KYSS: Mass spectrometry data quality assessment for protein analysis and large-scale proteomics

We introduce the computer tool “Know Your Samples” (KYSS) for assessment and visualisation of large scale proteomics datasets, obtained by mass spectrometry (MS) experiments. KYSS facilitates the evaluation of sample preparation protocols, LC peptide separation, and MS and MS/MS performance by monitoring the number of missed cleavages, precursor ion charge states, number of protein identifications and peptide mass error in experiments. KYSS generates several different protein profiles based on protein abundances, and allows for comparative analysis of multiple experiments. KYSS was adapted for blood plasma proteomics and provides concentrations of identified plasma proteins. We demonstrate the utility of the KYSS tool for MS based proteome analysis of blood plasma and for assessment of hydrogel particles for depletion of abundant proteins in plasma. The KYSS software is open source and is freely available at http://kyssproject.github.io/.     

PepDistiller: A quality control tool to improve the sensitivity and accuracy of peptide identifications in shotgun proteomics

In this study, we presented a quality control tool named PepDistiller to facilitate the validation of MASCOT search results. By including the number of tryptic termini, and integrating a refined false discovery rate (FDR) calculation method, we demonstrated the improved sensitivity of peptide identifications obtained from semitryptic search results. Based on the analysis of a complex data set, approximately 7% more peptide identifications were obtained using PepDistiller than using MASCOT Percolator. Moreover, the refined method generated lower FDR estimations than the percentage of incorrect target (PIT) fixed method applied in Percolator. Using a standard data set, we further demonstrated the increased accuracy of the refined FDR estimations relative to the PIT-fixed FDR estimations. PepDistiller is fast and convenient to use, and is freely available for academic access. The software can be downloaded from http://www.bprc.ac.cn/pepdistiller.     

Statistical quality control of variety trial data

I propose detection criteria for identifying an abnormal or erroneous data vector provided by a single variety trial in a longer series of variety trials. The test criteria are based on the linear effects estimated separately for each studied trial using global variance components estimated from the whole series of variety trials. The criteria comprise three mutually independent test statistics. The first one is a quadratic form in the estimated fixed effects, the second one is a quadratic form in the estimated realized linear random effects not including the residual effects, and the third one is a quadratic form in the estimated realized residual effects. Under the null hypothesis defining a valid data vector, the three quadratics have independent chi2 distributions. Under natural alternative hypotheses, they have noncentral chi2 distributions. Decomposing the total variation of the data vector studied into quadratic forms due to different types of the realized linear effects intuitively justifies the resulting test criteria. The decomposition may also be used to show that the resulting tests are likelihood ratio tests. I further present computational procedures that allow us to dispense with the need for prior estimation of the linear effects.     

A proteomics performance standard to support measurement quality in proteomics

The emergence of MS-based proteomic platforms as a prominent technology utilized in biochemical and biomedical research has increased the need for high-quality MS measurements. To address this need, National Institute of Standards and Technology (NIST) reference material (RM) 8323 yeast protein extract is introduced as a proteomics quality control material for benchmarking the preanalytical and analytical performance of proteomics-based experimental workflows. RM 8323 yeast protein extract is based upon the well-characterized eukaryote Saccharomyces cerevisiae and can be utilized in the design and optimization of proteomics-based methodologies from sample preparation to data analysis. To demonstrate its utility as a proteomics quality control material, we coupled LC-MS/MS measurements of RM 8323 with the NIST MS Quality Control (MSQC) performance metrics to quantitatively assess the LC-MS/MS instrumentation parameters that influence measurement accuracy, repeatability, and reproducibility. Due to the complexity of the yeast proteome, we also demonstrate how NIST RM 8323, along with the NIST MSQC performance metrics, can be used in the evaluation and optimization of proteomics-based sample preparation methods.     

