status report – Samposium https://samnicholls.net The Exciting Adventures of Sam Mon, 15 Jan 2018 22:12:03 +0000 en-GB hourly 1 https://wordpress.org/?v=5.7.5 101350222 Status Report: 2018: The light is at the end of the tunnel that I continue to build https://samnicholls.net/2018/01/15/status-jan18-p1/ https://samnicholls.net/2018/01/15/status-jan18-p1/#respond Mon, 15 Jan 2018 20:39:54 +0000 https://samnicholls.net/?p=2254 Happy New Year!
The guilt of not writing has reached a level where I feel sufficiently obligated to draft a post. You’ll likely notice from the upcoming contents that I am still a PhD student, despite a previous, more optimistic version of myself writing that 2016 would be my final Christmas as a PhD candidate.

Much has happened since my previous Status Report, and I’m sure much of it will spin-off to form several posts of their own, eventually. For the sake of brevity, I’ll give a high level overview.
I’m supposed to be writing a thesis anyway.

Previously on…

We last parted ways with a double–bill status report lamenting the troubles of generating suitable test data for my metagenomic haplotype recovery algorithm, and documenting the ups-and-downs-and-ups-again of analysing one of the synthetic data sets for my pre-print. In particular, I was on a quest to respond to our reviewer’s desire for more realistic data: real reads.

Gretel: Now with real reads!

Part Two of my previous report alluded to a Part Three that I never got around to finishing, on the creation and analysis of a test data set consisting of real reads. This was a major concern of the reviewers who gave feedback on our initial pre-print. Without getting into too much detail (I’m sure there’s time for that); I found a suitable data set consisting of real sequence reads from a lab-mix of five HIV strains, used to benchmark algorithms in the related problem of viral-quasispecies reconstruction. After fixing a small bug, and implementing deletion handling, it turns out we do well on this difficult problem. Very well.

In the same fashion as our synthetic DHFR metahaplome, this HIV data set provided five known haplotypes, representing five different HIV-1 strains. Importantly, we were also provided with real Illumina short-reads from a sequencing run containing a mix of the five known strains. This was our holy grail, finally: a benchmark with sequence reads and a set of known haplotypes. Gretel is capable of recovering long, highly variable genes with 100% accuracy. My favourite result is a recovery of env — the ridiculously hyper-variable envelope gene that encodes the HIV-1 virus’ protein shell — with Gretel correctly recovering all but one of 2,568 positions. Not bad.

A new pre-print

Armed with real-reads, and improved results for our original DHFR test data (thanks to some fiddling with bowtie2), we released a new pre-print. The manuscript was a substantial improvement over its predecessor, which meant it was all the more disappointing to be rejected from five different journals. But, more on this misery at another time.

Despite our best efforts to address the previous concerns, new reviewers felt that our data sets were still not a good representation of the problem-at-hand: “Where is the metagenome?”. It felt like the goal-posts had moved, suddenly real reads were not enough. But it’s both a frustrating and fair response, work should be empirically validated, but there are no metagenomic data sets with both a set of sequence reads, and known haplotypes. So, it was time to make one.

I’m a real scientist now…

And so, I embarked upon what would become the most exciting and frustrating adventure of my PhD. My first experiences of the lab as a computational biologist is a post sat in draft, but suffice to say that the learning curve was steep. I’ve discovered that there are many different types of water and that they all look the same, that 1ml is a gigantic volume, that you’ll lose your fingerprints if you touch a metal drawer inside a -80C freezer, and that contrary to what I might have thought before, transferring tiny volumes of colourless liquids between tiny tubes without fucking up a single thing, takes a lot of time, effort and skill. I have a new appreciation for the intricate and stochastic nature of lab work, and I understand what it’s like for someone to “borrow” a reagent that you spent hours of your time to make from scratch. And finally, I had a legitimate reason to wear an ill-fitting lab coat that I purchased in my first year (2010), to look cool at computer science socials.

With this new-found skill-tree to work on, I felt like I was becoming a proper interdisciplinary scientist, but this comes at a cost. Context switching isn’t cheap, and I was reminded of my undergraduate days where I juggled mathematics, statistics and computing to earn my joint honours degree. I had more lectures, more assignments and more exams than my peers, but this was and still is the cost of my decision to become an interdisciplinary scientist.

And it was often difficult to find much sympathy from either side of the venn diagram…

..and science can be awful

I’ve suffered many frustrations as a programmer. One can waste hours tracking down a bug that turns out to be a simple typo, or more likely, an off by one error that plagues much of bioinformatics. I’ve felt the self-directed anger having submitted thousands of cluster jobs that have failed with a missing parameter, or waited hours for a program to complete, only to discover the disk has run out of room to store the output. Yet, these problems pale into comparison in the face of problems at the bench.

I’ve spent days in the lab, setting-up and executing PCR, casting, loading and running gels, only to take a UV image of absolutely nothing at all.

Last year, I spent most of Christmas sheparding data through our cluster, much to my family’s dismay. This year, I had to miss a large family do for a sister’s milestone birthday. I spent many midnights in the lab, lamenting the life of a PhD student, and shuffling around with angry optimism; “Surely it has to fucking work this time?”. Until finally, I got what I wanted.

I screamed so loud with glee that security came to check on me. “I’m a fucking scientist now!”

New Nanopore Toys

My experiment was simple in practice. Computationally, I’d predicted haplotypes with my Gretel method from short-read Illumina data from a real rumen microbiome. I designed 10 pairs of primers to capture 10 genes of interest (with hydrolytic-activity) using the haplotypes. And finally, after several weeks of constant almost 24/7 lab work, building cDNA libraries and amplifying the genes of interest, I made enough product for the exciting next step: Nanopore sequencing.

With some invaluable assistance from our resident Nanopore expert Arwyn Edwards (@arwynedwards) and PhD student André (@GeoMicroSoares), I sequenced my amplicons on an Oxford Nanopore MinION, and the results were incredible.

Our Nanopore reads strongly supported our haplotypes, and concurred with the Sanger sequencing. Finally, we have empirical biological evidence that Gretel works.

The pre-print rises

With this bomb-shell in the bag, the third version of my pre-print rose from the ashes of the second. We demoted the DHFR and HIV-1 data sets to the Supplement, and included an analysis on our performance with a de facto benchmark mock community introduced by Chris Quince in its place. The data sets and evaluation mechanisms that our previous reviewers found unrepresentative and convoluted were gone. I even got to include a Circos plot.

Once more, we substantially updated the manuscript, and released a new pre-print. We made our to bioRxiv to much Twitter fanfare, earning over 1,500 views in our first week.

This work also addresses every piece of feedback we’ve had from reviewers in the past. Surely, the publishing process would now finally recognise our work and send us out for review, right?

Sadly, the journey of this work is still not smooth sailing, with three of my weekends marred by a Friday desk rejection…

…and a fourth desk rejection on the last working day before Christmas was pretty painful. But we are currently grateful to be in discussion with an editor and I am trying to remain hopeful we will get where we want to be in the end. Wish us luck!

In other news…

Of course, I am one for procrastination, and have been keeping busy while all this has been unfolding…

I hosted a national student conference

I am applying for some fellowships

I’ve officially started my thesis…

…which is just as well, because the money is gone

I’ve started making cheap lab tat with my best friend…

…it’s approved by polar bears

…and the UK Centre for Astrobiology

…and has been to the Arctic

I gave an invited talk at a big conference…

…it seemed to go down well

I hosted UKIEPC at Aber for the 4th year

We’ve applied to fund Monster Lab…

…and made a website to catalogue our monsters

For a change I chose my family over my PhD and had a fucking great Christmas

What’s next?

