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plantTFdb注释

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Merging two tables according to the merging type and default value.

197

Merging more than two tables according to the merging type and default value.

131

Transposing the row and column in a table

95

Pick out data of interest from the input table

190

Convert SVG images into other formats.

75

Identify the properties of a read based on its SAM flag value or obtain a SAM flag value based on the reads properties.

27

Covert fastq files to fasta files

39

align the query sequences to the Swiss-Prot protein database to obtain functional annotations

5

align nucleic acid or protein sequences to animalTFBD to predicte TF families

1

Sequences annotation for PHI database.

6

align the query sequences to the GO database.

12

align the query sequences to the KEGG database.

9

align the query sequences to the NR database.

7

annotation for bacteria resistance protein in ARDB

1

annation for virulence factors in VFDB

3

Resistence gene annotation in CARD

1

Sequences annotation for COG database

6

Sequences annotation for KOG database

4

Sequences annotation for eggNOG database

2

Sequences annotation for Pfam database.

6

Sequences annotation for CAZy database.

2

align nucleic acid or protein sequence to plantTFdb to predicte TF families

5

Analyze GO terms that significantly enriched in a given gene set comparing to the genome background.

140

Pathway enrichment analysis in given gene sets comparing to the genome background.

104

Using the linear model to reduce the sample distance dimension display

38

Demonstrate file data in a scatter plot.

104

Demonstrate file data in a line chart.

93

Demonstrate file data in a histogram.

104

Demonstrate the frequency distribution of file data in a frequency histogram.

72

Visualizing data with an area graph

46

Visualizing data with a boxplot.

191

Visualizing data with a bubble plot.

153

A graphical representation of data matrix with colors.

888

Shows all possible logical relations between different data sets

546

Constructing directed molecular interaction networks.

138

Displaying significant difference between two groups of data.

355

Displaying significant difference between two groups of data.

105

Krona allows hierarchical data to be explored with zoomable pie charts.

51

Demonstrate file data in a senior bubble plot.

397

Demonstrate file data in a scatter plot with auxiliary lines and correlation coefficient.

121

Constructing weighted molecular interaction networks.

122

Plotting the relationship between a scalar dependent variable y and one explanatory variable x.

90

Demonstrate file data in a three-dimension scatter plot.

82

Demonstrate file data in a pie chart.

107

Visualizing data with a Violin plot

67

correlation analysis between transcriptome and proteome

32

Using several table datas to draw a grouped violin diagram

31

Draw a circus plot to display relationship between sample and taxa

66

Convert linearly massive variables into several important variables and draw a two-dimension score plot.

385

Cluster data with similar properties.

234

Cluster genes with similar expression patterns

319

Convert linearly massive variables into several important variables and draw a three-dimension score plot.

157

Using the linear model to reduce the sample distance dimension display

115

Using the non-linear model to reduce the sample distance dimension display

81

cca

44

Shows all possible logical relations between different data sets

27

Find out differentially expressed genes between two groups of data.

303

Differential metabolites analysis tool is used to screen out differential metabolites between two groups of samples.

162

ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system(e.g. biomarker)

39

within groups

63

Inter group

67

this tool determine if two groups of data are significantly different from each other

54

this tool determine if two groups of data are significantly different from each other

14

Pathway enrichment analysis in given gene sets comparing to the genome background.

662

Analyze GO terms that significantly enriched in a given gene set comparing to the genome background.

654

Draw a histogram using COG/KOG IDs or COG/KOG codes of genes.

133

Plotting GO (gene ontology) annotations (the second level) in three GO ontologies from one or more groups of data.

171

An extremely versatile application for sequence reformatting.

42

Calculate sequence length.

56

Filter sequences with specific length.

51

Cut out a designated section of sequence.

57

Conversion between DNA and RNA

45

Translating CDS to protein according to the Standard Code (transl_table=1) or the Bacterial, Archaeal and Plant Plastid Code (transl_table=11)

56

Local alignment between two sequences to measure their similarity

94

Multiple Sequences Alignment with Muscle program

69