Help page of Whitefly Expression Altas

 

  • BLAST tools

     

    Like the most commonly used BLAST in NCBI. 1) Select a BLAST method. 2) Select a dataset. 3) Input your sequence or upload a fasta file. And then “ Blast ”. The results page will be displayed.

     

     

    Furthermore, the tool of “Extract fasta sequence(s) from ID list” can help users extract fasta sequence(s) according to the gene ID of BLAST result.

     

  • Querying by annotation or ID

     

    The functional annotation and sequence information of interesting genes can be queried by gene ID(s). The query can be shown in two ways, excel file(A) or online(B).

     

     

    The result of online query contains gene fuctional annotation and gene sequences.

    After click each link of gene ID, NR GI or Swissprot ID, detailed infomation page will be shown.

     

     

    The detailed gene information page contailns 4 parts: fuctional annation, sequences, predicted protein domains and gene expression in published articles.

     

  • RNA-seq tools in Galaxy

     

    In this page, you can map your RNA-seq data to whitefly transcriptome reference and anylysis differential expression across various expriments.

     

    1. Please login or register. Your data will be saved in the individual acount.

    2. Upload your RNA-seq raw data, from the upload button in tool panel . The time of this step depends on network speed and file size.

    1) Click the upload button; 2)Choose the raw data in fastq file or compressed in .qz,zip,bzip file; 3) Start to upload. The uploaded fastq will be shown in history panel.

     

     

    3. Run RSEM to estimate transcript abundances of each raw data from the link of “RSEM abundance estimation”. Select “Use a built-in transcriptome sequences” and “The trancriptome of bemisia tabaci MED version 2.0, trancript result of Trinity”. Select other parameters according to the sequencing method and library type of your raw data. Then press “Execute” button. The running status will be shown on history panel. Each raw data of sample will take about 0.5~2 hours which depends on number of raw reads.

     

     

    4. Join RSEM estimates from multiple samples which resulted in Step 3 into a single matrix from the link of “estimation to matrix”. After click "Add new RSEM abundance estimate for samples", choose the gene counts result which was done in previous step and write each sample name as column label in the matrix. Then press “Execute” button. This step will run about 1 minute.

     

     

    5. Identify differentially expressed genes using EdgeR from the link of “EdgeR differential expression”. Select “Matrix of RNA-Seq fragment counts for transcripts per condition”, “Matrix of FPKM for transcripts per condition” which resulted in Step 4. Select “Transcripts fasta file corresponding to matrix” in Step 2. Then press “Execute” button. This step will run about several minutes.

     

    If there are biological replication in samples, you need to write sample table indicating biological replicate relationships as below

     

     

    6. The output of Step 5 is the result of differential expression analyses. Press save button to save the results to your local disk.

     

  • KEGG & GO Enrichment Tools

    Enter sequences ID of list for testing. If you want to use a certain background for testing, paste the list to the second textarea.

     

    The result of enriched GO catagory or KEGG pathway.

     

    The detailed information of genes in each enriched GO catagory or KEGG pathway.

     

  • Gene Expression Anylysis

    This tool focus on gene expression pattern such as specific expression, correlated expression and similar expression across varies RNA-seq experiments.

     After inputting gene ID list in the textbox, users can choose the dataset to do the analysis: uploading gene expression matrix from Galaxy server, or only using expression data integrated in the database.

     

     

    The gene expression profiles for querying gene IDs were shown in heatmap representing in blue-white-red color scheme. Both FPKM and TPM normalize gene expression or log2-transformed expression value could be represented. In order to show correlated expression and similar expression across genes or different treatment, cluster function could be used across genes and/or RNA-seq experiments.