Quality Control Workflow Research Articles

QPortal: A platform for data-driven biomedical research

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis.We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software’s strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy).qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

Technical Note: A simple algorithm to convert EPID gray values into absorbed dose to water without prior knowledge.

Full integration of EPID-based dosimetry in a global quality control workflow is still complicated. All the actual solutions are based on a relation between image gray-level signal and total linac-delivered dose. In this study, we propose a simple algorithm relying pixel gray-level of EPID image with average linac delivered dose per acquisition frame. Calibration models are constructed for Varian and Elekta linacs including scattering conditions and EPID-arm backscatter-specific corrections. Only simple homogeneous fields are required to establish the EPID dose conversion model for each x-ray beam. Then, the model was evaluated by comparing calculated and converted dose distributions for homogeneous and modulated beams using gamma maps. To fit average dose per frame (Dfnorm ) vs pixel gray value (Ngnorm ) of each EPID image, a logarithmic curve Dfnorm=A+B∗lnNgnorm-C, has been chosen where A, B and C are constants depending on beam energy. Gamma comparison (2%, 2 mm, threshold 15%) between converted images and calculated dose distributions for linac control and pretreatment patient fields led to a gamma pass rate higher than 97% for all the analyzed fields. Without a prior irradiation settings knowledge except the incident energy beam, we use EPID as a reliable dose to water detector for both homogeneous and modulated beams.

Assessment of anti-inflammatory properties of extracts from Honeysuckle (Lonicera sp. L., Caprifoliaceae) by ATR-FTIR spectroscopy

Inflammation is a hallmark of some of today's most life-threatening diseases such as arteriosclerosis, cancer, diabetes and Alzheimer's disease. Herbal medicines (HMs) are re-emerging resources in the fight against these conditions and for many of them, anti-inflammatory activity has been demonstrated. However, several aspects of HMs such as their multi-component character, natural variability and pharmacodynamic interactions (e.g. synergism) hamper identification of their bioactive constituents and thus the development of appropriate quality control (QC) workflows. In this study, we investigated the potential use of Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy as a tool to rapidly and non-destructively assess different anti-inflammatory properties of ethanolic extracts from various species of the Genus Lonicera (Caprifoliaceae). Reference measurements for multivariate calibration comprised in vitro bioactivity of crude extracts towards four key players of inflammation: Nitric oxide (NO), interleukin 8 (IL-8), peroxisome proliferator-activated receptor β/δ (PPAR β/δ), and nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-κB). Multivariate analysis of variance (MANOVA) revealed a statistically significant, quantitative pattern-activity relationship between the extracts' ATR-FTIR spectra and their ability to modulate these targets in the corresponding cell models. Ensemble orthogonal partial least squares (OPLS) discriminant models were established for the identification of extracts exhibiting high and low activity with respect to their potential to suppress NO and IL-8 production. Predictions made on an independent test set revealed good generalizability of the models with overall sensitivity and specificity of 80% and 100%, respectively. Partial least squares (PLS) regression models were successfully established to predict the extracts' ability to suppress NO production and NF-κB activity with root mean squared errors of cross-validation (RMSECV) of 8.7% and 0.05-fold activity, respectively.

A Robust Method for Estimating B0 Inhomogeneity Field in the Liver by Mitigating Fat Signals and Phase-Wrapping.

