{"id":1637,"date":"2023-06-28T11:51:22","date_gmt":"2023-06-28T11:51:22","guid":{"rendered":"http:\/\/netbio.bgu.ac.il\/labwebsite\/?page_id=1637"},"modified":"2023-07-26T12:10:02","modified_gmt":"2023-07-26T12:10:02","slug":"motifnet-tuturial","status":"publish","type":"page","link":"https:\/\/netbio.bgu.ac.il\/labwebsite\/motifnet-tuturial\/","title":{"rendered":"MotifNet – Tutorial"},"content":{"rendered":"\t\t
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MotifNet: A web-server for network motif analysis<\/span><\/b><\/p>

Contact: Esti Yeger-Lotem <\/span>estiyl@bgu.ac.il<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Contents:<\/p>

1.MotifNet Overview<\/a><\/p>

1.1.Using MotifNet<\/a><\/p>

1.2.Running time<\/a><\/p>

1.3.The motif detection algorithm<\/a><\/p>

2.The Input Form<\/a><\/p>

2.1.Input Network Files<\/a><\/p>

2.2.Motif Parameters<\/a><\/p>

2.3.Advanced Search Options<\/a><\/p>

2.4.Job Details<\/a><\/p>

3.The Interactive Motif Browser<\/a><\/p>

3.1.The motif card<\/a><\/p>

3.2.The motifs filter form<\/a><\/p>

3.2.1.’Search by ID’<\/a><\/p>

3.2.2.Filter by Properties<\/a><\/p>

3.2.3.Filter by Nodes and Filter by Edges<\/a><\/p>

3.2.4.Filter by Genes<\/a><\/p>

3.3.The motif instance exploration window<\/a><\/p>

3.3.1.The instances table<\/a><\/p>

3.3.2.The frequency histogram<\/a><\/p>

3.3.3.Node\/Edge data<\/a><\/p>

4.Dispersity<\/a><\/p>

4.1.What is dispersity?<\/a><\/p>

4.2.How is it computed?<\/a><\/p>

5.The Navigation Menu<\/a><\/p>

6.Registration<\/a><\/p>

7.Implementation<\/a><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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1.\u00a0\u00a0\u00a0 MotifNet Overview<\/h2>

MotifNet is a tool for the identification of network motifs: small topological patterns that recur in the network significantly more often than expected randomly [1]. MotifNet allows researchers to analyze integrated networks, where nodes and edges may be labelled, and search for network motifs of up to eight nodes. The input to MotifNet includes a node\/edge-labelled network and arguments to configure the motif detection algorithm. The output consists of all identified network motifs and their instances in the network. The output motifs are presented graphically and the user can interactively filter the motifs and explore their instances in various ways. For a detailed description of the motifNet web-server see here.<\/a><\/p>

1.1.\u00a0 Using MotifNet<\/h4>

MotifNet contains two main views: The input form (section \u200e2), which is used to submit a network for motif analysis, and the interactive motif browser (section \u200e3), which is used to explore the output motifs and their instances. Upon submission of a new job in the input form, you will be provided with a link to the motif browser where you can view the results once results are ready. Because the execution may take several days, users can provide their e-mail address in the input form, and receive a notification when results are ready. Results remain accessible for two months after the submission of the job.<\/p>

1.2.\u00a0 Running time<\/h4>

The running-time of MotifNet depends on several factors, including the size of the sought motifs and the analyzed network, the number of random networks to be generated, and the type of enumeration. The table below shows the expected running-time for various inputs (the advances options, except for ‘Enumeration type’, were set to their default values). Exceeding the input sizes listed in this table might result in an overload on our server, and thus failure to perform the motif analysis<\/u>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t<\/colgroup>

