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plantTFdb注释

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

194

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

128

Transposing the row and column in a table

96

Pick out data of interest from the input table

187

Convert SVG images into other formats.

74

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

38

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

4

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.

8

align the query sequences to the KEGG database.

7

align the query sequences to the NR database.

7

annotation for bacteria resistance protein in ARDB

1

annation for virulence factors in VFDB

2

Resistence gene annotation in CARD

1

Sequences annotation for COG database

5

Sequences annotation for KOG database

3

Sequences annotation for eggNOG database

2

Sequences annotation for Pfam database.

5

Sequences annotation for CAZy database.

2

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

2

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

131

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

97

Using the linear model to reduce the sample distance dimension display

35

Demonstrate file data in a scatter plot.

101

Demonstrate file data in a line chart.

91

Demonstrate file data in a histogram.

104

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

71

Visualizing data with an area graph

46

Visualizing data with a boxplot.

188

Visualizing data with a bubble plot.

151

A graphical representation of data matrix with colors.

863

Shows all possible logical relations between different data sets

538

Constructing directed molecular interaction networks.

136

Displaying significant difference between two groups of data.

343

Displaying significant difference between two groups of data.

104

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

51

Demonstrate file data in a senior bubble plot.

393

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

118

Constructing weighted molecular interaction networks.

119

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

89

Demonstrate file data in a three-dimension scatter plot.

80

Demonstrate file data in a pie chart.

105

Visualizing data with a Violin plot

63

correlation analysis between transcriptome and proteome

32

Using several table datas to draw a grouped violin diagram

29

Draw a circus plot to display relationship between sample and taxa

63

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

377

Cluster data with similar properties.

231

Cluster genes with similar expression patterns

319

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

154

Using the linear model to reduce the sample distance dimension display

114

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

80

cca

42

Shows all possible logical relations between different data sets

25

Find out differentially expressed genes between two groups of data.

297

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

157

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

39

within groups

61

Inter group

62

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

51

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

13

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

652

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

644

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

130

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

165

An extremely versatile application for sequence reformatting.

42

Calculate sequence length.

56

Filter sequences with specific length.

50

Cut out a designated section of sequence.

56

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

92

Multiple Sequences Alignment with Muscle program

69