12,531 research outputs found
Entropy-scaling search of massive biological data
Many datasets exhibit a well-defined structure that can be exploited to
design faster search tools, but it is not always clear when such acceleration
is possible. Here, we introduce a framework for similarity search based on
characterizing a dataset's entropy and fractal dimension. We prove that
searching scales in time with metric entropy (number of covering hyperspheres),
if the fractal dimension of the dataset is low, and scales in space with the
sum of metric entropy and information-theoretic entropy (randomness of the
data). Using these ideas, we present accelerated versions of standard tools,
with no loss in specificity and little loss in sensitivity, for use in three
domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics
(MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search
(esFragBag, 10x speedup of FragBag). Our framework can be used to achieve
"compressive omics," and the general theory can be readily applied to data
science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo
Integration of Biological Sources: Exploring the Case of Protein Homology
Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heteroge- neous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioin- formatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Un- certain databases are able to contain several possi- ble worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration
SIG-DB: leveraging homomorphic encryption to Securely Interrogate privately held Genomic DataBases
Genomic data are becoming increasingly valuable as we develop methods to
utilize the information at scale and gain a greater understanding of how
genetic information relates to biological function. Advances in synthetic
biology and the decreased cost of sequencing are increasing the amount of
privately held genomic data. As the quantity and value of private genomic data
grows, so does the incentive to acquire and protect such data, which creates a
need to store and process these data securely. We present an algorithm for the
Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm
enables databases of genomic sequences to be searched with an encrypted query
sequence without revealing the query sequence to the Database Owner or any of
the database sequences to the Querier. SIG-DB is the first application of its
kind to take advantage of locality-sensitive hashing and homomorphic encryption
to allow generalized sequence-to-sequence comparisons of genomic data.Comment: 38 pages, 3 figures, 4 tables, 1 supplemental table, 7 supplemental
figure
A Molecular Biology Database Digest
Computational Biology or Bioinformatics has been defined as the application of mathematical
and Computer Science methods to solving problems in Molecular Biology that require large scale
data, computation, and analysis [18]. As expected, Molecular Biology databases play an essential
role in Computational Biology research and development. This paper introduces into current
Molecular Biology databases, stressing data modeling, data acquisition, data retrieval, and the
integration of Molecular Biology data from different sources. This paper is primarily intended
for an audience of computer scientists with a limited background in Biology
Integrating and Ranking Uncertain Scientific Data
Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates
A Data Transformation System for Biological Data Sources
Scientific data of importance to biologists in the Human Genome Project resides not only in conventional databases, but in structured files maintained in a number of different formats (e.g. ASN.1 and ACE) as well a.s sequence analysis packages (e.g. BLAST and FASTA). These formats and packages contain a number of data types not found in conventional databases, such as lists and variants, and may be deeply nested. We present in this paper techniques for querying and transforming such data, and illustrate their use in a prototype system developed in conjunction with the Human Genome Center for Chromosome 22. We also describe optimizations performed by the system, a crucial issue for bulk data
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