Tweet
Biopython Help
I am studying protein-protein interactions between the AR(fl) and its coactivators. The AR-NTD and AR-CTD homodimerize, and coactivators seem to also bind to the CTD, potentially in the region that AR-NTD binds. Therefore, I am trying to write a script to make it easy for me to align the sequences of AR-NTD and certain coactivators. However, I am very new to biopython, and to writing scripts. Would anyone be able to help me to write a script to download protein sequences from NCBI, align the sequences and produce a consensus sequence? I ultimately want the script to print the consensus sequence at the end. I'm also not very familiar with "clustal", but after talking to a friend, they had mentioned it could be useful. Thank you for any and all advice.
-
-
The NCBI Entrez utilities could be used via Biopython to search for and download protein sequences via Biopython - although depending on the numbers it might be simpler to manually select proteins of interest? How many proteins are you looking for? e.g. A dozen, or a hundred?
Aligning the sequences could be done with a tool like Clustal Omega or MUSCLE - both of which you can call from Biopython, but for one or two alignments there is little point compared to running the tool by hand.
Once you have an alignment, getting the consensus is possible with Biopython - or indeed many alignment tools (e.g. Jalview). Again, if you are only doing this once or twice I see little reason to automate it via Biopython.
-
Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
...07-09-2026, 11:10 AM -
Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...07-08-2026, 05:17 AM -
Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
Comment