Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins.

The expression of proteins can be quantified in high-throughput means using different types of mass spectrometers. In recent years, there have emerged label-free methods for determining protein abundance. Although the expression is initially measured at the peptide level, a common approach is to combine the peptide-level measurements into protein-level values before differential expression analysis. However, this simple combination is prone to inconsistencies between peptides and may lose valuable information. To this end, we introduce here a method for detecting differentially expressed proteins by combining peptide-level expression-change statistics. Using controlled spike-in experiments, we show that the approach of averaging peptide-level expression changes yields more accurate lists of differentially expressed proteins than does the conventional protein-level approach. This is particularly true when there are only few replicate samples or the differences between the sample groups are small. The proposed technique is implemented in the Bioconductor package PECA, and it can be downloaded from http://www.bioconductor.org.

Exploring the transcriptomic variation caused by the Finnish founder mutation of lysinuric protein intolerance (LPI).

Lysinuric protein intolerance (LPI) is an autosomal recessive disorder caused by mutations in cationic amino acid transporter gene SLC7A7. Although all Finnish patients share the same homozygous mutation, their clinical manifestations vary greatly. The symptoms range from failure to thrive, protein aversion, anemia and hyperammonaemia, to immunological abnormalities, nephropathy and pulmonary alveolar proteinosis. To unravel the molecular mechanisms behind those symptoms not explained directly by the primary mutation, gene expression profiles of LPI patients were studied using genome-wide microarray technology. As a result, we discovered 926 differentially-expressed genes, including cationic and neutral amino acid transporters. The functional annotation analysis revealed a significant accumulation of such biological processes as inflammatory response, immune system processes and apoptosis. We conclude that changes in the expression of genes other than SLC7A7 may be linked to the various symptoms of LPI, indicating a complex interplay between amino acid transporters and various cellular processes.

Pripper: prediction of caspase cleavage sites from whole proteomes.

BACKGROUND: Caspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments. RESULTS: Three different pattern recognition classifiers were developed for predicting caspase cleavage sites from protein sequences. Evaluation of the classifiers with quality measures indicated that all of the three classifiers performed well in predicting caspase cleavage sites, and when combining different classifiers the accuracy increased further. A new tool, Pripper, was developed to utilize the classifiers and predict the caspase cut sites from an arbitrary number of input sequences. A database was constructed with the developed tool, and it was used to identify caspase target proteins from tandem mass spectrometry data from two different proteomic experiments. Both known caspase cleavage products as well as novel cleavage products were identified using the database demonstrating the usefulness of the tool. Pripper is not restricted to predicting only caspase cut sites, but it gives the possibility to scan protein sequences for any given motif(s) and predict cut sites once a suitable cut site prediction model for any other protease has been developed. Pripper is freely available and can be downloaded from http://users.utu.fi/mijopi/Pripper. CONCLUSIONS: We have developed Pripper, a tool for reading an arbitrary number of proteins in FASTA format, predicting their caspase cleavage sites and outputting the cleaved sequences to a new FASTA format sequence file. We show that Pripper is a valuable tool in identifying novel caspase target proteins from modern proteomics experiments.

Compid: a new software tool to integrate and compare MS/MS based protein identification results from Mascot and Paragon.

Tandem mass spectrometry-based proteomics experiments produce large amounts of raw data, and different database search engines are needed to reliably identify all the proteins from this data. Here, we present Compid, an easy-to-use software tool that can be used to integrate and compare protein identification results from two search engines, Mascot and Paragon. Additionally, Compid enables extraction of information from large Mascot result files that cannot be opened via the Web interface and calculation of general statistical information about peptide and protein identifications in a data set. To demonstrate the usefulness of this tool, we used Compid to compare Mascot and Paragon database search results for mitochondrial proteome sample of human keratinocytes. The reports generated by Compid can be exported and opened as Excel documents or as text files using configurable delimiters, allowing the analysis and further processing of Compid output with a multitude of programs. Compid is freely available and can be downloaded from http://users.utu.fi/lanatr/compid. It is released under an open source license (GPL), enabling modification of the source code. Its modular architecture allows for creation of supplementary software components e.g. to enable support for additional input formats and report categories.

