RNAseqTRAPLINE

Annotation: RNA-sequencing data analysis in a Transparent Reproducible and Automated PipeLINE - TRAPLINE.

StepAnnotation
Step 1: Input dataset
select at runtime
This is the reference annotation input which has to be added to the history. To import my annotation set please go to shared data -> iGenome -> mm9
Step 2: Input dataset
select at runtime
This input should be our provided miRNA targets files for your species of interest that has to be downloaded.
Step 3: Input dataset
select at runtime
This input should be our provided protein-protein interaction file for your species of interest that has to be downloaded.
Step 4: Input dataset
select at runtime
Each experiment was done in triplicates, so each workflow will handle two conditions. Please assign the first experiment under condition 2.
Step 5: Input dataset
select at runtime
Each experiment was done in triplicates, so each workflow will handle two conditions. Please assign the second experiment under condition 2.
Step 6: Input dataset
select at runtime
Each experiment was done in triplicates, so each workflow will handle two conditions. Please assign the third experiment under condition 1.
Step 7: Input dataset
select at runtime
Each experiment was done in triplicates, so each workflow will handle two conditions. Please assign the second experiment under condition 1.
Step 8: Input dataset
select at runtime
Each experiment was done in triplicates, so each workflow will handle two conditions. Please assign the third experiment under condition 2.
Step 9: Input dataset
select at runtime
Each experiment was done in triplicates, so each workflow will handle two conditions. Please assign the first experiment under condition 1.
Step 10: Cut
c10,c11,c12,c13,c14,c15,c16,c17
Tab
Output dataset 'output' from step 2
This module cuts the non-conserved miRNAs from the miRNA target input dataset.
Step 11: FASTQ Groomer
Output dataset 'output' from step 4
Sanger & Illumina 1.8+
Hide Advanced Options
This tool is able to convert different FASTQ formats from different platforms (Illumina, SOLiD, Solexa) into a format suitable for TRAPLINE.
Step 12: FASTQ Groomer
Output dataset 'output' from step 5
Sanger & Illumina 1.8+
Hide Advanced Options
This tool is able to convert different FASTQ formats from different platforms (Illumina, SOLiD, Solexa) into a format suitable for TRAPLINE.
Step 13: FASTQ Groomer
Output dataset 'output' from step 6
Sanger & Illumina 1.8+
Show Advanced Options
Sanger (recommended)
ASCII
Summarize Input
This tool is able to convert different FASTQ formats from different platforms (Illumina, SOLiD, Solexa) into a format suitable for TRAPLINE.
Step 14: FASTQ Groomer
Output dataset 'output' from step 7
Sanger & Illumina 1.8+
Show Advanced Options
Sanger (recommended)
ASCII
Summarize Input
This tool is able to convert different FASTQ formats from different platforms (Illumina, SOLiD, Solexa) into a format suitable for TRAPLINE.
Step 15: FASTQ Groomer
Output dataset 'output' from step 8
Sanger & Illumina 1.8+
Hide Advanced Options
This tool is able to convert different FASTQ formats from different platforms (Illumina, SOLiD, Solexa) into a format suitable for TRAPLINE.
Step 16: FASTQ Groomer
Output dataset 'output' from step 9
Sanger & Illumina 1.8+
Show Advanced Options
Sanger (recommended)
ASCII
Summarize Input
This tool is able to convert different FASTQ formats from different platforms (Illumina, SOLiD, Solexa) into a format suitable for TRAPLINE.
Step 17: Clip
Output dataset 'output_file' from step 11
15
Enter custom sequence
GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTT
0
Yes
Output only non-clipped sequences (i.e. sequences which did not contained the adapter)
Clipping of Illumina adapter sequences. Step could be discarded if an other sequencing system than Illumina was used.
Step 18: Clip
Output dataset 'output_file' from step 12
15
Enter custom sequence
GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTT
0
Yes
Output only non-clipped sequences (i.e. sequences which did not contained the adapter)
Clipping of Illumina adapter sequences. Step could be discarded if an other sequencing system than Illumina was used.
Step 19: Clip
Output dataset 'output_file' from step 13
15
Enter custom sequence
GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTT
0
Yes
Output only non-clipped sequences (i.e. sequences which did not contained the adapter)
Clipping of Illumina adapter sequences. Step could be discarded if an other sequencing system than Illumina was used.
Step 20: Clip
Output dataset 'output_file' from step 14
15
Enter custom sequence
GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTT
0
Yes
Output only non-clipped sequences (i.e. sequences which did not contained the adapter)
Clipping of Illumina adapter sequences. Step could be discarded if an other sequencing system than Illumina was used.
Step 21: Clip
Output dataset 'output_file' from step 15
15
Enter custom sequence
GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTT
0
Yes
Output only non-clipped sequences (i.e. sequences which did not contained the adapter)
Clipping of Illumina adapter sequences. Step could be discarded if an other sequencing system than Illumina was used.
Step 22: Clip
Output dataset 'output_file' from step 16
15
Enter custom sequence
GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTT
0
Yes
Output only non-clipped sequences (i.e. sequences which did not contained the adapter)
Clipping of Illumina adapter sequences. Step could be discarded if an other sequencing system than Illumina was used.
Step 23: FASTQ Quality Trimmer
Output dataset 'output' from step 17
False
5' and 3'
1
1
0
min score
>=
20.0
fastq data pre processing step with significant downstream impact
Step 24: FASTQ Quality Trimmer
Output dataset 'output' from step 18
False
5' and 3'
1
1
0
min score
>=
20.0
Step 25: FASTQ Quality Trimmer
Output dataset 'output' from step 19
False
5' and 3'
1
1
0
min score
>=
20.0
fastq data pre processing step with significant downstream impact
Step 26: FASTQ Quality Trimmer
Output dataset 'output' from step 20
False
5' and 3'
1
1
0
min score
>=
20.0
fastq data pre processing step with significant downstream impact
Step 27: FASTQ Quality Trimmer
Output dataset 'output' from step 21
False
5' and 3'
1
1
0
min score
>=
20.0
fastq data pre processing step with significant downstream impact
Step 28: FASTQ Quality Trimmer
Output dataset 'output' from step 22
False
5' and 3'
1
1
0
min score
>=
20.0
fastq data pre processing step with significant downstream impact
Step 29: TopHat
Single-end
Output dataset 'output_file' from step 23
Use a built-in genome
mm9
Use Defaults
No
Use default job resource parameters
Best cooperated genome aligner within Galaxy according to Cufflinks2. Exemplarily 1 .bam file is stored for further proceedings and data handling optimisation. txt files are stored for read align accuracy.
Step 30: FastQC
Output dataset 'output_file' from step 23
select at runtime
select at runtime
Step 31: TopHat
Single-end
Output dataset 'output_file' from step 24
Use a built-in genome
mm9
Use Defaults
No
Use default job resource parameters
Best cooperated genome aligner within Galaxy according to Cufflinks2. txt files are stored for read align accuracy.
Step 32: FastQC
Output dataset 'output_file' from step 24
select at runtime
select at runtime
Step 33: TopHat
Single-end
Output dataset 'output_file' from step 25
Use a built-in genome
mm9
Use Defaults
No
Use default job resource parameters
Best cooperated genome aligner within Galaxy according to Cufflinks2. txt files are stored for read align accuracy.
Step 34: FastQC
Output dataset 'output_file' from step 25
select at runtime
select at runtime
Step 35: TopHat
Single-end
Output dataset 'output_file' from step 26
Use a built-in genome
mm9
Use Defaults
No
Use default job resource parameters
Best cooperated genome aligner within Galaxy according to Cufflinks2. txt files are stored for read align accuracy.
Step 36: FastQC
Output dataset 'output_file' from step 26
select at runtime
select at runtime
Step 37: TopHat
Single-end
Output dataset 'output_file' from step 27
Use a built-in genome
mm9
Use Defaults
No
Use default job resource parameters
Best cooperated genome aligner within Galaxy according to Cufflinks2. txt files are stored for read align accuracy.
Step 38: FastQC
Output dataset 'output_file' from step 27
select at runtime
select at runtime
Step 39: TopHat
Single-end
Output dataset 'output_file' from step 28
Use a built-in genome
mm9
Use Defaults
No
Use default job resource parameters
Best cooperated genome aligner within Galaxy according to Cufflinks2. Exemplarily 1 .bam file is stored for further proceedings and data handling optimisation. txt files are stored for read align accuracy.
Step 40: FastQC
Output dataset 'output_file' from step 28
select at runtime
select at runtime
Module to evaluate the fastq after pre processing, comparison to former module possible.
Step 41: Cufflinks
Output dataset 'accepted_hits' from step 29
300000
0.1
0.15
Use reference annotation as guide
Output dataset 'output' from step 1
600
50
No
No
No
Cufflinks Effective Length Correction
select at runtime

No value found for 'Global model (for use in Trackster)'. Using default: 'None'.

