Protein deletions are frequent among both disease-causing and tolerated variants. Several mechanisms at the DNA, RNA and protein levels can lead to deletions. Many deletions are misclassified in the literature and databases, especially when the mRNA is degraded by the cellular quality-control mechanism. We developed a novel predictor for sequence retaining protein deletions, i.e., variants that do not alter the sequence downstream of the deletion site. We collected an extensive dataset of verified protein deletions, each described by a comprehensive set of context, content, position, and gene-based features. We evaluated both statistical and deep learning algorithms and selected a gradient boosting-based approach to develop the PON-Del predictor for short, 1-10 amino acid, sequence-retaining deletions. Variants are typically classified into two categories: either pathogenic or benign. However, there is always a third class of variants: variants of uncertain significance (VUSs), which have been ignored by all previous methods. PON-Del is the first deletion interpretation method that includes VUSs. It provides two outputs, binary and three-state prediction with VUSs. The performance of PON-Del was superior to that of previous methods. The tool is freely available at https://structure.bmc.lu.se/pon_del/.

Computational tools are widely used for interpreting variants detected in sequencing projects.The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment.The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets.To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency �1% and <25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes.We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools.In addition to overall specificity of the tools, we tested their performance for variants in six geographical populations.PON-P2 had the best performance (95.5%) followed by FATHMM (86.4%) and VEST (83.5%).While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing.The results allow choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers. Author summaryIn precision/personalized medicine of many conditions it is essential to investigate individual's genome.Interpretation of the observed variation (mutation) sets is feasible only with computational approaches.We assessed the performance of variant pathogenicity/ tolerance prediction programs on benign variants.Variants were obtained from highquality ExAC database and selected to have minor allele frequency between 1 and 25%.We obtained 63,160 such cases and investigated 10 widely used predictors.Specificities of the methods showed large differences, from 64 to 96%, thus users of these methods have to be careful when choosing the one(s) they will use.We investigated further the performances on different populations, allele frequencies, separately for males and females, chromosome wise and for population unique and non-unique variants.The ranking of the tools remained the same in all these scenarios, i.e. the best methods were the best irrespective on how the data was filtered and grouped.This is to our knowledge the first large scale evaluation of method performance on benign variants.

BACKGROUND: Benchmark datasets are essential for both method development and performance assessment. These datasets have numerous requirements, representativeness being one. In the case of variant tolerance/pathogenicity prediction, representativeness means that the dataset covers the space of variations and their effects. RESULTS: We performed the first analysis of the representativeness of variation benchmark datasets. We used statistical approaches to investigate how proteins in the benchmark datasets were representative for the entire human protein universe. We investigated the distributions of variants in chromosomes, protein structures, CATH domains and classes, Pfam protein families, Enzyme Commission (EC) classifications and Gene Ontology annotations in 24 datasets that have been used for training and testing variant tolerance prediction methods. All the datasets were available in VariBench or VariSNP databases. We tested also whether the pathogenic variant datasets contained neutral variants defined as those that have high minor allele frequency in the ExAC database. The distributions of variants over the chromosomes and proteins varied greatly between the datasets. CONCLUSIONS: None of the datasets was found to be well representative. Many of the tested datasets had quite good coverage of the different protein characteristics. Dataset size correlates to representativeness but only weakly to the performance of methods trained on them. The results imply that dataset representativeness is an important factor and should be taken into account in predictor development and testing.

BACKGROUND: Amino acid substitutions due to DNA nucleotide replacements are frequently disease-causing because of affecting functionally important sites. If the substituting amino acid does not fit into the protein, it causes structural alterations that are often harmful. Clashes of amino acids cause local or global structural changes. Testing structural compatibility of variations has been difficult due to the lack of a dedicated method that could handle vast amounts of variation data produced by next generation sequencing technologies. RESULTS: We developed a method, PON-SC, for detecting protein structural clashes due to amino acid substitutions. The method utilizes side chain rotamer library and tests whether any of the common rotamers can be fitted into the protein structure. The tool was tested both with variants that cause and do not cause clashes and found to have accuracy of 0.71 over five test datasets. CONCLUSIONS: We developed a fast method for residue side chain clash detection. The method provides in addition to the prediction also visualization of the variant in three dimensional structure.

