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Datalog Extensions for Bioinformatic Data Analysis

Authors: Jiwon Seo 0002;

Datalog Extensions for Bioinformatic Data Analysis

Abstract

Recent growth in public bioinformatic databases has facilitated the analysis of genomic and proteomic data. However, the large size of the datasets makes it hard for nonexpert programmers to perform the analysis. In this paper we present B-Log, a high-level query language for bioinformatic data analysis. Based on Datalog, B-Log can simply express graph analysis algorithms; it is extended with nested tables, recursive aggregations, and foreign functions, which helps quick exploratory analyses. We implemented several analysis algorithms in B-Log; we also implemented a prototype system to explore TCGA dataset. We find B-Log to be useful for exploratory analysis and quick prototyping.

Related Organizations
Keywords

Data Analysis, Proteomics, Databases, Factual, Computational Biology, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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