We developed an optimized and robust method to estimate liver B0 field inhomogeneity for monitoring and correcting susceptibility-induced geometric distortion in magnetic resonance images for precision therapy. A triple-gradient-echo acquisition was optimized for the whole liver B0 field estimation within a single-exhale breath-hold scan on a 3 T scanner. To eliminate chemical-shift artifacts, fat signals were chosen in-phase between 2 echoes with an echo time difference (ΔTE) of 2.3 milliseconds. To avoid phase-wrapping, other 2 echoes provided a large field dynamic range (1/ΔTE) to cover the B0 field inhomogeneity. In addition, using high parallel imaging factor of 4 and a readout-bandwidth of 1955 Hz/pixel, an ∼18-second acquisition time for breath-held scans was achieved. A 2-step, 1-dimensional regularized method for the ΔB0 field map estimation was developed, tested and validated in phantom and patient studies. Our method was validated on a water phantom with fat components and air pockets; it yielded ΔB0-field maps that had no chemical-shift and phase-wrapping artifacts, and it had a <0.5 mm of geometric distortion near the air pockets. The ΔB0-field maps of the patients' abdominal regions were also free from phase-wrapping and chemical-shift artifacts. The maximum field inhomogeneity was found near the lung–liver interface, up to ∼300 Hz, resulting in ∼2 mm of distortions in anatomical images with a readout-bandwidth of 440 Hz/pixel. The field mapping method in the abdominal region is robust; it can be easily integrated in clinical workflow for patient-based quality control of magnetic resonance imaging geometric integrity.

High-throughput sequencing of multiple amplicons for barcoding and integrative taxonomy

Until now, the potential of NGS for the construction of barcode libraries or integrative taxonomy has been seldom realised. Here, we amplified (two-step PCR) and simultaneously sequenced (MiSeq) multiple markers from hundreds of fig wasp specimens. We also developed a workflow for quality control of the data. Illumina and Sanger sequences accumulated in the past years were compared. Interestingly, primers and PCR conditions used for the Sanger approach did not require optimisation to construct the MiSeq library. After quality controls, 87% of the species (76% of the specimens) had a valid MiSeq sequence for each marker. Importantly, major clusters did not always correspond to the targeted loci. Nine specimens exhibited two divergent sequences (up to 10%). In 95% of the species, MiSeq and Sanger sequences obtained from the same sampling were similar. For the remaining 5%, species were paraphyletic or the sequences clustered into divergent groups on the Sanger + MiSeq trees (>7%). These problematic cases may represent coding NUMTS or heteroplasms. Our results illustrate that Illumina approaches are not artefact-free and confirm that Sanger databases can contain non-target genes. This highlights the importance of quality controls, working with taxonomists and using multiple markers for DNA-taxonomy or species diversity assessment.

A comprehensive quality control workflow for paired tumor-normal NGS experiments.

Quality control (QC) is an important part of all NGS data analysis stages. Many available tools calculate QC metrics from different analysis steps of single sample experiments (raw reads, mapped reads and variant lists). Multi-sample experiments, as sequencing of tumor-normal pairs, require additional QC metrics to ensure validity of results. These multi-sample QC metrics still lack standardization. We therefore suggest a new workflow for QC of DNA sequencing of tumor-normal pairs. With this workflow well-known single-sample QC metrics and additional metrics specific for tumor-normal pairs can be calculated. The segmentation into different tools offers a high flexibility and allows reuse for other purposes. All tools produce qcML, a generic XML format for QC of -omics experiments. qcML uses quality metrics defined in an ontology, which was adapted for NGS. All QC tools are implemented in C ++ and run both under Linux and Windows. Plotting requires python 2.7 and matplotlib. The software is available under the 'GNU General Public License version 2' as part of the ngs-bits project: https://github.com/imgag/ngs-bits. christopher.schroeder@med.uni-tuebingen.de. Supplementary data are available at Bioinformatics online.

Tissue Requirements and DNA Quality Control for Clinical Targeted Next-Generation Sequencing of Formalin-Fixed, Paraffin-Embedded Samples: A Mini-Review of Practical Issues

Most molecular pathology laboratories perform mutational analyses to diagnose somatic cancer mutations and evaluate therapeutic options on formalin fixed paraffin embedded (FFPE) samples as daily practice using various methods. Recent studies show that targeted next-generation sequencing (NGS) is a promising diagnostic method with many benefits including simultaneous detection of multiple mutations in various genes in a single test. High quality DNA is essential for an efficient and successful NGS performance. However, low tumor tissue percentage and low DNA quality are the main limitations to use FFPE DNA for NGS assay. We reviewed and discussed the required tissue amount for the NGS assay, the effect of formalin fixation on DNA integrity, and the method for reduction of formalin-induced sequencing artifacts. We also review DNA extraction methods, DNA quality control methods and quality control workflow for nucleic acids and libraries. This review provides an overview for molecular pathology laboratories or researcher considering NGS to detect somatic cancer mutations using small FFPE samples.

