https://github.com/mrzresearcharena/blast
Easy Way to Generate PSSM from the FASTA Sequences
https://github.com/mrzresearcharena/blast
bioinformatics bioinformatics-software computational-biology
Last synced: about 1 month ago
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Easy Way to Generate PSSM from the FASTA Sequences
- Host: GitHub
- URL: https://github.com/mrzresearcharena/blast
- Owner: mrzResearchArena
- License: gpl-3.0
- Created: 2020-05-27T02:47:28.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-14T17:00:19.000Z (over 1 year ago)
- Last Synced: 2025-04-14T06:12:16.274Z (about 1 month ago)
- Topics: bioinformatics, bioinformatics-software, computational-biology
- Language: Jupyter Notebook
- Homepage: http://rafsanjani.pythonanywhere.com/
- Size: 8.5 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
```console
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:
```console
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):
```console
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).
4. The segments change frequently.
5. 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:
```console
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:
```bash
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](https://github.com/mrzResearchArena/BLAST/blob/master/decompress-NR.sh).
### Step 5: Split Multile FASTA File into Single FASTA Files:
```python
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](https://github.com/mrzResearchArena/BLAST/blob/master/splitFASTA.py).
5. We can also use Colab for the splitting Multiple FASTA sequence into single sequences [[Update Implementation](https://github.com/mrzResearchArena/BLAST/blob/master/Split-FASTA-using-BioPython-Colab.ipynb)].
### Step 6: Tracking the Original Sequence Name (Optional):
```python
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](https://github.com/mrzResearchArena/BLAST/blob/master/renamedSequencesOrder.py).
### Step 7: Implementation/Generate PSSMs:
```python
###
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](https://github.com/mrzResearchArena/BLAST/blob/master/asynParallel.py).
**Acknowledgement:** I would like to thank you to Professor [Iman Dehzangi](https://scholar.google.com/citations?user=RkamSRYAAAAJ&hl=en), who helped me initially for the PSSM generation.