Prianichnikov, N. | Search bioRxiv

bioRxiv2019

MaxQuant software for ion mobility enhanced shotgun proteomics

Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides, proteins and posttranslational modification sites in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

bioinformatics↗

bioRxiv2018

VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota

The resistance of bacterial pathogens to antibiotics is one of the most important issues of the modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microflora by means of horizontal gene transfer (HGT). Thus, at the moment it is important to develop methods of prediction and monitoring of antibiotics resistance in human population. We represent the agent-based VERA model, which allows to simulate the spread of pathogen with account of possible horizontal transfer of resistance determinants from commensal microbiota community. The model considers the opportunity of residents to stay in the town or in medical institution, have wrong self-treatment, reception of several antibiotics types, transfer and accumulation of resistance determinants from a microbiota to a pathogen. In this model we have also created assessment of optimum intensity of observation of infection spread among the population. Investigating model behavior we show a number of nonlinear dependencies, including the exponential nature of dependence of total of the diseased on average resistance of a pathogen. As the model infection we considered infection with Shigella spp, though it could be applied to a wide range of other pathogens.\n\nAvailability and implementationSource code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform.

bioinformatics↗