biopython | Official git repository for Biopython | Genomics library
kandi X-RAY | biopython Summary
kandi X-RAY | biopython Summary
Official git repository for Biopython (originally converted from CVS)
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Top functions reviewed by kandi - BETA
- Read a PIC file .
- Return the radius of an atom .
- Fetch the internal id list from the database .
- Returns an iterator over the FASTQ - M10 records .
- Draw a cross link .
- Parse coordinates .
- Writes a SCAD file to fp .
- r Compute the DNA sequence .
- Run a qblast query .
- Read a PFM file .
biopython Key Features
biopython Examples and Code Snippets
Lines of Code : 6License : Permissive (MIT)
Copygit clone https://github.com/chris-rands/biopython-coronavirus;
cd biopython-coronavirus
pip3 install jupyter biopython
conda env create -f environment.yml
conda activate biopython-coronavirus
jupyter-notebook biopython-coronavirus-notebook.ipynb
import re
string = 'VATLDSCBACSKVNDNVKNKVKVKNVKMLDHHHV'
re.findall(r"B[^P]C|M[^P]D", string)
['BAC', 'MLD']
AA_seq=input("write Amino Acid Sequence:" )
AA_seq=AA_seq.upper()
sum=0
value={"V": 3.1,"Y":3.5,"W":4.7,"T" :5.3,"S":5.1,"P":3.7,
"F":4.7,"M":1.5,"K":8.9,"L":6,"I":4.3,"H":3.3,"G":7.1,
"E":7,"Q":5.4,"C":0.6,"D":7.6,"N":6,"R":8.7,"A":3.4}with open(glob_path, "rU") as input_fq:
with open(file_path, "rU") as input_fq:
glob_path = path.glob('*.fastq')
for file_path in glob_path:
with open(file_path, "rU") as input_fq:
ATOM 25 N ALA E 5 48.087 97.950 74.514 1.00 9.33 N
ATOM 26 CA ALA E 5 48.052 99.292 73.904 1.00 9.37 C
ATOM 27 C ALA E 5 47.483 100.285 74.935 1.00 9.65 seqtk subseq > out.fa
seen_records = set()
records_to_keep = []
for record in SeqIO.parse('DNA_library', 'fasta'):
seq = str(record.seq)
if seq not in seen_records:
seen_records.add(seq)
records_to_keep.append(record)
SeqIO.write(records_to_keep, import itertools
import re
degen = {"A": 4,"R": 6,"N": 2,"D": 2,"C": 2, "E": 2,"Q": 2,"G": 2,"H": 2,"I": 3, "L": 6,"K": 2,"M": 1,"F": 2,"P": 4, "S": 6,"T": 4,"W": 1, "Y": 2, "V": 4}
d= {'A': ['GCA', 'GCC', 'GCG', 'GCT'], 'C': ['TGC', 'TGTdf['protein'] = df['DNA'].apply(lambda x: Seq(x).translate(), axis=1)
.
.
.
df.describe()
print('Total proteins:', len(df))
def conv(item):
return len(item)
def to_str(item):
return str(item)
df['sequence_str'] = df[0].apply(to_str)
df['length'] = df[0].apply(conv)
df.rename(columns={0: "sequence"}, Community Discussions
Trending Discussions on biopython
QUESTION
I'm trying to reproduce results of an older research paper and need tp run a singularity container with nvidia CUDA 9.0 and torch 1.2.0.
Locally I have Ubuntu 20.04 as VM where I run singularity build. I follow the guide to installing older CUDA versions.
This is the recipe file
ANSWER
Answered 2022-Apr-14 at 10:20As described in overview section of singularity build documentation
build can produce containers in two different formats that can be specified as follows.
- compressed read-only Singularity Image File (SIF) format suitable for production (default)
- writable (ch)root directory called a sandbox for interactive development (
--sandboxoption)
Adding --sandbox should make the system files writable which should resolve your issue.
Ideally, I'd suggest adding any apt-get install commands to the %post section in your recipe file.
QUESTION
I am trying to find a amino acid pattern (B-C or M-D, where '-' could be any alphabet other than 'P') in a protein sequence let say 'VATLDSCBACSKVNDNVKNKVKVKNVKMLDHHHV'. Protein sequence in in a fasta file.
I have tried a lot but couldn't find any solution.
I tried a lot. the following code is one of them
...ANSWER
Answered 2022-Mar-30 at 09:43In python you can use the Regex module (re):
QUESTION
I'd like to loop through every file in a directory given by the user and apply a specific transformation for every file that ends with ".fastq".
