https://bigdata.dongguk.edu/gene_project/AGORA/

Input

Reference sequence :

- In order to run the BLAST with query and references, you need to put reference sequences. If you have the user-defined amino acid or nucleotide sequences, you can upload it on the system. Otherwise, you can put the accesion number such as NC_000000.For more information, please refer NCBI site
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Input File Format :

- Input file is an aassembled contig. The format should be "FASTA". The AGORA allows only one assembled query contig. For more information about FASTA, please refer to NCBI site

Type :

- Select Choloroplast or Mitochondrion for your organellar genome

Genetic Code

- This code is used for running the tBLASTn

   Standard
   Vertebrate Mitochondrial
   Yeast Mitochondrial
   Mold Mitochondriali
   Invertebrate Mitochondrial
   Ciliate Nuclear
   Echinoderm Mitochondrial
   Euplotid Nuclear
   Bacteria and Archaea
   Alternative Yeast Nuclear
   Ascidian Mitochondrial
   Flatworm Mitochondrial
   Blepharisma Macronuclear

For more information about genetic code please see NCBI

Output

Output :

- As you see below examples, output file is the BLAST result that includes amino acid and nucloetide. The Query is set to the refereces and Data base is set to query. The number of matched position is decided upon the "Maximum matched sub gene's count"

The blast result of amino acid

The blast result of nucleotide

Amino acid db sequences :

- This file includes the amino acid data base sequences. If the user uploaded the user-defined sequence, this file is same to that uploaded file. Otherwise, system is automatically generated from the NCBI.

Amino acid sequences :

- This is CDS translation files that is matched from the BLAST

output CSV file :

- This file provides the start and end position, direction and gene product for each gene.

Nucleotide db sequences :

- This file is nucleotide data base sequences.

Nucleotide sequences :

-The FASTA formatted seuqneces file is includes the BLAST mached sequences.

GenBank File format :

- This file is GenBank formatted file. With this file we draw the circular gene map by running OGDRAW

OGDRAW :

- If all genes are matched correctly, you can see the figure. Here is example

https://bigdata.dongguk.edu/gene_project/genome_search_plotter

Reference accesion Number :
- In order to run the BLAST with query contigs and reference, Reference sequences are required. For more information, please refer to NCBI site

Input File Format :
- Input file is the assembled contigs and format should be in FASTA format. The number of contigs are not limimted, but, it will takes several hours. We recommend to copy the reslut page URL for your reference.

Maximum number of matched BLAST hit group :
- The number sets the maximum groups which is based on the BLAST result.

Minimum number of matched sub gene's count per each contig :
- Please refer to below Figure 1-B.

The provided files are "sorted sequences" and "PDF" files

Sorted by the number of subgene :
- BLAST result file which is sorted by BLAST e-value. In the sorted query sequences file, sequences are sorted by the number of matched sub-genes and the sequences that do not meet the minimum value of k are filtered out

PDF file:
- The results are shown as a graph depicting matches with the 149 reference genomes on the X-axis and query sequences uploaded by the user on the Y-axis. Each 150 line on the plot indicates that a query contig is matched with the reference sequences.

In comparative and evolutionary genomics, a detailed comparison of common features between organisms is essential to evaluate genetic distance. However, identifying differences in matched and mismatched genes among multiple genomes is difficult using current comparative genomic approaches due to complicated methodologies or the generation of meager information from obtained results. This study describes a visualized software tool, geneCo (gene Comparison), for comparing genome structure and gene arrangements between various organisms. User data are aligned, gene information is recognized, and genome structures are compared based on user-defined GenBank files. Information regarding inversion, gain, loss, duplication, and gene rearrangement among multiple organisms being compared is provided by geneCo, which uses a web-based interface that users can easily access without any need to consider the computational environment.

https://bigdata.dongguk.edu/geneCo

C-Hunter is a new clustering algorithm which incorporates knowledge of gene function derived from Gene Ontology, with the organization of genes on chromosomes. In order to use C-Hunter program, basic data sets are needed. All data sets for eight species(AT,CE,DM,DR,EC,HS,MM & SC), Data/Map file, GO, gene2accession and gene2go are supplied with C-Hunter program together. But, if you want to use new data sets, you can download from each website and you can make them again. C-Hunter program can be compiled under the Unix/Linux/Windows(Cygwin) environment, if the compiler supports STL.

Installation

• tar -zxvf C_Hunter_v.1.2.tar.gz
• cd C_Hunter_v.1.2
• ./install

Procedure for preparing data sets

If you are going to use alreay-made data sets, you don't need to do this procedure.

