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The paper “Genomic-scale capture and sequencing of endogenous DNA from feces” by George H. Perry, John C. Marioni, Páll Melsted and Yoav Gilad is literally full of feces. The word ‘fecal’ appears 100 times.

Poop jokes aside, the paper presents an interesting idea that has legs. Perry et al. show that clever use of Agilent’s SureSelect allows for capturing nuclear genomic regions from fecal DNA. Intellectually, it is the predecessor of T. Mercer et al.‘s “Targeted RNA sequencing reveals deep complexity of the human transcriptome“, Nature Biotechnology, 2011 (another paper I like and for which I wrote a research highlight). Even though the Perry et al. paper does not have many citations, the Mercer et al. paper does (although unfortunately the authors forgot to cite Perry et al., which I think they should have). In other words, the Perry et al. paper is not as well known as it ought to be, and this post is an attempt to rectify that.

The ‘*’ in sh*t in my title is for *Seq. At a high level,  the Perry et al. paper shows how high-throughput sequencing technology can be leveraged to sequence deeply a single genome from among a community of metagenomes. For this reason, and for convenience, I henceforth will refer to the Perry et al. paper as the Sh*t-Seq paper. The “*” is inserted in lieu of the “i” not as censorship, but to highlight the point that the method is general and applies not only to sequencing nuclear genome from fecal DNA, but also as Mercer et al. shows, for targeted transcriptome sequencing (one can imagine also many other applications).

The Sh*t-Seq protocol is conceptually simple yet complicated in practice. DNA was captured using the Agilent SureSelect target enrichment system coupled to the Illumina single-end sequencing platform library prep protocol (of note is that the paper is from 2010 and the kits are from 2009). However because of the very small amount of DNA targeted (the authors claim ~1.8%), a number of adjustments to standard SureSelect capture / Illumina library prep had to be implemented. To the authors credit, the paragraphs in the section “DNA Capture” are exemplary in their level of detail and presumably greatly facilitate replicability. I won’t repeat all the detail here. However there are two steps that caught my attention as possibly problematic. First, the the authors performed substantial PCR amplification of the  adapter-ligated fecal DNA. This affects the computational analysis they discuss later and leads to a computational step they implemented that I have some issues with (more on this later). Second, they performed two rounds of capture as one round was insufficient for capturing the needed material for sequencing. This necessitated additional PCR, which also is possibly problematic.

The samples collected were from six chimpanzees. This is a fairly small n but the paper is a proof of principle and I think this is sufficient. Both fecal and blood samples were collected allowing for comparison of the fecally derived nuclear DNA to the actual genome of the primates. In what is clearly an attempt to channel James Bond, they collected fecal samples (2 g of stool) within 1 hour of defecation in tubes containing RNALater and these were then “shaken vigorously” (not stirred).

The next part of the paper is devoted to computational analyses to confirm that the Sh*t-Seq protocol can in fact be used to target nuclear endogenous DNA. As a sanity check, mtDNA was considered first. They noted too much diversity to align reads to a reference genome with BWA, and opted instead for de novo assembly using ABySS. This is certainly overkill, possible only because of the high copy count of mitochondrion reads. But I suppose it worked (after filtering out all the low coverage ABySS sequence, which was presumably junk). One interesting idea given more modern RNA-Seq assembly tools would be to assemble the resulting reads with an RNA-Seq de novo assembler that allows for different abundances of sequences. Ideally, such an assembly should indicate naturally the sought after enrichment.

Next, nuclear DNA was investigated, specifically the X chromosome and chromosome 21. Here the analyses is very pre-2014. First, all multi-mapping reads were removed. This is not a good idea for many reasons, and I am quite certain that with the new GRCh38 (with alternate sequence representation for variant regions) it is a practice that will rapidly be phased out. I’d like to give Perry et al. the benefit of the doubt for making this mistake since they published in 2010, but their paper appeared 6 months after the Cufflinks paper so they could have, in principle, known better. Having said that, while I do think multi-mapping would have allowed them to obtain much stronger results as to the accuracy and extent of their enrichment by avoiding the tossing of a large number of reads, their paper does manage to prove their principle so its not a big deal.