Basically this would be the pipeline:
- User puts the directory of where those files are (in command line)
- Script loops through every file that has the format ".fastq" and applies specific transformation
- Script saves new output in ".fasta" format
This is what I have (python and biopython):
...ANSWER
Answered 2022-Mar-22 at 15:55Your problem is with this line:
QUESTION
I have a file containing a sequence:
...ANSWER
Answered 2022-Mar-14 at 15:14Using awk with empty FS. This may not work with every awk version or with arbitrarily long sequences:
QUESTION
I have an almost similar question to the topic : https://www.biostars.org/p/154993/
I have a fasta file with align sequence and I want to generate a consensus by using IUPAC code.
So far I wrote :
...ANSWER
Answered 2022-Mar-08 at 21:32The raw solving. Although, Biopython code on GitHub looks not better. You can extend this for your aims.
QUESTION
I have been sorting through a ~1.5m read fasta file ('V1_6D_contigs_5kbp.fa') to determine which of the reads are likely to be 'viral' in origin. The reads in this file are denoted as Vx_Cz - where x is 1-6, depending on which trial group it came from, and z is the contig number/name from 1-~1.5m. e.g V1_C10810 or V3_C587937...
Through varying bioinformatic pipelines I have produced a .txt file with a list (2699 long) of the contig names that are predicted (<0.05) to be viral. I now need to use this list of predicted contigs to extract and produce a new fasta file that contains only these contigs.
The theoretical idea behind my code is that it opens the .txt file (names of each significant contig) and the original fasta file, goes through each line of the .txt file and sets the line (contig name) as a variable. It should then loop through the original fasta file which contains all the sequence information and if the contig name matches the record.id (contig name from original file) it should then export the full record information to a new file.
I think I am close, but my current iterations seems to run only one or the the other loop as I expect them to.
Please see the code below. I have added notes below to what runs wrong with each program I have tried.
I am using Python, including SeqIO the Biopython application.
...ANSWER
Answered 2022-Mar-08 at 09:43Among quite a few typos, the main issue is that the line from lines=f.readlines() will still contain the newline character \n and will therefore never match the id from SeqIO, the solution is to use a simple strip() call:
QUESTION
I am working on Next Generation Sequencing (NGS) analysis of DNA. I am using SeqIO Biopython module to parse the DNA libraries in Fasta format. I want to filter the unique clones (unique records) only. I am using the following python code for this purpose.
...ANSWER
Answered 2022-Mar-06 at 15:24I don't have your files so I cannot test the actual performance gain you'll get, but here are some things that stick out as slow to me:
- the line
records=list(SeqIO.parse('DNA_library', 'fasta'))converts the records into a list of records, which may sound inoffensive but becomes costly if you have millions of records. According to the docs,SeqIO.parse(...)returns an iterator so you can simply iterate over it directly. - Use a
setinstead of alistwhen keeping track of seen records. When performing membership checking usingin, lists must iterate through every element while sets perform the operation in constant time (more info here).
With those changes, your code becomes:
QUESTION
I have a pandas dataframe that contains DNA sequences and gene names. I want to translate the DNA sequences into protein sequences, and store the protein sequences in a new column.
The data frame looks like:
DNA gene_name ATGGATAAG gene_1 ATGCAGGAT gene_2After translating and storing the DNA, the dataframe would look like:
DNA gene_name protein ATGGATAAG... gene_1 MDK... ATGCAGGAT... gene_2 MQD...I am aware of biopython's (https://biopython.org/wiki/Seq) ability to translate DNA to protein, for example:
...ANSWER
Answered 2022-Feb-17 at 17:57Since you want to translate each sequence in the "DNA" column, you could use a list comprehension:
QUESTION
I am working with amino acid sequences using the Biopython parser, but regardless of data format (the format is fasta, that is, you can imagine them as strings of letters as follows preceded by the id), my problem is that I have a huge amount of data and despite having tried to parallelize with joblib the estimate of the hours it would take me to run this simple code is 400.
Basically I have a file that contains a series of ids that I have to remove (ids_to_drop) from the original dataset (original_dataset), to create a new file (new_dataset) that contains all the ids contained in the original dataset without the ids_to_drop.
I've tried them all but I don't know how else to do it and I'm stuck right now. Thanks so much!
...ANSWER
Answered 2021-Dec-31 at 18:43This looks like a simple file filter operation. Turn the ids to remove into a set one time, and then just read/filter/write the original dataset. Sets are optimized for fast lookup. This operation will be I/O bound and would not benefit from parallelization.
QUESTION
The code below is from the Biopython tutorial. I intend to add 'N5' after every contig. Why is the trailing N10 not present after the third contig "TTGCA"?
...ANSWER
Answered 2021-Dec-19 at 16:40This has nothing to do with biopython.
This is just how string.join works:
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Install biopython
You can use biopython like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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