Tag: NGS

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Should you trim your RNA-Seq reads?

According to a new paper, basically, no.   Actually that is an oversimplification, but the authors find that quality trimming of RNA-Seq reads results in skewed gene expression estimates for up to 10% of genes.   Furthermore, the authors claim that:

“Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming results in the most biologically accurate gene expression estimates.”

First, the authors show how aggressive trimming affects mappability in Figure 2:

Rna-Seq reads trimming effects.
Influence of quality-based trimming on mappability of reads.

You can see that as the threshold becomes more severe (approaching 40), the number of RNA-Seq reads remaining drops off considerably, and the overall % mappability increases.  Overall, you’d think this would be a good thing, but it leads to problems as shown in Figure 4 of the paper:

Rna-Seq reads.
Isoform and gene expression levels after trimming.

Here you can see in (a) how increasingly aggressive trimming thresholds lead to increased differential expression estimates between untrimmed and trimmed data (red dots).  Section (b) and (c) also show that the number of biased isoforms and genes, respectively, increases dramatically as one approaches the Q40 threshold.

One way to correct this bias is to introduce length filtering on the quality-trimmed RNA-Seq reads.  In Figure 5, the authors show that this can recover much of the bias in gene expression estimates:

Isoform and gene expression levels after length-filtering.
Isoform and gene expression levels after length-filtering.

Now in (b-d) it is clear that as the length filter increases to 36, the number of biased expression estimates goes rapidly down.   There seems to be a sweet spot around L20, where you get the maximum decrease in bias while keeping as many reads as possible.

Taken together, the authors suggest that aggressive trimming can strongly bias gene expression estimates through the incorrect alignment of short reads that result from quality trimming.  A secondary length filter step can mitigate some of the damage.   In the end, the use of trimming depends on your project type and goals.  If you have tons of reads, some modest trimming and length filtering may not be too destructive.  Similarly, if your data are initially of low quality, trimming may be necessary to recover low-quality reads.  However, you should be restrained in your trimming and look at the resulting length distributions if possible before deciding on quality thresholds for your project.

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Filtering variants for cancer mutational signature analysis

Recently, I’ve been working to help prepare a manuscript on Vestibular Schwannomas (VS), a type of benign cancer of the myelin-forming cells along the nerves of the ear.  I’ve been thinking a lot about strategies for filtering exome variant calls to feed into mutational signature analysis.

Mutational signatures are important because they describe the types of mutational processes operating on the genome of the tumor cells.  Many of these processes are known (see the COSMIC database), however, some are entirely novel.  The variants that are used for calculating such signatures are somatic in nature, and have to be carefully curated from the raw variant calls that you get from a pipeline like GATK.

Looking at the existing literature, I find that there is no common or “best practices” methodology for filtering variants in whole exome data.  Some groups are very stringent, others less so.  The first step in most cases is to just subtract normal variant calls from tumor in most cases.  However, there are further filtering steps that should be undertaken.

If I had to describe some overall commonalities in the literature approaches to somatic variant filters, it could include:

1) removing variants that are present in dbSNP or 1000genomes or other non-cancer exome data
2) taking only variants in coding regions (exons) or splicing sites
3) variants must appear in more than X reads in the tumor, and fewer than X reads in the normal (generally ~5 and ~2, respectively)
4) subtraction of “normals” from “tumor” (either pooled normals, or paired)
5) variant position must be covered by a minimum depth (usually > 10X)
6) throwing away reads from low mapping quality (MQ) regions

Some papers only consider non-synonymous variants, but for mutational signatures, to me it makes sense to take all validated variants (especially in exome data because you are starting with fewer raw variant calls than whole genome data).

As far as actual numbers of variants that are “fed” into the mutational signature analysis, most papers do not report this directly (surprisingly).  If you dig around in the SI sections, sometimes you can find it indirectly.

It looks like, generally, the number of variants is somewhere around 10,000 for papers dealing with specific tumor types (not pan-cancer analyses of public databases). Several papers end up with ~1000 variants per tumor (ranging from 1,000 up to 7,000).  So with 10 tumors sequenced, that would be 10,000 filtered, high-confidence SNVs.

If you’re working on exome mutational signature analysis and you have your own filtering criteria, I’d love for you to share it in the comments.

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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.

nature12477-f2

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'''
    try:
        handle = Entrez.efetch(db="nucleotide", 
                    id=hg19_chrom[chrom], 
                    rettype="fasta", 
                    strand=1, 
                    seq_start=int(pos) - 1, 
                    seq_stop=int(pos) + 1)
                       
        record = SeqIO.read(handle, "fasta")
        handle.close()
    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.