Mahalanobis distance screening of Arabidopsis mutants with chlorophyll fluorescence.

Rapid nondestructive screening of mutants is a common step in many research projects in plant biology. Here we report the development of a method that uses kinetic imaging of chlorophyll fluorescence to detect phenotypes that differ from wild-type plants. The method uses multiple fluorescence features simultaneously in order to catch different types of photosynthesis-related mutants with a single assay. The Mahalanobis distance was used to evaluate the degree of similarity in fluorescence features between the wild-type and test plants, and plants differing strongly from the wild-type were classified as mutants. The method was tested on a collection of photosynthesis-related mutants of Arabidopsis thaliana. The plants were evaluated from images in which the color of each pixel depended on the Mahalanobis distance of the fluorescence features. Two parameters of the color-coding procedure were used to adjust the trade-off between detection of true mutants and erratic classification of wild-type plants as mutants. We found that a large percentage of photosynthesis-related mutants can be detected with this method. Scripts for the free statistics software R are provided to facilitate the practical application of the method.

Filtering strategies for improving protein identification in high-throughput MS/MS studies.

Despite the recent advances in streamlining high-throughput proteomic pipelines using tandem mass spectrometry (MS/MS), reliable identification of peptides and proteins on a larger scale has remained a challenging task, still involving a considerable degree of user interaction. Recently, a number of papers have proposed computational strategies both for distinguishing poor MS/MS spectra prior to database search (pre-filtering) as well as for verifying the peptide identifications made by the search programs (post-filtering). Both of these filtering approaches can be very beneficial to the overall protein identification pipeline, since they can remove a substantial part of the time consuming manual validation work and convert large sets of MS/MS spectra into more reliable and interpretable proteome information. The choice of the filtering method depends both on the properties of the data and on the goals of the experiment. This review discusses the different pre- and post-filtering strategies available to the researchers, together with their relative merits and potential pitfalls. We also highlight some additional research topics, such as spectral denoising and statistical assessment of the identification results, which aim at further improving the coverage and accuracy of high-throughput protein identification studies.

Missing value imputation improves clustering and interpretation of gene expression microarray data.

BACKGROUND: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used. RESULTS: We show that this discrepancy can mostly be attributed to the way in which imputation methods have traditionally been developed and evaluated. By comparing a number of advanced imputation methods on recent microarray datasets, we show that even when there are marked differences in the measurement-level imputation accuracies across the datasets, these differences become negligible when the methods are evaluated in terms of how well they can reproduce the original gene clusters or their biological interpretations. Regardless of the evaluation approach, however, imputation always gave better results than ignoring missing data points or replacing them with zeros or average values, emphasizing the continued importance of using more advanced imputation methods. CONCLUSION: The results demonstrate that, while missing values are still severely complicating microarray data analysis, their impact on the discovery of biologically meaningful gene groups can - up to a certain degree - be reduced by using readily available and relatively fast imputation methods, such as the Bayesian Principal Components Algorithm (BPCA).

Inspiratory flow shape clustering: an automated method to monitor upper airway performance during sleep.

We describe an automated method for monitoring airflow dynamics in the upper airway of a sleeping subject. Its main task is to determine a set of inspiratory flow shape representatives and their relative incidence in a given respiratory airflow material. The flow shape clustering aims at reducing redundant information in the data, and thereby decreases the time needed to score overnight sleep recordings. Compared with previous computer-assisted systems, built on a pre-defined classification of prototype shapes, we require no a priori assumptions of the flow shape clusters to be discovered. The intrinsic flow shape clustering is performed with a modification of the Isodata algorithm, and the K-means clustering is used as a reference in comparison studies. The operation of the method is demonstrated on clinical sleep recordings both from patients with nocturnal breathing disorders and from non-symptomatic individuals. The feasible results obtained in the practical research design suggest that application of clustering algorithms to respiratory airflow measurements could give important insights into the subtle flow shape abnormalities underlying obstructive sleep-disordered breathing.

