Table 1 Open in new tab Available...

Method

Type

Open source availa-bility

Short summary

Link

LEAP

Correlation

Yes

LEAP infers gene regulatory networks based on gene co-expression relationships and considers possible lags in time.

https://cran.r-project.org/web/packages/LEAP/index.html

dynGENIE3

Regression

Yes

dynGENIE3 extends GENIE3 by considering changes in expression over time and building dynamic models based on ordinary differential equations.

https://github.com/vahuynh/dynGENIE3

Inferelator

Regression

Yes

Inferelator infers gene regulatory networks by selecting the regulators whose levels are most predictive of gene expression based on a LASSO regression model.

https://github.com/baliga-lab/cMonkeyNwInf

SWING

Granger causality

Yes

SWING is a gene regulatory network inference framework based on multivariate Granger causality and sliding window regression.

https://github.com/bagherilab/SWING

DREM

Probabilistic graph model

Yes

DREM integrates time-series gene expression data and static or dynamic transcription factor–gene interaction data (e.g. ChIP-seq data) and produces as output a dynamic regulatory map.

http://sb.cs.cmu.edu/drem/

Table 1

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Available methods/tools for regulatory network prediction

Method	Type	Open source availa-bility	Short summary	Link
LEAP	Correlation	Yes	LEAP infers gene regulatory networks based on gene co-expression relationships and considers possible lags in time.	https://cran.r-project.org/web/packages/LEAP/index.html
dynGENIE3	Regression	Yes	dynGENIE3 extends GENIE3 by considering changes in expression over time and building dynamic models based on ordinary differential equations.	https://github.com/vahuynh/dynGENIE3
Inferelator	Regression	Yes	Inferelator infers gene regulatory networks by selecting the regulators whose levels are most predictive of gene expression based on a LASSO regression model.	https://github.com/baliga-lab/cMonkeyNwInf
SWING	Granger causality	Yes	SWING is a gene regulatory network inference framework based on multivariate Granger causality and sliding window regression.	https://github.com/bagherilab/SWING
DREM	Probabilistic graph model	Yes	DREM integrates time-series gene expression data and static or dynamic transcription factor–gene interaction data (e.g. ChIP-seq data) and produces as output a dynamic regulatory map.	http://sb.cs.cmu.edu/drem/

Method	Type	Open source availa-bility	Short summary	Link
LEAP	Correlation	Yes	LEAP infers gene regulatory networks based on gene co-expression relationships and considers possible lags in time.	https://cran.r-project.org/web/packages/LEAP/index.html
dynGENIE3	Regression	Yes	dynGENIE3 extends GENIE3 by considering changes in expression over time and building dynamic models based on ordinary differential equations.	https://github.com/vahuynh/dynGENIE3
Inferelator	Regression	Yes	Inferelator infers gene regulatory networks by selecting the regulators whose levels are most predictive of gene expression based on a LASSO regression model.	https://github.com/baliga-lab/cMonkeyNwInf
SWING	Granger causality	Yes	SWING is a gene regulatory network inference framework based on multivariate Granger causality and sliding window regression.	https://github.com/bagherilab/SWING
DREM	Probabilistic graph model	Yes	DREM integrates time-series gene expression data and static or dynamic transcription factor–gene interaction data (e.g. ChIP-seq data) and produces as output a dynamic regulatory map.	http://sb.cs.cmu.edu/drem/

LEAP, lag-based expression association for pseudotime-series; dynGENIE3, dynamical GENIE3; SWING, sliding window inference for network generation; DREM, Dynamic Regulatory Events Miner.

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