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  • 1.
    Bendz, Maria
    et al.
    Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Skwark, Marcin
    Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Nilsson, Daniel
    Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Granholm, Viktor
    Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Cristobal, Susana
    Kall, Lukas
    Elofsson, Arne
    Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Membrane protein shaving with thermolysin can be used to evaluate topology predictors2013In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 13, no 9, p. 1467-1480Article in journal (Refereed)
    Abstract [en]

    Topology analysis of membrane proteins can be obtained by enzymatic shaving in combination with MS identification of peptides. Ideally, such analysis could provide quite detailed information about the membrane spanning regions. Here, we examine the ability of some shaving enzymes to provide large-scale analysis of membrane proteome topologies. To compare different shaving enzymes, we first analyzed the detected peptides from two over-expressed proteins. Second, we analyzed the peptides from non-over-expressed Escherichia coli membrane proteins with known structure to evaluate the shaving methods. Finally, the identified peptides were used to test the accuracy of a number of topology predictors. At the end we suggest that the usage of thermolysin, an enzyme working at the natural pH of the cell for membrane shaving, is superior because: (i) we detect a similar number of peptides and proteins using thermolysin and trypsin; (ii) thermolysin shaving can be run at a natural pH and (iii) the incubation time is quite short. (iv) Fewer detected peptides from thermolysin shaving originate from the transmembrane regions. Using thermolysin shaving we can also provide a clear separation between the best and the less accurate topology predictors, indicating that using data from shaving can provide valuable information when developing new topology predictors.

  • 2. Branca, Rui M. M.
    et al.
    Orre, Lukas M.
    Johansson, Henrik J.
    Granholm, Viktor
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Huss, Mikael
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Perez-Bercoff, Åsa
    Forshed, Jenny
    Käll, Lukas
    Lehtio, Janne
    HiRIEF LC-MSMS enables deep proteome coverage and unbiased proteogenomics2014In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 11, no 1, p. 59-+Article in journal (Refereed)
    Abstract [en]

    We present a liquid chromatography-mass spectrometry (LC-MSMS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resolution isoelectric focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelectric point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, respectively. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.

  • 3.
    Granholm, Viktor
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    The accuracy of statistical confidence estimates in shotgun proteomics2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    High-throughput techniques are currently some of the most promising methods to study molecular biology, with the potential to improve medicine and enable new biological applications. In proteomics, the large scale study of proteins, the leading method is mass spectrometry. At present researchers can routinely identify and quantify thousands of proteins in a single experiment with the technique called shotgun proteomics.

    A challenge of these experiments is the computational analysis and the interpretation of the mass spectra. A shotgun proteomics experiment easily generates tens of thousands of spectra, each thought to represent a peptide from a protein. Due to the immense biological and technical complexity, however, our computational tools often misinterpret these spectra and derive incorrect peptides. As a consequence, the biological interpretation of the experiment relies heavily on the statistical confidence that we estimate for the identifications.

    In this thesis, I have included four articles from my research on the accuracy of the statistical confidence estimates in shotgun proteomics, how to accomplish and evaluate it. In the first two papers a new method to use pre-characterized protein samples to evaluate this accuracy is presented. The third paper deals with how to avoid statistical inaccuracies when using machine learning techniques to analyze the data. In the fourth paper, we present a new tool for analyzing shotgun proteomics results, and evaluate the accuracy of  its statistical estimates using the method from the first papers.

    The work I have included here can facilitate the development of new and accurate computational tools in mass spectrometry-based proteomics. Such tools will help making the interpretation of the spectra and the downstream biological conclusions more reliable.

  • 4.
    Granholm, Viktor
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Kall, Lukas
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Quality assessments of peptide-spectrum matches in shotgun proteomics2011In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 11, no 6, p. 1086-1093Article in journal (Refereed)
    Abstract [en]

    The peptide identification process in shotgun proteomics is most frequently solved with search engines. Such search engines assign scores that reflect similarity between the measured fragmentation spectrum and the theoretical spectra of the peptides of a given database. However, the scores from most search engines do not have a direct statistical interpretation. To understand and make use of the significance of peptide identifications, one must thus be familiar with some statistical concepts. Here, we discuss different statistical scores used to show the confidence of an identification and a set of methods to estimate these scores. We also describe the variance of statistical scores and imperfections of scoring functions of peptide-spectrum matches.