  • Get this fucking great paper off my desk and out of my life
  • Hopefully get invited to some fellowship interviews
  • Continue making cool stuff with Sam and Tom Industrys
  • Do more cool stuff with Monster Lab
  • Finish this fucking thesis so I can finally do something else

tl;dr

  • Happy New Year
  • For more information, please re-read
]]> https://samnicholls.net/2018/01/15/status-jan18-p1/feed/ 0 2254 Status Report: November 2016 (Part I): Triviomes, Treeviomes & Fuck Everything https://samnicholls.net/2016/12/19/status-nov16-p1/ https://samnicholls.net/2016/12/19/status-nov16-p1/#respond Mon, 19 Dec 2016 23:14:33 +0000 https://samnicholls.net/?p=1720 Once again, I have adequately confounded progress since my last report to both myself, and my supervisorial team such that it must be outlaid here. Since I’ve got back from having a lovely time away from bioinformatics, the focus has been to build on top of our highly shared but unfortunately rejected pre-print: Advances in the recovery of haplotypes from the metagenome.

I’d hoped to have a new-and-improved draft ready by Christmas, in time for an invited talk at Oxford, but sadly I’ve had to postpone both. Admittedly, it has taken quite some time for me to dust myself down after having the entire premise of my PhD so far rejected without re-submission, but I have finally built up the motivation to revisit what is quite a mammoth piece of work, and am hopeful that I can take some of the feedback on board to rein in the new year with an even better paper.

This will likely be the final update of the year.
This is also the last Christmas I hope to be a PhD candidate.

Friends and family can skip to the tldr

The adventure continues…

We left off with a lengthy introduction to my novel data structure; Hansel and algorithm; Gretel. In that post I briefly described some of the core concepts of my approach, such as how the Hansel matrix is reweighted after Gretel successfully creates a path (haplotype), how we automatically select a suitable value for the “lookback” parameter (i.e. the order of the Markov chain used when calculating probabilities for the next variant of a haplotype), and the current strategy for smoothing.

In particular, I described our current testing methodologies. In the absence of metagenomic data sets with known haplotypes, I improvised two strategies:

  • Trivial Haplomes (Triviomes)
    Data sets designed to be finely controlled, and well-defined. Short, random haplotypes and sets of reads are generated. We also generate the alignment and variant calls automatically to eliminate noise arising from the biases of external tools. These data sets are not expected to be indicative of performance on actual sequence data, but rather represent a platform on which we can test some of the limitations of the approach.

  • Synthetic Metahaplomes
    Designed to be more representative of the problem, we generate synthetic reads from a set of similar genes. The goal is to recover the known input genes, from an alignment of their reads against a pseudo-reference.

I felt our reviewers misunderstood both the purpose and results of the “triviomes”. In retrospect, this was probably due to the (albeit intentional) lack of any biological grounding distracting readers from the story at hand. The trivial haplotypes were randomly generated, such that none of them had any shared phylogeny. Every position across those haplotypes was deemed a SNP, and were often tetra-allelic. The idea behind this was to cut out the intermediate stage of needing to remove homogeneous positions across the haplotypes (or in fact, from even having to generate haplotypes that had homogeneous positions). Generated reads were thus seemingly unrealistic, at a length of 3-5bp. However they meant to represent not a 3-5bp piece of sequence, but the 3-5bp sequence that remains when one only considers genomic positions with variation, i.e. our reads were simulated such they spanned between 3 and 5 SNPs of our generated haplotypes.

I believe these confusing properties and their justifications got in the way of expressing their purpose, which was not to emulate the real metahaplotying problem, but to introduce some of the concepts and limitations of our approach in a controlled environment.

Additionally, our reviewers argued that the paper is lacking an extension to the evaluation of synthetic metahaplomes: data sets that contain real sequencing reads. Indeed, I felt that this was probably the largest weakness of my own paper, especially as it would not require an annotated metagenome. Though, I had purposefully stayed on the periphery of simulating a “proper” metagenome, as there are ongoing arguments in the literature as to the correct methodology and I wanted to avoid the simulation itself being used against our work. That said, it would be prudent to at least present small synthetic metahaplomes akin to the DHFR and AIMP1, using real reads.

So this leaves us with a few major plot points to work on before I can peddle the paper elsewhere:

  • Improve Triviomes
    We are already doing something interesting and novel, but the “triviomes” are evidently convoluting the explanation. We need something with more biological grounding such that we don’t need to spend many paragraphs explaining why we’ve made certain simplifications, or cause readers to question why we are doing things in a particular way. Note this new method will still need to give us a controlled environment to test the limitations of Hansel and Gretel.

  • Polish DHFR and AIMP1 analysis
    One of our reviewers misinterpreted some of the results, and drew a negative conclusion about Gretel‘s overall accuracy. I’d like to revisit the *DHFR* and *AIMP1* data sets to both improve the story we tell, but also to describe in more detail (with more experiments) under what conditions we can and cannot recover haplotypes accurately.

  • Real Reads
    Create and analyse a data set consisting of real reads.

The remainder of this post will focus on the first point, because otherwise no-one will read it.

Triviomes and Treeviomes

After a discussion about how my Triviomes did not pay off, where I believe I likened them to “random garbage”. It was clear that we needed a different tactic to introduce this work. Ideally this would be something simple enough that we could still have total control over both the metahaplome to be recovered, and the reads to recover it from, but also yield a simpler explanation for our readers.

My biology-sided supervisor, Chris, is an evolutionary biologist with a fetish for trees. Throughout my PhD so far, I have managed to steer away from phylogenetic trees and the like, especially after my terrifying first year foray into taxonomy, where I discovered that not only can nobody agree on what anything is, or where it should go, but there are many ways to skin a cat draw a tree.

Previously, I presented the aggregated recovery rates of randomly generated metahaplomes, for a series of experiments, where I varied the number of haplotypes, and their length. Remember that every position of these generated haplotypes was a variant. Thus, one may argue that the length of these random haplotypes was a poor proxy for genetic diversity. That is, we increased the number of variants (by making longer haplotypes) to artificially increase the level of diversity in the random metahaplome, and make recoveries more difficult. Chris pointed out that actually, we could specify and fix the level of diversity, and generate our haplotypes according to some… tree.

This seemed like an annoyingly neat and tidy solution to my problem. Biologically speaking, this is a much easier explanation to readers; our sequences will have meaning, our reads will look somewhat more realistic and most importantly, the recovery goal is all the more tangible. Yet at the same time, we still have precise control over the tree, and we can generate the synthetic reads in exactly the same way as before, allowing us to maintain tight control of their attributes. So, despite my aversion to anything that remotely resembles a dendrogram, on this occasion, I have yielded. I introduce the evaluation strategy to supplant1 my Triviomes: Treeviomes.

(Brief) Methodology

  • Heartlessly throw the Triviomes section in the bin
  • Generate a random start DNA sequence
  • Generate a Newick format tree. The tree is a representation of the metahaplome that we will attempt to recover. Each branch (taxa) of the tree corresponds to a haplotype. The shape of the tree will be a star, with each branch of uniform length. Thus, the tree depicts a number of equally diverse taxa from a shared origin
  • Use the tree to simulate evolution of the start DNA sequence to create the haplotypes that comprise the synthetic metahaplome
  • As before, generate reads (of a given length, at some level of coverage) from each haplotype, and automatically generate the alignment (we know where our generated reads should start and end on the reference without external tools) and variant calls (any heterogeneous genomic position when the reads are piled up)
  • Rinse and repeat, make pretty pictures

The foundation for this part of the work is set. Chris even recommended seq-gen as a tool that can simulate evolution from a starting DNA sequence, following a Newick tree, which I am using to generate our haplotypes. So I now have a push-buttan-to-metahaplome workflow that generates the necessary tree, haplotypes, and reads for testing Gretel.