No
Use default job resource parameters
Caution! Bias correction and Multiple-read correction were not included as default, but this should be checked always on your own dataset!
Step 42: MarkDuplicates
Output dataset 'accepted_hits' from step 29
Comments
Comment 1
SNP Output 2.1
True
True
SUM_OF_BASE_QUALITIES
[a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.
100
Lenient
Necessary step for multiple read correction. Output will can be used for SNP analysis.
Step 43: Cufflinks
Output dataset 'accepted_hits' from step 31
300000
0.1
0.15
Use reference annotation as guide
Output dataset 'output' from step 1
600
50
No
No
No
Cufflinks Effective Length Correction
select at runtime

No value found for 'Global model (for use in Trackster)'. Using default: 'None'.

No
Use default job resource parameters
Caution! Bias correction and Multiple-read correction were not included as default, but this should be checked always on your own dataset!
Step 44: MarkDuplicates
Output dataset 'accepted_hits' from step 31
Comments
Comment 1
SNP Output 2.2
True
True
SUM_OF_BASE_QUALITIES
[a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.
100
Lenient
Necessary step for multiple read correction. Output will can be used for SNP analysis.
Step 45: Cufflinks
Output dataset 'accepted_hits' from step 33
300000
0.1
0.15
Use reference annotation as guide
Output dataset 'output' from step 1
600
50
No
No
No
Cufflinks Effective Length Correction
select at runtime

No value found for 'Global model (for use in Trackster)'. Using default: 'None'.

No
Use default job resource parameters
Caution! Bias correction and Multiple-read correction were not included as default, but this should be checked always on your own dataset!
Step 46: MarkDuplicates
Output dataset 'accepted_hits' from step 33
Comments
Comment 1
SNP Output 1.3
True
True
SUM_OF_BASE_QUALITIES
[a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.
100
Lenient
Necessary step for multiple read correction. Output will can be used for SNP analysis.
Step 47: Cufflinks
Output dataset 'accepted_hits' from step 35
300000
0.1
0.15
Use reference annotation as guide
Output dataset 'output' from step 1
600
50
No
No
No
Cufflinks Effective Length Correction
select at runtime

No value found for 'Global model (for use in Trackster)'. Using default: 'None'.

No
Use default job resource parameters
Caution! Bias correction and Multiple-read correction were not included as default, but this should be checked always on your own dataset!
Step 48: MarkDuplicates
Output dataset 'accepted_hits' from step 35
Comments
Comment 1
SNP Output 1.2
True
True
SUM_OF_BASE_QUALITIES
[a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.
100
Lenient
Necessary step for multiple read correction. Output will can be used for SNP analysis.
Step 49: Cufflinks
Output dataset 'accepted_hits' from step 37
300000
0.1
0.15
Use reference annotation as guide
Output dataset 'output' from step 1
600
50
No
No
No
Cufflinks Effective Length Correction
select at runtime

No value found for 'Global model (for use in Trackster)'. Using default: 'None'.

No
Use default job resource parameters
Caution! Bias correction and Multiple-read correction were not included as default, but this should be checked always on your own dataset!
Step 50: MarkDuplicates
Output dataset 'accepted_hits' from step 37
Comments
Comment 1
SNP Output 2.3
True
True
SUM_OF_BASE_QUALITIES
[a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.
100
Lenient
Necessary step for multiple read correction. Output will can be used for SNP analysis.
Step 51: Cufflinks
Output dataset 'accepted_hits' from step 39
300000
0.1
0.15
Use reference annotation as guide
Output dataset 'output' from step 1
600
50
No
No
No
Cufflinks Effective Length Correction
select at runtime

No value found for 'Global model (for use in Trackster)'. Using default: 'None'.