Automated pattern ranking in differential display data analysis.

Gene expression analysis by differential display (DD) is limited by the labor-intensive visual evaluation of the electrophoretic data traces. We describe a flexible method for computer-assisted ranking of expression patterns in data from DD experiments. The method is based on a pairwise alignment and comparison of the quantitative trace data with respect to specific expression patterns defined by the investigator. The observed patterns are ranked according to a score value that identifies the most potential findings to be confirmed visually instead of the vast amount of original results. This two-step approach, enabled by the efficient computer algorithm for gene expression pattern comparison, will increase the percentage of true-positive findings chosen for the tedious downstream processing, while minimizing the cost and labor involved in large scale DD data analysis.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

BACKGROUND: Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. METHODS: We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. RESULTS: The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. CONCLUSIONS: By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

PolyAlign: A Versatile LC-MS Data Alignment Tool for Landmark-Selected and -Automated Use.

We present a versatile user-friendly software tool, PolyAlign, for the alignment of multiple LC-MS signal maps with the option of manual landmark setting or automated alignment. One of the spectral images is selected as a reference map, and after manually setting the landmarks, the program warps the images using either polynomial or Hermite transformation. The software provides an option for automated landmark finding. The software includes a very fast zoom-in function synchronized between the images, which facilitate detecting correspondences between the adjacent images. Such an interactive visual process enables the analyst to decide when the alignment is satisfactory and to correct known irregularities. We demonstrate that the software provides significant improvements in the alignment of LC-MALDI data, with 10-15 landmark pairs, and it is also applicable to correcting electrospray LC-MS data. The results with practical data show substantial improvement in peak alignment compared to MZmine, which was among the best analysis packages in a recent assessment. The PolyAlign software is freely available and easily accessible as an integrated component of the popular MZmine software, and also as a simpler stand-alone Perl implementation to preview data and apply landmark directed polynomial transformation.

Gaussian mixture model-based segmentation of MR images taken from premature infant brains.

Segmentation of Magnetic Resonance multi-layer images of premature infant brain has additional challenges in comparison to normal adult brain segmentation. Images of premature infants contain lower signal to noise ratio due to shorter scanning times. Further, anatomic structure include still greater variations which can impair the accuracy of standard brain models. A fully automatic brain segmentation method for T1-weighted images is proposed in present paper. The method uses watershed segmentation with Gaussian mixture model clustering for segmenting cerebrospinal fluid from brain matter and other head tissues. The effect of the myelination process is considered by utilizing information from T2-weighted images. The performance of the new method is compared voxel-by-voxel to the corresponding expert segmentation. The proposed method is found to produce more uniform results in comparison to three accustomary segmentation methods originally developed for adults. This is the case in particular when anatomic forms are still under development and differ in their form from those of adults.

Computer-assisted identification of multitrace electrophoretic patterns in differential display experiments.

Modern multicapillary devices allow researchers to address increasingly complex biological questions involving comparisons of gene expression patterns across electrophoretic samples under various experimental conditions. As labor-intensive visual evaluation of the electrophoretic results is often the bottleneck of large-scale differential display (DD) studies, one way to further streamline this process is to focus only on a highly compressed list of the most potential patterns that are likely to provide reliable findings. To enable the identification of such candidate patterns, we present a computer-assisted method for objective ranking of multitrace peak patterns in DD experiments. The fundamental component of the multitrace pattern ranking method (MRANK) is the multiple alignment algorithm that allows for discovery of patterns involving sets of peak complexes from various electrophoretic samples. A score value is attached to each detected pattern which characterizes how accurately the pattern resembles the desired pattern query, freely defined by the researcher. The ranked pattern list produced by MRANK is validated against visual evaluation in terms of detecting and ranking a group of relevant patterns in a DD analysis of T-helper cell differentiation. We demonstrate high enrichment of the desired patterns on top of the score-ranked list (e.g., 90% of the visually selected patterns are discovered by looking through the first 3% of patterns in the ranked list of all patterns). The results suggest that a substantial amount of manual labor can be saved without compromising the accuracy of the findings by prioritizing the patterns according to MRANK output in the visual confirmation phase.