I’ve had two main difficulties with Treeviomes…

• Throughput

Once again, running anything thousands of times has proven the bane of my life. Despite having a well defined workflow to generate and test a metahaplome, getting the various tools and scripts to work on the cluster here has been a complete pain in my arse. So much so, I ended up generating all of the data on my laptop (sequentially, over the course of a few days) and merely uploading the final BAMs and VCFs to our compute cluster to run Gretel. This has been pretty frustrating, especially when last weekend I set my laptop to work on creating a few thousand synthetic metahaplomes and promised some friends that I’d take the weekend off work for a change, only to find on Monday that my laptop had done exactly the same.

• Analysis

Rather unexpectedly, initial results raised more questions than answers. This was pretty unwelcome news following the faff involved in just generating and testing the many metahaplomes. Once Gretel‘s recoveries were finished (the smoothest part of the operation, which was a surprise in itself, given the presence of Sun Grid Engine), another disgusting munging script of my own doing spat out the convoluted plot below:

The figure is a matrix of boxplots where:

  • Horizontal facets are the number of taxa in the tree (i.e. haplotypes)
  • Vertical facets are per-haplotype, per-base mutation rates (i.e. the probability that any genomic position on any of the taxa may be mutated from the common origin sequence)
  • X-axis of each boxplot represents each haplotype in the metahaplome, labelled A – O
  • Y-axis of each boxplot quantifies the average best recovery rate made by Gretel for a given haplotype A – O, over ten executions of Gretel (each using a different randomly generated, uniformly distributed read set of 150bp at 7x per-haplotype coverage)

We could make a few wild speculations, but no concrete conclusions:

  • At low diversity, it may be impossible to recover haplotypes, especially for metahaplomes containing fewer haplotypes
  • Increasing diversity appears to create more variance in accuracy, but mean accuracy increases slightly in datasets with 3-5 haplotypes, but falls under 10+
  • Increasing the number of haplotypes in the metahaplome appears to increase recovery accuracy
  • In general, whilst there is variation, recovery rates across haplotypes is fairly clustered
  • It is possible to achieve 100% accuracy for some haplotypes under high diversity, and few true haplotypes

The data is not substantial on the surface. But, if anything, I had seemed to refute my own pre-print. Counter-intuitively, we now seem to have shown that the problem is easier in the presence of more haplotypes, and more variation. I was particularly disappointed with the ~80% accuracy rates on mid-level diversity on just 3 haplotypes. Overall, comparing the recovery accuracy to that of my less realistic Triviomes, appeared worse.

This made me sad, but mostly cross.

The beginning of the end of my sanity

I despaired at the apparent loss of accuracy. Where had my over 90% recoveries gone? I could feel my PhD pouring away through my fingers like sand. What changed here? Indeed, I had altered the way I generated reads since the pre-print, was it the new read shredder? Or are we just less good at recovering from more realistic metahaplomes? With the astute assumption that everything I am working on equating to garbage, I decided to miserably withdraw from my PhD for a few days to play Eve Online…

I enjoyed my experiences of space. I began to wonder whether I should quit my PhD and become an astronaut, shortly before my multi-million ISK ship was obliterated by pirates. I lamented my inability to enjoy games that lack copious micromanagement, before accepting that I am destined to be grumpy in all universes and that perhaps for now I should be grumpy in the one where I have a PhD to write.

In retrospect, I figure that perhaps the results in my pre-print and the ones in our new megaboxplot were not in disagreement, but rather incomparable in the first place. Whilst an inconclusive conclusion on that front would not answer any of the other questions introduced by the boxplots, it would at least make me a bit feel better.

Scattering recovery rates by variant count

So I constructed a scatter plot to show the relationship between the number of called variants (i.e. SNPs), and best Gretel recovery rate for each haplotype of all of the tested metahaplomes (dots coloured by coverage level below), against the overall best average recovery rates from my pre-print (large black dots).

Immediately, it is obvious that we are discussing a difference in magnitude when it comes to numbers of called variants, particularly when base mutation rates are high. But if we are still looking for excuses, we can consider the additional caveats:

  • Read coverage from the paper is 3-5x per haplotype, whereas our new data set uses a fixed coverage of 7x
  • The number of variants on the original data sets (black dots) are guaranteed, and bounded, by their length (250bp max)
  • Haplotypes from the paper were generated randomly, with equal probabilities for nucleotide selection. We can consider this as a 3 in 4 chance of disagreeing with the pseudo-reference: a 0.75 base mutation rate). The most equivalent subset of our new data consists of metahaplomes with a base mutation rate of “just” 0.25.

Perhaps the most pertinent point here is the last. Without an insane 0.75 mutation rate dataset, it really is quite sketchy to debate how recovery rates of these two data sets should be compared. This said, from the graph we can see:

  • Those 90+% average recoveries I’m missing so badly belong to a very small subset of the original data, with very few SNPs (10-25)
  • There are still recovery rates stretching toward 100%, particularly for the 3 haplotype data set, but for base mutation of 2.5% and above
  • Actually, recovery rates are not so sad overall, considering the significant number of SNPs, particularly for the 5 and 10 haplotype metahaplomes

Recoveries are high for unrealistic variation

Given that a variation rate of 0.75 is incomparable, what’s a sensible amount of variation to concern ourselves with anyway? I ran the numbers on my DHFR and AIMP1 data sets; dividing the number of called variants on my contigs by their total length. Naively distributing the number of SNPs across each haplotype evenly, I found the magic number representing per-haplotype, per-base variation to be around 1.5% (0.015). Of course, that isn’t exactly a vigorous analysis, but perhaps points us in the right direction, if not the correct order of magnitude.

So the jig is up? We report high recovery rates for unnecessarily high variation rates (>2.5%), but our current data sets don’t seem to support the idea that Gretel needs to be capable of recovering from metahaplomes demonstrating that much variation. This is bad news, as conversely, both our megaboxplot and scatter plot show that for rates of 0.5%, Gretel recoveries were not possible in either of the 3 or 5 taxa metahaplomes. Additionally at a level of 1% (0.01), recovery success was mixed in our 3 taxa datasets. Even at the magic 1.5%, for both the 3 and 5 taxa, average recoveries sit uninterestingly between 75% and 87.5%.

Confounding variables are the true source of misery

Even with the feeling that my PhD is going through rapid unplanned disassembly with me still inside of it, I cannot shake off the curious result that increasing the number of taxa in the tree appears to improve recovery accuracy. Each faceted column of the megaboxplot shares elements of the same tree. That is, the 3 taxa 0.1 (or 1%) diversity rate tree, is a subtree of the 15 taxa 0.1 diversity tree. The haplotypes A, B and C, are shared. Yet why does the only reliable way to improve results among those haplotypes seem to be the addition of more haplotypes? In fact, why are the recovery rates of all the 10+ metahaplomes so good, even under per-base variation of half a percent?

We’ve found the trap door, and it is confounding.

Look again at the pretty scatter plot. Notice how the number of called variants increases as we increase the number of haplotypes, for the same level of variation. Notice that it is also possible to actually recover the same A, B, and C haplotype from 3-taxa trees, at low diversity when there are 10 or 15 taxa present.

Recall that each branch of our tree is weighted by the same diversity rate. Thus, when aligned to a pseudo-reference, synthetic reads generated from metahaplomes with more original haplotypes have a much higher per-position probability for containing at least one disagreeing nucleotide in a pileup. i.e. The number of variants is a function of the number of original haplotypes, not just their diversity.

The confounding factor is the influence of Gretel‘s lookback parameter: L. We automatically set the order of the Markov chain used to determine the next nucleotide variant given the last L selected variants, to be equal to the average number of SNPs spanned by all valid reads that populated the Hansel structure. A higher number of called variants in a dataset offers not only more pairwise evidence for Hansel and Gretel to consider (as there are more pairs of SNPs), but also a higher order Markov chain (as there are more pairs of SNPs, on the same read). Thus, with more SNPs, the hypothesis is Gretel has at her disposal, sequences of length L that are not only longer, but more unique to the haplotype that must be recovered.