No
Use default job resource parameters
Caution! Bias correction and Multiple-read correction were not included as default, but this should be checked always on your own dataset!
Step 52: MarkDuplicates
Output dataset 'accepted_hits' from step 39
Comments
Comment 1
SNP output 1.1
True
True
SUM_OF_BASE_QUALITIES
[a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.
100
Lenient
Necessary step for multiple read correction. Output will can be used for SNP analysis.
Step 53: Cuffmerge
Output dataset 'assembled_isoforms' from step 51,Output dataset 'assembled_isoforms' from step 47,Output dataset 'assembled_isoforms' from step 45,Output dataset 'assembled_isoforms' from step 41,Output dataset 'assembled_isoforms' from step 43,Output dataset 'assembled_isoforms' from step 49
Additional GTF Inputs (Lists)s
Yes
Output dataset 'output' from step 1
No
0.05
Use default job resource parameters
Cuffmerge combines all discovered transcripts of the various experimental conditions into a single gtf file that is used for an overall Cuffdiff analysis.
Step 54: Cuffdiff
Output dataset 'merged_transcripts' from step 53
False
False
SAM/BAM
Conditions
Condition 1
Experimental Condition 1
Output dataset 'accepted_hits' from step 39,Output dataset 'accepted_hits' from step 35,Output dataset 'accepted_hits' from step 33
Condition 2
Experimental Condition 2
Output dataset 'accepted_hits' from step 29,Output dataset 'accepted_hits' from step 31,Output dataset 'accepted_hits' from step 37
classic-fpkm
pooled
0.05
10
False
No
No
No
cufflinks effective length correction
No
No
Use default job resource parameters
Cuffdiff output suitable for cummeRbund usage. Please adjust your preferred parameter according to false discovery rate as well as bias correction and multiple-read correction which were not included as default. Be aware of the default parameters!
Step 55: Filter
Output dataset 'promoters_diff' from step 54
c14=='yes'
0
This module filters the significantly enriched multi promoter regions.
Step 56: Filter
Output dataset 'splicing_diff' from step 54
c14=='yes'
0
This module filters the significant differntially expressed spliced forms.
Step 57: Sort
Output dataset 'genes_exp' from step 54
8
Numerical sort
Descending order
Column selections
0
Data sort on genes to acquire most expressed; based on FPKM. Two sorts are a comparison of the two Cuffdiff modules (Should be the same output).
Step 58: Filter
Output dataset 'out_file1' from step 57
c14=='yes'
0
Filtering for significant different genes.
Step 59: Compute
c8-c9
Output dataset 'out_file1' from step 58
NO
This is the main step to seperate the up regulated and down regulated significantly differentially expressed genes.
Step 60: Filter
Output dataset 'out_file1' from step 59
c15>=1
0
This step filters the significantly up regulated genes.
Step 61: Filter
Output dataset 'out_file1' from step 59
c15<=-1
0
This step filters the significantly down regulated genes.
Step 62: Compare two Datasets
Output dataset 'out_file1' from step 56
3
Output dataset 'out_file1' from step 60
3
Matching rows of 1st dataset
This module shows the significantly upregulated splice variants.
Step 63: Select first
150
Output dataset 'out_file1' from step 60
False

No value found for 'Dataset has a header'. Using default: 'False'.

If there are too many significantly up regulated genes there might me an error during the DAVID process. We can overcome this by selecting less genes. You can also put in your gene list directly to the DAVID web-page.
Step 64: Select
Output dataset 'out_file1' from step 60
Matching

parameter 'invert': an invalid option ('false') was selected (valid options: ,-v) Using default: ''.

Mir
False

No value found for 'Keep header line'. Using default: 'False'.

This module identifies the significantly upregulated miRNAs.
Step 65: DAVID
Output dataset 'out_file1' from step 60
None
OFFICIAL_GENE_SYMBOL
Step 66: Compare two Datasets
Output dataset 'output' from step 3
3
Output dataset 'out_file1' from step 60
3
Matching rows of 1st dataset
This module compares all up regulated mRNAs with the protein-protein interaction file to identify the first interacting protein.
Step 67: Compare two Datasets
Output dataset 'out_file1' from step 56
3
Output dataset 'out_file1' from step 61
3
Matching rows of 1st dataset
This module shows the significantly downregulated splice variants.
Step 68: Select first
150
Output dataset 'out_file1' from step 61
False

No value found for 'Dataset has a header'. Using default: 'False'.