Motif size <\/span><\/p>

(# nodes)<\/span><\/p><\/td>

Network size <\/span><\/p>

(# edges)<\/span><\/p><\/td>

Enumeration type<\/span><\/p><\/td>

Run time(d=days, h=hours, m=minutes) <\/span><\/p><\/td><\/tr>

3<\/span><\/p><\/td>

166,302<\/span><\/p><\/td>

Random<\/span><\/p><\/td>

3h 27m<\/span><\/p><\/td><\/tr>

3<\/span><\/p><\/td>

166,302<\/span><\/p><\/td>

Exact<\/span><\/p><\/td>

15h 8m<\/span><\/p><\/td><\/tr>

4<\/span><\/p><\/td>

32,330<\/span><\/p><\/td>

Random<\/span><\/p><\/td>

21h 36m<\/span><\/p><\/td><\/tr>

4<\/span><\/p><\/td>

23,056<\/span><\/p><\/td>

Exact<\/span><\/p><\/td>

3d 6h 13m<\/span><\/p><\/td><\/tr>

4<\/span><\/p><\/td>

32,330<\/span><\/p><\/td>

Exact<\/span><\/p><\/td>

8d 5h 18m<\/span><\/p><\/td><\/tr>

5<\/span><\/p><\/td>

7,882<\/span><\/p><\/td>

Random<\/span><\/p><\/td>

37h 46m<\/span><\/p><\/td><\/tr>

5<\/span><\/p><\/td>

1,373<\/span><\/p><\/td>

Exact<\/span><\/p><\/td>

10h 18m<\/span><\/p><\/td><\/tr>

6<\/span><\/p><\/td>

2,746<\/span><\/p><\/td>

Random<\/span><\/p><\/td>

5h 42m<\/span><\/p><\/td><\/tr>

6<\/span><\/p><\/td>

1,373<\/span><\/p><\/td>

Exact<\/span><\/p><\/td>

8d 23h 41m<\/span><\/p><\/td><\/tr>

7<\/span><\/p><\/td>

1,373<\/span><\/p><\/td>

Random<\/span><\/p><\/td>

46h 31m<\/span><\/p><\/td><\/tr>

7<\/span><\/p><\/td>

200<\/span><\/p><\/td>

Exact<\/span><\/p><\/td>

3d 10h 14m<\/span><\/p><\/td><\/tr>

8<\/span><\/p><\/td>

300<\/span><\/p><\/td>

Random<\/span><\/p><\/td>

23h 54m<\/span><\/p><\/td><\/tr>

8<\/span><\/p><\/td>

200<\/span><\/p><\/td>

Exact<\/span><\/p><\/td>

6d 14h 34m<\/span><\/p><\/td><\/tr><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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1.3.\u00a0 \u00a0<\/a>The motif detection algorithm<\/h4>\nThe identification of the motifs is done with FANMOD algorithm [2,3]. The basic workflow of the algorithm is as follows. The algorithm generates numerous random networks and counts the instances of each candidate motif in each random network. The p-value of the candidate motif is calculated as the fraction of random networks in which it occurs more frequently than in the input network. The generation of random networks is done by a series of edge swapping operations applied on the input network, while preserving the node-degree distribution. During this process, the edges are swapped one after the other. When an edge is swapped, a partner edge is selected randomly, and the two edges swap one of their endpoint nodes.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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<\/a>2.\u00a0\u00a0\u00a0 The Input Form<\/h2>\nIn this form, the user submits the input network to be analyzed, sets the arguments to configure the motif detection algorithm and may provide additional information about the job.\n

<\/a>2.1.\u00a0 Input Network Files<\/h4>\nEnter the input network to be analyzed. The input network may contain several node\/edge-labels. The network node and edge labels are given as several tab-delimited files, one file per label.\n\n‘Directed’<\/em> \u2013 Checked (default) if the edges are uni-directional.\n\n‘FileName’ <\/em>\u2013 The name of the file.\n\n‘Lines’ <\/em>\u2013 The number of lines in the file.\n\n‘Remove’ <\/em>\u2013 Remove the file from the list.\n\nImportant notes:<\/strong>\n