Evaluation of partial volume effect correction methods for brain positron emission tomography: Quantification and reproducibility.

Quantitative accuracy of positron emission tomography (PET) is decreased by the partial volume effect (PVE). The PVE correction (PVC) methods proposed by Alfano et al., Rousset et al., Müller-Gärtner et al. and Meltzer et al. were evaluated in the present study to obtain guidelines for selecting among them. For accuracy evaluation, the Hoffman brain phantom was scanned with three PETs of differing spatial resolution in order to measure the effect of PVC on radioactivity distribution. Test-retest data consisting of duplicate dynamic emission recordings of the dopamine D2-receptor ligand [(11)C] raclopride obtained in eight healthy control subjects were used to test the correction effect in different regions of interest. The PVC method proposed by Alfano et al. gave the best quantification accuracy in the brain gray matter region. When the effect of PVC on reliability was tested with human data, the method of Meltzer et al. proved to be the most reliable. The method by Alfano et al. may be better for group comparison studies and the method by Meltzer et al. for intra-subject drug-effect studies.

Improving missing value estimation in microarray data with gene ontology.

MOTIVATION: Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. RESULTS: We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. AVAILABILITY: Java and Matlab codes are available on request from the authors. SUPPLEMENTARY MATERIAL: Available online at http://users.utu.fi/jotatu/GOImpute.html.

Quality classification of tandem mass spectrometry data.

UNLABELLED: Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. RESULTS: We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.

A comparative evaluation of software for the analysis of liquid chromatography-tandem mass spectrometry data from isotope coded affinity tag experiments.

The options available for processing quantitative data from isotope coded affinity tag (ICAT) experiments have mostly been confined to software specific to the instrument of acquisition. However, recent developments with data format conversion have subsequently increased such processing opportunities. In the present study, data sets from ICAT experiments, analysed with liquid chromatography/tandem mass spectrometry (MS/MS), using an Applied Biosystems QSTAR Pulsar quadrupole-TOF mass spectrometer, were processed in triplicate using separate mass spectrometry software packages. The programs Pro ICAT, Spectrum Mill and SEQUEST with XPRESS were employed. Attention was paid towards the extent of common identification and agreement of quantitative results, with additional interest in the flexibility and productivity of these programs. The comparisons were made with data from the analysis of a specifically prepared test mixture, nine proteins at a range of relative concentration ratios from 0.1 to 10 (light to heavy labelled forms), as a known control, and data selected from an ICAT study involving the measurement of cytokine induced protein expression in human lymphoblasts, as an applied example. Dissimilarities were detected in peptide identification that reflected how the associated scoring parameters favoured information from the MS/MS data sets. Accordingly, there were differences in the numbers of peptides and protein identifications, although from these it was apparent that both confirmatory and complementary information was present. In the quantitative results from the three programs, no statistically significant differences were observed.

Comparison of PDQuest and Progenesis software packages in the analysis of two-dimensional electrophoresis gels.

Efficient analysis of protein expression by using two-dimensional electrophoresis (2-DE) data relies on the use of automated image processing techniques. The overall success of this research depends critically on the accuracy and the reliability of the analysis software. In addition, the software has a profound effect on the interpretation of the results obtained, and the amount of user intervention demanded during the analysis. The choice of analysis software that best meets specific needs is therefore of interest to the research laboratory. In this paper we compare two advanced analysis software packages, PDQuest and Progenesis. Their evaluation is based on quantitative tests at three different levels of standard 2-DE analysis: spot detection, gel matching and spot quantitation. As test materials we use three gel sets previously used in a similar comparison of Z3 and Melanie, and three sets of gels from our own research. It was observed that the quality of the test gels critically influences the spot detection and gel matching results. Both packages were sensitive to the parameter or filter settings with respect to the tendency of finding true positive and false positive spots. Quantitation results were very accurate for both analysis software packages.