  • 5.
    Granholm, Viktor
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Kim, Sangtae
    Navarro, José C. F.
    Sjölund, Erik
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Smith, Richard D.
    Käll, Lukas
    Fast and Accurate Database Searches with MS-GF plus Percolator:  2014In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 13, no 2, p. 890-897Article in journal (Refereed)
    Abstract [en]

    One can interpret fragmentation spectra stemming from peptides in mass-spectrometry-based proteomics experiments using so-called database search engines. Frequently, one also runs post-processors such as Percolator to assess the confidence, infer unique peptides, and increase the number of identifications. A recent search engine, MS-GF+, has shown promising results, due to a new and efficient scoring algorithm. However, MS-GF+ provides few statistical estimates about the peptide-spectrum matches, hence limiting the biological interpretation. Here, we enabled Percolator processing for MS-GF+ output and observed an increased number of identified peptides for a wide variety of data sets. In addition, Percolator directly reports p values and false discovery rate estimates, such as q values and posterior error probabilities, for peptide-spectrum matches, peptides, and proteins, functions that are useful for the whole proteomics community.

  • 6.
    Granholm, Viktor
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Navarro, Jose Fernandez
    Noble, William Stafford
    Käll, Lukas
    Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics2013In: Journal of Proteomics, ISSN 1874-3919, E-ISSN 1876-7737, Vol. 80, p. 123-131Article in journal (Refereed)
    Abstract [en]

    The analysis of a shotgun proteomics experiment results in a list of peptide-spectrum matches (PSMs) in which each fragmentation spectrum has been matched to a peptide in a database. Subsequently, most protein inference algorithms rank peptides according to the best-scoring PSM for each peptide. However, there is disagreement in the scientific literature on the best method to assess the statistical significance of the resulting peptide identifications. Here, we use a previously described calibration protocol to evaluate the accuracy of three different peptide-level statistical confidence estimation procedures: the classical Fisher's method, and two complementary procedures that estimate significance, respectively, before and after selecting the top-scoring PSM for each spectrum. Our experiments show that the latter method, which is employed by MaxQuant and Percolator, produces the most accurate, well-calibrated results.

  • 7.
    Granholm, Viktor
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Noble, William Stafford
    Käll, Lukas
    A cross-validation scheme for machine learning algorithms in shotgun proteomics2012In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 13, p. S3-Article in journal (Refereed)
    Abstract [en]

    Peptides are routinely identified from mass spectrometry-based proteomics experiments by matching observed spectra to peptides derived from protein databases. The error rates of these identifications can be estimated by target-decoy analysis, which involves matching spectra to shuffled or reversed peptides. Besides estimating error rates, decoy searches can be used by semi-supervised machine learning algorithms to increase the number of confidently identified peptides. As for all machine learning algorithms, however, the results must be validated to avoid issues such as overfitting or biased learning, which would produce unreliable peptide identifications. Here, we discuss how the target-decoy method is employed in machine learning for shotgun proteomics, focusing on how the results can be validated by cross-validation, a frequently used validation scheme in machine learning. We also use simulated data to demonstrate the proposed cross-validation scheme's ability to detect overfitting.

  • 8.
    Granholm, Viktor
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Noble, William Stafford
    Käll, Lukas
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    On Using Samples of Known Protein Content to Assess the Statistical Calibration of Scores Assigned to Peptide-Spectrum Matches in Shotgun Proteomics2011In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 10, no 5, p. 2671-2678Article in journal (Refereed)
    Abstract [en]

    In shotgun proteomics, the quality of a hypothesized match between an observed spectrum and a peptide sequence is quantified by a score function. Because the score function lies at the heart of any peptide identification pipeline, this function greatly affects the final results of a proteomics assay. Consequently, valid statistical methods for assessing the quality of a given score function are extremely important. Previously, several research groups have used samples of known protein composition to assess the quality of a given score function. We demonstrate that this approach is problematic, because the outcome can depend on factors other than the score function itself. We then propose an alternative use of the same type of data to validate a score function. The central idea of our approach is that database matches that are not explained by any protein in the purified sample comprise a robust representation of incorrect matches. We apply our alternative assessment scheme to several commonly used score functions, and we show that our approach generates a reproducible measure of the calibration of a given peptide identification method. Furthermore, we show how our quality test can be useful in the development of novel score functions.

  • 9.
    Moruz, Luminita
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Hoopmann, MR
    Rosenlund, Magnus
    Granholm, Viktor
    Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Moritz, RL
    Käll, Lukas
    Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times2013In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 12, no 12, p. 5730-5741Article in journal (Refereed)
    Abstract [en]

    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.

1 - 9 of 9
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