It seems my counter-intuitive result of more variants and more haplotypes making the problem easier, has the potential to be true.

This theory explains the converse problem of being unable to recover any haplotypes from 3 and 5-taxa trees at low diversity. There simply aren’t enough variants to inform Gretel. After all, at a rate of 0.5%, one would expect a mere 5 variants per 1000bp. Our scatter plot shows for our 3000bp pseudo-reference, at the 0.5% level we observe fewer than 50 SNPs total, across the haplotypes of our 3-taxa tree. Our 150bp reads are not long enough to span the gaps between variants, and Gretel cannot make decisions on how to cross these gaps.

This doesn’t necessarily mean everything is not terrible, but it certainly means the megaboxplot is not only an awful way to demonstrate our results, but probably a poorly designed experiment too. We currently confound the average number of SNPs on reads by observing just the number of haplotypes, and their diversity. To add insult to statistical injury, we then plot them in facets that imply they can be fairly compared. Yet increasing the number of haplotypes, increases the number of variants, which increases the density of SNPs on reads, and improves Gretel‘s performance: we cannot compare the 3-taxa and 15-taxa trees of the same diversity in this way as the 15-taxa tree has an unfair advantage.

I debated with my resident PhD tree pervert about this. In particular, I suggested that perhaps the diversity could be equally split between the branches, such that synthetic read sets from a 3-taxa tree and 15-taxa tree should expect to have the same number of called variants, even if the individual haplotypes themselves have a different level of variation between the trees. Chris argued that whilst that would fix the problem and make the trees more comparable, but going against the grain of simple biological explanations would reintroduce the boilerplate explanation bloat to the paper that we were trying to avoid in the first place.

Around this time I decided to say fuck everything, gave up and wrote a shell for a little while.

Deconfounding the megabox

So where are we now? Firstly, I agreed with Chris. I think splitting the diversity between haplotypes, whilst yielding datasets that might be more readily comparable, will just make for more difficult explanations in our paper. But fundamentally, I don’t think these comparisons actually help us to tell the story of Hansel and Gretel. I thought afterwards, and there are other nasty, unobserved variables in our megaboxplot experiment that directly affect the density of variants on reads, namely: read length and read coverage. We had fixed these to 150bp and 7x coverage for the purpose of our analysis, which felt like a dirty trick.

At this point, bioinformatics was starting to feel like a grand conspiracy, and I was in on it. Would it even be possible to fairly test and describe how our algorithm works through the noise of all of these confounding factors?

I envisaged the most honest method to describe the efficacy of my approach, as a sort of lookup table. I want our prospective users to be able to determine what sort of haplotype recovery rates might be possible from their metagenome, given a few known attributes, such as read length and coverage, at their region of interest. I also feel obligated to show under what circumstances Gretel performs less well, and offer reasoning for why. But ultimately, I want readers to know that this stuff is really fucking hard.

Introducing the all new low-fat less-garbage megaboxplot

Here is where I am right now. I took this lookup idea, and ran a new experiment consisting of some 1500 sets of reads, and runs of Gretel, and threw the results together to make this:

  • Horizontal facets represent synthetic read length
  • Vertical facets are (again) per-haplotype, per-base mutation rates, this time expressed as a percentage (so a rate of 0.01, is now 1%)
  • Colour coded X-axis of each boxplot depicts the average per-haplotype read coverage
  • Y-axis of each boxplot quantifies the average best recovery rate made by Gretel for all of the five haplotypes, over ten executions of Gretel (each using a different randomly generated, uniformly distributed read set)

I feel this graph is much more tangible to users and readers. I feel much more comfortable expressing our recovery rates in this format, and I hope eventually our reviewers and real users will agree. Immediately we can see this figure reinforces some expectations, primarily increasing the read length and/or coverage, has a large improvement on Gretel‘s performance. Increasing read length also lowers the requirements on coverage for accuracy.

This seems like a reasonable proof of concept, so what’s next?

  • Generate a significant amount more input data, preferably in a way that doesn’t make me feel ill or depressed
  • Battle with the cluster to execute more experiments
  • Generate many more pretty graphs

I’d like to run this test for metahaplomes with a different number of taxa, just to satisfy my curiosity. I also want to investigate the 1 – 2% diversity region in a more fine grain fashion. Particularly important will be to repeat the experiments with multiple metahaplomes for each read length, coverage and sequence diversity parameter triplet, to randomise away the influence of the tree itself. I’m confident this is the reason for inconsistencies in the latest plot, such as the 1.5% diversity tree with 100bp reads yielding no results (likely due to this particular tree generating haplotypes such that piled up reads contain a pair of variants more than 100bp apart).

Conclusion

  • Generate more fucking metahaplomes
  • Get this fucking paper out

tl;dr

  • I don’t want to be doing this PhD thing in a year’s time
  • I’ve finally started looking again at our glorious rejected pre-print
  • The Trivial haplomes tanked, they were too hard to explain to reviewers and actually don’t provide that much context on Gretel anyway
  • New tree-based datasets have superseded the triviomes2
  • Phylogenetics maybe isn’t so bad (but I’m still not sure)
  • Once again, the cluster and parallelism in general has proven to be the bane of my fucking life
  • It can be quite difficult to present results in a sensible and meaningful fashion
  • There are so many confounding factors in analysis and I feel obligated to control for them all because it feels like bad science otherwise
  • I’m fucking losing it lately
  • Playing spaceships in space is great but don’t expect to not be blown out of fucking orbit just because you are trying to have a nice time
  • I really love ggplot2, even if the rest of R is garbage
  • I’ve been testing Gretel at “silly” levels of variation thinking that this gives proof that we are good at really hard problems, but actually more variation seems to make the problem of recovery easier
  • 1.5% per-haplotype per-base mutation seems to be my current magic number (n=2, because fuck you)
  • I wrote a shell because keeping track of all of this has been an unmitigated clusterfuck
  • I now have some plots that make me feel less like I want to jump off something tall
  • I only seem to enjoy video games that have plenty of micromanagement that stress me out more than my PhD
  • I think Bioinformatics PhD Simulator 2018 would make a great game
  • Unrealistic testing cannot give realistic answers
  • My supervisor, Chris is a massive dendrophile3
  • HR bullshit makes a grumpy PhD student much more grumpy
  • This stuff, is really fucking hard
  1. supplant HAH GET IT ↩
  2. superseeded HAHAH I AM ON FIRE ↩
  3. phylogenphile? ↩
]]> https://samnicholls.net/2016/12/19/status-nov16-p1/feed/ 0 1720 Status Report: Jul Aug September 2016 https://samnicholls.net/2016/10/24/status-sep16/ https://samnicholls.net/2016/10/24/status-sep16/#respond Mon, 24 Oct 2016 15:37:02 +0000 https://samnicholls.net/?p=1201 A sufficient time had passed since my previous report such that there were both a number of things to report, and I crossed the required threshold of edginess to provide an update on the progress of the long list of things I am supposed to be doing in my quest to become Dr. Nicholls. I began a draft of this post shortly before I attended the European Conference on Computational Biology at the start of September. However at the end of the conference, I spontaneously decided to temporarily abort my responsibilities to bioinformatics, not return to Aberystwyth, electing to spend a few weeks traversing Eastern Europe instead. I imagine there will be more on this in a future post, but for now let’s focus on the long overdue insight into the work that I am supposed to be doing.

In our last installment, I finally managed to describe in words how the metahaplome is somewhat like a graph, but possesses properties that also make it not like a graph. The main outstanding issues were related to reweighting evidence in the metahaplome structure, the ongoing saga of generating sufficient data sets for evaluation and writing this all up so people believe us when we say it is possible. Of the many elephants in the room, the largest was my work still being described as the metahaplome or some flavour of Sam’s algorithm. It was time to try and conquer one of the more difficult problems in computer science: naming things.

