| 1. |
- Littink, Karin W., et al.
(författare)
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Homozygosity Mapping in Patients with Cone-Rod Dystrophy : Novel Mutations and Clinical Characterizations
- 2010
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Ingår i: Investigative Ophthalmology and Visual Science. - : Association for Research in Vision and Ophthalmology (ARVO). - 0146-0404 .- 1552-5783. ; 51:11, s. 5943-5951
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE. To determine the genetic defect and to describe the clinical characteristics in a cohort of mainly nonconsanguineous cone-rod dystrophy (CRD) patients. METHODS. One hundred thirty-nine patients with diagnosed CRD were recruited. Ninety of them were screened for known mutations in ABCA4, and those carrying one or two mutations were excluded from further research. Genome-wide homozygosity mapping was performed in the remaining 108. Known genes associated with autosomal recessive retinal dystrophies located within a homozygous region were screened for mutations. Patients in whom a mutation was detected underwent further ophthalmic examination. RESULTS. Homozygous sequence variants were identified in eight CRD families, six of which were nonconsanguineous. The variants were detected in the following six genes: ABCA4, CABP4, CERKL, EYS, KCNV2, and PROM1. Patients carrying mutations in ABCA4, CERKL, and PROM1 had typical CRD symptoms, but a variety of retinal appearances on funduscopy, optical coherence tomography, and autofluorescence imaging. CONCLUSIONS. Homozygosity mapping led to the identification of new mutations in consanguineous and nonconsanguineous patients with retinal dystrophy. Detailed clinical characterization revealed a variety of retinal appearances, ranging from nearly normal to extensive retinal remodeling, retinal thinning, and debris accumulation. Although CRD was initially diagnosed in all patients, the molecular findings led to a reappraisal of the diagnosis in patients carrying mutations in EYS, CABP4, and KCNV2.
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| 2. |
- Moruz, Luminita, 1982-
(författare)
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Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Mass spectrometry-based methods are among the most commonly used techniques to characterize proteins in biological samples. With rapid technological developments allowing increasing throughput, thousands of proteins can now be monitored in a matter of hours. However, these advances brought a whole new set of analytical challenges. At the moment, it is no longer possible to rely on human experts to process the data. Instead, accurate computational tools are required.In line with these observations, my research work has involved development of computational methods to facilitate the analysis of mass spectrometry-based experiments. In particular, the projects included in this thesis revolve around the chromatography step of such experiments, where peptides are separated according to their hydrophobicity.The first part of the thesis describes an algorithm to predict retention time from peptide sequences. The method provides more accurate predictions compared to previous approaches, while being easily transferable to other chromatography setups. In addition, it gives equally good predictions for peptides carrying arbitrary posttranslational modifications as for unmodified peptides.The second part of the thesis includes two applications of retention time predictions in the context of mass spectrometry-based proteomics experiments. First, we show how theoretical calculations of masses and retention times can be used to infer proteins in shotgun proteomics experiments. Secondly, we illustrate the use of retention time predictions to calculate optimized gradient functions for reversed-phase liquid chromatography.
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| 3. |
- Moruz, Luminita, et al.
(författare)
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Chromatographic retention time prediction for posttranslationally modified peptides
- 2012
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 12:8, s. 1151-1159
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Tidskriftsartikel (refereegranskat)abstract
- Retention time prediction of peptides in liquid chromatography has proven to be a valuable tool for mass spectrometry-based proteomics, especially in designing more efficient procedures for state-of-the-art targeted workflows. Additionally, accurate retention time predictions can also be used to increase confidence in identifications in shotgun experiments. Despite these obvious benefits, the use of such methods has so far not been extended to (posttranslationally) modified peptides due to the absence of efficient predictors for such peptides. We here therefore describe a new retention time predictor for modified peptides, built on the foundations of our existing Elude algorithm. We evaluated our software by applying it on five types of commonly encountered modifications. Our results show that Elude now yields equally good prediction performances for modified and unmodified peptides, with correlation coefficients between predicted and observed retention times ranging from 0.93 to 0.98 for all the investigated datasets. Furthermore, we show that our predictor handles peptides carrying multiple modifications as well. This latest version of Elude is fully portable to new chromatographic conditions and can readily be applied to other types of posttranslational modifications. Elude is available under the permissive Apache2 open source License at or can be run via a web-interface at.
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| 4. |
- Moruz, Luminita, et al.
