One of our long-term interests has been in figuring out what the !$!$!#!#%! assemblers actually do to real data, given all their heuristics. A continuing challenge in this space is that short-read assemblers deal with really large amounts of noisy data, and it can be extremely hard to look at assembly results without running into this noise head-on. It turns out that being able to label De Bruijn graphs efficiently and align reads to graphs can help us explore assemblies in a variety of ways.
The two basic challenges are noisy data and lots of data. When (for example) looking at what fraction of reads has been incorporated into an assembly, noise causes problems because a read may have been corrected during assembly. This is where graph alignment comes in handy, because we can use it to align reads to the full graph and get rid of much of this noise. Lots of data complicates things because it's very hard to look at reads individually - you need to treat them in aggregate, and it's much easier just to look at the reads that match to your assembly than to investigate the oddball reads that don't assemble. And this is where the combination of graph alignment and labeling helps, because it's easy to count and extract reads based on overlaps with labels, as well as to summarize those overlaps.
The main question we will be asking below is: can we measure overlaps and disjoint components in graph extents, that is, in unique portions of assembly graphs? We will be doing this using our sparse graph instead of counting nodes or k-mers, for two reasons: first, the covering is largely independent of coverage, and second, the number of sparse nodes is a lot smaller than the total number of k-mers.
The underlying approach is straightforward:
- load contigs or reads from A into the graph, tagging sparse nodes as we go;
- load contigs or reads from B into the graph, tagging sparse nodes as we go;
- count the number of tagged nodes that are unique to A, unique to B, and in the overlap;
- optionally do graph alignment as you load in reads, to ignore errors.
Let's start with simulations, as usual. We'll set up two randomly generated chromosomes, a and b, of equal size, both in genomes.fa, and look at genome-a extent within the context of both (target 'fake_a' in Makefile):
./compare-graphs.py genomes.fa genome-b.fa all tags: 52 n tags in A: 52 n tags in B: 26 tags in A but not in B 26 tags in B but not in A 0
So far so good -- there's a 50% overlap between one of the chromosomes and the total.
If we now generate reads from genome-b.fa and do the graph comparison with the reads, we get silly results (target 'fake_b' in Makefile):
./compare-graphs.py genomes.fa reads-b.fa all tags: 135 n tags in A: 109 n tags in B: 107 tags in A but not in B 28 tags in B but not in A 26
Despite knowing by construction that all of the reads came from genome-b, we're getting results that there's a lot of tags in the reads that aren't in the genome. This is because of errors in the reads, which introduce many spurious branches in the graph.
This is now where the read aligner comes in; we can do the same comparison, but this time we can ask that the reads be aligned to the genome, thus eliminating most of the errors in the comparison:
./compare-graphs.py genomes.fa reads-b.fa --align-b all tags: 99 n tags in A: 99 n tags in B: 72 tags in A but not in B 27 tags in B but not in A 0
At this point we can go in and look at the original tags in A that aren't covered in B (there are 52) and note that B is missing approximately half of the graph extent in A.
Trying it out on some real data
Let's try evaluating a reference against some low-coverage reads. Using the same mouse reference transcriptome & subset of reads that we've been using in previous blog posts, we can ask "how many sparse nodes are unaccounted for in the mouse transcriptome when we look at the reads?" (Note, the mouse transcriptome was not generated from this data set; this is the reference transcriptome.)
all tags: 1959121 n tags in A: 1878475 n tags in B: 644963 tags in A but not in B 1314158 tags in B but not in A 80646
About 12.5% of the reads in (B; 80646 / 644963) don't pick up tags in the official reference transcriptome (A).
Interestingly, the results with alignment are essentially the same (target rna-compare-align.txt):
all tags: 1958219 n tags in A: 1877685 n tags in B: 643655 tags in A but not in B 1314564 tags in B but not in A 80534
suggesting that, by and large, these reads are disjoint from the existing assembly, and not mere sequencing errors. (This may be because we require that the entire read be mappable to the graph in order to count it, though.)
One of the interesting questions that's somewhat hard to investigate in terms of transcriptome assembly is, how beneficial is read trimming to the assembly? The intuition here (that I agree with) is that generally sequence trimming lowers the effective coverage for assembly, and hence loses you assembled sequence. Typically this is measured by running an assembler against the reads, which is slightly problematic because the assembler could have all sorts of strange interactions with the trimming.
So, can we look at the effect of trimming in terms of sparse nodes? Sure!
Suppose we do a stringent round of trimming on our RNAseq (Trimmomatic SLIDINGWINDOW:4:30) - what do we lose?
On this low coverage data set, where A is the graph formed from the trimmed reads and B is the graph from the raw reads, we see (target rseq-hardtrim-ba-noalign.txt):
all tags: 588615 n tags in A: 518980 n tags in B: 588615 tags in A but not in B 0 tags in B but not in A 69635
we see about 12% of the sparse nodes missing from the trimmed data.
If we run the read aligner with a low coverage cutoff (target rseq-hardtrim-ba-align1.txt), we see:
all tags: 569280 n tags in A: 519396 n tags in B: 561757 tags in A but not in B 7523 tags in B but not in A 49884
Basically, we recover about 20,000 tags in B (69,635 - 49,884) with alignment vs exact matches, so a few percent; but we also lose about half that (7,500) for reasons that we don't entirely understand (wiggle in the graph aligner?)
We have no firm conclusions here, except to say that this should be a way to evaluate the effect of different trimming on graph extent, which should be more reliable than looking at the effect on assemblies.
Notes and miscellany
- There is no inherent coverage model embedded here, so as long as we can correct for the density of tags, we can apply these approaches to genomes, metagenomes, and transcriptomes.
- It's actually very easy to extract the reads that do or don't match, but our current scripts don't let us do so based on labels.
- We aren't really using the labeling here, just the tagging - but labeling can enable n-way comparisons between e.g. different assemblies and different treatments, because it lets us examine which tags show up in different combinations of data sets.
Appendix: Running this code
The computational results in this blog post are Rather Reproducible (TM). Please see https://github.com/dib-lab/2015-khmer-wok5-eval/blob/master/README.rst for instructions on replicating the results on a virtual machine or using a Docker container.