Introducing Hansel and Gretel

Harbouring a dislike for the apparent convention in bioinformatics that software names should be unimaginative1 or awkwardly constructed acronyms, and spotting an opportunity to acquire a whimsical theme, I decided to follow in the footsteps of Goldilocks, and continue the fairy tale naming scheme; on the condition I could find a name that fit comfortably.

Unsurprisingly, this proved quite difficult. After some debate, the most suitable fairy tale name I could find was Rumpelstiltskin, for its ‘straw-to-gold’ reference. I liked the idea of your straw-like reads being converted to golden haplotypes. However as I am not remotely capable of spelling the name without Google, and the link between the name and software feels a tad tenuous, I vetoed the option and forged forward with the paper, with the algorithm Untitled.

As I considered the logistics of packaging the implementation as it stood, I realised that I had essentially created both a novel data structure for storing the metahaplome, as well as an actual algorithm for utilising that information to recover haplotypes from a metagenome. At this point everything fell into place; a nice packaging solution and a fitting pair of names had resolved themselves. Thus, I introduce the Hansel data structure, and the Gretel algorithm; a framework for recovering real haplotypes from metagenomes.

Hansel

Hansel is a Python package that houses our novel data structure. I describe it as a “graph-inspired data structure for determining likely chains of symbol sequences from crummy evidence”. Hansel is designed to work with counts of pairwise co-occurrences of symbols in space or time. For our purposes, those symbols are the chemical bases of DNA (or RNA, or perhaps even amino acids of proteins), and the “space” dimension is their position along some sequence.

Three corresponding representations, (top) aligned reads, (middle) the Hansel structure, (bottom) a graph that can be derived from the Hansel structure

We fill this structure with counts of the number of times we observe some pair of nucleotides, at some pair of positions on the same read. Hansel provides a numpy ndarray backed class that offers a user friendly API for operating on our data structure: including adding, adjusting and fetching the counts of pairwise co-occurrences of pairs of symbols in space and time, and making probabilistic queries on the likelihood of symbols occurring, given some sequence of observed symbols thus far.

Gretel

Gretel is a Python package that now houses the actual algorithm that exploits the spun-out API offered by Hansel to attempt to recover haplotypes from the metagenome. Gretel provides a command line based tool that accepts a BAM of aligned reads (against a metagenomic pseudo-reference, typically the assembly) and a VCF of single nucleotide polymorphisms.

Gretel populates the Hansel matrix with observations by parsing the reads in the BAM at the genomic positions described in the provided VCF. Once parsing the reads is complete, Gretel exploits the ability to traverse the Hansel structure like a graph, creating chains of nucleotides that represent the most likely sequence of SNPs that appear on reads that in turn align to some region of interest on the pseudo-reference.

At each node, the decision to move to the next node (i.e. nucleotide) is made by ranking each of the probabilities of the node on the end of each current outgoing edge appearing after some subset of the previously seen nodes (i.e. the current path). Thus both the availability and associated probabilities of future edges are dependent on not only the current or previous node, but the path itself.

Pairwise conditionals between L last variants on the observed path, and each of the possible next variants are calculated and the best option (highest likelihood) is chosen

Pairwise conditionals between L last variants on the observed path,
and each of the possible next variants are calculated and the
best option (highest likelihood) is chosen

Gretel will construct a path in this way until a dummy sentinel representing the end of the graph is reached. After a path has been constructed from the dummy (or “sentinel”) start node to the end, observations in the Hansel structure are reweighted; to ensure that the same path is not returned again (as traversal is deterministic) and to allow Gretel to return the next most likely haplotype instead.

Gretel repeatedly attempts to traverse the graph, each time returning the next most likely haplotype given the reweighted Hansel structure, until a node with no outgoing edges is encountered (i.e. all observations between two genomic positions have been culled by re-weighting).

Going Public

After surgically separating Hansel from Gretel, the codebases were finally in a state where I wasn’t sick at the thought of anyone looking at them. Thus Hansel and Gretel now have homes on Github: see github.com/samstudio8/hansel and github.com/samstudio8/gretel. In an attempt to not be an awful person, partial documentation now also exists on ReadTheDocs: see hansel.readthedocs.io and gretel.readthedocs.io. Hansel and Gretel are both open source and distributed under the MIT Licence.

Reweighting

For the time being, I am settled on the methodology for reweighting the Hansel matrix as described in February’s status report, with the only recent difference being that it is now implemented correctly. Given a path through the metahaplome found by Gretel (that is, a predicted haplotype), we consider the marginal distribution of each of the selected variants that compose that path, and select the smallest (i.e. the least likely variant to be selected across all SNPs). That probability is then used as a ratio to reduce the observations in the Hansel data structure for all pairs of variants on the path.

As I have mentioned in the past, reweighting requires care: an overly aggressive methodology would dismiss similar looking paths (haplotypes) that have shared variants, but an overly cautious algorithm would limit exploration of the metahaplome structure and inhibit recovery of real haplotypes.

Testing so far has indicated that out of the methods I have proposed, this is the most robust, striking a happy balance between exploration-potential and accuracy. At this time I’m happy to leave it as is and work on something else, before I break it horribly.

The “Lookback” Parameter

As the calculation of edge likelihoods depends on a subset of the current path, a keen reader might wonder how such a subset is defined. Your typical bioinformatics software author would likely leave this as an exercise:
-l, --lookback [mysteriously important non-optional integer that fundamentally changes how well this software works, correct selection is a dark art, never set to 0, good luck.]

Luckily for both bioinformatics and my viva, I refuse to inflict the field’s next k-mer size, and there exists a reasonable intuition for the selection of L: the number of nodes over which to lookback (from and including the head of the current path) when considering the next node (nucleotide of the sequence). My intuition is that sequenced read fragments cover some limited number of SNP sites. Thus there will be some L after which it is typically unlikely that pairs of SNPs will co-occur on the same read between the next variant node i+1 and the already observed node (i+1) - L.

I say typically here because in actuality, SNPs will not be uniformly distributed2 and as such the value of L according to this intuition is going to vary across the region of interest depending on both the read sizes, mate pair insert sizes, coverage and indeed the distribution of the SNPs themselves. We thus consider the average number of SNPs covered by all reads parsed from the BAM, and use this to set the value of L.

Smoothing

Smoothing is a component of Hansel that has flown under the radar in my previous discussions. This is likely because I have not given it a second thought since being Belgian last year. Smoothing attempts to awkwardly sidestep two potential issues, namely overfitting and ZeroDivisionError.

For the former, we want to avoid scenarios where variant sites with very low read coverage (and thus few informative observations) are assumed to be fully representative of the true variation. In this case, smoothing effectively inserts additional observations that were not actually present in the provided reads to attempt to make up for potentially unseen observations.

For the latter case, consider the conditional probability of symbol a at position i occurring, given some symbol b at position j. This is defined (before smoothing is applied) by the Hansel framework as:

\frac{\text{Reads featuring} ~a~ \text{at} ~i~ \text{and} ~b~ \text{at} ~j}{\text{Reads spanning} ~i~ \text{and feature} ~b~ \text{at} ~j}

If one were to query the Hansel API with some selection of i, b and j such that there are no reads spanning i that feature symbol b at position j, a ZeroDivisionError will be raised. This is undesirable and cannot be circumvented by simply “catching” the error and returning 0, as the inclusion of a probability of 0 in a sequence of products renders the entire sequence of probabilities as 0, too.

The current smoothing method is merely “add one smoothing”, which modifies the above equation to artificially insert one (more) observation for each possible combination of symbols a and b between i and j. This avoids division by zero as there will always be at least one valid observation. However I suspect that to truly address the former problem, a more sophisticated solution is necessary.