MOTIVATION: Solubility is one of the fundamental protein properties. It is of great interest because of its relevance to protein expression. Reduced solubility and protein aggregation are also associated with many diseases. RESULTS: We collected from literature the largest experimentally verified solubility affecting amino acid substitution (AAS) dataset and used it to train a predictor called PON-Sol. The predictor can distinguish both solubility decreasing and increasing variants from those not affecting solubility. PON-Sol has normalized correct prediction ratio of 0.491 on cross-validation and 0.432 for independent test set. The performance of the method was compared both to solubility and aggregation predictors and found to be superior. PON-Sol can be used for the prediction of effects of disease-related substitutions, effects on heterologous recombinant protein expression and enhanced crystallizability. One application is to investigate effects of all possible AASs in a protein to aid protein engineering. AVAILABILITY AND IMPLEMENTATION: PON-Sol is freely available at http://structure.bmc.lu.se/PON-Sol The training and test data are available at http://structure.bmc.lu.se/VariBench/ponsol.php CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. RESULTS: The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. CONCLUSIONS: VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

MOTIVATION: Multiple sequence alignment (MSA) is an essential prerequisite for many sequence analysis methods and valuable tool itself for describing relationships between protein sequences. Since the success of the sequence analysis is highly dependent on the reliability of alignments, measures for assessing the quality of alignments are highly requisite. RESULTS: We present a statistical model-based alignment quality score. Unlike other quality scores, it does not require several parallel alignments for the same set of sequences or additional structural information. Our quality score is based on measuring the conservation level of reference alignments in Homstrad. Reference sequences were realigned with the Mafft, Muscle and Probcons alignment programs, and a sum-of-pairs (SP) score was used to measure the quality of the realignments. Statistical modelling of the SP score as a function of conservation level and other alignment characteristics makes it possible to predict the SP score for any global MSA. The predicted SP scores are highly correlated with the correct SP scores, when tested on the Homstrad and SABmark databases. The results are comparable to that of multiple overlap score (MOS) and better than those of normalized mean distance (NorMD) and normalized iRMSD (NiRMSD) alignment quality criteria. Furthermore, the predicted SP score is able to detect alignments with badly aligned or unrelated sequences. AVAILABILITY: The method is freely available at http://www.mtt.fi/AlignmentQuality/.

BACKGROUND: Pseudogenes, nonfunctional copies of genes, evolve fast due the lack of evolutionary pressures and thus appear in several different forms. PseudoGeneQuest is an online tool to search the human genome for a given query sequence and to identify different types of pseudogenes as well as novel genes and gene fragments. DESCRIPTION: The service can detect pseudogenes, that have arisen either by retrotransposition or segmental genome duplication, many of which are not listed in the public pseudogene databases. The service has a user-friendly web interface and uses a powerful computer cluster in order to perform parallel searches and provide relatively fast runtimes despite exhaustive database searches and analyses. CONCLUSION: PseudoGeneQuest is a versatile tool for detecting novel pseudogene candidates from the human genome. The service searches human genome sequences for five types of pseudogenes and provides an output that allows easy further analysis of observations. In addition to the result file the system provides visualization of the results linked to Ensembl Genome Browser. PseudoGeneQuest service is freely available.

Hydropathy is a dominant force in protein folding and has been measured with numerous methods. Several hydropathy scales are widely used in sequence-based predictions, however, without knowledge about their reliability. We investigated the prediction accuracy of 56 hydropathy scales by correlating predicted values with the accessible surface area in known 3-D structures of proteins. The correlations for the best scales are in the order of - 0.26, whereas the weakest have on average merely - 0.11. Results for different amino acids vary greatly within the scales, but are more consistent between the scales. One of the most common applications of hydropathy scales is to predict antigenic regions. Our analysis indicated that some epitopes are located among the most exposed regions. Despite poor overall correlation, hydropathy predictions can still be used in certain applications where qualitative analysis is sufficient.

BACKGROUND: Multiple sequence alignment is the foundation of many important applications in bioinformatics that aim at detecting functionally important regions, predicting protein structures, building phylogenetic trees etc. Although the automatic construction of a multiple sequence alignment for a set of remotely related sequences cause a very challenging and error-prone task, many downstream analyses still rely heavily on the accuracy of the alignments. RESULTS: To address the need for an objective evaluation framework, we introduce a statistical score that assesses the quality of a given multiple sequence alignment. The quality assessment is based on counting the number of significantly conserved positions in the alignment using importance sampling method in conjunction with statistical profile analysis framework. We first evaluate a novel objective function used in the alignment quality score for measuring the positional conservation. The results for the Src homology 2 (SH2) domain, Ras-like proteins, peptidase M13, subtilase and beta-lactamase families demonstrate that the score can distinguish sequence patterns with different degrees of conservation. Secondly, we evaluate the quality of the alignments produced by several widely used multiple sequence alignment programs using a novel alignment quality score and a commonly used sum of pairs method. According to these results, the Mafft strategy L-INS-i outperforms the other methods, although the difference between the Probcons, TCoffee and Muscle is mostly insignificant. The novel alignment quality score provides similar results than the sum of pairs method. CONCLUSION: The results indicate that the proposed statistical score is useful in assessing the quality of multiple sequence alignments.

