Conditional network

Network Search

Network Remodel

Module Finder

Network Comparison

Network Search Tool was used to search the co-expressed genes for one or several genes based on ten global/conditional networks that we provided.
Please select one of the background networks:

B73 global RNA-seq network

We collected 456 RNA-seq samples from Illumina platform of B73 inbred line with a rigid screening by the count of reads and mapping rate. The B73 global RNA-seq network was constructed by all the samples including 294 tissue specific, 122 abiotic and 40 biotic samples of B73 maize inbred line. We use Tophat and Cufflinks to compute the expression profile and further to construct the expression matrix. We combined PCC and MR algorithms to construct the network.

B73 development Microarray conditional network

The Nimblegen Maize Whole-Genome Microarray 385K (VersionV1_4a.53) GSE27004. It contained 16 kinds of tissues.

B73 tissue-specific RNA-seq conditional network

The B73 tissue-specific RNA-seq conditional network was constructed by 294 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 abiotic stress conditional RNA-seq network

The B73 abiotic stress RNA-seq conditional network was constructed by 122 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 biotic stress RNA-seq conditional network

The B73 abiotic stress RNA-seq conditional network was constructed by 40 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 kernel RNA-seq conditional network

The B73 kernel RNA-seq conditional network was constructed by 22 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 embryo and endosperm RNA-seq conditional network

B73 embryo and endosperm RNA-seq conditional network was constructed by 65 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 development RNA-seq conditional network

B73 development RNA-seq conditional network was constructed by 58 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 leaf section RNA-seq conditional network

B73 leaf section RNA-seq conditional network was constructed by 15 samples picked out from the 294 tissue specific samples. All samples belong to the SRP018823 series. And the calculation process is the same as described above.

Mo17 tissue-specific RNA-seq network

We collected 65 RNA-seq samples from Illumina platform of Mo17 inbred line with a rigid screening by the count of reads and mapping rate. And the calculation process is the same as described above.
Network Comparison Tool was used to compare the network structure for two networks (such as cross platforms: Microarray VS. RNA-seq; cross inbred lines: B73 VS. Mo17;cross conditions: global/conditional VS. conditional) and to display the functional plasticity and otherness.
Please select two of the background networks:

Cross Platforms: Microarray VS. RNA-seq

Cross Inbreds: B73 VS. Mo17

Cross Conditions: Global/Conditional VS Conditional

B73 global RNA-seq network

We collected 456 RNA-seq samples from Illumina platform of B73 inbred line with a rigid screening by the count of reads and mapping rate. The B73 global RNA-seq network was constructed by all the samples including 294 tissue specific, 122 abiotic and 40 biotic samples of B73 maize inbred line. We use Tophat and Cufflinks to compute the expression profile and further to construct the expression matrix. We combined PCC and MR algorithms to construct the network.

B73 development Microarray conditional network

The Nimblegen Maize Whole-Genome Microarray 385K (VersionV1_4a.53) GSE27004. It contained 16 kinds of tissues.

B73 tissue-specific RNA-seq conditional network

The B73 tissue-specific RNA-seq conditional network was constructed by 294 samples picked out from the 456 samples. And the calculation process is the same as described above.

BB73 abiotic stress RNA-seq conditional network

The B73 abiotic stress RNA-seq conditional network was constructed by 122 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 biotic stress RNA-seq conditional network

The B73 biotic stress RNA-seq conditional network was constructed by 40 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 kernel RNA-seq conditional network

The B73 kernel RNA-seq conditional network was constructed by 22 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 embryo and endosperm RNA-seq conditional network

B73 embryo and endosperm RNA-seq conditional network was constructed by 65 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 development RNA-seq conditional network

B73 development RNA-seq conditional network was constructed by 58 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 leaf section RNA-seq conditional network

B73 leaf section RNA-seq conditional network was constructed by 15 samples picked out from the 294 tissue specific samples. All samples belong to the SRP018823 series. And the calculation process is the same as described above.

Mo17 tissue-specific RNA-seq conditional network

We collected 65 RNA-seq samples from Illumina platform of Mo17 inbred line with a rigid screening by the count of reads and mapping rate. And the calculation process is the same as described above.
Network Remodel Tool was used to search the inner relationships (the first, second, third hierarchy) for a gene list based on ten global/conditional networks that we provided.
Please select one of the background networks:

B73 global RNA-seq network

We collected 456 RNA-seq samples from Illumina platform of B73 inbred line with a rigid screening by the count of reads and mapping rate. The B73 global RNA-seq network was constructed by all the samples including 294 tissue specific, 122 abiotic and 40 biotic samples of B73 maize inbred line. We use Tophat and Cufflinks to compute the expression profile and further to construct the expression matrix. We combined PCC and MR algorithms to construct the network.

B73 development Microarray conditional network

The Nimblegen Maize Whole-Genome Microarray 385K (VersionV1_4a.53) GSE27004. It contained 16 kinds of tissues.