Fortunately, it appears that in practice, for metagenomes with reasonable coverage, the first problem falls away and smoothing has a negligible effect on evaluation of edge probabilities. Despite this, the method of smoothing employed is admittedly naive and future work could potentially benefit from replacement. It should be noted that the influence of smoothing has the potential to become particularly pronounced after significant reweighting of the Hansel matrix (i.e. when very few observations remain).

Evaluation

Avid readers will be aware that evaluation of this method has been a persistent thorn in my side since the very beginning. There are no metagenomic test data sets that offer raw sequence reads, an assembly of those reads and a set of all expected (or indeed, even just some) haplotypes for a gene known to truly exist in that metagenome. Clearly this makes evaluation of a method for enumerating the most likely haplotypes of a particular gene from sequenced metagenomic reads somewhat difficult.

You might remember from my last status report that generating my own test data sets for real genes was both convoluted, and fraught with inconsistencies that made it difficult to determine whether unrecovered variants were a result of my approach, or an artefact of the data itself. I decided to take a step back and consider a simpler form of the problem, in the hope that I could construct an adequate testing framework for future development, and provide an empirical proof that the approach works (as well as a platform to investigate the conditions under which it doesn’t).

Trivial Haplomes: Triviomes

To truly understand and diagnose the decisions my algorithm was making, I needed a source of reads that could be finely controlled and well-defined. I also needed the workflow to generate those reads to be free of uncontrolled external influences (i.e. reads dropped by alignment, difficult to predict SNP calling).

To accomplish this I created a script to construct trivial haplomes: sets of short, randomly generated haplotypes, each of the same fixed length. Every genomic position of such a trivial haplotype was considered to be a site of variation (i.e. a SNP). Tiny reads (of a configurable size range, set to 3-5bp for my work) are then constructed by sliding windows across each of the random haplotypes. Additional options exist to set a per-base error rate and “slip” the window (to reduce the quality of some read overlaps, decreasing the number of shared paired observations).

Although this appears to be grossly unrepresentative of the real problem at hand — what technology even generates reads of 3-5bp? Don’t forget, Hansel and Gretel are designed to work directly with SNPs. Aligned reads are only parsed at positions specified in the VCF (i.e. the list of called variants) and so real sequences collapse to a handful of SNPs anyway. The goal here is not so much about accurately emulating real reads with real variation and error, but to establish a framework for controlled performance testing under varying conditions (e.g. how do recovery rates change with respect to alignment rate, error rate, number of SNPs, haplotypes etc).

We must also isolate the generation of input files (alignments and variant lists) from external processes. That is, we cannot use established tools for read alignment and variant calling. Whilst the results of these processes are tractable and deterministic, they confound the testing of my triviomes due to their non-trivial behaviour. For example, my tiny reads have a known correct alignment: each read is yielded from some window with a defined start and end position on a given haplotype. However read aligners discard, clip and “incorrectly” align these tiny test reads3. My reads are no longer under my direct control.

In the same fashion, despite my intention that every genomic position of my trivial haplome is a SNP, established variant callers and their diploid assumptions can simply ignore, or warp the calling of tri- or tetra-alleleic positions.

Thus my script is responsible for generating a SAM file, describing the alignment of the generated reads against a reference that does not exist, and a VCF, which simply enumerates all genomic positions on the triviome as a potential variant.
It’s important to note here that for Gretel (and recovery in general) the actual content of the reference sequence is irrelevant: the job of the reference (or pseudo-reference, as I have taken to call it for metagenomes) is to provide a shared co-ordinate system for the sequenced reads via alignment. In this case, the co-ordinates of the reads are known (we generated them!) and so the process of alignment is redundant and the reference need not exist at all.

Indeed, this framework has proved valuable. A harness script now rapidly and repeatedly generates hundreds of triviomes. Each test creates a number of haplotypes, with some number of SNPs. The harness can also specify an error rate, and how to sample the reads from each generated haplotype (uniformly, exponentially etc.). The resulting read alignment and variant list is thrown at Hansel and Gretel as part of the harness. Haplotypes recovered and output by Gretel can then be compared to the randomly generated input sequences for accuracy by merely constructing a matrix of Hamming distances (i.e. how many bases do not match for each output-input pair?). This is simple, fast and pretty effective, even if I do say so myself.

Despite its simplicity, this framework forms a basis for testing the packages during development, as well as giving us a platform on which to investigate the influence on recovery rate that parameters such as read length, number of haplotypes, number of SNPs, error rate, alignment rate have. Neat!

Synthetic Metagenomes

Of course, this triviome stuff is all well and good, but we need to prove that recovery is also possible on real genomic data. So we still left executing the rather convoluted looking workflow that I left you with at towards the end of my last report?

On the surface, that appears to be the case. Indeed, we must still simulate reads from some set of related genes, align those reads to some pseudo-reference, call for SNPs on that alignment and use the alignment and those called SNPs as the input to Gretel to recover the original genes that we generated reads from in the first place. Of course, we must also compare the haplotypes returned by Gretel to the known genes to evaluate the results.

But actually, the difficulty in the existing workflow is in the evaluation. Currently we use an alignment step to determine where each input gene maps to the selected pseudo-reference. This alignment is independent from the alignment of generated reads to the pseudo-reference. The hit table that describes how each input gene maps to the master is actually parsed as part of the evaluation step. To compare the input sequences against the recovered haplotypes, we need to know which parts of the input sequence actually overlap the recovered sequences (which share the same co-ordinates as the pseudo-reference), and where the start and end of that overlapping region exists on that particular input. The hit table effectively allows us to convert the co-ordinates of the recovered haplotypes, to those of the input gene. We can then compare bases by position and count how many match (and how many don’t).

Unsurprisingly, this got messy quite quickly, and the situation was exacerbated by subtle disagreements between the alignments of genes to the reference with BLAST and reads to the reference with bowtie2. This caused quite a bit of pain and ultimately ended with me manually inspecting sequences and writing my own BLAST-style hit tables to correct the evaluation process.

One afternoon whilst staring at Tablet and pondering my life choices, I wondered why I was even comparing the input and output sequences in this way. We’re effectively performing a really poor local alignment search between the input and output sequences. Using an aligner such as BLAST to compare the recovered haplotypes to the input sequences seems to be a rather intuitive idea. So why don’t we just do an actual local alignment?

Without a good answer, I tore my haplotype evaluation logic out of Gretel and put it in a metaphorical skip. Now we’ve dramatically simplified the process for generating and evaluating data sets. Hooray.

A bunch of small data sets now exist at github.com/SamStudio8/gretel-test and a framework of questionable bash scripts make the creation of new data sets rather trivial. Wonderful.

Results

So does this all actually work? Without wanting to step on the toes of my next blog post, it seems to, yes.

Accuracy across the triviome harness in general, is very good. Trivial haplotypes with up to 250 SNPs can be recovered in full, even in haplomes consisting of reads from 10 distinct, randomly generated haplotypes. Unsurprisingly, we’ve confirmed that increasing the number of haplotypes, and the number of SNPs on those haplotypes makes recovery more difficult.

To investigate recovery from metahaplomes of real genes, I’ve followed my previous protocol: select some gene (I’ve chosen DHFR and AIMP1), use BLAST to locate five arbitrary but similar genes from the same family, break them into reads and feed the alignment and called SNPs to Gretel with the goal of recovering the original genes. For both the DHFR and AIMP1 data sets, it is possible to get recovery rates of 95-100% for genes that look similar to the psuedo-reference and 80+% for those that are more dissimilar.

The relationship between pseudo-reference similarity and haplotype recovery rates might appear discouraging at first, but after digging around for the reasoning behind this result, it turns out not to be Gretel‘s fault. Reads generated from sequences that have less identity to the pseudo-reference are less likely to align to that pseudo-reference, and are more likely to be discarded. bowtie2 denies Gretel access to critical evidence required to accurately recover these less similar haplotypes.

