BLAST: Basic Local Alignment Search Tool

I describe the procedure for the PSSM generation from the FASTA sequences. It is asynchronous parallel processing that can process up to n-sequence at a time. I spend a considerable amount of time on the PSSM generation purpose. It is definitely a hard and tedious procedure, but I make it easy so that other researchers can use it efficiently. People can use it for PSSM generation; unfortunately, I did not check the benchmark yet.

   

Step 0: Graphical Representation: How can we generate PSSM using asynchronous parallel processing?

   

Step 1: Download the BLAST Tool:

Please find the latest version of BLAST tool from the given website (https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/). Then download one of the files as per your operating system (OS) requirement. As I am a Linux OS user, that is why I downloaded "ncbi-blast-...-linux.tar.gz". Please don't worry about the version; it usually changes over time.

 

user@machine:~$ wget 'https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ncbi-blast-2.10.1+-x64-linux.tar.gz' ### Fetch from website
user@machine:~$ tar -xvzf ncbi-blast-2.10.1+-x64-linux.tar.gz                                                           ### Extract the tool after the download

   

Step 2: Download the Non-redundant (NR) Proteins Database:

We can download the nr database from official website (https://ftp.ncbi.nlm.nih.gov/blast/db/), and the downloading processes are given below.

 

Option-1: Downloading Process:

user@machine:~$ wget 'ftp://ftp.ncbi.nlm.nih.gov/blast/db/nr.*.tar.gz'
user@machine:~$ wget 'ftp://ftp.ncbi.nlm.nih.gov/blast/db/nr.*.tar.gz.md5'

Option-2: Downloading Process (aka Update the Current Database):

user@machine:~$ /home/user/ncbi-blast-2.10.1+/bin/update_blastdb.pl --decompress nr [*]

Notes:

1. The database had 39 segments, and the initial file size was approximately 100GB; we would get around 450GB after the extraction (Until 2020).
   • The database was 54 segments (Last Update: August 1, 2021).
   • The database is now 78 segments; the initial file size was approximately 193GB, and we got around xxGB after the extraction; (Last Update: July 12, 2023).
2. The segments change frequently.
3. We can get the update_blastdb.pl file from BLAST tool.

   

Step 3: Update the Non-redundant (NR) Proteins Database (Optional):

When the nr database will be old, no need to download (or upgrade) rather than update the previous one.

 

Update Process:

user@machine:~$ /home/user/ncbi-blast-2.10.1+/bin/update_blastdb.pl --decompress nr [*]

Notes:

1. We can get the update_blastdb.pl file from BLAST tool.
2. Plese run the script from the nr directory (or folder), otherwise it won't work.

   

Step 4: Extract the Non-redundant (NR) Proteins Database:

Extract *.tar.gz Files:

n=38   ### If the number of the segment is n, then we will use n-1.

for i in $(seq 0 1 $n); do
    if [ $i -lt 10 ]; then
        tar -xvzf nr.0$i.tar.gz
    else
        tar -xvzf nr.$i.tar.gz
    fi
done

Notes:

1. A question can arise why I used 38 in the loop? The answer is, I got the 39 segments in the nr directory (or folder).
2. Please make sure that how many segments you have, then update the value of n. We can find it from nr.pal in nr directory (or folder).
3. Plese run the script from the nr directory (or folder), otherwise it won't work.
4. We will find the update decompress procedure from given URL.

   

Step 5: Split Multile FASTA File into Single FASTA Files:

File = '/home/user/Bioinformatics/multiSequences.fa'

from Bio import SeqIO # Install (If you don't have it.): pip install biopython

C= 1
for record in SeqIO.parse(File, 'fasta'):
    openFile = open(str(C) + '.fasta', 'w')
    SeqIO.write(record, openFile, 'fasta')
    C += 1
#end-for

 

Notes:

1. I renamed the origial name of FASTA sequence as it is helpful for tracking the implementation.
2. I used sequential numerical order rather than the original sequence name.
3. Renaming the sequence is optional.
4. We will find the updated FASTA splitting procedure from given URL.
5. We can also use Colab for the splitting Multiple FASTA sequence into single sequences [Update Implementation].

   

Step 6: Tracking the Original Sequence Name (Optional):

File = '/home/rafsanjani/Downloads/TS88.fa'

from Bio import SeqIO

C= 1

print('Original-Sequence-Name, Renamed-Sequence, Corresponding-PSSM')
for record in SeqIO.parse(File, 'fasta'):
    print('{}, {}.fasta, {}.fasta.pssm'.format(record.id, C, C))
    C += 1
#end-for

 

Notes:

1. As I rename the sequences, you can track the original sequences.
2. You will find the update procedure from given URL.

   

Step 7: Implementation/Generate PSSMs:

###
database = '/home/learning/mrzResearchArena/NR/nr'   # Please, set path where "nr" database directory is located.
PSSM = '/home/learning/mrzResearchArena/PSSM'        # Please, set path where PSSM directory is located.
core = 8                                             # multiprocessing.cpu_count()
###

###
import multiprocessing
import time
import glob
import os
os.chdir(PSSM)
###

###
def runPSIBLAST(file):
    try:
        os.system('/home/learning/ncbi-blast-2.10.1+/bin/psiblast -query {} -db {} -out {}.out -num_iterations 3 -out_ascii_pssm {}.pssm -inclusion_ethresh 0.001 -comp_based_stats 0 -num_threads 1'.format(file, database, file, file))
    except:
        print('PSI-BLAST is error for the sequence {}!'.format(file))
        return '{}, is error.'.format(file)

    return '{}, is done.'.format(file)
#end-def
###

###
begin   = time.time()
pool    = multiprocessing.Pool(processes=core)
results = [ pool.apply_async(runPSIBLAST, args=(file,)) for file in glob.glob('*.fasta') ] # for x in range(1, 10)
###

###
outputs = [result.get() for result in results]
end = time.time()
###

###
print(sorted(outputs))
print()
print('Time elapsed: {} seconds.'.format(end - begin))
###

 

Notes:

1. You will find the update procedure from given URL.

   

Acknowledgement: I would like to thank you to Professor Iman Dehzangi, who helped me initially for the PSSM generation.