Juan Felipe Beltran

mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome

A new algorithm and Web server, mutation3D (http://mutation3d.org), proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D Web interface. On a large scale, we show that clustering with mutation3D is able to separate functional from nonfunctional mutations by analyzing a combination of 8,869 known inherited disease mutations and 2,004 SNPs overlaid together upon the same sets of crystal structures and homology models. Further, we present a systematic analysis of whole‐genome and whole‐exome cancer datasets to demonstrate that mutation3D identifies many known cancer genes as well as previously underexplored target genes. The mutation3D Web interface allows users to analyze their own mutation data in a variety of popular formats and provides seamless access to explore mutation clusters derived from over 975,000 somatic mutations reported by 6,811 cancer sequencing studies. The mutation3D Web interface is freely available with all major browsers supported.

Scalable package queries in relational database systems

Traditional database queries follow a simple model: they define constraints that each tuple in the result must satisfy. This model is computationally efficient, as the database system can evaluate the query conditions on each tuple individually. However, many practical, real-world problems require a collection of result tuples to satisfy constraints collectively, rather than individually. In this paper, we present package queries, a new query model that extends traditional database queries to handle complex constraints and preferences over answer sets. We develop a full-fledged package query system, implemented on top of a traditional database engine. Our work makes several contributions. First, we design PaQL, a SQL-based query language that supports the declarative specification of package queries. We prove that PaQL is at least as expressive as integer linear programming, and therefore, evaluation of package queries is in general NP-hard. Second, we present a fundamental evaluation strategy that combines the capabilities of databases and constraint optimization solvers to derive solutions to package queries. The core of our approach is a set of translation rules that transform a package query to an integer linear program. Third, we introduce an offline data partitioning strategy allowing query evaluation to scale to large data sizes. Fourth, we introduce SketchRefine, a scalable algorithm for package evaluation, with strong approximation guarantees ((1 ± e)6-factor approximation). Finally, we present extensive experiments over real-world and benchmark data. The results demonstrate that SketchRefine is effective at deriving high-quality package results, and achieves runtime performance that is an order of magnitude faster than directly using ILP solvers over large datasets.

Subsymmetries predict auditory and visual pattern complexity.

A mathematical measure of pattern complexity based on subsymmetries possessed by the pattern, previously shown to correlate highly with empirically derived measures of cognitive complexity in the visual domain, is found to also correlate significantly with empirically derived complexity measures of perception and production of auditory temporal and musical rhythmic patterns. Not only does the subsymmetry measure correlate highly with the difficulty of reproducing the rhythms by tapping after listening to them, but also the empirical measures exhibit similar behavior, for both the visual and auditory patterns, as a function of the relative number of subsymmetries present in the patterns.

Interactome INSIDER: a structural interactome browser for genomic studies

We present Interactome INSIDER, a tool to link genomic variant information with structural protein–protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

Codo: Fundraising with Conditional Donations

Crowdfunding websites like Kickstarter and Indiegogo offer project organizers the ability to market, fund, and build a community around their campaign. While offering support and flexibility for organizers, crowdfunding sites provide very little control to donors. In this paper, we investigate the idea of empowering donors by allowing them to specify conditions for their crowdfunding contributions. We introduce a crowdfunding system, Codo, that allows donors to specify conditional donations. Codo allow donors to contribute to a campaign but hold off on their contribution until certain specific conditions are met (e.g. specific members or groups contribute a certain amount). We begin with a micro study to assess several specific conditional donations based on their comprehensibility and usage likelihood. Based on this study, we formalize conditional donations into a general grammar that captures a broad set of useful conditions. We demonstrate the feasibility of resolving conditions in our grammar by elegantly transforming conditional donations into a system of linear inequalities that are efficiently resolved using off-the-shelf linear program solvers. Finally, we designed a user-friendly crowdfunding interface that supports conditional donations for an actual fund raising campaign and assess the potential of conditional donations through this campaign. We find preliminary evidence that roughly 1 in 3 donors make conditional donations and that conditional donors donate more compared to direct donors.

Measuring Musical Rhythm Similarity: Statistical Features Versus Transformation Methods

Two approaches to measuring the similarity between symbolically notated musical rhythms are compared with each other and with human judgments of perceived similarity. The first is the edit-distance, a popular transformation method, applied to the symbolic rhythm sequences. The second approach employs the histograms of the inter-onset-intervals (IOIs) calculated from the rhythms. Furthermore, two methods for dealing with the histograms are also compared. The first utilizes the Mallows distance, a transformation method akin to the Earth-Movers distance popular in computer vision, and the second extracts a group of standard statistical features, used in music information retrieval, from the IOI-histograms. The measures are compared using four contrastive musical rhythm data sets by means of statistical Mantel tests that compute correlation coefficients between the various dissimilarity matrices. The results provide evidence from the aural domain, that transformation methods such as the edit distance are superior to feature-based methods for predicting human judgments of similarity. The evidence also supports the hypothesis that IOI-histogram-based methods are better than music-theoretical structural features computed from the rhythms themselves, provided that the rhythms do not share identical IOI histograms.

Interactome INSIDER: A Multi-Scale Structural Interactome Browser For Genomic Studies

Protein interactions underlie nearly all known cellular function, making knowledge of their binding conformations paramount to understanding the physical workings of the cell. Studying binding conformations has allowed scientists to explore some of the mechanistic underpinnings of disease caused by disruption of protein interactions. However, since experimentally determined interaction structures are only available for a small fraction of the known interactome such inquiry has largely excluded functional genomic studies of the human interactome and broad observations of the inner workings of disease. Here we present Interactome INSIDER, an information center for genomic studies using the first full-interactome map of human interaction interfaces. We applied a new, unified framework to predict protein interaction interfaces for 184,605 protein interactions with previously unresolved interfaces in human and 7 model organisms, including the entire experimentally determined human binary interactome. We find that predicted interfaces share several known functional properties of interfaces, including an enrichment for disease mutations and recurrent cancer mutations, suggesting their applicability to functional genomic studies. We also performed 2,164 de novo mutagenesis experiments and show that mutations of predicted interface residues disrupt interactions at a similar rate to known interface residues and at a much higher rate than mutations outside of predicted interfaces. To spur functional genomic studies in the human interactome, Interactome INSIDER (http://interactomeinsider.yulab.org) allows users to explore known population variants, disease mutations, and somatic cancer mutations, or upload their own set of mutations to find enrichment at the level of protein domains, residues, and 3D atomic clustering in known and predicted interaction interfaces.

GeMSTONE: orchestrated prioritization of human germline mutations in the cloud

Abstract Integrative analysis of whole-genome/exome-sequencing data has been challenging, especially for the non-programming research community, as it requires simultaneously managing a large number of computational tools. Even computational biologists find it unexpectedly difficult to reproduce results from others or optimize their strategies in an end-to-end workflow. We introduce Germline Mutation Scoring Tool fOr Next-generation sEquencing data (GeMSTONE), a cloud-based variant prioritization tool with high-level customization and a comprehensive collection of bioinformatics tools and data libraries (http://gemstone.yulab.org/). GeMSTONE generates and readily accepts a shareable ‘recipe’ file for each run to either replicate previous results or analyze new data with identical parameters and provides a centralized workflow for prioritizing germline mutations in human disease within a streamlined workflow rather than a pool of program executions.