Massimiliano Orsini and Simone Carcangiu develope a new and fast fast package "BlaSTorage" to parse and store BLAST results. BlaSTorage shows comparable speed of more basic parser written in compiled languages as C++ and can be easily integrated into web applications or software pipelines.

Find more @ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571973/

http://biowiki.crs4.it/biowiki/MassimilianoOrsini

If you have any questions or comments, post them on one of the trackers on BRIG’s SourceForge page: http://sourceforge.net/tracker/?group_id=328245.

Features:

• Images show similarity between a central reference sequence and other sequences as concentric rings.
• BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.
• Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.
• Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.
• BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously

Address of the bookmark: http://brig.sourceforge.net/

The goal of this tutorial is to run you through a demonstration of the command line, which you may not have seen or used much before.

All of the commands below can copy/pasted.

sudo apt-get update && sudo apt-get -y install python ncbi-blast+

wget ftp://ftp.ncbi.nih.gov/refseq/B_taurus/mRNA_Prot/cow.1.protein.faa.gz
wget ftp://ftp.ncbi.nih.gov/refseq/H_sapiens/mRNA_Prot/human.1.protein.faa.gz

gunzip *gz
ls

head cow.1.protein.faa
head human.1.protein.faa

grep -c '^>' cow.1.protein.faa
grep -c '^>' human.1.protein.faa

head -6 cow.1.protein.faa > cow.small.faa

makeblastdb -in human.1.protein.faa -dbtype prot
ls

blastp -query cow.small.faa -db human.1.protein.faa

    blastp -query cow.small.faa -db human.1.protein.faa -out cow_vs_human_blast_results.txt
ls

less cow_vs_human_blast_results.txt

blastp -help

blastp -help | less

blastp \
-query cow.small.faa \
-db human.1.protein.faa \
-out cow_vs_human_blast_results.tab \
-evalue 1e-5 \
-outfmt 7

head -199 cow.1.protein.faa > cow.medium.faa

grep -c '^>' cow.medium.faa

blastp \
-query cow.medium.faa \
-db human.1.protein.faa \
-out cow_vs_human_blast_results.tab \
-evalue 1e-5 \
-outfmt 6 \
-max_target_seqs 1

Here is a list of what is changing:

1. All databases at the root level will be dbV5.
2. The dbV5 file naming,  “_v5” will be removed. Databases with  no “_vX” descriptor will be dbV5.
3. dbV4 tarballs will be renamed with "_v4", files included in tarball will not be renamed.
4. dbV4 databases will be moved to a v4 subdirectory.
5. As of 1/13/20 the Cloud directory will be frozen with no more new entries.
6. The will be no more updates to dbV4 databases.
7. The FASTA directory will contain nr, nt, swissprot, and pdbaa files.

If you have any questions or concerns, please contact blast-help@ncbi.nlm.nih.gov

Core_nt contains the same eukaryotic transcript and gene-related sequences as nt. The core_nt database is nt without most eukaryotic chromosome sequences. Most nucleotide BLAST searches with core_nt will be similar to the nt database. However, core_nt is better than nt for accomplishing your most common BLAST search goals, such as identifying gene-related sequences like transcript sequences and complete bacterial chromosomes. This is because, in recent years, nt has acquired more low-relevance, non-annotated, and non-gene content. 

Learn more: https://ncbiinsights.ncbi.nlm.nih.gov/2024/07/18/new-blast-core-nucleotide-database/

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

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

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!

mScaffolder scaffolds a genome using an existing high quality genome as the reference. It aligns the two genomes using nucmer utility from MUMmer and then orders and orients the contigs of the candidate genome guided by their alignments to the reference genome. Please send your questions and comments to mchakrab@uci.edu.

Citation https://www.nature.com/articles/s41588-017-0010-y

Address of the bookmark: https://github.com/mahulchak/mscaffolder

Take a look at the PRICE software from the DeRisi lab. Its meant to do something very similar. http://derisilab.ucsf.edu/index.php?page=software

You could also look at SSPACE (http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/sspacev12/), ATLAS tools (http://www.hgsc.bcm.tmc.edu/content/bcm-hgsc-software), and SCARPA (http://compbio.cs.toronto.edu/hapsembler/scarpa.html).

See the PAGIT protocol: http://www.sanger.ac.uk/resources/software/pagit/

In particular, take a look at the IMAGE tool: http://genomebiology.com/2010/11/4/R41

Also SOAPdenovo has ha function for scaffolding. Not sure about ABYSS

Here there is a useful explanation of several tools.

https://bioinformaticsonline.com/search?q=scaffolding&entity_type=object&entity_subtype=bookmarks&offset=0&search_type=entities

I could be wrong, but the above answers to your hypothetical scenario appear to miss the point that you aren't interested in assembling the full genome, just the 100 kb part you're interested in. I suggest the following algorithm:

1. Start with the initial assembly C0 of the contigs you have identified as overlapping your region of interest, and the set S of reads those contigs contain. Let C = C0.

2. Repeat:
a. Identify paired-end reads (not in C) for which one or both ends align within, or extending, contigs in C.
b. Identify unpaired reads that align extending these new paired-end reads.
c. Construct a new assembly C' from C and the new reads identified in (a) and (b).
d. Trim C' so it does not extend more than 100 kb to either end of C0. Set C = C'.
e. Let S' denote the reads that contribute to C'. If S' does not contain any reads not present in S, stop. Otherwise, Set S = S'.

3. If you don't have a complete assembly of the region of interest, generate an STS for each end of each contig, probe a library for clones including these STSes, subclone these clones into a paired-end sequencing vector, and generate paired-end reads for this library; then try steps (1) and (2) again, adding these new sequencing reads to what you had before.

4. If your average sequencing depth for the region of interest exceeds 25 or so without filling all gaps, it is likely that the remaining gaps represent sequences that are not getting cloned in your sequencing vectors. Try different sequencing vectors.