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Pavlenko, A.

Publications and source records attributed to Pavlenko, A..

2 recordsLinked to original sources

Direct empirical in-house assessment of peptide proteotypicity for targeted proteomics

In bottom-up proteomics peptide it was early shown that despite a certain protein is present in a sample, only a subset of its proteolytic peptide products will be detected with LC-MS analysis. Property of peptide being frequently detected given its source proteins identification was called proteotypicity. Much effort has been since applied to predict proteotypic peptides and summarize evidence on peptide detection. Nevertheless, when targeted proteomics method is being developed, prediction or inference from communal experience might be inaccurate and prior knowledge of true peptide proteotypicity in a selected setup for a selected population is necessary. In this work we test fully in-house approach for proteotypicity assessment including comprehensive peptide synthesis and detection verification. Proteotypicity and contribution of sample processing and biology-related factors are estimated in a model experiment for three plasma proteins, albumin, ceruloplasmin and C-reactive protein.

genomics↗

EZTraits: a Programmable Tool to Evaluate Multi-site Deterministic Traits

The vast majority of human traits, including many disease phenotypes, are affected by alleles at numerous genomic loci. With a continually increasing set of variants with published clinical disease or biomarker associations, an easy-to-use tool for non-programmers to rapidly screen VCF files for risk alleles is needed. We have developed EZTraits as a tool to quickly evaluate genotype data (e.g., from microarrays) against a set of rules defined by the user. These rules can be defined directly in the scripting language Lua, for genotype calls using variant ID (RS number) or chromosomal position. Alternatively, EZTraits can parse simple and intuitive text including concepts like any or all. Thus, EZTraits is designed to support rapid genetic analysis and hypothesis-testing by researchers, regardless of programming experience or technical background. The software is implemented in C++ and compiles and runs on Linux and MacOS. The source code is available under the MIT license from https://github.com/selfdecode/rd-eztraits Contact: manfred@selfdecode.com

bioinformatics↗