Quality Control of Structural MRI Images Applied Using FreeSurfer-A Hands-On Workflow to Rate Motion Artifacts.

In structural magnetic resonance imaging motion artifacts are common, especially when not scanning healthy young adults. It has been shown that motion affects the analysis with automated image-processing techniques (e.g., FreeSurfer). This can bias results. Several developmental and adult studies have found reduced volume and thickness of gray matter due to motion artifacts. Thus, quality control is necessary in order to ensure an acceptable level of quality and to define exclusion criteria of images (i.e., determine participants with most severe artifacts). However, information about the quality control workflow and image exclusion procedure is largely lacking in the current literature and the existing rating systems differ. Here, we propose a stringent workflow of quality control steps during and after acquisition of T1-weighted images, which enables researchers dealing with populations that are typically affected by motion artifacts to enhance data quality and maximize sample sizes. As an underlying aim we established a thorough quality control rating system for T1-weighted images and applied it to the analysis of developmental clinical data using the automated processing pipeline FreeSurfer. This hands-on workflow and quality control rating system will aid researchers in minimizing motion artifacts in the final data set, and therefore enhance the quality of structural magnetic resonance imaging studies.

An automated workflow for patient-specific quality control of contour propagation

Contour propagation is an essential component of adaptive radiotherapy, but current contour propagation algorithms are not yet sufficiently accurate to be used without manual supervision. Manual review of propagated contours is time-consuming, making routine implementation of real-time adaptive radiotherapy unrealistic. Automated methods of monitoring the performance of contour propagation algorithms are therefore required. We have developed an automated workflow for patient-specific quality control of contour propagation and validated it on a cohort of head and neck patients, on which parotids were outlined by two observers. Two types of error were simulated—mislabelling of contours and introducing noise in the scans before propagation. The ability of the workflow to correctly predict the occurrence of errors was tested, taking both sets of observer contours as ground truth, using receiver operator characteristic analysis. The area under the curve was 0.90 and 0.85 for the observers, indicating good ability to predict the occurrence of errors. This tool could potentially be used to identify propagated contours that are likely to be incorrect, acting as a flag for manual review of these contours. This would make contour propagation more efficient, facilitating the routine implementation of adaptive radiotherapy.

MicroTalk: Using Argumentation to Improve Crowdsourcing Accuracy

Crowd workers are human and thus sometimes make mistakes. In order to ensure the highest quality output, requesters often issue redundant jobs with gold test questions and sophisticated aggregation mechanisms based on expectation maximization (EM). While these methods yield accurate results in many cases, they fail on extremely difficult problems with local minima, such as situations where the majority of workers get the answer wrong. Indeed, this has caused some researchers to conclude that on some tasks crowdsourcing can never achieve high accuracies, no matter how many workers are involved. This paper presents a new quality-control workflow, called MicroTalk, that requires some workers to Justify their reasoning and asks others to Reconsider their decisions after reading counter-arguments from workers with opposing views. Experiments on a challenging NLP annotation task with workers from Amazon Mechanical Turk show that (1) argumentation improves the accuracy of individual workers by 20%, (2) restricting consideration to workers with complex explanations improves accuracy even more, and (3) our complete MicroTalk aggregation workflow produces much higher accuracy than simpler voting approaches for a range of budgets.

Practical considerations and quality control for an FWI workflow

Prestack processing skills are essential in full-waveform inversion (FWI). In practical applications, without careful guidance and continuous monitoring of intermediate data and model updates, the output of the inversion process can diverge and produce nongeologic output models and inversion artifacts. A synthetic case study demonstrates challenges in real-life FWI projects. The FWI workflow is described in terms used by seismic processors. Detailed understanding of the input data set and the selection of starting models, combined with creative analysis tools and quality-control workflows, are paramount for a successful inversion project.

