the identification of spectra for which peptides are present in the database, preventing the identification of peptides from mutated or alternatively spliced sequences. A variety of methods has been developed to search a spectrum against a sequence allowing for variations.

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Gene annotation underpins genome science. Most often protein coding sequence is inferred from the genome based on transcript evidence and computational predictions. While generally correct, gene models suffer from errors in reading frame, exon border definition, and exon identification.

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