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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!

bart-simpson-generator.phpMy new year ‘s resolution.

My students run a journal club that meets once a week and last Friday the paper we discussed was M. Imakaev et al., Iterative correction of Hi-C data reveals hallmarks of chromosome organization, Nature Methods 9 (2012), p 999–1003. The paper, from Leonid Mirny’s group, describes an approach to analyzing Hi-C data, and is one of only a few papers that have been published focusing on methods for this *Seq assay. Present at journal club were my students Nicolas Bray, Shannon Hateley, Harold Pimentel, Atif Rahman, Lorian Schaeffer, Akshay Tambe, Faraz Tavakoli, postdocs Sreeram Kannan and Shannon McCurdy, and guest Emily Brown from Doris Bachtrog’s group. They all read the paper prior to our meeting, and the notes below are a summary of our discussion (although I have taken some more time to flesh out the math for the interested reader). For those who are impatient, the bottom line is that I think the title of the paper by Imakaev et al. should have had a few extra words (mine in italics) so that it would be “Iterative proportional fitting of a log-linear model for correction of Hi-C data reveals hallmarks of chromosome organization”. Read the rest of this entry »

RNA-Seq is the new kid on the block, but there is still something to be learned from the stodgy microarray. One of the lessons is hidden in a tech report by Daniela Witten and Robert Tibshirani from 2007: “A comparison of fold-change  and the t-statistic for microarray data analysis“.

The tech report makes three main points. The first is that it is preferable to use a modified t-statistic rather than the ordinary t-statistic. This means that rather than comparing (normalized) means using

T_i = \frac{\bar{x_i} - \bar{y_i}}{s_i}

where s_i is the standard deviation of the replicates x_i (respectively y_i) of gene i in two different conditions, it is better to use

T'_i = \frac{\bar{x_i} - \bar{y_i}}{s_i+s_0}

 where s_0 minimizes the coefficient of variation of T'_i.

The second point made is that the intuition that reproducibility implies accuracy is not correct (fold change had been proposed for use instead of a t-statistic because the results were more reproducible).

The third point, in my opinion the most important one, I quote directly from the report:

“A researcher should choose the measure of differential expression based on the biological system of interest. If large absolute changes in expression are relevant to the system, then fold-change should be used; on the other hand, if changes in expression relative to the underlying noise are important, then a modified t-statistic is preferable.”

How does this pertain to RNA-Seq? Microarray experiments and RNA-Seq both measure expression but the translation of methods for the analysis of one platform to the other can be non-trivial. One reason is that in RNA-Seq experiments accurately measuring “fold-change” is difficult. Read counts accumulated across a gene cannot be used directly to estimate fold change because the transcripts making up the gene may have different lengths. For this reason, methods such as Cufflinks, RSEM or eXpress (and most recently Sailfish recently reviewed on this blog) use the EM algorithm to “deconvolute” ambiguously mapped reads. The following thought experiment (Figure 1 in our paper describing Cufflinks/Cuffdiff 2) illustrates the issue:

Wrongdoesnotcancelwrong

Changes in fragment counts for a gene do not necessarily equal a change in expression. The “exon-union” method counts reads falling on any of a gene’s exons, whereas the “exon-intersection” method counts only reads
on constitutive exons. Both of the exon-union and exon-intersection counting schemes may incorrectly estimate a change in expression in genes with multiple isoforms as shown in the table. It is important to note that the problem of fragment assignment described here in the context of RNA-Seq is crucial for accurate estimation of parameters in many other *Seq assays.

“Count-based” methods for differential expression, such as DESeq, work directly with accumulated gene counts and are based on the premise that even if estimated fold-change is wrong, statistical significance can be assessed based on differences between replicates.  In our recent paper describing Cuffdiff 2 (with a new method for differential abundance analysis) we examine DESeq (as a proxy for count-based methods) carefully and show using both simulation and real data that fold-change is not estimated accurately. In fact, even when DESeq and Cufflinks both deem a gene to be differentially expressed, and even when the effect is in the same direction (e.g. up-regulation), DESeq can (and many times does) estimate fold-change incorrectly. This problem is not specific to DESeq. All “count based” methods that employ naive heuristics for computing fold change will produce inaccurate estimates:

fold_change

Comparison of fold-change estimated by Cufflinks (tail of arrows) vs. “intersection-count” (head of arrows) reproduced from Figure 5 of the supplementary material of the Cuffdiff 2 paper. “Intersection-count” consists of the accumulated read counts in the regions shared among transcripts in a gene. The x-axis shows array fold change vs. the estimated fold-change on the y-axis.  For more details on the experiment see the Cuffdiff 2 paper.

In other words,

it is essential to perform fragment assignment in a biological context where absolute expression differences are relevant to the system.

What might that biological context be? This is a subjective question but in my experience users of microarrays or RNA-Seq (including myself) always examine fold-change in addition to p-values obtained from (modified) t-statistics or other model based statistics because the raw fold-change is more directly connected to the data from the experiment.

In many settings though, statistical significance remains the gold standard for discovery. In the recent epic “On the immortality of television sets: ‘function’ in the human genome according to the evolution-free gospel of ENCODE“, Dan Graur criticizes the ENCODE project for reaching an “absurd conclusion” through various means, among them the emphasis of “statistical significance rather than magnitude of effect”. Or, to paraphrase Samuel Johnson,

statistical significance is the last refuge from a poor analysis of data.

When the organizers of ISMB 2013 kindly invited me to give a keynote presentation this year, I decided to use the opportunity to survey “sequence census” methods. These are functional genomics assays based on high throughput sequencing. It has become customary to append the suffix “-Seq” to such assays (e.g. RNA-Seq), and I therefore prefer the term *Seq where the * denotes a wildcard.

The starting point for preparing the talk was a molecular biology seminar I organized in the Spring of 2010, where we discussed new high-throughput sequencing based assays with a focus on the diverse range of applications being explored. At the time I had performed a brief literature search to find *Seq papers for students to present, and this was helpful as a starting point for building a more complete bibliography for my talk. Finding *Seq assays is not easy- there is no central repository for them- but after some work I put together a (likely incomplete) bibliography that is appended to the end of the post. Update: I’ve created a page for actively maintaining a bibliography of *Seq assays.

The goal for my talk was to distill what appears to be a plethora of complex and seemingly unrelated experiments (see, e.g., Drukier et al. on the *Seq list) into a descriptive framework useful for thinking about their commonalities and differences. I came up with this cartoonish figure that I briefly explain in the remainder of this post. In future posts I plan to review in more detail some of these papers and the research they have enabled.

*SeqCartoon

A typical assay first involves thinking of a (molecular) measurement to be made. The problem of making the measurement is then “reduced” (in the computer science sense of the word) to sequencing. This means that the measurement will be inferred from sequencing bits of DNA from “target” sequences (created during the reduction), and then counting the resulting fragments.  It is important to keep in mind that the small fragments of DNA are sampled randomly from the targets, but the sampling may not be uniform.

The inference step is represented in the “Solve inverse problem” box in the figure, and involves developing a model of the experiment, together with an algorithm for inferring the desired measurement from the data (the sequenced DNA reads). Finally, the measurement becomes a starting point for further (computational) biology inquiry.  Read the rest of this entry »

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