Microsatellite instability (MSI) is a phenomenon seen in several cancer types, which can be used as a biomarker to help guide immune checkpoint inhibitor treatment. To facilitate this, researchers have developed computational tools to categorize samples as having high microsatellite instability, or as being microsatellite stable using next-generation sequencing data. Most of these tools were published with unclear scope and usage, and they have yet to be independently benchmarked. To address these issues, we assessed the performance of eight leading MSI tools across several unique datasets that encompass a wide variety of sequencing methods. While we were able to replicate the original findings of each tool on whole exome sequencing data, most tools had worse receiver operating characteristic and precision-recall area under the curve values on whole genome sequencing data. We also found that they lacked agreement with one another and with commercial MSI software on gene panel data, and that optimal threshold cut-offs vary by sequencing type. Lastly, we tested tools made specifically for RNA sequencing data and found they were outperformed by tools designed for use with DNA sequencing data. Out of all, two tools (MSIsensor2, MANTIS) performed well across nearly all datasets, but when all datasets were combined, their precision decreased. Our results caution that MSI tools can have much lower performance on datasets other than those on which they were originally evaluated, and in the case of RNA sequencing tools, can even perform poorly on the type of data for which they were created.

BACKGROUND: As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. RESULTS: In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation . CONCLUSIONS: GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.

Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods implicitly test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates by reducing the influence of genes with greater uncertainty on the estimation of distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes.

MOTIVATION: The expression of tissue-restricted antigens (TRAs) in the thymus is required to ensure efficient negative selection of potentially auto-reactive T lymphocytes and avoid autoimmune disease. This promiscuous expression is under the control of the autoimmune regulator (AIRE), a transcription factor expressed in medullary thymic epithelial cells (mTECs). Tissue-specific alternative splicing may also produce TRAs but the extent to which splice isoforms that are restricted to specific tissues are expressed in mTECs is yet to be investigated. RESULTS: We reanalyzed microarray and RNA-Seq datasets from mouse mTECs and other epithelial and non-epithelial cell types and found that the diversity of splice isoforms in mTECs was greater than in any of the other cell types or tissues studied. We identified tissue-specific isoforms from a panel of mouse tissues and found several examples of such isoforms that are expressed in mTECs. The number of isoforms with restricted expression found in mTECs was significantly higher than for comparable cell types. Furthermore, we found evidence that AIRE influences the increased splicing diversity observed in mTECs as the genes for which tissue restricted isoforms are produced in mTECs were significantly more likely than other genes to be differentially spliced between AIRE knock-out and wild-type samples. Our results suggest that developing T lymphocytes are exposed to diverse tissue-restricted splice isoforms in the thymus and that AIRE has a direct or indirect role in this process, representing a novel aspect of its role in the maintenance of immune self-tolerance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use, demonstrated by the ever-growing numbers of samples deposited in public repositories. RESULTS: We propose a novel approach to microarray analysis that attains many of the advantages of RNA-Seq. This method, called Machine Learning of Transcript Expression (MaLTE), leverages samples for which both microarray and RNA-Seq data are available, using a Random Forest to learn the relationship between the fluorescence intensity of sets of microarray probes and RNA-Seq transcript expression estimates. We trained MaLTE on data from the Genotype-Tissue Expression (GTEx) project, consisting of Affymetrix gene arrays and RNA-Seq from over 700 samples across a broad range of human tissues. CONCLUSION: This approach can be used to accurately estimate absolute expression levels from microarray data, at both gene and transcript level, which has not previously been possible. This methodology will facilitate re-analysis of archived microarray data and broaden the utility of the vast quantities of data still being generated.

UNLABELLED: Gene expression data are typically generated from heterogeneous biological samples that are composed of multiple cell or tissue types, in varying proportions, each contributing to global gene expression. This heterogeneity is a major confounder in standard analysis such as differential expression analysis, where differences in the relative proportions of the constituent cells may prevent or bias the detection of cell-specific differences. Computational deconvolution of global gene expression is an appealing alternative to costly physical sample separation techniques and enables a more detailed analysis of the underlying biological processes at the cell-type level. To facilitate and popularize the application of such methods, we developed CellMix, an R package that incorporates most state-of-the-art deconvolution methods, into an intuitive and extendible framework, providing a single entry point to explore, assess and disentangle gene expression data from heterogeneous samples. AVAILABILITY AND IMPLEMENTATION: The CellMix package builds on R/BioConductor and is available from http://web.cbio.uct.ac.za/∼renaud/CRAN/web/CellMix. It is currently being submitted to BioConductor. The package's vignettes notably contain additional information, examples and references.

MOTIVATION: DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genome-wide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters. RESULTS: We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research. RESULTS: We show that paralogs, which typically have high sequence identity and similar molecular functions, also exhibit high correlation in their expression patterns. We investigate this correlation as a potential confounding factor common to current GSA methods using Indygene http://www.cbio.uct.ac.za/indygene, a web tool that reduces a supplied list of genes so that it includes no pairwise paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs. CONCLUSIONS: The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

BACKGROUND: We carried out an analysis of intron length conservation across a diverse group of nineteen mammalian species. Motivated by recent research suggesting a role for time delays associated with intron transcription in gene expression oscillations required for early embryonic patterning, we searched for examples of genes that showed the most extreme conservation of total intron content in mammals. RESULTS: Gene sets annotated as being involved in pattern specification in the early embryo or containing the homeobox DNA-binding domain, were significantly enriched among genes with highly conserved intron content. We used ancestral sequences reconstructed with probabilistic models that account for insertion and deletion mutations to distinguish insertion and deletion events on lineages leading to human and mouse from their last common ancestor. Using a randomization procedure, we show that genes containing the homeobox domain show less change in intron content than expected, given the number of insertion and deletion events within their introns. CONCLUSIONS: Our results suggest selection for gene expression precision or the existence of additional development-associated genes for which transcriptional delay is functionally significant.

BACKGROUND: Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community. RESULTS: Our objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results. CONCLUSIONS: The NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN.

Probabilistic models of sequence evolution are in widespread use in phylogenetics and molecular sequence evolution. These models have become increasingly sophisticated and combined with statistical model comparison techniques have helped to shed light on how genes and proteins evolve. Models of codon evolution have been particularly useful, because, in addition to providing a significant improvement in model realism for protein-coding sequences, codon models can also be designed to test hypotheses about the selective pressures that shape the evolution of the sequences. Such models typically assume a phylogeny and can be used to identify sites or lineages that have evolved adaptively. Recently some of the key assumptions that underlie phylogenetic tests of selection have been questioned, such as the assumption that the rate of synonymous changes is constant across sites or that a single phylogenetic tree can be assumed at all sites for recombining sequences. While some of these issues have been addressed through the development of novel methods, others remain as caveats that need to be considered on a case-by-case basis. Here, we outline the theory of codon models and their application to the detection of positive selection. We review some of the more recent developments that have improved their power and utility, laying a foundation for further advances in the modeling of coding sequence evolution.

BACKGROUND: Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. RESULTS: MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning results ranked by decreasing probability of relevance. For maximum speed it uses the Medical Subject Headings (MeSH) and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline. The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline. We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic. In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92. CONCLUSION: MScanner is an effective non-domain-specific classifier that operates on the entire Medline database, and is suited to retrieving topics for which many features may indicate relevance. Its web interface simplifies the task of classifying Medline citations, compared to building a pre-filter and classifier specific to the topic. The data sets and open source code used to obtain the results in this paper are available on-line and as supplementary material, and the web interface may be accessed at http://mscanner.stanford.edu.

MOTIVATION: Accurate detection of positive Darwinian selection can provide important insights to researchers investigating the evolution of pathogens. However, many pathogens (particularly viruses) undergo frequent recombination and the phylogenetic methods commonly applied to detect positive selection have been shown to give misleading results when applied to recombining sequences. We propose a method that makes maximum likelihood inference of positive selection robust to the presence of recombination. This is achieved by allowing tree topologies and branch lengths to change across detected recombination breakpoints. Further improvements are obtained by allowing synonymous substitution rates to vary across sites. RESULTS: Using simulation we show that, even for extreme cases where recombination causes standard methods to reach false positive rates >90%, the proposed method decreases the false positive rate to acceptable levels while retaining high power. We applied the method to two HIV-1 datasets for which we have previously found that inference of positive selection is invalid owing to high rates of recombination. In one of these (env gene) we still detected positive selection using the proposed method, while in the other (gag gene) we found no significant evidence of positive selection. AVAILABILITY: A HyPhy batch language implementation of the proposed methods and the HIV-1 datasets analysed are available at http://www.cbio.uct.ac.za/pub_support/bioinf06. The HyPhy package is available at http://www.hyphy.org, and it is planned that the proposed methods will be included in the next distribution. RDP2 is available at http://darwin.uvigo.es/rdp/rdp.html

MOTIVATION: In a diploid organism the proportion of transcripts that are produced from the two parental alleles can differ substantially due, for example to epigenetic modification that causes complete or partial silencing of one parental allele or to cis acting polymorphisms that affect transcriptional regulation. Counts of SNP alleles derived from EST sequences have been used to identify both novel candidates for genomic imprinting as well as examples of genes with allelic differences in expression. RESULTS: We have developed a set of statistical models in a maximum likelihood framework that can make highly efficient use of public transcript data to identify genes with unequal representation of alternative alleles in cDNA libraries. We modelled both imprinting and allele-specific expression and applied the models to a large dataset of SNPs mapped to EST sequences. Using simulations, matched closely to real data, we demonstrate significantly improved performance over existing methods that have been applied to the same data. We further validated the power of this approach to detect imprinting using a set of known imprinted genes and inferred a set of candidate imprinted genes, several of which are in close proximity to known imprinted genes. We report evidence that there are undiscovered imprinted genes in known imprinted regions. Overall, more than half of the genes for which the most data are available show some evidence of allele-specific expression. AVAILABILITY: Software is available from the authors on request.

BACKGROUND: Rates of substitution in protein-coding sequences can provide important insights into evolutionary processes that are of biomedical and theoretical interest. Increased availability of coding sequence data has enabled researchers to estimate more accurately the coding sequence divergence of pairs of organisms. However the use of different data sources, alignment protocols and methods to estimate substitution rates leads to widely varying estimates of key parameters that define the coding sequence divergence of orthologous genes. Although complete genome sequence data are not available for all organisms, fragmentary sequence data can provide accurate estimates of substitution rates provided that an appropriate and consistent methodology is used and that differences in the estimates obtainable from different data sources are taken into account. RESULTS: We have developed FRAGS, an application framework that uses existing, freely available software components to construct in-frame alignments and estimate coding substitution rates from fragmentary sequence data. Coding sequence substitution estimates for human and chimpanzee sequences, generated by FRAGS, reveal that methodological differences can give rise to significantly different estimates of important substitution parameters. The estimated substitution rates were also used to infer upper-bounds on the amount of sequencing error in the datasets that we have analysed. CONCLUSION: We have developed a system that performs robust estimation of substitution rates for orthologous sequences from a pair of organisms. Our system can be used when fragmentary genomic or transcript data is available from one of the organisms and the other is a completely sequenced genome within the Ensembl database. As well as estimating substitution statistics our system enables the user to manage and query alignment and substitution data.