The removal of multi-mapping reads was just the first step in a series of “filters” designed to narrow down the nuclear DNA reads to regions of the chimpanzee genome that could be argued to be unambiguously representative of the target. I won’t go into details, although they are all in the paper. As with the experimental methods, I applaud the authors on publishing reproducible methods, especially computational methods, with all details included. But there was a final red flag for me in the computational methods: namely the selection of a single unique fragment (at random) for each genomic (start) site for the purposes of calling SNPs. This was done to eliminate problems due to amplification biases, which is indeed a serious concern, but if heterozygous sites appear due to the PCR steps, then there ought to have been telltale signatures. For example, a PCR “SNP’ would, I think, appear only in reads specific to a single position, but not in other overlapping reads of the site. It would have been very helpful if they would have done a detailed analysis of this issue, rather than just pick a single read at random for each genomic (start) site. They kicked the can down the road.

Having removed multiple mapping reads, repetitive regions, low coverage regions, etc. etc. Dayenu, they ended up estimating a false positive rate for heterozygous sites (using the X chromosome in males) at 0.0007% for fecal DNA and 0.0010% for blood DNA. This led them to conclude that incorrectly-identified heterozygous sites in their study were 0.8%, 2.0%, 1.1%, and 2.7% for fecal DNA chromosome 21, fecal DNA chromosome X in females, blood DNA chromosome 21, and blood DNA chromosome X in females, respectively. Such good news is certainly the result of their extraordinarily stringent filtering, but I think it does prove that they were able to target effectively. They give further proof using PCR and Sanger sequencing of 20 regions.

I have a final nitpick and it relates to Figure 4. It is a a companion to Figure 3 which shows the Chimpanzee phylogeny for their samples based on the mtDNA. As expected in that figure, the fecal and blood samples cluster together. Figure 4 shows two phylogenies, one based on chr 21, the other on chr X. My issue here is with the way that distances were constructed. Its a technical point, but it looks like they used hamming distance, and I don’t think that makes a lot of sense, not to mention the fact that neighbor-joining does not seem like the appropriate algorithm for building a tree in this setting (I plan to blog about neighbor-joining shortly). But this is a methodological point not really relevant to the main result of the paper, namely proof of principle for targeted sequencing of endogenous DNA from fecal matter.

I think Sh*t-Seq has a future. The idea of targeted capture coupled to high-throughput sequencing has more than an economic rationale. It provides the possibility to probe the “deep field” as discussed in the previously mentioned review on targeted RNA-Seq. This is a general principle that should be more widely recognized.

And of course, dung is just cool. Happy new year!

My new year ‘s resolution.

[Update April 6, 2014: The initial title of this post was “23andme genotypes are all wrong”. While that was and remains a technically correct statement, I have changed it because the readership of my blog, and this post in particular, has changed. Initially, when I made this post, the readers of the blog were (computational) biologists with extensive knowledge of genotyping and association mapping, and they could understand the point I was trying to make with the title. However in the past few months the readership of my blog has grown greatly, and the post is now reaching a wide public audience. The revised title clarifies that the content of this post relates to the point that low error rates in genotyping can be problematic in the context of genome-wide association reports because of multiple-testing.]

I have been reading the flurry of news articles and blog posts written this week about 23andme and the FDA with some interest. In my research talks, I am fond of displaying 23andme results, and have found that people always respond with interest. On the teaching side, I have subsidized 23andme testing for volunteer students in Math127 who were interested in genetics so that they could learn about personalized genomics first-hand. Finally, a number of my former and current students have worked at 23andme, and some are current employees.

Despite lots of opinions being expressed about the 23andme vs. FDA kerfuffle, I believe that two key points have been ignored in the discussions:

1. All 23andme genotypes that have ever been reported to customers are wrong. This is the case despite very accurate genotyping technology used by 23andme.
2. The interpretation of 23andme results involves examining a large number of odds ratios. The presence of errors leads to a huge multiple-testing problem.

Together, these issues lead to an interesting conundrum for the company, for customers, and for the FDA.

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