BACKGROUND: Cells react to changing intra- and extracellular signals by dynamically modulating complex biochemical networks. Cellular responses to extracellular signals lead to changes in gene and protein expression. Since the majority of genes encode proteins, we investigated possible correlations between protein parameters and gene expression patterns to identify proteome-wide characteristics indicative of trends common to expressed proteins. RESULTS: Numerous bioinformatics methods were used to filter and merge information regarding gene and protein annotations. A new statistical time point-oriented analysis was developed for the study of dynamic correlations in large time series data. The method was applied to investigate microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle. CONCLUSION: We show that the properties of proteins synthesized correlate dynamically with the gene expression profile, indicating that not only is the actual identity and function of expressed proteins important for cellular responses but that several physicochemical and other protein properties correlate with gene expression as well. Gene expression correlates strongly with amino acid composition, composition- and sequence-derived variables, functional, structural, localization and gene ontology parameters. Thus, our results suggest that a dynamic relationship exists between proteome properties and gene expression in many biological systems, and therefore this relationship is fundamental to understanding cellular mechanisms in health and disease.

Signal transduction pathways are crucial for the regulation of a very wide variety of cellular functions ranging, for example, from translation to intercellular communication, and from metabolism to apoptosis. Protein kinases and phosphatases, together with their binding partners, are key players in these cascades. They also form a substantial part of the genes in genomes and proteins in proteomes in all animals. Signalling can be studied in many different levels and ways. This has resulted in large body of publications and Internet services. This paper describes open-access databases and software aiming at presenting the kind of data available and how to perform bioinformatics analyses.

MOTIVATION: Profile hidden Markov models provide a sensitive method for performing sequence database search and aligning multiple sequences. One of the drawbacks of the hidden Markov model is that the conserved amino acids are not emphasized, but signal and noise are treated equally. For this reason, the number of estimated emission parameters is often enormous. Focusing the analysis on conserved residues only should increase the accuracy of sequence database search. RESULTS: We address this issue with a new method for efficient emission probability (EEP) estimation, in which amino acids are divided into effective and ineffective residues at each conserved alignment position. A practical study with 20 protein families demonstrated that the EEP method is capable of detecting family members from other proteins with sensitivity of 98% and specificity of 99% on the average, even if the number of free emission parameters was decreased to 15% of the original. In the database search for TIM barrel sequences, EEP recognizes the family members nearly as accurately as HMMER or Blast, but the number of false positive sequences was significantly less than that obtained with the other methods. AVAILABILITY: The algorithms written in C language are available on request from the authors.

SUMMARY: RankViaContact is a web service for calculation of residue-residue contact energies in proteins based on a coarse-grained model, and for visualization of interactions. The service provides information about ranked contact energies of residues, coordination numbers and the relative solvent accessibility of selected residues, as well as sequence and structure information. The program can be used to design stabilizing mutations, to analyze residue-residue contacts and to study the consequences of mutations. AVAILABILITY: http://bioinf.uta.fi/Rank.htm.

UNLABELLED: We have developed a new Internet service, which provides mobile access to bioinformatics databases and software tools. The BioWAP service facilitates access to basic bioinformatics databases and analysis tools from everywhere without a PC or a laptop computer. Both open source bioinformatics program suites and Internet services, which are not designed for mobile Internet access, were utilized in the BioWAP service. AVAILABILITY: The BioWAP service starting page can be browsed with any WAP terminal from http://bioinf.uta.fi/wml/welcome.wml.

MOTIVATION: To develop tools for the submission of mutations to databases and maintenance of locus-specific mutation databases. Advanced, integrated computer systems are needed to store and organize the increasing mutation information. RESULTS: The MUTbase program suite provides an easy, interactive and quality-controlled submission of information to mutation databases. For further study of the databases on the World Wide Web, a number of tools are provided. The program package also writes and updates a large number of Web pages, e.g. about the distribution and statistics of disease-causing mutations, and changes in restriction patterns.

An algorithm for multiple sequence comparison was implemented in FORTRAN 77 for VAX/VMS in GCG-compatible format. The MULTICOMP program package includes several procedures with which one query sequence can be compared simultaneously to several DNA, RNA or amino acid sequences. The same technique was also introduced for comparing propensities of secondary structural features, which can be predicted on the basis of amino acid sequences. The technique has been applied to a wide range of sequence and structural analyses.

An algorithm was developed to compare simultaneously several DNA, RNA or protein sequences. With the algorithm, conserved regions of one sequence are located by doing pairwise comparisons with other sequences, which is advantageous in planning site-directed mutagenesis studies. The observation matrices filled with scores of comparisons are superimposed and added together and those points having values greater than or equal to stringency are accepted. The predicted secondary structural features can also be compared.