1. Download go.obo text file (include Molecular Function, Biological Process, Cellular) at http://www.geneontology.org/page/download-ontology
2. Download gene2accession at NCBI, ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
3. Download gene2go at NCBI, ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
4. Make Chromosome list file (text file by tab-separated format)
   1st_Column = Chromsome Number
   2nd_Column = Accession
   3rd_Column = GI

5. Run "obo2scheme.py" with 1 Gene Ontology data
   Usage : python obo2scheme.py go.obo (file from #1)
   The output file name will be "scheme.GO.data"
6. Run "Make_GO_Map_Data.php" with gene2accession, gene2go and Chr_list
   Usage : php Make_GO_Map_Data.php gene2accession, gene2go, Chr_list, Ouput file name
   This converting program makes map files and GO data files.

Download:

• C_Hunter_v.1.2.tar.gz
• C_Hunter_v.1.2.Fasta.tar.gz

SClassify is a supervised protein family classification algorithm that overcomes the problems of existing supervised and unsupervised algorithms and achieves much improved accuracy. It can assign proteins to existing families in databases, and by taking into account similarities between the unclassified proteins, can assign them to new families.

Installation

The SClassify source code, including sample input and output files, can be compiled under the Unix/Linux/Windows(Cygwin) environment. The following steps will create a directory called sclassify. Detailed usage of SClassify is provided in a README file.

• gunzip sclassify.tar.gz
• tar xvf sclassify.tar
• cd sclassify
• ./install

How to use

The program assumes that e-values between each unclassified protein and each protein in existing families and e-values between each pair of unclassified proteins have already been obtained by other software such as BLAST or SSEARCH. The following files are needed:

1. A file that lists the name of each protein in existing families along with the name of its family in a two-column tab-separated format (example file: pfam.list).

2. A file that lists the name of each unclassified protein in a one-column format (example file: test.list).

3. A file that lists the e-values between each unclassified protein and each protein in existing families in a three-column tab-separated format that gives the name of an unclassified protein, the name of a protein in an existing family, and the e-value between them. There is no need to have an e-value for each pair if some of them are missing. The file is optional (example files: blast/test_pfam.score, ssearch/test_pfam.score).

4. A file that lists the e-values between each pair of unclassified proteins in a three-column tab-separated format that gives the names of two unclassified proteins and the e-value between them. There is no need to have an e-value for each pair if some of them are missing. The file is optional (example files: blast/test_test.score, ssearch/test_test.score).

USAGE
./sclassify -c infile1 -u infile2 -p infile3 -n infile4 -e cutoff -o outfile
where infile1 to infile4 are the input files described above, cutoff is the e-value cutoff, and outfile is the output file.

EXAMPLES
./sclassify -c pfam.list -u test.list -p blast/test_pfam.score -n blast/test_test.score -e 0.1 -o test.out
./sclassify -c pfam.list -u test.list -p blast/test_pfam.score -e 1e-10 -o test.out

./sclassify -c pfam.list -u test.list -p ssearch/test_pfam.score -n ssearch/test_test.score -e 0.1 -o test.out
./sclassify -c pfam.list -u test.list -p ssearch/test_pfam.score -e 1e-10 -o test.out

OUTPUT
The output file is in a two-column tab-separated format that lists the name of each protein that is classified and the name of its assigned family. A distinct name is generated for each new family, and the same name is used for all proteins that are classified to the same family.

SCRIPTS
Two scripts are provided to convert the results from BLAST and from SSEARCH to a three-column tab-separated format.



1. BLAST converter

Usage: python convert_blast.py infile outfile
where infile contains the results from BLAST, and outfile is the output file.

Examples:
python convert_blast.py blast/test_pfam.blast blast/test_pfam.score
python convert_blast.py blast/test_test.blast blast/test_test.score

Note: If BLAST is applied with option -m 8, then there is no need to run the python script to convert the BLAST output.

Example:
blastall -p blastp -m 8 -d pfam -i test.fasta | cut -f 1,2,11 > blast/test_pfam.score

2. SEARCH converter

Usage: python convert_ssearch.py infile outfile
where infile contains the results from SSEARCH, and outfile is the output file.

Examples:
python convert_ssearch.py ssearch/test_pfam.ssearch ssearch/test_pfam.score
python convert_ssearch.py ssearch/test_test.ssearch ssearch/test_test.score

Download:
sclassify.tar.gz

Advances in the next-generation sequencing technology have led to a dramatic decrease in read-generation cost and an increase in read output. Reconstruction of short DNA sequence reads generated by next-generation sequencing requires a read alignment method that reconstructs a reference genome. In addition, it is essential to analyze the results of read alignments for a biologically meaningful inference. However, read alignment from vast amounts of genomic data from various organisms is challenging in that it involves repeated automatic and manual analysis steps. We, here, devised cPlot software for read alignment of nucleotide sequences, with automated read alignment and position analysis, which allows visual assessment of the analysis results by the user. cPlot compares sequence similarity of reads by performing multiple read alignments, with FASTA format files as the input. This application provides a web-based interface for the user for facile implementation, without the need for a dedicated computing environment. cPlot identifies the location and order of the sequencing reads by comparing the sequence to a genetically close reference sequence in a way that is effective for visualizing the assembly of short reads generated by NGS and rapid gene map construction.

https://bigdata.dongguk.edu/cPlot