Hands-on with cancer mutational signatures

In my last post, I wrote about the biological context of mutational signatures in cancer and how a recent series of papers has addressed this notion by creating an algorithm for extracting signatures from a catalogue of tumor SNPs.


In this follow-up post, I wanted to offer practical advice, based on my experience, about how to prepare data for mutational signature analysis and how to interpret the results.

First, in order to analyze your SNPs for mutational signatures, one needs to derive the trimer context of each SNP (i.e., the upstream base and downstream base flanking the SNP) for technical reasons described in the paper linked above.

In order to do this,  a reference genome must be queried with the positions of the SNPs in order to find those bases adjacent to them.  One could either query a remote database, like Entrez Nucleotide, or download an entire 40GB reference genome and search it locally.

In my code, I opted for the former option:  querying the NCBI/Entrez Nucleotide database using HTTP requests.   The advantage of this approach is that I can reuse the same code to query multiple reference genomes (e.g., hg38 vs. hg19), depending on the changing needs of future projects.

The relevant section of code is as follows:

def get_record(chrom, pos):               
    '''Fetch record, and minus/plus one base from Entrez'''
        handle = Entrez.efetch(db="nucleotide", 
                    seq_start=int(pos) - 1, 
                    seq_stop=int(pos) + 1)
        record = SeqIO.read(handle, "fasta")
    except BaseException:
        return None
    return record


You can see from the code that I am using a dictionary (‘hg19_chrom’)  to translate between chromosome numbers and their Entrez Nucleotide IDs in the eFetch request.

The disadvantage of this approach is that Entrez HTTP tools limits the user to 3 queries per second (in fact this limitation is hard-coded into Biopython).  Even with my mediocre coding skills, this turns out to be the rate-limiting step.   Thus, this code would have to be run pretty much overnight for any sizable number of SNPs (~50,000 SNPs would take ~4.6 hrs).  However, it’s easy to let the script run overnight, so this wasn’t a deal breaker for me.

In the third and final post next two posts on this topic I will address how to create the MatLab .mat file from the output of this script and how to compare the signatures generated by the MatLab framework to the COSMIC reference database in a non-biased way.