A Novel Work Flow of Density-Log Normalization for Coalbed-Methane Wells: An Example From the Surat Basin in Australia

Summary In coalbed-methane (CBM) projects, conventional logs, such as density were used heavily for coal identification, coal-property derivation, and also potentially for coal ranking. This paper presents a novel work flow of density-log normalization for CBM wells. It addresses two major challenges to current normalization methods: absence of a pervasive reference layer and removal of geological effects after normalization. To overcome these disadvantages, the proposed work flow undertakes six steps, which are data quality control (QC), geological-area segmentation, subarea type-well selection, density-control points, variable linear transformation and normalized-log result QC with box plots, matrix crossplots, and several histograms. This work flow removes the systematic errors caused by acquiring logs at different times with different logging instruments provided by different vendors. Furthermore, it possesses the advantages of the automated QC work flow, type-well control, and reference-layer methods without incurring their respective disadvantages. It was applied successfully to approximately 600 wells covering a large area in the Surat basin of Australia. Proper log normalization will reduce the uncertainties involved in 3D geological modeling and will increase the accuracy of reservoir evaluation. This paper should therefore be of interest to petrophysicists and geologists involved in CBM-field characterization.

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov–Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

Comparison of iterated single-step and Bayesian regressions on genomic evaluations for age at 100 kg in swine.

The effects of modified single-step genomic best linear unbiased prediction (ssGBLUP) iterations on GEBV and SNP were investigated using 85,388 age at 100 kg phenotypes from the BRF SA breeding program Landrace pure line animals, off-tested between 2002 and 2013. Pedigree data comprised animals born between 1999 and 2013. A total of 1,068 animals were assigned to the training population, in which all of them had genotypes, original and corrected age at 100 kg phenotypes, and weighted deregressed proof records. A total of 100 genotyped animals, with high accuracy age at 100 kg estimated breeding values, were assigned to the validation population. After applying the quality control workflow, a set of 41,042 SNP was used for the analysis. Standard and modified ssGBLUP, BayesCπ, and Bayesian Lasso were compared, and their predictive abilities were accessed by approximate true and GEBV correlations. Modified ssGBLUP iteration effects on SNP estimates and GEBV were relevant, in which assigned differential weights and shrinkage caused important losses on ssGBLUP predictive ability for age at 100 kg GEBV. Even though ssGBLUP accuracy can be equal or better than the compared Bayesian methods, additional gains can be obtained by correctly identifying the number of iterations required for best ssGBLUP performance.

A semi-automated workflow for biodiversity data retrieval, cleaning, and quality control.

The compilation and cleaning of data needed for analyses and prediction of species distributions is a time consuming process requiring a solid understanding of data formats and service APIs provided by biodiversity informatics infrastructures. We designed and implemented a Taverna-based Data Refinement Workflow which integrates taxonomic data retrieval, data cleaning, and data selection into a consistent, standards-based, and effective system hiding the complexity of underlying service infrastructures. The workflow can be freely used both locally and through a web-portal which does not require additional software installations by users.

High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately.

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application – SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary “Manual Initialize” and “Hand Draw” tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application – SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary “Manual Initialize” and “Hand Draw” tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.

Cost and benefit of quality control visual checks in large-scale digitization of archival manuscripts