This finding echoes an overarching theme I have encountered with current genomic tools and pipelines: not only are our current protocols and software not suitable for the analysis of metagenomes, but their underlying assumptions of diploidy are actually detrimental to the analyses we are conducting.

Introducing our Preprint

My work on everything so far has culminated in the production of a preprint: Advances in the recovery of haplotypes from the metagenome. It’s quite humbling to see the sum total of around 18 months of my life summed into a document. Flicking through it a few months after it went online, I still get a warm fuzzy feeling: it looks like real science! I provide a proper definition of the metahaplome and introduce both the underlying graph theory for Hansel and the probability equations for Gretel. It also goes into a whole heap of results obtained so far, some historical background into the problem and where we are now as a field, and an insight into how this approach is different from other methodologies.

Our work is an advance in computational methods for extracting exciting exploitable enzymes from metagenomes.

Conclusion

Things appear to be going well. Next steps are to get in the lab and try Gretel out for real. We are still hunting around for some DNA in a freezer that has a corresponding set of good quality sequenced reads.

In terms of development for Hansel and Gretel, there is still plenty of room for future work (helpfully outlined by my preprint) but in particular I still need to come up with a good way to incorporate quality scores and paired end information. I expect both attributes will improve performance more than the pretty awesome results we are getting already.

For more heavy refactoring, I’d like to also look at replacing Gretel‘s inherent greedy bias (the best choice is always the edge with the highest probability) with something a little more clever. Additionally, the handling of indels is more than certainly going to become the next thorn in my side.

I’d also like to come up with some way of visualising results (I suspect it will involve Circos), because everybody can get behind a pretty graph.

In other news

I went in the lab for the first time…

…it was not a success

Biologists on Reddit actually liked my sassy PCR protocol

Our sys admin left

This happened.

Someone is actually using Goldilocks

I am accidentally in charge of the department 3D printer

Writing a best man’s speech is definitely harder than an academic paper

I am a proper biologist now

Someone trusts me to play with lasers

I acquired some bees

I went up some mountains with a nice man from the internet

I went to a conference…

…I presented some work!

I accidentally went on holiday

…and now I am back.

tl;dr

  • Yes hello, I am still here and appear to be worse at both blog and unit testing
  • My work now has a name: a data structure called Hansel and an algorithm called Gretel
  • Reweighting appears to work in a robust fashion now that it is implemented correctly
  • Smoothing the conditional probabilities of observations should be looked at again
  • Triviomes provide a framework for investigating the effects of various aspects of metagenomes on the success of haplotype recovery
  • Generating synthetic metahaplomes for testing is significantly less of a pain in the ass now I have simplified the process for evaluating them
  • Current protocols and software are not suitable for the analysis of metagenomes (apart from mine)
  • I have a preprint!
  • Indels are almost certainly going to be the next pain in my ass
  • I shunned my responsibilities as a PhD student for a month and travelled Eastern Europe enjoying the sun and telling strangers about how awful bioinformatics is
  • I am now back telling North Western Europeans how awful bioinformatics is
  1. Mhaplotyper? Oh yes, the M is for metagenomic, and it is also silent. ↩
  2. Having written this paragraph, I wonder what the real impact of this intuition actually is. I’ve now opened an issue on my own repo to track my investigation. In the case where more than L pairs of evidence exist, is there quantifiable loss in accuracy by only considering L pairs of evidence? In the case where fewer than L pairs of evidence exist, does the smoothing have a non-negligible affect on performance? ↩
  3. Of course, the concept of alignment in a triviome is somewhat undefined as we have no “reference” sequence anyway. Although one could select one of the random haplotypes as a pseudo-reference against which to align all tiny reads, considering the very short read lengths and the low sequence identity between the randomly generated haplotype sequences, it is highly likely that the majority of reads will fail to align correctly (if at all) to such a reference. ↩
]]> https://samnicholls.net/2016/10/24/status-sep16/feed/ 0 1201 Status Report: May 2016 (Metahaplomes: The graph that isn’t) https://samnicholls.net/2016/06/12/status-may16/ https://samnicholls.net/2016/06/12/status-may16/#respond Sun, 12 Jun 2016 20:03:24 +0000 https://samnicholls.net/?p=699 It would seem that a sufficient amount of time has passed since my previous report to discuss how everything has broken in the meantime. You would have left off with a version of me who had not long solidified the concept of the metahaplome: a graph-inspired representation of the variation observed across aligned reads from a sequenced metagenome. Where am I now?

Metahaplomes

The graph that isn’t

At the end of my first year, I returned from my reconnaissance mission to extract data mining knowledge from a leading Belgian university with a prototype for a data structure that was fit to house a metahaplome; a probabilistically weighted graph that can be traversed to extract likely sequences of variants on some gene of interest. I say graph, because the structure and API does not look or particularly act like a traditional graph at all. Indeed, the current representation is a four dimensional matrix that stores the number of observations of a symbol (SNP) A at position i, co-occurring with a symbol B at position j.

This has proved problematic as I’ve had difficulty in explaining the significance of this to people who dare to ask what my project is about. “What do you mean it’s not a graph? There’s a picture of a graph on your poster right there!?”. Yes, the matrix can be exploited to build a simple graph representation, but not without some information loss. As a valid gene must select a variant at each site, one cannot draw a graph that contains edges from sites of polymorphisms that are not adjacent (as a path that traverses such an edge would skip a variant site1). We therefore lose the ability to encode any information regarding co-occurrence of non-adjacent variants (abs(i - j) != 1) if we depict the problem with a simple graph alone.

To circumvent this, edges are not weighted upfront. Instead, to take advantage of the evidence available, the graph is dynamically weighted during traversal (the movement to the next node is variable, and depends on the nodes that have been visited already) using the elements stored in the matrix.

Thus we have a data structure capable of being utilised like a graph, with some caveats: it is not possible to enumerate all possibilities or assign weights to all edges upfront before traversal (or for that matter, a random edge), and a fog of war exists during any traversal (i.e. it is not possible to predict where a path may end without exploring). Essentially we have no idea what the graph looks like, until we explore it. Despite this, my solution fuses the advantage of a graph’s simple representation, with the advantage of an adjacency matrix that permits storage of all pertinent information. Finally, I’ve been able to describe the structure and algorithm verbally and mathematically.

Reweighting

Of course, having this traversable structure that takes all the evidence seen across the reads into account is great, but we need a reliable method for rescuing more than just one possible gene variant from the target metahaplome. My initial attempts at this involved invoking stochastic jitter during traversal to quite poor effect. It was not until some time after I’d got back from putting mayonnaise on everything that I considered altering the observation matrix that backs the graph itself to achieve this.

My previous report described the current methodology: given a complete path, check the marginal probability for each variant at each position of the path (i.e. the probability one would select the same nucleotide if you were to look at variant site in isolation) and determine the smallest marginal. Then iterate over the path, down-weighting the element of the observation matrix that stores the number of occurrences of the i‘th nucleotide and the i+1‘th selected nucleotide, by multiplying the existing value by the lowest marginal (which will be greater than 0, but smaller than 1) and subtracting that value from the current count.

Initial testing yielded more accurate results with this method than anything I had tried previously, where accuracy is quantified by this not happening:

The algorithm is evaluated with a data set of several known genes from which a metagenome is simulated. The coloured lines on the chart above refer to each known input gene. The y axis represents the percentage of variants that are “recovered” from the metagenome, the x axis is the iteration (or path) number. In this example, a questionable strategy caused poor performance (other than the 100% recovery of the blue gene), and a bug in handling elements that are reweighted below 1 allowed the algorithm to enter a periodic state.

