BOL: Related items

BLAST options, setting and defaults

Jit — Mon, 10 Dec 2018 08:29:37 -0600

BLAST stands for Basic Local Alignment Search Tool and was developed by Altschul et al. (1990) and significantly improved by Altschul et al. (1997). It is a very fast search algorithm that is used to separately search protein or DNA databases. BLAST is best used for sequence similarity searching, rather than for motif searching. For searches using a query sequence of fewer than twenty residues, PatMatch is the best choice. Another sequence alignment tool that may yield different results from BLAST, and may be useful for motif searching, is FASTA. To search nonplant datasets, try NCGR BLAST or NCBI BLAST.

A fairly complete on-line guide to BLAST searching can be found at the NCBI BLAST Help Manual. For a theoretical overview of BLAST, see the NCBI BLAST Course. Additional information can be found in the BLAST 2.0 Release Notes

	BLASTN	BLASTP	BLASTX	TBLASTN	TBLASTX	PSIBLAST
Gap opening penalty: cost to open a gap [integer]	default = 5	default = 11 limited values are supported	default = 11 limited values are supported	default = 11 limited values are supported	default = 11 limited values are supported	default = 5
Gap extension penalty: cost to extend a gap [integer]	default = 2	default = 1 a 0 in this field means to use the default	default = 1 a 0 in this field means to use the default	default = 1 a 0 in this field means to use the default	default = 1 a 0 in this field means to use the default	default = 2
Nucleic match: reward for a match in the BLAST portion of run [integer]	default = 1	n/a	n/a	n/a	n/a	default = 1
Nucleic mismatch: penalty for a mismatch in the blast portion of run [integer]	default = -3	n/a	n/a	n/a	n/a	default = -3
Expectation value: (E) [real]	default = 10.0	default = 10.0	default = 10.0	default = 10.0	default = 10.0	default = 10.0
Word size: the size of the initial word that must be matched between the database and the query sequence	default = 11	default = 3	default = 3	default = 3	default = 3	default = 11
Max scores: Number of one-line descriptions (V) [Integer]	default = 25	default = 25	default = 25	default = 25	default = 25	default = 25
Max alignments: number of alignments to show (B) [integer]	default = 15	default = 15	default = 15	default = 15	default = 15	default = 15
Query filter: filter applied to the query sequence	default = DUST	default = SEG	default = SEG	default = SEG	default = SEG	default = DUST
Query genetic code: genetic code to be used in BLASTX translation of the query	n/a	n/a	default = universal	default = universal	default = universal	n/a
Matrix: substitution matrix to be used for amino acid comparisons	no default	default = blosum62	default = blosum62	default = blosum62	default = blosum62	no default

Supported and Suggested Values for Gap Open and Extension in BLASTP, BLASTX, TBLASTN, and TBLASTX

Gaps Open	Gap Extension
10	1
10	2
11	1
8	2
9	2

Address of the bookmark: https://www.arabidopsis.org/Blast/BLASToptions.jsp

Genomics and Personalized Medicine

Sun, 01 Jun 2014 23:38:42 -0500

(October 20, 2009) Michael Snyder, Professor of Genetics and Chair of the Department of Genetics at Stanford, discusses advances in gene sequencing, the impact of genomics on medicine, the potential for personalized medicine. and efforts at Stanford to further study these issues. Stanford Mini Med School is a series arranged and directed by Stanford's School of Medicine, and presented by the Stanford Continuing Studies program. Featuring more than thirty distinguished, faculty, scientists and physicians from Stanford's medical school, the series offers students a dynamic introduction to the world of human biology, health and disease, and the groundbreaking changes taking place in medical research and health care. Stanford University http://www.stanford.edu Stanford University School of Medicine http://med.stanford.edu Stanford Continuing Studies http://continuingstudies.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford

Adhoc Bioinformatics Faculty Position @ NIT

Tue, 03 Jun 2014 16:19:52 -0500

NATIONAL INSTITUTE OF TECHNOLOGY, DEPARTMENT OF BIOTECHNOLOGY, WARANGAL – 506 021, Andhra Pradesh

No.NITW/BT/2014/adhoc

APPLICATIONS ARE INVITED FOR THE APPOINTMENT OF ADHOC FACULTY ON CONTRACT BASIS IN THE DEAPARTMENT OF BIOTECHNOLOGY

Period of Contract: Initially the appointment is for one semester i.e., from July 2014 up to December 2014 only.

Essential Qualifications:

i) B. Tech or equivalent in Biotechnology/ Industrial Biotechnology/ Biochemical Engineering / Chemical Engg. Or M. Sc in Microbiology/ Botany/ Zoology/ Biochemistry/Biotechnology and ii) M. Tech or equivalent in Biotechnology/Industrial Biotechnology/Bioinformatics

Integrated M. Tech in Biotechnology/Industrial Biotechnology/ Bioinformatics

Candidates must possess First class (60% aggregate marks or 6.5 CGPA) at B. Tech/ M. Sc and M. Tech.

