Welcome to EGRNI


Consensus Approach For Gene Regulatory Network Construction

An Consensus Approach for constructing Gene Regulatory Network using Fisher's Weighted Method. The probability values of each Edge obtained from four different methods i.e. Correlation, Principal component regression, Partial least square regression and Ridge regression are combined using Fisher's Weighted method. Only the significant Edges obtained from four methods are considered for Ensemble network construction.

About EGRNI


EGRNI Workflow

Citation: Sarkar C, Parsad R, Mishra DC and Rai A (2021) A Web Tool for Consensus Gene Regulatory Network Construction. Front. Genet. 12:745827. doi: 10.3389/fgene.2021.745827

Our developed web tool mainly follows three steps—data uploading, data analysis, and combining the outputs of four methods. The input data of gene expressions can be provided in comma separated value (.csv) file format or in Microsoft excel format containing the list of genes in rows and the conditions or various experiments in columns. The user-uploaded input data are renamed with the date and time of data uploading to avoid repetition in the uploaded file name. The edge scores are computed using four methods, i.e., correlation, PCR, PLS, and ridge regression methods. Probability values are computed for edges from the mixture distribution of edge scores obtained from each method. The probability values are combined using Fisher’s weighted method. Different steps of analysis are done using R programming. Few R packages like “dna” and “fdrtool” are used in writing the R script for the analysis. The outputs of the analysis are available in downloadable format in the result tab. Each output file contains the names of the interacting genes and the connectivity score.

  • User Interface Layer (UIL)
    The UIL for the web tool was developed using HTML (Hyper Text Markup Language), CSS, and JavaScript. The UIL consists of forms to interact with users. In UIL, users can:
    • Upload the gene expression dataset in Excel format
    • Download the result file

  • Application Layer (APL)
    The APL of the web tool has been designed using PHP and R code.
    • The R script for constructing GRN has been integrated with PHP for analysis of gene expression data
    • The R script is executed in the background of the web tool, which is not visible to the user

  • Database Layer (DBL)
    The DBL has been designed as server-side file storage. This layer stores:
    • The user-provided input file
    • Intermediate files generated during R script execution, such as:
      • Files containing a pairwise scoring matrix from four individual methods
      • Files containing p-values, FDR values, and Fw scores
    • The final result file

EGRNI Workflow

Citation: Sarkar C, Parsad R, Mishra DC and Rai A (2021) A Web Tool for Consensus Gene Regulatory Network Construction. Front. Genet. 12:745827. doi: 10.3389/fgene.2021.745827

Analyse with data


Select file to upload and analyse:

Input Data details


Showing Data from: Example_data.csv

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Os.11687.1.S1_a_at5.916.216.126.696.756.666.7810.4100484910.3383352710.5658161110.5572530710.233883279.9248021148.9950611249.3425897699.162515539.179813399.4041772339.2588427579.1574304119.2663808059.0742570319.3533981139.4480170019.3071422799.3007835649.3509524529.1309779419.3613664199.302443029.265643571
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Os.11719.1.S1_at2.82.582.425.215.346.46.5411.9065223611.8045626411.9569133511.848603387.7127058227.0671869445.7088753535.4201916225.4489799764.9647605764.9043688794.8856779724.8697164914.7946876414.6950256014.5290700874.5692574.076875815.5223727064.9316961624.7590430244.6813743414.4535568884.664500595
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Os.11749.1.S1_at5.575.294.647.47.737.327.746.485135366.3631535527.0960404226.994941715.4385989266.0115828534.5342035644.0942885614.242059334.3435699323.9314256594.1654045354.5301829733.8487709263.9817852584.1860367413.8671933584.0143675914.5124653633.8489588394.0409243864.2198751994.1871820654.052624132
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Os.12059.1.S1_at6.294.533.759.769.419.9410.7514.0565784514.3243985714.4023259414.5432735413.2697286512.1380413610.2593216510.6173365910.192433289.4738958748.2783705989.40105941210.100731910.6476172510.131264849.505961248.2015987498.74190977810.3036773310.003104410.2242244810.011819339.03958794610.25923231
Os.12063.1.S1_at7.035.125.1610.9710.2411.0412.0714.1837361714.4813832113.900225313.9216643114.2364776714.0920633711.8937704511.921900711.6562683711.109556989.57861986110.7522785311.5769087412.1069055111.8664216810.874274499.53148313110.148078111.7610926611.6236355611.5446855611.1519912910.6138295511.14449611
Os.12067.2.S1_at2.782.932.866.065.026.746.5811.9699944111.9873919312.5686283212.556764689.7573249998.3419146073.7094806663.9788791844.0850847614.1982037033.2695760994.114374584.2791488313.8523428834.3062627363.9178031733.9174452513.8315116093.8091612693.9914300193.952664674.1862641983.8931053824.041753534
Os.12074.1.S1_at2.862.422.535.26.337.117.125.820268335.0669932835.7961824116.1574277855.6548364884.7985793713.1533636393.0459965553.3805212033.0284758323.1913224113.2130013743.3865945693.2665785523.1829075573.247379083.4320345343.4763349563.0469728833.2563160023.2828321473.1976569513.3667274843.084023728