After implementing the latest strategy, performance compared to the above increased significantly (at least on the limited data sets I have spent the time curating), but I was still not entirely satisfied. Recognising this was going to take much more time and thought, I procrastinated by writing up the technical aspects of my work in excruciating mathematical detail in preparation for my next paper. To wrap my head around my own equations, I commandeered the large whiteboards in the undergraduate computing room and primed myself with coffee and Galantis. Unfortunately, after a hour or two of excited scribbling, this happened:

I encountered an oversight. Bluntly:

Despite waxing lyrical about the importance of evidence arising from non-adjacent variant sites, I’d overlooked them in the reweighting process. Although frustrated with my own incompetence, this issue was uncovered at a somewhat opportune time as I was looking for a likely explanation for what felt like an upper bound on the performance of the algorithm. As evidence (observation counts) for adjacent pairwise variants was decreased through reweighting, non-adjacent evidence was becoming an increasingly important factor in the decision making process for path traversal, simply by virtue of the counts being larger (as they were left untouched). Thus paths were still being heavily coerced along particular routes and were not afforded the opportunity to explore more of the graph, yielding less accurate results (less recovered variants) for more divergent input genes.

As usual in these critical oversights, the fix was trivial (just ensure to apply the same rules for reweighting the adjacent pairs of variants to the non-adjacent ones too), and indeed, performance was bumped by around 5%p. Hooray.

Evaluation

Generating test data (is still a pain in the arse)

So here we are, I’m still somewhat stuck in a data rut. Generating data sets (that can be verified) is a somewhat convoluted procedure. Whilst, to run the algorithm all one needs is a BAM of aligned reads, and an associated VCF of called SNP sites; to empirically test output, we also need to know what the output genes should look like. Currently this requires a “master” FASTA (the origin gene), a FASTA of similar genes (the ones we actually want to recover) and a blast hit table that documents how those similar genes align to the master. The workflow for generating and testing a data set looks like this:

  • Select an interesting, arbitrary master gene from a database (master.fa)
  • blast for similar genes and select several hits with decreasing identity
  • Download FASTA (genes.fa) and associated blast hit table (hits.txt) for selected genes
  • Simulate reads by shredding genes.fa (reads.fq)
  • Align reads (reads.fq) with bowtie to pseudo-reference (master.fa) to create (hoot.bam)
  • Call for SNPs on (hoot.bam) to create a VCF (hoot.vcf)
  • Construct metahaplome and traverse paths with reads (hoot.bam) and SNPs (hoot.vcf)
  • Output potential genes (out.fa)
  • Evaluate each result in out.fa against each hit in hits.txt
    • Extract DNA between subject start and end for record from genes.fa
    • Determine segment of output (from out.fa) overlapping current hit (from genes.fa)
    • Convert co-ordinates of SNP to current hit (gene.fa)
    • Confirm consistency between SNP on output, to
    • Return matrix of consistency for each output gene, to each input gene

Discordant alignments between discordant aligners

The issue at hand primarily arises from discordant alignment decisions between the two alignment processes that make up components of the pipeline; blast and bowtie. Although blast is used to select the initial genes (given some “master”), and its resulting hit table is also used to evaluate the approach at the end of the algorithm, bowtie is used to align the reads (reads.fq) to that same master. Occasional disagreements between both algorithms are inevitable on real data, but I assumed that given the simplicity of the data (simulated reads of uniform length, no errors, reasonable identity) that they would behave the same. It may sound like an obvious problem source, but when several genes are reported as correctly extracted with high accuracy (identity) and one or two are not, you might forgive me for thinking that the algorithm just needed tweaking rather than an underlying problem stemming from the alignment steps! This led to much more tail chasing than I would care to admit to.

For one example: I investigated a poorly reconstructed gene by visualising the input BAM produced by bowtie with Tablet (a rather nifty BAM+VCF+FA viewer). It turned out that for input reads belonging to one of the input genes, bowtie had called an indel1, causing a disagreement as to what the empirical base at each SNP following that indel should have been. That is, although all of the reads from that particular input gene were aligned by bowtie as having an indel (and thus shifting bases in those reads), and processed by my algorithm with that indel taken into account, at the point of evaluation, the blast hit table is the gold standard; what may have been the correct variant (indel withstanding) would be determined as incorrect by the alignment of the hit table.

I suppose the solution might be to switch to one aligner, but I’m aware that even the same aligner can make different decisions under differing conditions (read length).
It’s important to note that currently the hit table is also used to define where to begin sharding reads for the simulated metagenome, which in turn causes trouble if bowtie disagrees with where an alignment begins and ends. I’ve had cases where blast aligns the first base of the subject (input gene) to the first base of the query (master) but on inspection with Tablet, it becomes evident that bowtie clips the first few bases when aligning opening reads to the master. This problem is a little more subtle, and in current practice causes little trouble. Although the effect would be a reduction in observed evidence for variants at SNPs that happen to occur within the first few bases of the gene, my test sets so far do not have a SNP site so close to the start of the master. This is obviously something to watch out for, though.

At this time I’ve just been manually altering the hit table to reconcile differences between the two aligners, which is gross.

Bugs in the machine

Of course, a status report from me is not complete without some paragraph where I hold my hands up in the air and say everything was broken due to my own incompetence. Indeed, a pair of off-by-one bugs in my evaluation algorithm also warped reported results. The first, a regression introduced after altering the parameters under which the evaluation function determines an overlap between the current output gene and current hit, led to a miscalculation when translating the base position on the master to the base position on the expected input under infrequent circumstances, causing the incorrect base to be compared to the expected output. This was also accidentally fixed when I refactored the code and saw a very small increase in performance.

The second, an off-by-one error in the reporting of a new metric: “deviations from reference”, caused the results to suddenly appear rather unimpressive. The metric measures the number of bases that are different from the pseudo-reference (master.fa) that were correctly recovered by my algorithm to match an original gene from gene.fa. Running my algorithm now yielded a results table describing impressive gene recovery scores (>89%) but those genes only appeared to differ from the reference by merely a few SNPs (<10). How could we suck at recovering sequences that barely deviate from the master? Why does it take so many iterations? After getting off the floor and picking up the shattered pieces of my ego and PhD, I checked the VCF and confirmed there were over a hundred SNPs across all the genes. Curious, I inspected the genes manually with Tablet to see how they compared to the reference. Indeed, there were definitely more than the four reported for one particular case, so what was going on?

To finish quickly; path iteration numbers start from 0, but are reported to the user as iter + 1, because the 0’th iteration is not catchy. My mistake was using the iter + 1 to also access the number of deviations from the reference detected in the current iteration – in a zero indexed structure. I was fetching the number of deviations successfully extracted by the path after the this one, which we would expect to be poor, as the structure would have been reweighted to prevent that path from appearing again. Nice work, me. This fix made things a little more interesting:

More testing of the testing, is evidently necessary.

Conclusion

So where does that leave us? Performance is up, primarily because the code that I wrote to evaluate performance (and reweight) is now less broken. Generating data sets is still a pain in the arse, but I have got the hang of the manual process involved so I can at least stop hiding from the work to be done. It might be worth investigating consolidating all of my alignment activities into one aligner to improve my credit score. Results are looking promising, this algorithm is now capable of extracting genes (almost or entirely whole) from simulated (albeit quite simple) metagenomes.

Next steps will be more testing, writing the method itself as a paper, and getting some proper biological evidence from the lab that this work can do what I tell people it can do.

In other news

tl;dr

  • I continue to be alive and bad at both blog and implementing experimental data structures
  • I fixed my program not working by fixing the thing that told me it wasn’t working
  • If your evaluator has holes in, you’ll spend weeks chasing problems that don’t exist
  • Never assume how someone else’s software will work, especially if you are assuming it will work like a different piece of software that you are already making assumptions about
  • Always be testing (especially testing the testing)
  • This thing actually fucking works
  • An unpleasant side effect of doing a PhD is the rate of observed existential crises increases
  • Life continues to be a series of off-by-one errors, punctuated with occasional trips to the seaside
  1. Let’s not even talk about indels for now. ↩ ↩
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