(författare)
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GradientOptimizer : An open-source graphical environment for calculating optimized gradients in reversed-phase liquid chromatography
- 2014
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 14:12, s. 1464-1466
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Tidskriftsartikel (refereegranskat)abstract
- We here present GradientOptimizer, an intuitive, lightweight graphical user interface to design nonlinear gradients for separation of peptides by reversed-phase liquid chromatography. The software allows to calculate three types of nonlinear gradients, each of them optimizing a certain retention time distribution of interest. GradientOptimizer is straightforward to use, requires minimum processing of the input files, and is supported under Windows, Linux, and OS X platforms. The software is open-source and can be downloaded under an Apache 2.0 license at https://github.com/statisticalbiotechnology/NonlinearGradientsUI.
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| 5. |
- Moruz, Luminita, 1982-, et al.
(författare)
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Mass Fingerprinting of Complex Mixtures : Protein Inference from High-Resolution Peptide Masses and Predicted Retention Times
- 2013
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 12:12, s. 5730-5741
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Tidskriftsartikel (refereegranskat)abstract
- In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.
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| 6. |
- Moruz, Luminita, et al.
(författare)
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Optimized Nonlinear Gradients for Reversed-Phase Liquid Chromatography in Shotgun Proteomics
- 2013
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Ingår i: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 85:16, s. 7777-7785
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Tidskriftsartikel (refereegranskat)abstract
- Reversed-phase liquid chromatography has become the preferred method for separating peptides in most of the mass spectrometry-based proteomics workflows of today. In the way the technique is typically applied, the peptides are released from the chromatography column by the gradual addition of an organic buffer according to a linear function. However, when applied to complex peptide mixtures, this approach leads to unequal spreads of the peptides over the chromatography time. To address this, we investigated the use of nonlinear gradients, customized for each setup at hand. We developed an algorithm to generate optimized gradient functions for shotgun proteomics experiments and evaluated it for two data sets consisting each of four replicate runs of a human complex sample. Our results show that the optimized gradients produce a more even spread of the peptides over the chromatography run, while leading to increased numbers of confident peptide identifications. In addition, the list of peptides identified using nonlinear gradients differed considerably from those found with the linear ones, suggesting that such gradients can be a valuable tool for increasing the proteome coverage of mass spectrometry-based experiments.
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| 7. |
- Moruz, Luminita, et al.
(författare)
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Peptide retention time prediction
- 2016
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Ingår i: Mass spectrometry reviews (Print). - : Wiley. - 0277-7037 .- 1098-2787.
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Tidskriftsartikel (refereegranskat)abstract
- Most methods for interpreting data from shotgun proteomics experiments are to large degree dependent on being able to predict properties of peptide-ions. Often such predicted properties are limited to molecular mass and fragment spectra, but here we put focus on a perhaps underutilized property, a peptide's chromatographic retention time. We review a couple of different principles of retention time prediction,and their applications within computational proteomics.
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| 8. |
- Moruz, Luminita, et al.
(författare)
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Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics
- 2010
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 9:10, s. 5209-5216
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Tidskriftsartikel (refereegranskat)abstract
- Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Most notably such predictions are used to weed out incorrect peptide-spectrum matches, and to design targeted proteomics experiments. In this study, we describe a RT predictor, ELUDE, which can be employed in both applications. ELUDE's predictions are based on 60 features derived from the peptide's amino acid composition and optimally combined using kernel regression. When sufficient data is available, ELUDE derives a retention time index for the condition at hand making it fully portable to new chromatographic conditions. In cases when little training data is available, as often is the case in targeted proteomics experiments, ELUDE selects and calibrates a model from a library of pretrained predictors. Both model selection and calibration are carried out via robust statistical methods and thus ELUDE can handle situations where the calibration data contains erroneous data points. We benchmarked our method against two state-of-the-art predictors and showed that ELUDE outperforms these methods and tracked up to 34% more peptides in a theoretical SRM method creation experiment. ELUDE is freely available under Apache License from http://per-colator.com.
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| 9. |
- Serang, Oliver, et al.
(författare)
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Recognizing Uncertainty Increases Robustness and Reproducibility of Mass Spectrometry-based Protein Inferences
- 2012
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 11:12, s. 5586-5591
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Tidskriftsartikel (refereegranskat)abstract
- Parsimony and protein grouping are widely employed to enforce economy in the number of identified proteins, with the goal of increasing the quality and reliability of protein identifications; however, in a counterintuitive manner, parsimony and protein grouping may actually decrease the reproducibility and interpretability of protein identifications. We present a simple illustration demonstrating ways in which parsimony and protein grouping may lower the reproducibility or interpretability of results. We then provide an example of a data set where a probabilistic method increases the reproducibility and interpretability of identifications made on replicate analyses of Human Du145 prostate cancer cell lines.
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