B73 tissue-specific RNA-seq conditional network

The B73 tissue-specific RNA-seq conditional network was constructed by 294 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 abiotic stress RNA-seq conditional network

The B73 abiotic stress RNA-seq conditional network was constructed by 122 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 biotic stress RNA-seq conditional network

The B73 abiotic stress RNA-seq conditional network was constructed by 40 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 kernel RNA-seq conditional network

The B73 kernel RNA-seq conditional network was constructed by 22 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 embryo and endosperm RNA-seq conditional network

The B73 embryo and endosperm RNA-seq conditional network was constructed by 65 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 development RNA-seq conditional network

B73 development RNA-seq conditional network was constructed by 58 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 leaf section RNA-seq conditional network

B73 leaf section RNA-seq conditional network was constructed by 15 samples picked out from the 294 tissue specific samples. All samples belong to the SRP018823 series. And the calculation process is the same as described above.

Mo17 tissue-specific RNA-seq conditional network

We collected 65 RNA-seq samples from Illumina platform of Mo17 inbred line with a rigid screening by the count of reads and mapping rate. And the calculation process is the same as described above.
Module Finder Tool was used to find out candidate functional modules for a gene list with potential network structure based on ten global/conditional networks that we provided.
Please select one of the background networks:

B73 global RNA-seq network

We collected 456 RNA-seq samples from Illumina platform of B73 inbred line with a rigid screening by the count of reads and mapping rate. The B73 global RNA-seq network was constructed by all the samples including 294 tissue specific, 122 abiotic and 40 biotic samples of B73 maize inbred line. We use Tophat and Cufflinks to compute the expression profile and further to construct the expression matrix. We combined PCC and MR algorithms to construct the network.

B73 development Microarray conditional network

The Nimblegen Maize Whole-Genome Microarray 385K (VersionV1_4a.53) GSE27004. It contained 16 kinds of tissues.

B73 tissue-specific RNA-seq conditional network

The B73 tissue-specific RNA-seq conditional network was constructed by 294 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 abiotic stress RNA-seq conditional network

The B73 abiotic stress RNA-seq conditional network was constructed by 122 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 biotic stress RNA-seq conditional network

The B73 abiotic stress RNA-seq conditional network was constructed by 40 samples picked out from the 456 samples. And the calculation process is the same as described above.

B73 kernel RNA-seq conditional network

The B73 kernel RNA-seq conditional network was constructed by 22 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 embryo and endosperm RNA-seq conditional network

B73 embryo and endosperm RNA-seq conditional network was constructed by 65 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 development RNA-seq conditional network

B73 development RNA-seq conditional network was constructed by 58 samples picked out from the 294 tissue specific samples. And the calculation process is the same as described above.

B73 leaf section RNA-seq conditional network

B73 leaf section RNA-seq conditional network was constructed by 15 samples picked out from the 294 tissue specific samples. All samples belong to the SRP018823 series. And the calculation process is the same as described above.

Mo17 tissue-specific RNA-seq conditional network

We collected 65 RNA-seq samples from Illumina platform of Mo17 inbred line with a rigid screening by the count of reads and mapping rate. And the calculation process is the same as described above.

B73 global RNA-seq network

B73 development Microarray conditional network

B73 tissue-specific RNA-seq conditional network

B73 abiotic stress conditional RNA-seq network

B73 biotic stress RNA-seq conditional network

B73 kernel RNA-seq conditional network

B73 embryo and endosperm RNA-seq conditional network

B73 development RNA-seq conditional network

B73 leaf section RNA-seq conditional network

Mo17 tissue-specific RNA-seq network

B73 global RNA-seq network

B73 development Microarray conditional network

B73 tissue-specific RNA-seq conditional network

BB73 abiotic stress RNA-seq conditional network

B73 biotic stress RNA-seq conditional network

B73 kernel RNA-seq conditional network

B73 embryo and endosperm RNA-seq conditional network

B73 development RNA-seq conditional network

B73 leaf section RNA-seq conditional network

Mo17 tissue-specific RNA-seq conditional network

B73 global RNA-seq network

B73 development Microarray conditional network

B73 tissue-specific RNA-seq conditional network

B73 abiotic stress RNA-seq conditional network

B73 biotic stress RNA-seq conditional network

B73 kernel RNA-seq conditional network

B73 embryo and endosperm RNA-seq conditional network

B73 development RNA-seq conditional network

B73 leaf section RNA-seq conditional network

Mo17 tissue-specific RNA-seq conditional network

B73 global RNA-seq network

B73 development Microarray conditional network

B73 tissue-specific RNA-seq conditional network

B73 abiotic stress RNA-seq conditional network

B73 biotic stress RNA-seq conditional network

B73 kernel RNA-seq conditional network

B73 embryo and endosperm RNA-seq conditional network

B73 development RNA-seq conditional network

B73 leaf section RNA-seq conditional network

Mo17 tissue-specific RNA-seq conditional network