If there are too many significantly down regulated genes there might me an error during the DAVID process. We can overcome this by selecting less genes. You can also put in your gene list directly to the DAVID web-page.
Step 69: Select
Output dataset 'out_file1' from step 61
Matching

parameter 'invert': an invalid option ('false') was selected (valid options: ,-v) Using default: ''.

Mir
False

No value found for 'Keep header line'. Using default: 'False'.

This module identifies the significantly downregulated miRNAs.
Step 70: DAVID
Output dataset 'out_file1' from step 61
None
OFFICIAL_GENE_SYMBOL
Step 71: Compare two Datasets
Output dataset 'output' from step 3
3
Output dataset 'out_file1' from step 61
3
Matching rows of 1st dataset
This module compares all down regulated mRNAs with the protein-protein interaction file to identify the first interacting protein.
Step 72: Join two Datasets
Output dataset 'out_file1' from step 60
1
Output dataset 'out_file1' from step 61
1
Yes
Yes
No
No
This modules combines the results of the DE Analysis to a single table.
Step 73: DAVID
Output dataset 'out_file1' from step 63
None
OFFICIAL_GENE_SYMBOL
Step 74: Convert
Commas
Output dataset 'out_file1' from step 64
True
True
Sometimes it could be possible that a gene/miRNA has serveral names. This module fixes the problem.
Step 75: Compare two Datasets
Output dataset 'out_file1' from step 66
4
Output dataset 'out_file1' from step 60
3
Matching rows of 1st dataset
This module includes all upregulated first interacting proteins and compares it again to the up regulated mRNAs to get the second significant interacting protein and, finally, a list of protein-protein interactions.
Step 76: DAVID
Output dataset 'out_file1' from step 68
None
OFFICIAL_GENE_SYMBOL
Step 77: Convert
Commas
Output dataset 'out_file1' from step 69
True
True
Step 78: Compare two Datasets
Output dataset 'out_file1' from step 71
4
Output dataset 'out_file1' from step 61
3
Matching rows of 1st dataset
This module includes all downregulated first interacting proteins and compares it again to the up regulated mRNAs to get the second significant interacting protein and, finally, a list of protein-protein interactions.
Step 79: Compare two Datasets
Output dataset 'output' from step 2
1
Output dataset 'out_file1' from step 74
3
Matching rows of 1st dataset
This module identifies upregulated CONSERVED miRNAs and predictes their possible mRNA targets (based on miRanda).
Step 80: Compare two Datasets
Output dataset 'out_file1' from step 10
1
Output dataset 'out_file1' from step 74
3
Matching rows of 1st dataset
This module identifies upregulated NON-CONSERVED miRNAs and predictes their possible mRNA targets (based on miRanda).
Step 81: Compare two Datasets
Output dataset 'output' from step 2
1
Output dataset 'out_file1' from step 74
4
Matching rows of 1st dataset
Step 82: Compare two Datasets
Output dataset 'out_file1' from step 10
1
Output dataset 'out_file1' from step 74
4
Matching rows of 1st dataset
Step 83: Join two Datasets
Output dataset 'out_file1' from step 72
1
Output dataset 'out_file1' from step 75
1
Yes
Yes
No
No
This modules combines the results of the former table and the protein-protein interactions of the up regulated mRNAs.
Step 84: Compare two Datasets
Output dataset 'output' from step 2
1
Output dataset 'out_file1' from step 77
3
Matching rows of 1st dataset
This module identifies downregulated CONSERVED miRNAs and predictes their possible mRNA targets (based on miRanda).
Step 85: Compare two Datasets
Output dataset 'out_file1' from step 10
1
Output dataset 'out_file1' from step 77
3
Matching rows of 1st dataset
This module identifies downregulated NON-CONSERVED miRNAs and predictes their possible mRNA targets (based on miRanda).
Step 86: Compare two Datasets
Output dataset 'output' from step 2
1
Output dataset 'out_file1' from step 77
4
Matching rows of 1st dataset
Step 87: Compare two Datasets
Output dataset 'out_file1' from step 10
1
Output dataset 'out_file1' from step 77
4
