OBJECTIVE: Many genetic variants are classified, but many more are variants of uncertain significance (VUS). Clinical observations of patients and their families may provide sufficient evidence to classify VUS. Understanding how long it takes to accumulate sufficient patient data to classify VUS can inform decisions in data sharing, disease management, and functional assay development. MATERIALS AND METHODS: Our software models the accumulation of clinical evidence (and excludes all other types of evidence) to measure their unique impact on variant interpretation. We illustrate the time and probability for VUS classification when laboratories share evidence, when they silo evidence, and when they share only variant interpretations. RESULTS: Using conservative assumptions for frequencies of observed clinical evidence, our models show the probability of classifying rare pathogenic variants with an allele frequency of 1/100 000 increases from less than 25% with no data sharing to nearly 80% after one year when labs share data, with nearly 100% classification after 5 years. Conversely, our models found that extremely rare (1/1 000 000) variants have a low probability of classification using only clinical data. DISCUSSION: These results quantify the utility of data sharing and demonstrate the importance of alternative lines of evidence for interpreting rare variants. Understanding variant classification circumstances and timelines provides valuable insight for data owners, patients, and service providers. While our modeling parameters are based on our own assumptions of the rate of accumulation of clinical observations, users may download the software and run simulations with updated parameters. CONCLUSIONS: The modeling software is available at https://github.com/BRCAChallenge/classification-timelines.

MOTIVATION: The past decade has seen the introduction of fast and relatively inexpensive methods to detect genetic variation across the genome and exponential growth in the number of known single nucleotide variants (SNVs). There is increasing interest in bioinformatics approaches to identify variants that are functionally important from millions of candidate variants. Here, we describe the essential components of bioinformatics tools that predict functional SNVs. RESULTS: Bioinformatics tools have great potential to identify functional SNVs, but the black box nature of many tools can be a pitfall for researchers. Understanding the underlying methods, assumptions and biases of these tools is essential to their intelligent application.

SUMMARY: Most genes in human, mouse and rat produce more than one transcript isoform. The Affymetrix Exon Array is a tool for studying the many processes that regulate RNA production, with separate probesets measuring RNA levels at known and putative exons. For insights on how exons levels vary between normal tissues, we constructed the Affy Exon Tissues track from tissue data published by Affymetrix. This track reports exon probeset intensities as log ratios relative to median values across the dataset and renders them as colored heat maps, to yield quick visual identification of exons with intensities that vary between normal tissues. AVAILABILITY: Affy Exon Tissues track is freely available under the UCSC Genome Browser (http://genome.ucsc.edu/) for human (hg18), mouse (mm8 and mm9), and rat (rn4).

UNLABELLED: Recent studies have revealed that alternative splicing plays an important role in the observed protein and interaction diversity. Special microarrays allow for measuring gene expression at the exon level and thus for studying alternative transcripts and their corresponding protein domain architecture. We have developed the Cytoscape plugin DomainGraph that enables the visualization and detailed study of domain-domain interactions forming protein interaction networks. In addition, the integration of exon expression data supports the analysis of alternative splicing events and the characterization of their effects on the protein and domain interaction network. Different expression patterns between human tissues or cells can be identified by comparing the generated domain graphs. AVAILABILITY: The plugin DomainGraph and the online documentation are available at http://domaingraph.bioinf.mpi-inf.mpg.de.

UNLABELLED: We have implemented a graph layout algorithm that exposes Gene Ontology (GO) class structure on the network nodes. It can be used in conjunction with BiNGO plug-in to Cytoscape, which finds the GO categories over-represented in a given network. Our plug-in, named GOlorize, first highlights the class members with category-specific color-coding and then constructs an enhanced visualization of the network using a class-directed layout algorithm. AVAILABILITY: http://www.cytoscape.org/plugins2.php. SUPPLEMENTARY INFORMATION: Installation instructions and tutorial at http://www.cytoscape.org/plugins/GOlorize/GOlorizeUserGuide.pdf.

Alternative splicing contributes to both gene regulation and protein diversity. To discover broad relationships between regulation of alternative splicing and sequence conservation, we applied a systems approach, using oligonucleotide microarrays designed to capture splicing information across the mouse genome. In a set of 22 adult tissues, we observe differential expression of RNA containing at least two alternative splice junctions for about 40% of the 6,216 alternative events we could detect. Statistical comparisons identify 171 cassette exons whose inclusion or skipping is different in brain relative to other tissues and another 28 exons whose splicing is different in muscle. A subset of these exons is associated with unusual blocks of intron sequence whose conservation in vertebrates rivals that of protein-coding exons. By focusing on sets of exons with similar regulatory patterns, we have identified new sequence motifs implicated in brain and muscle splicing regulation. Of note is a motif that is strikingly similar to the branchpoint consensus but is located downstream of the 5' splice site of exons included in muscle. Analysis of three paralogous membrane-associated guanylate kinase genes reveals that each contains a paralogous tissue-regulated exon with a similar tissue inclusion pattern. While the intron sequences flanking these exons remain highly conserved among mammalian orthologs, the paralogous flanking intron sequences have diverged considerably, suggesting unusually complex evolution of the regulation of alternative splicing in multigene families.

Abstract Summary: GPCR-GRAPA-LIB is a library of HMMs describing G protein coupled receptor families. These families are initially defined by class of receptor ligand, with divergent families divided into subfamilies using phylogenic analysis and knowledge of GPCR function. Protein sequences are applied to the models with the GRAPA curve-based selection criteria. RefSeq sequences for Homo sapiens, Drosophila melanogaster, and Caenorhabditis elegans have been annotated using this approach. Availability: The HMMs and sequence alignments are available at http://www.affymetrix.com/community/publications/affymetrix/index.affx. Annotation results are available at http://www.affymetrix.com/analysis/index.affx Contact: [email protected] * To whom correspondence should be addressed.

Abstract Motivation: Protein sequence alignments have a myriad of applications in bioinformatics, including secondary and tertiary structure prediction, homology modeling, and phylogeny. Unfortunately, all alignment methods make mistakes, and mistakes in alignments often yield mistakes in their application. Thus, a method to identify and remove suspect alignment positions could benefit many areas in protein sequence analysis. Results: We tested four predictors of alignment position reliability, including near-optimal alignment information, column score, and secondary structural information. We validated each predictor against a large library of alignments, removing positions predicted as unreliable. Near-optimal alignment information was the best predictor, removing 70% of the substantially-misaligned positions and 58% of the over-aligned positions, while retaining 86% of those aligned accurately. Availability: The shift score alignment comparison algorithm is available online at http://www.soe.ucsc.edu/research/compbio/HMM-apps/compare-align.html and from the authors on request. Contact: [email protected]