FINET: Fast Inferring NETwork
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(2020) 13:521 Wang and Hai BMC Res Notes https://doi.org/10.1186/s13104-020-05371-0
Open Access
RESEARCH NOTE
FINET: Fast Inferring NETwork Anyou Wang1* and Rong Hai1,2*
Abstract Objectives: Numerous software has been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hamper their applications to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork), to infer a network with high accuracy and rapidity from big data. Results: The high accuracy results from integrating algorithms with stability-selection, elastic-net, and parameter optimization. Tested by a known biological network, FINET infers interactions with over 94% precision. The high speed comes from partnering parallel computations implemented with Julia, a new compiled language that runs much faster than existing languages used in the current software, such as R, Python, and MATLAB. Regardless of FINET’s implementations with Julia, users with no background in the language or computer science can easily operate it, with only a user-friendly single command line. In addition, FINET can infer other networks such as chemical networks and social networks. Overall, FINET provides a confident way to efficiently and accurately infer any type of network for any scale of data. Keywords: FINET, Network, Inference, Julia, Stability selection, Elastic-net, LASSO, Accuracy Introduction All biological phenotypes are achieved from fine regulation of gene expression. Thus, understanding gene regulations is a crucially fundamental topic in the biology. Conventionally, manipulating gene mutations such as knockout and knockdown helps to digest the gene regulations. However, these approaches suffer several drawbacks such as transcript compensatory and side effects [1]. Gene mutation approaches also assume that the genome remains stable after mutations. However, the genome varies dramatically with even a single gene mutation, which alters gene expressions of thousand genes as shown in RNA sequencing data. As a result, there is no way to fully comprehend the complete regulatory interactions of any single gene. Computational biology and bioinformatics have attempted to infer gene regulatory networks from gene *Correspondence: [email protected]; [email protected] 1 The Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA 92521, USA Full list of author information is available at the end of the article
expression data, and have established software and tools to execute their works [2–9]. However, the efficiency of current software suffers from high noise and lagging. They usually generate overly complicated network interactions—mostly false positives [2]. Therefore, these results actually provide more questions than answers to true biology regulatory interactions. In addition, the current software face challenges when applied to big sequencing data. With the software FIN
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