SUMMARY Modern biology is experiencing a rapid increase in data volumes that challenges our analytical skills and existing cyberinfrastructure. Exponential expansion of the protein sequence universe (PSU), the protein sequence space, together with the costs and complexities of manual curation creates a major bottleneck in life sciences research. Existing resources lack scalable visualization tools that are instrumental for functional annotation. Here, we describe a new visualization tool using multidimensional scaling to create a 3D embedding of the protein space. The advantages of the proposed PSU method include the ability to scale to large numbers of sequences, integrate different similarity measures with other functional and experimental data, and facilitate protein annotation. We applied the method to visualize the prokaryotic PSU using sequence alignment scores. As an annotation example, we used the interpolation approach to map the set of annotated archaeal proteins into the prokaryotic PSU. Transdisciplinary approaches akin to the one described in this paper are urgently needed to quickly and efficiently translate the influx of new data into tangible innovations and groundbreaking discoveries. Copyright © 2013 John Wiley & Sons, Ltd.

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.

Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway.

A common task in biological research is to predict function for proteins by comparing sequences between proteins of known and unknown function. This is often done using pair-wise sequence alignment algorithms (e.g. BLAST). A problem with this approach is the assumption of a simple equivalence between a minimum sequence similarity threshold and the function similarity between proteins. This assumption is based on the binary concept of homology in that proteins are or not homologous. The relationship between sequence and function however is more complex as well as pertinent for predicting protein function, e.g. evaluating BLAST alignments or developing training sets for profile models based on functional rather than homologous groupings. Our motivation for this study was to model sequence and function similarity between proteins to gain insights into the "sequence-function similarity relationship between proteins for predicting function. Using our model we found that function similarity generally increases with sequence similarity but with a high degree of variability. This result has implications for pair-wise approaches in that it appears sequence similarity must be very high to ensure high function similarity. Profile models which enable higher sensitivity are a potential solution. However, multiple sequences alignments (a necessary prerequisite) are a problem in that current algorithms have difficulty aligning sequences with very low sequence similarity, which is common in our data set, or are intractable for high numbers of sequences. Given the importance of predicting protein function and the need for multiple sequence alignments, algorithms for accomplishing this task should be further refined and developed.

MOTIVATION: Enrichment tests are used in high-throughput experimentation to measure the association between gene or protein expression and membership in groups or pathways. The Fisher's exact test is commonly used. We specifically examined the associations produced by the Fisher test between protein identification by mass spectrometry discovery proteomics, and their Gene Ontology (GO) term assignments in a large yeast dataset. We found that direct application of the Fisher test is misleading in proteomics due to the bias in mass spectrometry to preferentially identify proteins based on their biochemical properties. False inference about associations can be made if this bias is not corrected. Our method adjusts Fisher tests for these biases and produces associations more directly attributable to protein expression rather than experimental bias. RESULTS: Using logistic regression, we modeled the association between protein identification and GO term assignments while adjusting for identification bias in mass spectrometry. The model accounts for five biochemical properties of peptides: (i) hydrophobicity, (ii) molecular weight, (iii) transfer energy, (iv) beta turn frequency and (v) isoelectric point. The model was fit on 181 060 peptides from 2678 proteins identified in 24 yeast proteomics datasets with a 1% false discovery rate. In analyzing the association between protein identification and their GO term assignments, we found that 25% (134 out of 544) of Fisher tests that showed significant association (q-value ≤0.05) were non-significant after adjustment using our model. Simulations generating yeast protein sets enriched for identification propensity show that unadjusted enrichment tests were biased while our approach worked well.

MOTIVATION: The false discovery rate (FDR) has been widely adopted to address the multiple comparisons issue in high-throughput experiments such as microarray gene-expression studies. However, while the FDR is quite useful as an approach to limit false discoveries within a single experiment, like other multiple comparison corrections it may be an inappropriate way to compare results across experiments. This article uses several examples based on gene-expression data to demonstrate the potential misinterpretations that can arise from using FDR to compare across experiments. Researchers should be aware of these pitfalls and wary of using FDR to compare experimental results. FDR should be augmented with other measures such as p-values and expression ratios. It is worth including standard error and variance information for meta-analyses and, if possible, the raw data for re-analyses. This is especially important for high-throughput studies because data are often re-used for different objectives, including comparing common elements across many experiments. No single error rate or data summary may be appropriate for all of the different objectives.

MOTIVATION: Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, > or = 2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68-98% of the correct single-hit proteins with an error rate of < 2%. This results in a 22-65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.