A guide to the Proteomics Identifications Database proteomics data repository

The Proteomics Identifications Database (PRIDE, www.ebi.ac.uk/pride ) is one of the main repositories of MS derived proteomics data. Here, we point out the main functionalities of PRIDE both as a submission repository and as a source for proteomics data. We describe the main features for data retrieval and visualization available through the PRIDE web and BioMart interfaces. We also highlight the mechanism by which tailored queries in the BioMart can join PRIDE to other resources such as Reactome, Ensembl or UniProt to execute extremely powerful across‐domain queries. We then present the latest improvements in the PRIDE submission process, using the new easy‐to‐use, platform‐independent graphical user interface submission tool PRIDE Converter. Finally, we speak about future plans and the role of PRIDE in the ProteomExchange consortium.     

Heteromer score—using internal standards to assess the quality of proteomic data

In the cell, the majority of proteins exist in complexes. Most of these complexes have a constant stoichiometry and thus can be used as internal standards. In this rapid communication, we show that it is possible to calculate a correlation coefficient that reflects the reproducibility of the analytical approach used. The abundance of one subunit in a heterodimer is plotted against the abundance of the other, and this is repeated for all subunits in all heteromers found in the data set. The correlation coefficient obtained (the “heteromer score”) is a new bioinformatic tool that is independent of the method used to collect the data, requires no special sample preparation and can be used retrospectively on old datasets. It can be used for quality control, to indicate when a change becomes significant or identify complexes whose stoichiometry has been perturbed during the experiment.     

A proposed enhancement to the in air sensitivity test for ultrasound quality assurance

PurposeA number of guidelines for ultrasound quality assurance recommend the use of the in air reverberation depth as a proxy measure for sensitivity. The test is quantised, i.e. it depends on the brightness of the deepest in air reverberation. The aim of this study was to investigate a possible enhancement to the test, where the gain is reduced to determine the “reverberation threshold”.MethodsThe test was introduced in several ultrasound departments. Results were audited to determine agreement with annual tests of sensitivity using a tissue mimicking test object.ResultsThe new test was performed on 100 probes. A change in reverberation threshold was demonstrated in 9 probes; 8 of these also had changes in penetration and/or grey level in a tissue mimicking test object. Reduced penetration but no change in reverberation threshold was seen in 2 probes.ConclusionsThe reverberation threshold provides a simple enhancement to the in air sensitivity test. Periodic sensitivity testing with a tissue mimicking test object remains important.     

A QconCAT informatics pipeline for the analysis, visualization and sharing of absolute quantitative proteomics data

Absolute protein concentration determination is becoming increasingly important in a number of fields including diagnostics, biomarker discovery and systems biology modeling. The recently introduced quantification concatamer methodology provides a novel approach to performing such determinations, and it has been applied to both microbial and mammalian systems. While a number of software tools exist for performing analyses of quantitative data generated by related methodologies such as SILAC, there is currently no analysis package dedicated to the quantification concatamer approach. Furthermore, most tools that are currently available in the field of quantitative proteomics do not manage storage and dissemination of such data sets.     

Informatics and data management in proteomics

Proteomics has become dominated by large amounts of experimental data and interpreted results. This experimental data cannot be effectively used without understanding the fundamental structure of its information content and representing that information in such a way that knowledge can be extracted from it. This review explores the structure of this information with regard to three fundamental issues: the extraction of relevant information from raw data, the scale of the projects involved and the statistical significance of protein identification results.     

The need to revisit published data: A concept and framework for complementary proteomics

Tandem proteomic strategies based on large‐scale and high‐resolution mass spectrometry have been widely applied in various biomedical studies. However, protein sequence databases and proteomic software are continuously updated. Proteomic studies should not be ended with a stable list of proteins. It is necessary and beneficial to regularly revise the results. Besides, the original proteomic studies usually focused on a limited aspect of protein information and valuable information may remain undiscovered in the raw spectra. Several studies have reported novel findings by reanalyzing previously published raw data. However, there are still no standard guidelines for comprehensive reanalysis. In the present study, we proposed the concept and draft framework for complementary proteomics, which are aimed to revise protein list or mine new discoveries by revisiting published data.     