Final Results


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Output Data


Final Results Preview

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Node-1Node-2F
1038Gene 43Gene 62.07533641193754
1470Gene 77Gene 82.67932171348268
1859Gene 68Gene 102.90919752092164
1871Gene 80Gene 101.65469300867888
4474Gene 96Gene 232.66784941636971
4517Gene 139Gene 231.74022555993762
4824Gene 48Gene 251.98225455599166
4972Gene 196Gene 251.66826436248836
5490Gene 117Gene 282.00861253683748
6249Gene 80Gene 321.65426513450621
7410Gene 47Gene 381.7743422788415
9144Gene 189Gene 461.86024059486891
11286Gene 142Gene 573.15539536891948
11303Gene 159Gene 573.2498046826483
11408Gene 65Gene 583.22278393362338
12078Gene 138Gene 611.67332189596762
12446Gene 108Gene 632.24331994958835
14311Gene 182Gene 722.30866925966963
14611Gene 84Gene 742.38223369993818
14696Gene 169Gene 741.73902548954385
19657Gene 155Gene 991.69518605318268
19668Gene 166Gene 993.23955778183941
19802Gene 101Gene 1002.80380202138369
19849Gene 148Gene 1002.30350219265626
19851Gene 150Gene 1002.11946891644346
19865Gene 164Gene 1001.66909170693691
19871Gene 170Gene 1002.49779128139153
20006Gene 106Gene 1013.17136102436191
20029Gene 129Gene 1012.62373880288803
20227Gene 128Gene 1021.85702630689776
21069Gene 174Gene 1062.26871907562458
21673Gene 181Gene 1091.89815730720017
22480Gene 192Gene 1131.66836067331989
23645Gene 163Gene 1192.96425305483896
24025Gene 145Gene 1211.84929915750232
25236Gene 162Gene 1271.75547137491976
26436Gene 168Gene 1333.3794961531026
27459Gene 196Gene 1381.7756899468232
27610Gene 148Gene 1392.82331093048325
27642Gene 180Gene 1392.96038284037054
28010Gene 150Gene 1412.44437110245577
28237Gene 178Gene 1421.77277455799809
29645Gene 193Gene 1491.86428288500138

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Contact No.: +91 7011456880

Email: cschiranjib9@gmail.com

Location: Department of Bioinformatics, University of North Bengal, Siliguri, Darjeeling, West Bengal, India

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We place the highest priority on the confidentiality and integrity of your data. All files and information submitted through this platform are processed under strict security protocols to ensure complete protection against unauthorized access, misuse, or loss. Secure server-side storage and controlled execution of analysis scripts further enhance data protection.

Acknowledgement

The EGRNI project has been developed and is actively maintained by the Computational Systems Biology Laboratory, Department of Bioinformatics, University of North Bengal. This effort is guided and supported by domain experts dedicated to advancing research in computational biology and network inference. We sincerely thank all our users for their trust and support. Your feedback is invaluable in helping us continuously improve the platform and its features. We are committed to providing a reliable and effective tool for gene regulatory network construction.