WALK-IN-INTERVIEW

A walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132001 at 11:30 AM on 18.12.2013 to select One RA and One SRF as per details given below:

1. One post of Research Associate under DBT sponsored Support under BIPP for the “SanGenix: A comprehensive Next Generation Sequence (NGS) data analysis solution” as Grants in AID. Thepost duration is Upto 31st March 2015 or earlier.

2. One post of Senior Research Fellow under NAIP (Component-4) Bioprospecting of genes and allele mining for abiotic stress tolerance. The post duration is Upto 31st March 2014 or earlier

Essential Qualifications: Ph.D. in Bioinformatics/ Computer Application or
First Class Masters degree in Bioinformatics/ Computer Application with two years experience as evidenced by Publications.

Desirable: Experience in the field of handling Next generation Sequencing Data.

Emolument: Rs. 22,000/- per month + HRA as per admissibility

Age Limit:

40 years for Men
45 years for women as on date of interview

Research Associate: ONE

Duration of engagement: Upto

31st March 2015 or earlier & Coterminus with the project

Responsibilities: To help the PI for Beta testing and development of the SanGenix Tool for NGS data.

Essential Qualifications: First Class Masters’ degree in Bioinformatics/Biotechnology.

Desirable: Experience in the field of Biotechnology/ Bioinformatics

Emoluments:

Rs. 16,000/- per month + HRA as per admissibility.
Senior Research Fellow: ONE
Duration of engagement: Upto 31st March 2014 or earlier & Coterminus with the project

Age Limit

35 years for men
40 years for women as on date of interview

Note: Relaxation in age will be admissible for SC/ST & OBC candidates as per Govt. of India /ICAR norms

1. The applicants must bring with them original documents and brief of research work done during post graduation along with a set of photocopy and latest two passport size photographs.
2. A panel of selected candidates will also be made which may be utilized for filling of positions of shorter durations in future if demand arises.
3. Experience certificate in original, if any 4. The above positions are purely on temporary basis and are co-terminus with the project. No TA/DA will be paid to attend the interview.
5. Any other clarifications can be had on the date of interview.
6. The Director’s decision will be final and binding on all respects.

Advertisement: http://210.212.93.85/rasrfadvertise.pdf

Key Features of BLAST:
1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.

2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.

3. Versatility: There are several versions of BLAST for different types of sequence comparisons:
- blastn: Compares a nucleotide query sequence against a nucleotide sequence database.
- blastp: Compares a protein query sequence against a protein sequence database.
- blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.
- tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.
- tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.

5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.

Applications of BLAST:
- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.
- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.
- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.
- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.

Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.

More at http://www.mathivananlab.org/index.html

http://scholar.google.com/citations?user=U6PyEdYAAAAJ&hl=en

RMBlast supports RepeatMasker searches by adding a few necessary features to the stock NCBI blastn program. These include:

• Support for custom matrices ( without KA-Statistics ).
• Support for cross_match-like complexity adjusted scoring. Cross_match is Phil Green's seeded smith-waterman search algorithm.
• Support for cross_match-like masklevel filtering.

https://anaconda.org/bioconda/rmblast

Address of the bookmark: http://www.repeatmasker.org/RMBlast.html

More at http://www.ircc.iitb.ac.in/IRCC-Webpage/rnd/RecruitmentGenerateCircular.jsp?srno=2013086

Address of the bookmark: https://kablammo.wasmuthlab.org/

- Plan more efficient experiments
- Correctly interpret results
- Communicate results in publications more effectively

The course focus is on methodologies, not on particular software tools. After the course participants should be able to apply the methods in their respective environment. However, during the course, hands-on sessions will be performed using the Genedata Expressionist® software, which enables participants to quickly apply the discussed methods and visualize results. No previous knowledge on Expressionist® is required; access to the software is free of charge during the course.

More @ http://www.dixa-fp7.eu/dixa-training/dixa-training-agenda/genedata-academy#!

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!

Now my main interests are in gene regulation, where we work on promoter analysis; non-coding RNA, where miRNAs and structure prediction are the main areas; and protein structure, where the group is working on methods for structure prediction from sequence. To read more about these topics, please see the research pages.

Lab page @ http://wiki.binf.ku.dk/User:Krogh