An abbreviated method for the quality control of pollen counters

We present an abbreviated method for conducting large scale quality control (QC) exercises over limited time periods, which was used for examining the proficiency of technicians involved in the electronic Pollen Information Network (ePIN). The goal was for technicians to have their analysis skills evaluated at least twice: (1) by having at least one of their slides successfully checked by other counters in the ePIN network and (2) by successfully examining at least one additional slide from other sites. Success was judged as a relative difference (RDif %) ≤ 30% between the two daily average pollen concentrations. A total of 21 sites participated in the ePIN QC exercise. All of the results for total pollen had RDif % < 30%. Only five results had RDif > 30%, three for Betula and two for Poaceae pollen. Of these, three were slides containing < 40 pollen/m³ daily average and two were for sites that had microscopes with small fields of view and examined < 10% of the slide surface. More than 80% of the participants had at least two slides successfully checked by someone else in the network, and all of the participants had one slide successfully examined. The latter is comparable to a traditional ring test where only one slide is sent to participating sites. The method described here enabled a large number of technicians to be examined in a short period of time and represents a viable alternative to other approaches that can take many months to complete.     

Rapid evaluation and quality control of next generation sequencing data with FaQCs

Background

Next generation sequencing (NGS) technologies that parallelize the sequencing process and produce thousands to millions, or even hundreds of millions of sequences in a single sequencing run, have revolutionized genomic and genetic research. Because of the vagaries of any platform’s sequencing chemistry, the experimental processing, machine failure, and so on, the quality of sequencing reads is never perfect, and often declines as the read is extended. These errors invariably affect downstream analysis/application and should therefore be identified early on to mitigate any unforeseen effects.

Results

Here we present a novel FastQ Quality Control Software (FaQCs) that can rapidly process large volumes of data, and which improves upon previous solutions to monitor the quality and remove poor quality data from sequencing runs. Both the speed of processing and the memory footprint of storing all required information have been optimized via algorithmic and parallel processing solutions. The trimmed output compared side-by-side with the original data is part of the automated PDF output. We show how this tool can help data analysis by providing a few examples, including an increased percentage of reads recruited to references, improved single nucleotide polymorphism identification as well as de novo sequence assembly metrics.

Conclusion

FaQCs combines several features of currently available applications into a single, user-friendly process, and includes additional unique capabilities such as filtering the PhiX control sequences, conversion of FASTQ formats, and multi-threading. The original data and trimmed summaries are reported within a variety of graphics and reports, providing a simple way to do data quality control and assurance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0366-2) contains supplementary material, which is available to authorized users.     

The PSI semantic validator: A framework to check MIAPE compliance of proteomics data

The Human Proteome Organization's Proteomics Standards Initiative (PSI) promotes the development of exchange standards to improve data integration and interoperability. PSI specifies the suitable level of detail required when reporting a proteomics experiment (via the Minimum Information About a Proteomics Experiment), and provides extensible markup language (XML) exchange formats and dedicated controlled vocabularies (CVs) that must be combined to generate a standard compliant document. The framework presented here tackles the issue of checking that experimental data reported using a specific format, CVs and public bio‐ontologies (e.g. Gene Ontology, NCBI taxonomy) are compliant with the Minimum Information About a Proteomics Experiment recommendations. The semantic validator not only checks the XML syntax but it also enforces rules regarding the use of an ontology class or CV terms by checking that the terms exist in the resource and that they are used in the correct location of a document. Moreover, this framework is extremely fast, even on sizable data files, and flexible, as it can be adapted to any standard by customizing the parameters it requires: an XML Schema Definition, one or more CVs or ontologies, and a mapping file describing in a formal way how the semantic resources and the format are interrelated. As such, the validator provides a general solution to the common problem in data exchange: how to validate the correct usage of a data standard beyond simple XML Schema Definition validation. The framework source code and its various applications can be found at http://psidev.info/validator .     

