Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Developing an internationalization strategy using diffusion modeling:the case of Greater Amberjack

\u3cp\u3eFor farmers of new fish species, market adoption is needed in order to grow a viable business. Farmers may try to sell the new species in their firms’ domestic markets, but they might also look at other markets. However, as markets are becoming more global and competitors more international, considering internationalization may be a necessity rather than a choice. Using diffusion modelling, and based on results of an online supermarket experiment, the innovation and imitation parameters are estimated and diffusion curves for five countries predicted in an attempt to determine the best lead market for introducing fillets of farmed greater amberjack (Seriola dumerili). The production capacity consequences of implementing different internationalization strategies (i.e. “sprinkler” and “waterfall”) were also explored. A waterfall strategy refers to the sequential introduction of a product in different markets, whereas the sprinkler strategy concerns the simultaneous introduction of a product in multiple international markets. Since a sprinkler approach requires many resources and the ability to quickly ramp up production capacity, a waterfall approach appears more suitable for farmers of greater amberjack. Italy and Spain appear to be the best lead markets for greater amberjack farmers to enter first.\u3c/p\u3

Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy

Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls

MotivationUntargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). In order to judge if metabolite levels are abnormal, analysis of a large number of reference samples is crucial to correct for variations in metabolite concentrations resulting from factors such as diet, age and gender. However, a large number of controls requires the use of out-of-batch controls, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e. technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed.Methods &amp; resultsBased on six metrics, we compared existing normalization methods on their ability to reduce batch effects from eight independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method which uses 17 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age- and sex as covariates fitted on control samples obtained from all eight batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal as well as in the detection of 178 known biomarkers across 45 IEM patient samples and performed at least similar to an approach using 15 within-batch controls. Furthermore, our regression model indicates that 10-24% of the considered features showed significant age-dependent variations.ConclusionsOur comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch controls to establish clinically-relevant reference values for metabolite concentrations. These findings opens possibilities to use large scale out-of-batch control samples in a clinical setting, increasing throughput and detection accuracy.AvailabilityMetchalizer is available at https://github.com/mbongaerts/Metchalizer/</jats:sec

Glycine amidinotransferase (GATM), renal Fanconi syndrome, and kidney failure

Background For many patients with kidney failure, the cause and underlying defect remain unknown. Here, we describe a novel mechanism of a genetic order characterized by renal Fanconi syndrome and kidney failure.Methods We clinically and genetically characterized members of five families with autosomal dominant renal Fanconi syndrome and kidney failure. We performed genome-wide linkage analysis, sequencing, and expression studies in kidney biopsy specimens and renal cells along with knockout mouse studies and evaluations of mitochondrial morphology and function. Structural studies examined the effects of recognized mutations.Results The renal disease in these patients resulted from monoallelic mutations in the gene encoding glycine amidinotransferase (GATM), a renal proximal tubular enzyme in the creatine biosynthetic pathway that is otherwise associated with a recessive disorder of creatine deficiency. In silico analysis showed that the particular GATM mutations, identified in 28 members of the five families, create an additional interaction interface within the GATM protein and likely cause the linear aggregation of GATM observed in patient biopsy specimens and cultured proximal tubule cells. GATM aggregates-containing mitochondria were elongated and associated with increased ROS production, activation of the NLRP3 inflammasome, enhanced expression of the profibrotic cytokine IL-18, and increased cell death.Conclusions In this novel genetic disorder, fully penetrant heterozygous missense mutations in GATM trigger intramitochondrial fibrillary deposition of GATM and lead to elongated and abnormal mitochondria. We speculate that this renal proximal tubular mitochondrial pathology initiates a response from the inflammasome, with subsequent development of kidney fibrosis