Purpose – The purpose of this paper is to provide much needed data to staff working with archival digitization on cost and benefit of visual checks during quality control workflows, and to encourage those in the field of digitization to take a data-driven approach to planning and workflow development as they transition into large-scale digitization. Design/methodology/approach – This is a case study of a cost benefit analysis at the Triangle Research Libraries Network. Data were tracked on time spent performing visual checks compared to scanning production and error type/discovery rates for the consortial grant “Content, context, and capacity: a collaborative large-scale digitization project on the long civil rights movement in North Carolina”. Findings – Findings show that 85 percent of time was spent scanning and 15 percent was spent on quality control with visual checks of every scan. Only one error was discovered for every 223 scans reviewed (0.4 percent of scans). Of the six types of error identified, only half cause critical user experience issues. Of all errors detected, only 32 percent fell into the critical category. One critical error was found for every 700 scans (0.1 percent of scans). If all the time spent performing visual checks were instead spent on scanning, production would have increased by 18 percent. Folders with 100 or more scans comprised only 11.5 percent of all folders and 37 percent of folders in this group contained errors (for comparison, only 8 percent of folders with 50 or more scans contained errors). Additionally, 52 percent of all critical errors occurred in these folders. The errors in larger folders represented 30 percent of total errors, and performing visual checks on the large folders required 32 percent of all visual check time. Practical implications – The data gathered during this research can be repurposed by others wishing to consider or conduct cost benefit analysis of visual check workflows for large-scale digitization. Originality/value – To the authors' knowledge, this is the only available dataset on rate of error detection and error type compared to time spent on quality control visual checks in digitization.

SU-E-P-19: Patient-Specific Quality Control Workflow for Dedicated SBRT Vero System

Purpose/Objectives: To set up patient specific quality control (QC) process that ensures the integrity, deliverability and accurate dose for the SBRT lung plan. Materials/Methods: In 11/2012, SBRT Vero system (C‐band 6MV linac on gimbals, mounted on a rotating (+/− 60 degree) ring) was commissioned at our facility. The gimbals allow pan/tilt of the linac to mechanically account for gantry sag during gantry rotation, and track the motion of the target by moving the axis of the treatment beam. The linac is equipped with 0.5 mm (at isocenter) MLC. The treatment planning system (BrainLab iPlanTM) has the following algorithms available: Pencil Beam, Monte‐Carlo (Dose‐to‐water and Dose‐to‐Medium options). For the treated 37 targets (15 patients), 24 were located in lung. Monte‐Carlo (dose‐to‐water) algorithm was used, as it best corresponded to the film/ion chamber commissioning dosimetry. Quality assurance (QA) for lung targets utilizes 0.01cc ion chamber measurements in CIRSTM heterogeneous lung motion phantom with a range of small targets available. Per institutional policy, point dose from the patient plan mapped onto the phantom is compared to measured dose in the same heterogeneous phantom in static configuration. Additionally, the same QC process was repeated using 3D sinusoidally moving target with the amplitudes similar or exceeding the results of patient‐specific target motion evaluation performed for each 4DCT patient at UFPTI. A range of planning geometries was used and QC‐verified: ring angles within ±15degrees and fields 2.5‐5.0cm diameter. Results and Conclusions: The measured dose was within 2.5% from the Monte Carlo calculation, with 1.2 % average difference between plan and measurement in the static case, and within 3% with average 1.6% and 1% SD for dynamic case. The agreement was satisfactory and ensured the integrity of the treatment planning and delivery process

Automated quality control system for LC-SRM setups

Selected reaction monitoring (SRM) is emerging as a standard tool for high-throughput protein quantification. For reliable and reproducible SRM protein quantification it is essential that system performance is stable. We present here a quality control workflow that is based on repeated analysis of a standard sample to allow insight into the stability of the key properties of a SRM setup. This is supported by automated software to monitor system performance and display information like signal intensities and retention time stability over time, and alert upon deviations from expected metrics. Utilising the software to evaluate 407 repeated injections of a standard sample during half a year, outliers in relative peptide signal intensities and relative peptide fragment ratios are identified, indicating the need for instrument maintenance. We therefore believe that the software could be a vital and powerful tool for any lab regularly performing SRM, increasing the reliability and quality of the SRM platform. Selected reaction monitoring (SRM) mass spectrometry is becoming established as a standard technique for accurate protein quantification. However, to achieve the required quantification reproducibility of the liquid chromatography (LC)-SRM setup, system performance needs to be monitored over time. Here we introduce a workflow with associated software to enable automated monitoring of LC-SRM setups. We believe that usage of the presented concepts will further strengthen the role of SRM as a reliable tool for protein quantification. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.