Desirable: Ph. D Pay Package: All selected candidates shall be eligible for a consolidated pay of Rs.30, 000/- per month. Candidates with Ph. D shall be eligible for an additional amount of Rs.5, 000/- per month.

How to apply : Applications on plain paper with attested photocopies of certificate and bio data along with justification for eligibility should reach to the Head, Department of Biotechnology, National Institute of Technology, Warangal AP 506004 in the form of soft or hard copy on or before 21st June 2014 email : biotech_hod@nitw.ac.in

Intimation: No separate call letters will be sent to the candidates. All the eligible candidates will be notified in the institute web site on 23rd June 2014. All the eligible candidates are requested to report for the interview to the Head, Department of Biotechnology at 9:00 AM on 27th June 2014

Joining: Selected candidates will be informed and they are expected to join immediately.

http://www.nitw.ac.in/nitw/announcements/2014/Bio-Adhoc%20Advt.%20May-2014.pdf

A Step-by-Step Guide to Running BLAST Offline

LEGE — Sat, 07 Dec 2024 22:32:37 -0600

BLAST (Basic Local Alignment Search Tool) is a powerful algorithm used to compare nucleotide or protein sequences to sequence databases, identifying regions of similarity. Running BLAST offline provides more control, ensures data security, and allows customization for specific research needs. Here’s a detailed guide to set up and run BLAST locally on your system.

Step 1: Install BLAST

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

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
```
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

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.
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

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

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

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

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

Search Shell Command History

Rahul Nayak — Thu, 12 Jun 2014 17:43:34 -0500

We use couple of hundreads of command in daily basis. Most of them are actually repeated several time. The question remain open how do I search old command history under bash shell and modify or reuse it?

Now a days almost all modern shell allows you to search command history if enabled by user. Use history command to display the history list with line numbers. Lines listed with with a * have been modified by user.

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

I-PV: Interactive Protein Sequence Visualization

Jit — Mon, 03 Jul 2017 07:52:51 -0500

I-PV is a interactive data visualization software designed for inspection of protein sequences and mutation information. It is mainly used for Genetics and Bioinformatics. So what exactly makes it standout?

http://i-pv.org/ipv_rec

Address of the bookmark: http://i-pv.org/

Bioinformatician’s Pocket Reference !!

RAJESH DETROJA — Sun, 08 Jun 2014 09:56:58 -0500

It is amusing how brain of bioinformaticians work! Learning a new programming language for days feels so much of fun that making 5 minute discussion with neighbours (unless under special circumstances!) in our own mother-tongue. Today every bioinformatician keeps more than few languages and core IT toolkits on their plate. It has become mandatory to be able to mould different code snippets to build our own custom workflows, and thus keeping syntax at our fingertips has become essential.Although Google is best way to get syntax problem solved, it is not a bad idea to keep reference sheets is our smartphones or stick out some printed sheets on the back of your door, in the old fashion way!!

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

ASAR: Advanced metagenomic Sequence Analysis in R

Jit — Mon, 09 Jul 2018 05:20:50 -0500

An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.

To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

Assistant Professor in Medical Bioinformatics

Tue, 24 Jun 2014 01:46:36 -0500

Advt. No : ME-I/A-IV/03/14
No.of Posts:01 (SC)
Pay Scale:
Pay Band of Rs.15600-39100 + Rs.6000/- GP +NPA @ 25% of Basic Pay +Learning Resource Allowance @ Rs.20,000/-P.A.+ Conveyance Allowance @ Rs. 1650/-P.M.+ Academic Allowance @ Rs.2500/- P.M. and other admissible allowances.
Qualifications:
Area of Specialization:-
Bioinformatics/Computational/Biology/Genomics/ Proteomics/ Structural Biology
1. Postgraduate qualification, e.g. Master’s Degree in Biotechnology/Bioinformatics/ Biophysics.
2. A Doctorate Degree of recognized University/Institute in a basic or allied Medical Science subject e.g. Medical Biotechnology/Biophysics. Bioinformatics/X-ray Crystallography/
Immunology/Structural Biology etc
Experience:
1.Minimum three years teaching and/or research experience in a recognized medical/research Institution in an allied medical subject after obtaining doctorate degree and preferably in Medical
Molecular Biology/ Biophysics/Structural Biology/Genomics and Clinical Proteomics/Computational Biology.
2. Minimum two publication with atleast one in international journal and atleast one as first author
Desirable:-
Consistently excellent scholastic/academic record, demonstrated ability to write grant proposal/(s) successfully, Post Doctoral training in a frontier area of medical Bioinformatics Research and of direct relevance to clinical diagnosis or patient care (preferably from a recognized top-ranking medical institution abroad)
Send your applications to O/O, Deputy Registrar, Recruitment & Establishment Cell, University of Health Sciences, Rohtak by 08.7.2014
For more details,please visit website: http://pgimsrohtak.nic.in/2014%20AP%20Advt.pdf
Last Apply Date: 08 Jul 2014

medaka: Sequence correction provided by ONT Research

BioStar — Mon, 18 May 2020 16:28:00 -0500

medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.

Address of the bookmark: https://github.com/nanoporetech/medaka