A quality assurance initiative for commercial-scale production in high-throughput cryopreservation of blue catfish sperm

Cryopreservation of fish sperm has been studied for decades at a laboratory (research) scale. However, high-throughput cryopreservation of fish sperm has recently been developed to enable industrial-scale production. This study treated blue catfish (Ictalurus furcatus) sperm high-throughput cryopreservation as a manufacturing production line and initiated quality assurance plan development. The main objectives were to identify: (1) the main production quality characteristics; (2) the process features for quality assurance; (3) the internal quality characteristics and their specification designs; (4) the quality control and process capability evaluation methods, and (5) the directions for further improvements and applications. The essential product quality characteristics were identified as fertility-related characteristics. Specification design which established the tolerance levels according to demand and process constraints was performed based on these quality characteristics. Meanwhile, to ensure integrity throughout the process, internal quality characteristics (characteristics at each quality control point within process) that could affect fertility-related quality characteristics were defined with specifications. Due to the process feature of 100% inspection (quality inspection of every fish), a specific calculation method, use of cumulative sum (CUSUM) control charts, was applied to monitor each quality characteristic. An index of overall process evaluation, process capacity, was analyzed based on in-control process and the designed specifications, which further integrates the quality assurance plan. With the established quality assurance plan, the process could operate stably and quality of products would be reliable.     

Consolidation of proteomics data in the Cancer Proteomics database

Cancer is a class of diseases characterized by abnormal cell growth and one of the major reasons for human deaths. Proteins are involved in the molecular mechanisms leading to cancer, furthermore they are affected by anti‐cancer drugs, and protein biomarkers can be used to diagnose certain cancer types. Therefore, it is important to explore the proteomics background of cancer. In this report, we developed the Cancer Proteomics database to re‐interrogate published proteome studies investigating cancer. The database is divided in three sections related to cancer processes, cancer types, and anti‐cancer drugs. Currently, the Cancer Proteomics database contains 9778 entries of 4118 proteins extracted from 143 scientific articles covering all three sections: cell death (cancer process), prostate cancer (cancer type) and platinum‐based anti‐cancer drugs including carboplatin, cisplatin, and oxaliplatin (anti‐cancer drugs). The detailed information extracted from the literature includes basic information about the articles (e.g., PubMed ID, authors, journal name, publication year), information about the samples (type, study/reference, prognosis factor), and the proteomics workflow (Subcellular fractionation, protein, and peptide separation, mass spectrometry, quantification). Useful annotations such as hyperlinks to UniProt and PubMed were included. In addition, many filtering options were established as well as export functions. The database is freely available at http://cancerproteomics.uio.no .     

How to measure CT image quality: Variations in CT-numbers,uniformity and low contrast resolution for a CT quality assurance phantom

PurposeQuality assurance (QA) phantoms for testing different image quality parameters in computed tomography (CT) are commercially available. Such phantoms are also used as reference for acceptance in the specifications of CT-scanners. The aim of this study was to analyze the characteristics of the most commonly used QA phantom in CT: Catphan 500/504/600.MethodsNine different phantoms were scanned on the same day, on one CT-scanner with the same parameter settings. Interphantom variations in CT-number values, image uniformity and low contrast resolution were evaluated for the phantoms. Comparisons between manual image analysis and results obtained from the automatic evaluation software QAlite were performed.ResultsSome interphantom variations were observed in the low contrast resolution and the CT-number modules of the phantoms. Depending on the chosen regulatory framework, the variations in CT-numbers can be interpreted as substantial. The homogenous modules were found more invariable. However, the automatic image analysis software QAlite measures image uniformity differently than recommended in international standards, and will not necessarily give results in agreement with these standards.ConclusionsIt is important to consider the interphantom variations in relation to ones framework, and to be aware of which phantom is used to study CT-numbers and low contrast resolution for a specific scanner. Comparisons with predicted values from manual and acceptance values should be performed with care and consideration. If automatic software-based evaluations are to be used, users should be aware that large differences can exist for the image uniformity testing.