Step 1: Install BLAST

1. Download BLAST:

   • Visit the NCBI BLAST+ download page to download the appropriate version for your operating system (Windows, macOS, or Linux).

2. Install BLAST:

   • Extract the downloaded archive. For Linux/Mac, use:

     tar -xvzf ncbi-blast-*.tar.gz
     cd ncbi-blast-*
     

   • Add the BLAST binary folder to your system PATH for easier access:

     export PATH=$PATH:/path/to/ncbi-blast-*/bin
     

3. Verify Installation:
   Run the following command to ensure BLAST is installed correctly:

   blastn -version
   

Step 2: Prepare a Local Database

To run BLAST offline, you’ll need a sequence database.

1. Download a Pre-Built Database (Optional):

   • NCBI provides ready-to-use databases such as nt, nr, and Swiss-Prot. Use the update_blastdb.pl script (bundled with BLAST) to download these:

     update_blastdb.pl --decompress nt
     

2. Create a Custom Database:
   If you have specific sequences to use as a database:

   • Prepare a FASTA file containing the sequences.
   • Use makeblastdb to create a database:

     makeblastdb -in your_sequences.fasta -dbtype [nucl|prot] -out custom_db
     

     Replace [nucl|prot] with nucl for nucleotide sequences or prot for protein sequences.

Step 3: Prepare the Query Sequence

• Save your query sequence(s) in FASTA format.
• Ensure the file is properly formatted, with a header line starting with > followed by the sequence name, and the sequence on subsequent lines.

Example:

>query_sequence
ATGCGTAGCTAGCGTAGCTAGCTAGCTA

Step 4: Run BLAST

1. Choose the Appropriate BLAST Tool:
   Depending on your data type:

   • blastn: For nucleotide-nucleotide searches.
   • blastp: For protein-protein searches.
   • blastx: Translates nucleotide sequences into proteins and compares them to a protein database.
   • tblastn: Compares protein sequences to a nucleotide database.
   • tblastx: Translates both nucleotide query and database sequences.

2. Run the Command:
   Example command for blastn:

   blastn -query query.fasta -db custom_db -out results.txt -outfmt 6 -evalue 1e-5
   

   Explanation of Parameters:

   • -query: Specifies the query file.
   • -db: Points to the local database.
   • -out: Output file name.
   • -outfmt: Output format (e.g., 6 for tabular format).
   • -evalue: E-value cutoff for significance.

Step 5: Interpret Results

1. Output Formats:

   • Default (outfmt 0): Human-readable format.
   • Tabular (outfmt 6): Includes fields like query ID, subject ID, percent identity, alignment length, etc.

2. Analyze Results:
   Use tools like grep, Python, or R to parse and filter results for downstream analysis.

Step 6: Optimize Performance

For large datasets, BLAST can be resource-intensive. To improve performance:

1. Multithreading:
   Use the -num_threads option to leverage multiple CPU cores:

   blastn -query query.fasta -db custom_db -out results.txt -num_threads 4
   

2. Database Subsetting:
   Split large databases into smaller chunks for faster searches.

3. Adjust Parameters:

   • Lower the -evalue threshold for stricter matches.
   • Use -max_target_seqs to limit the number of results per query.

Step 7: Update Databases (Optional)

If using NCBI databases, regularly update them to ensure the inclusion of the latest sequences:

update_blastdb.pl --decompress nt

Conclusion

Running BLAST offline is a straightforward process that offers flexibility and security for bioinformaticians working with sensitive data. By following this guide, you can harness the power of BLAST to analyze sequences efficiently and gain valuable biological insights.

For advanced use cases, explore BLAST’s customization options, such as custom scoring matrices, filtering, and iterative searches with tools like PSI-BLAST. Happy BLASTing!

Shell history search command

Type history at a shell prompt:
$ history

It will display the list of all used commandline history with an serial number.

To search particular command, enter:
$ history | grep command-name
$ history | egrep -i 'scp|ssh|ftp'
Emacs Line-Edit Mode Command History Searching

To get previous command containing string, hit [CTRL]+[r] followed by search string:

(reverse-i-search):

To get previous command, hit [CTRL]+[p]. You can also use up arrow key.

CTRL-p

To get next command, hit [CTRL]+[n]. You can also use down arrow key.

CTRL-n

fc command

Apart from hostory command there are fc command to extract the command from history. The fc stands for either "find command" or "fix command.

For example list last 10 command, enter:
$ fc -l 10
To list commands 130 through 150, enter:
$ fc -l 130 150
To list all commands since the last command beginning with ssh, enter:
$ fc -l ssh
You can edit commands 1 through 5 using vi text editor, enter:
$ fc -e vi 1 5

Delete command history

The -c option causes the history list to be cleared by deleting all of the entries:
$ history -c

If you use circlize in your publications, I would be appreciated if you can cite:

Gu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics. DOI: 10.1093/bioinformatics/btu393

Address of the bookmark: http://zuguang.de/circlize_book/book/

Address of the bookmark: http://infoplatter.wordpress.com/2014/04/06/bioinformaticians-pocket-reference/

You should consider using Circos to show:

• relationships between entities
• periodical data

Address of the bookmark: https://github.com/nicgirault/circosJS

It takes as an input a set of genomes represented as sequences of genes (or synteny blocks) and produces such sequences for ancestral genomes at the internal nodes of the phylogenetic tree.

The phylogenetic tree may be also specified completely or partially, in the latter case MGRA can reconstruct conserved ancestral regions (CARs) of the ancestral genome of interest.

Since version 2 MGRA supports gene insertion and deletions in addition to genome rearrangements and allows the input genomes to have different gene content.

It also can reconstruct most plausible phylogenetic tree based on the rearrangement characters.

Address of the bookmark: http://mgra.cblab.org/

The work will focus on the functional studies of small RNAs by using next-generation sequencing approaches.

Candidates should hold a Ph.D. degree and have strong background in bioinformatics.
The University of Nice Sophia-Antipolis provides a wide range of facilities and training essential for biomedical research.
Interested applicants should send a PDF with a cover letter stating research interests and qualifications, an updated CV, a summary of previous research experience and contact information for two references to Michele Trabucchi ( mtrabucchi@unice.fr )

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Address of the bookmark: http://genomicus.biologie.ens.fr/genomicus-90.01/cgi-bin/search.pl

Address of the bookmark: https://github.com/reubwn/hgt