Matching rows of 1st dataset
Step 88: Compare two Datasets
Output dataset 'out_file1' from step 79
3
Output dataset 'out_file1' from step 61
3
Matching rows of 1st dataset
This module compares the identified miRNA targets towards the downregulated mRNAs of the former analysis.
Step 89: Compare two Datasets
Output dataset 'out_file1' from step 80
3
Output dataset 'out_file1' from step 61
3
Matching rows of 1st dataset
This module compares the identified miRNA targets towards the downregulated mRNAs of the former analysis.
Step 90: Compare two Datasets
Output dataset 'out_file1' from step 81
3
Output dataset 'out_file1' from step 61
3
Matching rows of 1st dataset
Step 91: Compare two Datasets
Output dataset 'out_file1' from step 82
3
Output dataset 'out_file1' from step 61
3
Matching rows of 1st dataset
Step 92: Join two Datasets
Output dataset 'out_file1' from step 83
1
Output dataset 'out_file1' from step 78
1
Yes
Yes
No
No
This modules combines the results of the former table and the protein-protein interactions of the down regulated mRNAs.
Step 93: Compare two Datasets
Output dataset 'out_file1' from step 84
3
Output dataset 'out_file1' from step 60
3
Matching rows of 1st dataset
This module compares the identified miRNA targets towards the upregulated mRNAs of the former analysis.
Step 94: Compare two Datasets
Output dataset 'out_file1' from step 85
3
Output dataset 'out_file1' from step 60
3
Matching rows of 1st dataset
This module compares the identified miRNA targets towards the upregulated mRNAs of the former analysis.
Step 95: Compare two Datasets
Output dataset 'out_file1' from step 86
3
Output dataset 'out_file1' from step 60
3
Matching rows of 1st dataset
Step 96: Compare two Datasets
Output dataset 'out_file1' from step 87
3
Output dataset 'out_file1' from step 60
3
Matching rows of 1st dataset
Step 97: Join two Datasets
Output dataset 'out_file1' from step 88
1
Output dataset 'out_file1' from step 89
1
Yes
Yes
No
No
Joins the two miRNA prediction datasets to finally obtain a set of conserved and non-conserved miRNA targets.
Step 98: Join two Datasets
Output dataset 'out_file1' from step 90
1
Output dataset 'out_file1' from step 91
1
Yes
Yes
No
No
Step 99: Join two Datasets
Output dataset 'out_file1' from step 93
1
Output dataset 'out_file1' from step 94
1
Yes
Yes
No
No
Joins the two miRNA prediction datasets to finally obtain a set of conserved and non-conserved miRNA targets.
Step 100: Join two Datasets
Output dataset 'out_file1' from step 95
1
Output dataset 'out_file1' from step 96
1
Yes
Yes
No
No
Step 101: Join two Datasets
Output dataset 'out_file1' from step 97
1
Output dataset 'out_file1' from step 98
1
Yes
Yes
No
No
Step 102: Join two Datasets
Output dataset 'out_file1' from step 99
1
Output dataset 'out_file1' from step 100
1
Yes
Yes
No
No
Step 103: Cut
c1,c3,c9,c11
Tab
Output dataset 'out_file1' from step 101
This module cuts the relevant information for the network constructions.
Step 104: Cut
c1,c3,c9,c11
Tab
Output dataset 'out_file1' from step 102
This module cuts the relevant information for the network constructions.
Step 105: Join two Datasets
Output dataset 'out_file1' from step 92
1
Output dataset 'out_file1' from step 103
1
Yes
Yes
No
No
This modules combines the results of the former table and the up regulated miRNAs including their down regulated targets.
Step 106: DAVID
Output dataset 'out_file1' from step 103
None
OFFICIAL_GENE_SYMBOL
Step 107: DAVID
Output dataset 'out_file1' from step 104
None
OFFICIAL_GENE_SYMBOL
Step 108: Join two Datasets
Output dataset 'out_file1' from step 105
1
Output dataset 'out_file1' from step 104
1
Yes
Yes
No
No
This modules combines the results of the former table and the down regulated miRNAs including their up regulated targets.
Step 109: Cut
c3,c11,c24,c25,c33,c35
Tab
Output dataset 'out_file1' from step 108
This is the primary output file that contains all obtained results during the data analysis and data evaluation modules. The file ready-to-use as Cytoscape input. Columns are as follows: