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Motivation: Inferring lengths of inherited microsatellite alleles with single base pair resolution from short sequence reads is challenging due to several sources of noise caused by the repetitive nature of microsatellites and the technologies used to generate raw sequence data., Results: We have developed a program, GenoTan, using a discretized Gaussian mixture model combined with a rules-based approach to identify inherited variation of microsatellite loci from short sequence reads without paired-end information. It effectively distinguishes length variants from noise including insertion/deletion errors in homopolymer runs by addressing the bidirectional aspect of insertion and deletion errors in sequence reads. Here we first introduce a homopolymer decomposition method which estimates error bias toward insertion or deletion in homopolymer sequence runs. Combining these approaches, GenoTan was able to genotype 94.9% of microsatellite loci accurately from simulated data with 40x sequence coverage quickly while the other programs showed <90% correct calls for the same data and required 5∼30× more computational time than GenoTan. It also showed the highest true-positive rate for real data using mixed sequence data of two Drosophila inbred lines, which was a novel validation approach for genotyping., Availability: GenoTan is open-source software available at http://genotan.sourceforge.net.

Motivation: Simple tandem repeats are highly variable genetic elements and widespread in genomes of many organisms. Next-generation sequencing technologies have enabled a robust comparison of large numbers of simple tandem repeat loci; however, analysis of their variation using traditional sequence analysis approaches still remains limiting and problematic due to variants occurring in repeat sequences confusing alignment programs into mapping sequence reads to incorrect loci when the sequence reads are significantly different from the reference sequence., Results: We have developed a program, ReviSTER, which is an automated pipeline using a 'local mapping reference reconstruction method' to revise mismapped or partially misaligned reads at simple tandem repeat loci. RevisSTER estimates alleles of repeat loci using a local alignment method and creates temporary local mapping reference sequences, and finally remaps reads to the local mapping references. Using this approach, ReviSTER was able to successfully revise reads misaligned to repeat loci from both simulated data and real data., Availability: ReviSTER is open-source software available at http://revister.sourceforge.net., Contact: garner@vbi.vt.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Document similarity metrics such as PubMed's 'Find related articles' feature, which have been primarily used to identify studies with similar topics, can now also be used to detect duplicated or potentially plagiarized papers within literature reference databases. However, the CPU-intensive nature of document comparison has limited MEDLINE text similarity studies to the comparison of abstracts, which constitute only a small fraction of a publication's total text. Extending searches to include text archived by online search engines would drastically increase comparison ability. For large-scale studies, submitting short phrases encased in direct quotes to search engines for exact matches would be optimal for both individual queries and programmatic interfaces. We have derived a method of analyzing statistically improbable phrases (SIPs) for assistance in identifying duplicate content., Results: When applied to MEDLINE citations, this method substantially improves upon previous algorithms in the detection of duplication citations, yielding a precision and recall of 78.9% (versus 50.3% for eTBLAST) and 99.6% (versus 99.8% for eTBLAST), respectively., Availability: Similar citations identified by this work are freely accessible in the Déjà vu database, under the SIP discovery method category at http://dejavu.vbi.vt.edu/dejavu/.

Motivation: Duplicate publication impacts the quality of the scientific corpus, has been difficult to detect, and studies this far have been limited in scope and size. Using text similarity searches, we were able to identify signatures of duplicate citations among a body of abstracts., Results: A sample of 62,213 Medline citations was examined and a database of manually verified duplicate citations was created to study author publication behavior. We found that 0.04% of the citations with no shared authors were highly similar and are thus potential cases of plagiarism. 1.35% with shared authors were sufficiently similar to be considered a duplicate. Extrapolating, this would correspond to 3500 and 117,500 duplicate citations in total, respectively., Availability: eTBLAST, an automated citation matching tool, and Déjà vu, the duplicate citation database, are freely available at http://invention.swmed.edu/ and http://spore.swmed.edu/dejavu

Motivation: Microarrays rapidly generate large quantities of gene expression information, but interpreting such data within a biological context is still relatively complex and laborious. New methods that can identify functionally related genes via shared literature concepts will be useful in addressing these needs., Results: We have developed a novel method that uses implicit literature relationships (concepts related via shared, intermediate concepts) to cluster related genes. Genes are evaluated for implicit connections within a network of biomedical objects (other genes, ontological concepts and diseases) that are connected via their co-occurrences in Medline titles and/or abstracts. On the basis of these implicit relationships, individual gene pairs are scored using a probability-based algorithm. Scores are generated for all pairwise combinations of genes, which are then clustered based on the scores. We applied this method to a test set composed of nine functional groups with known relationships. The method scored highly for all nine groups and significantly better than a benchmark co-occurrence-based method for six groups. We then applied this method to gene sets specific to two previously defined breast tumor subtypes. Analysis of the results recapitulated known biological relationships and identified novel pathway relationships unique to each tumor subtype. We demonstrate that this method provides a valuable new means of identifying and visualizing significantly related genes within gene lists via their implicit relationships in the literature.

Motivation: The most widely used literature search techniques, such as those offered by NCBI's PubMed system, require significant effort on the part of the searcher, and inexperienced searchers do not use these systems as effectively as experienced users. Improved literature search engines can save researchers time and effort by making it easier to locate the most important and relevant literature., Results: We have created and optimized a new, hybrid search system for Medline that takes natural text as input and then delivers results with high precision and recall. The combination of a fast, low-sensitivity weighted keyword-based first pass algorithm to cast a wide net to gather an initial set of literature, followed by a unique sentence-alignment based similarity algorithm to rank order those results was developed that is sensitive, fast and easy to use. Several text similarity search algorithms, both standard and novel, were implemented and tested in order to determine which obtained the best results in information retrieval exercises., Availability: Literature searching algorithms are implemented in a system called eTBLAST, freely accessible over the web at http://invention.swmed.edu. A variety of other derivative systems and visualization tools provides the user with an enhanced experience and additional capabilities., Contact: Harold.Garner@UTSouthwestern.edu.

Motivation: While the mechanism for regulating alternative splicing is poorly understood, secondary structure has been shown to be integral to this process. Due to their propensity for forming complementary hairpin loops and their elevated mutation rates, tandem repeated sequences have the potential to influence splicing regulation., Results: An analysis of human intronic sequences reveals a strong correlation between alternative splicing and the prevalence of mono- through hexanucleotide tandem repeats that may engage in complementary pairing in introns that flank alternatively spliced exons. While only 44% of the 18 173 genes in the Human Alternative Splicing Database are known to be alternatively spliced, they contain 84% of the 694 237 intronic complementary repeat pairs. Significantly, the normalized frequency and distribution of repeat sequences, independent of their potential for pairing, are indistinguishable between alternatively spliced and non-alternatively spliced genes. Thus, the increased prevalence of repeats with pairing potential in alternatively spliced genes is not merely a consequence of more repeats or repeat composition bias. These results suggest that complementary repeats may play a role in the regulation of alternative splicing., Contact: harold.garner@utsouthwestern.edu.

Motivation: New relationships are often implicit from existing information, but the amount and growth of published literature limits the scope of analysis an individual can accomplish. Our goal was to develop and test a computational method to identify relationships within scientific reports, such that large sets of relationships between unrelated items could be sought out and statistically ranked for their potential relevance as a set., Results: We first construct a network of tentative relationships between 'objects' of biomedical research interest (e.g. genes, diseases, phenotypes, chemicals) by identifying their co-occurrences within all electronically available MEDLINE records. Relationships shared by two unrelated objects are then ranked against a random network model to estimate the statistical significance of any given grouping. When compared against known relationships, we find that this ranking correlates with both the probability and frequency of object co-occurrence, demonstrating the method is well suited to discover novel relationships based upon existing shared relationships. To test this, we identified compounds whose shared relationships predicted they might affect the development and/or progression of cardiac hypertrophy. When laboratory tests were performed in a rodent model, chlorpromazine was found to reduce the progression of cardiac hypertrophy.

Motivation: There is a general scientific need to be able to identify and evaluate what any given set of 'objects' (e.g. genes, phenotypes, chemicals, diseases) has in common. Whether it is to classify, expand upon or identify commonalities and functional groupings, informational needs can be diverse and the best source to identify relationships among a potentially heterogeneous set of objects is the scientific literature., Results: We first establish a network of related objects by their co-occurrence within MEDLINE records. A set of objects within this network can then be queried to identify shared relationships, and a method is presented to score their statistical relevance by comparing observed frequencies with what would be expected in a random network model. Using Gene Ontology (GO) categories, we demonstrate that this method enables a quantitative ranking of the 'cohesiveness' of a set of objects and, importantly, allows other objects related to this set to be identified and evaluated for their 'cohesion' to it. Supplemental information: A list of ranked genes related to each GO category analyzed can be found at http://innovation.swmed.edu/IRIDESCENT/GO&#95;relationships.htm

ARROGANT (ARRay OrGANizing Tool) is a software tool developed to facilitate the identification, annotation and comparison of large collections of genes or clones. The objective is to enable users to compile gene/clone collections from different databases, allowing them to design experiments and analyze the collections as well as associated experimental data efficiently. ARROGANT can relate different sequence identifiers to their common reference sequence using the UniGene database, allowing for the comparison of data from two different microarray experiments. ARROGANT has been successfully used to analyze microarray expression data for colon cancer, to compile genes potentially related to cardiac diseases for subsequent resequencing (to identify single nucleotide polymorphisms, SNPs), to design a new comprehensive human cDNA microarray for cancer, to combine and compare expression data generated by different microarrays and to provide annotation for genes on custom and Affymetrix chips.

Summary: Microarray data management and processing (MAD) is a set of Windows integrated software for microarray analysis. It consists of a relational database for data storage with many user-interfaces for data manipulation, several text file parsers and Microsoft Excel macros for automation of data processing, and a generator to produce text files that are ready for cluster analysis., Availability: Executable is available free of charge on http://pompous.swmed.edu. The source code is also available upon request.