shahab-sarmashghi
commented
1 year ago Hi Eric,
Thank you! I am glad that you have found our tool useful and easy to use. CCing Vineet Bafna and Siavash Mirarab who are PI's that co-lead genome skimming related projects at UCSD.
Regarding the way you are running RESPECT, I don't think there is a problem with it. Usually in the realm of genome skimming we work with low coverage data, and since our methods are alignment-free and do not exploit PE data, we merge read pairs in the preprocessing step (using BBtools) to get higher effective coverage (rather than running each read pair separately). That being said, if you have higher coverage data, there is no problem with running them separately as you did. Ideally, we would like to get identical results from forward and reverse reads, but there could be read-pair-specific sequencing artifacts that are not fully resolved in the preprocessing and result in differences. In your case, the difference is less than %10, which is within the typical estimation error of RESPECT, so this could just be due to noise and variation in the output of its (randomized) iterative algorithm.
Now regarding the interpretation of repetitiveness, you are right, 0.35 uniqueness ratio is on the quite low end of the spectrum (Figure 5 of RESPECT paper). This shows that at the k-mer level (by default 31-mer substrings of the genome), only 35% of them are unique and 65% are at higher copies, which include both low-copy (2,3,.. copies) and high-copy (thousands of copies) repeats. To get some resolution on this, we define and estimate HCRM, which quantifies the contribution of high copy repeats. Based on that, we assume when the uniqueness ratio is low (so we have many repeats) and HCRM is low (so not many are from high copy repeats), the repeats come from low copy repeats which we hypothesized can be a signature of genome duplication. We indeed observed that r1/L < 0.8 and HCRM < 200 is enriched for species with known WGD in their evolutionary history (Figure 5 of RESPECT paper).
For your sample, HCRM is ~375, and that could mean that the repetitiveness is mostly from high copy repeats (e.g., DNA transposons and retrotransposons). However, we do see some species like Atlantic salmon that have known WGD and still their HCRM is higher than 200, especially when the r1/L is well below 0.8, which is also the case with your sample. That suggests WGD is still a possibility. An important point to consider when interpreting these results is that, we use k-mers, and so the signature of old WGD in distant ancestral lineages is preserved much longer among short-length k-mers, compared to other methods that are based on finding gene duplication through alignment and paralog detection etc. If you know how the publishers of the genome concluded there is no genome duplication, or can point us to the corresponding study, we can look into it and discuss it further.
In the end I should say that our work is by no means a comprehensive effort at finding WGDs or certainly ruling in favor or against them. However, we think there is a potential to use k-mer based methods to dissect the repetitive structure of the genome, and identify signatures of whole genome doubling or other repeat-forming events across species. Vineet and Siavash are actively leading related repeat-aware computational methods, and applying those to study the underlying science is certainly within their (future) research interests.
Best, Shahab
On Mon, Oct 31, 2022 at 7:48 AM Eric Edsinger @.***> wrote:
Hi!
I'm using RESPECT to evaluate 10 octopus genomes - genomes that are expected to be human-sized or larger and repeat rich. Years ago people thought the genomes would be duplicated - but this has not held up since the first cephalopod genome came out in 2015 for Octopus bimaculoides.
To get a sense of how RESPECT works and what its accuracy is like, I characterized two Octopus bimaculoides NCBI SRR data sets at 4-5x coverage to compare to the published genome. RESPECT's prediction of genome size seems fairly accurate - and I think it's HCRM values are indicating the genome is repeat rich - which would be true. However, the uniqueness ratio is very low and if I understand correctly this could indicate genome duplication. If the published genome indicates no genome duplication - what else could be causing the low uniqueness ratio that RESPECT is finding - or how much trust would you have in interpreting a low uniqueness ratio (much lower than the 0.8 cutoff indicated in the README) as an indication of a genome duplication?
Or am I misunderstanding how to interpret the output?
One thing that maybe I am doing incorrectly is that I am running RESPECT on a PE forward read fastq file and then on a PE reverse read fastq file - rather than including pairs together. But maybe this is throwing things off somehow. I feel like it shouldn't - and just did it this way as an easy method to get close to the recommended 4x coverage for RESPECT.
Any comments or suggestions would be greatly appreciated!
Thank you very much - and thank you for such a fantastic tool. It seems potentially very effective and is simple to work with in a great way - and is a pleasure to use - I'm just unsure on the interpretation of things afterwards.
Eric
respect -i trimomatic-octopus-bimaculoides-1-paired.fastq trimomatic-octopus-bimaculoides-2-paired.fastq --debug
sample input_type sequence_type coverage genome_length uniqueness_ratio HCRM sequencing_error_rate average_read_length trimomatic-octopus-bimaculoides-1-paired.fastq sequence genome-skim 4.85 2916125971 0.34 363.86 0.0043 149.7477 trimomatic-octopus-bimaculoides-2-paired.fastq sequence genome-skim 5.27 2686674457 0.37 385.22 0.0070 149.7511
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000generic
Hi!
I'm using RESPECT to evaluate 10 octopus genomes - genomes that are expected to be human-sized or larger and repeat rich. Years ago people thought the genomes would be duplicated - but this has not held up since the first cephalopod genome came out in 2015 for Octopus bimaculoides.
To get a sense of how RESPECT works and what its accuracy is like, I characterized a Octopus bimaculoides NCBI SRR PE fastq data set - split up and run separately as PE forward/1 and as PE reverse/2 - each at 4-5x coverage - to compare to the published genome. RESPECT's prediction of genome size in bimaculoides seems fairly accurate - and I think it's HCRM values are indicating the genome is repeat rich - which would be true. However, the uniqueness ratio is very low and if I understand correctly, this could indicate genome duplication. If the published genome indicates no genome duplication - what else could be causing the low uniqueness ratio that RESPECT is finding - or how much trust would you have in interpreting a low uniqueness ratio (much lower than the 0.8 cutoff indicated in the README) as an indication of a genome duplication?
Or am I misunderstanding how to interpret the output?
One thing that maybe I am doing incorrectly is that I am running RESPECT on a PE forward read fastq file and then on a PE reverse read fastq file - rather than including pairs together. But maybe this is throwing things off somehow. I feel like it shouldn't - I did it this way as an easy method to get close to the recommended 4x coverage for RESPECT.
Any comments or suggestions would be greatly appreciated!
Thank you very much - and thank you for such a fantastic tool. It seems potentially very effective and is simple to work with in a great way - and is a pleasure to use - I'm just unsure on the interpretation of things afterwards.
Eric
respect -i trimomatic-octopus-bimaculoides-1-paired.fastq trimomatic-octopus-bimaculoides-2-paired.fastq --debug