With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography-mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t-test or analysis of variance, often after the application of data imputation to reduce the number of missing values. In high-throughput proteomics, statistical analysis methods and imputation techniques are difficult to evaluate, given the lack of gold standard data sets. Here, we use experimental and resampled data to evaluate the performance of four statistical analysis methods and the added value of imputation, for different numbers of biological replicates. We find that three or four replicates are the minimum requirement for high-throughput data analysis and confident assignment of significant changes. Data imputation does increase sensitivity in some cases, but leads to a much higher actual false discovery rate. Additionally, we find that empirical Bayes method (limma) achieves the highest sensitivity, and we thus recommend its use for performing differential expression analysis at the peptide level.

MOTIVATION: High Throughput Sequencing (HTS) has enabled researchers to probe the human T cell receptor (TCR) repertoire, which consists of many rare sequences. Distinguishing between true but rare TCR sequences and variants generated by polymerase chain reaction (PCR) and sequencing errors remains a formidable challenge. The conventional approach to handle errors is to remove low quality reads, and/or rare TCR sequences. Such filtering discards a large number of true and often rare TCR sequences. However, accurate identification and quantification of rare TCR sequences is essential for repertoire diversity estimation. RESULTS: We devised a pipeline, called Recover TCR (RTCR), that accurately recovers TCR sequences, including rare TCR sequences, from HTS data (including barcoded data) even at low coverage. RTCR employs a data-driven statistical model to rectify PCR and sequencing errors in an adaptive manner. Using simulations, we demonstrate that RTCR can easily adapt to the error profiles of different types of sequencers and exhibits consistently high recall and high precision even at low coverages where other pipelines perform poorly. Using published real data, we show that RTCR accurately resolves sequencing errors and outperforms all other pipelines. AVAILABILITY AND IMPLEMENTATION: The RTCR pipeline is implemented in Python (v2.7) and C and is freely available at http://uubram.github.io/RTCR/along with documentation and examples of typical usage. CONTACT: [email protected].

The RIG-I-like receptor (RLR) pathway is essential for detecting cytosolic viral RNA to trigger the production of type I interferons (IFNα/β) that initiate an innate antiviral response. Through systematic assessment of a wide variety of genomics data, we discovered 10 molecular signatures of known RLR pathway components that collectively predict novel members. We demonstrate that RLR pathway genes, among others, tend to evolve rapidly, interact with viral proteins, contain a limited set of protein domains, are regulated by specific transcription factors, and form a tightly connected interaction network. Using a Bayesian approach to integrate these signatures, we propose likely novel RLR regulators. RNAi knockdown experiments revealed a high prediction accuracy, identifying 94 genes among 187 candidates tested (~50%) that affected viral RNA-induced production of IFNβ. The discovered antiviral regulators may participate in a wide range of processes that highlight the complexity of antiviral defense (e.g. MAP3K11, CDK11B, PSMA3, TRIM14, HSPA9B, CDC37, NUP98, G3BP1), and include uncharacterized factors (DDX17, C6orf58, C16orf57, PKN2, SNW1). Our validated RLR pathway list (http://rlr.cmbi.umcn.nl/), obtained using a combination of integrative genomics and experiments, is a new resource for innate antiviral immunity research.

BACKGROUND: A Java application is presented, which compares large numbers (n > 100) of raw FTICR mass spectra from patients and controls. Two peptide profile matrices can be produced simultaneously, one with occurrences of peptide masses in samples and another with the intensity of common peak masses in all the measured samples, using the peak- and background intensities of the raw data. In latter way, more significantly differentially expressed peptides are found between groups than just using the presence or absence in samples of common peak masses. The software application is tested by searching angiogenesis related proteins in glioma by comparing laser capture micro dissected- and enzymatic by trypsin digested tissue sections. RESULTS: By hierarchical clustering of the presence-absence matrix, it appears that proteins, such as hemoglobin alpha and delta subunit, fibrinogen beta and gamma chain precursor, tubulin specific chaperone A, epidermal fatty acid binding protein, neutrophil gelatinase-associated lipocalin precursor, peptidyl tRNA hydrolase 2 mitochondrial precursor, placenta specific growth hormone, and zinc finger CCHC domain containing protein 13 are significantly different expressed in glioma vessels. The up-regulated proteins in the glioma vessels with respect to the normal vessels determined by the Wilcoxon-Mann-Whitney test on the intensity matrix are vimentin, glial fibrillary acidic protein, serum albumin precursor, annexin A5, alpha cardiac and beta actin, type I cytoskeletal 10 keratin, calcium binding protein p22, and desmin. Peptide masses of calcium binding protein p22, Cdc42 effector protein 3, fibronectin precursor, and myosin-9 are exclusively present in glioma vessels. Some peptide fragments of non-muscular myosin-9 at the C-terminus are strongly up-regulated in the glioma vessels with respect to the normal vessels. CONCLUSION: The less rigorous than in general used commercial propriety software de-isotope algorithm results in more mono-isotopic peptide masses and consequently more proteins. Centroiding of peptide masses takes place by taking the average over more spectra in the profile matrix. Cytoskeleton proteins and proteins involved in the calcium signaling pathway seem to be most up-regulated in glioma vessels. The finding that peptides at the C-terminus of myosin-9 are up-regulated could be ascribed to splicing or fragmentation by proteases.