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R handles statistics. It does not simulate complex biochemical interactions in different parts of the body. From the description, they did something that requ

by btilly 6y ago

R handles statistics. It does not simulate complex biochemical interactions in different parts of the body. From the description, they did something that requires a lot more horsepower.

dekhn 6y ago
I read the underlying article (https://elifesciences.org/articles/59177 https://elifesciences.org/articles/59177) and was unable to find any evidence of that. In fact the paper doesn't mention anything about SUmmit or details on the computations :( There certainly wasn't any "heavy biochemical calculations"; this work is entirely comparative genomics, so just operating on DNA strings. I see this fluff article https://www.ornl.gov/blog/genomics-code-exceeds-exaops-summit-supercomputer https://www.ornl.gov/blog/genomics-code-exceeds-exaops-summi... and there may be more detail here: https://www.hpcuserforum.com/presentations/april2019/JoubertORNL.pdf https://www.hpcuserforum.com/presentations/april2019/Joubert... which shows near-linear performance they ascribe to "Made possible by aggressive communication overlap and low-congestion Mellanox Infiniband fat tree network with adaptive routing" So there may actually be an HPC/supercomputer story in there, but I'm having trouble figuring out what they did in this most recent work.
- rrss 6y ago
  I can't find details either, but I think they just used R for the post-processing, and there's a lot of computation behind this sentence: > RNA-Seq analysis was performed using the latest version of the human transcriptome I found this article discussing read mapping for RNA sequence analysis: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833417/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833417/ > In particular, RNA sequencing (RNA-seq) technology,1 which provides a comprehensive profile of a transcriptome, is increasingly replacing conventional expression microarrays.2 Primary data processing in RNA-seq (as well as in other massive sequencing experiments, including genome resequencing) involves mapping reads onto a reference genome. This step constitutes a computationally expensive process in which, in addition, sensitivity is a serious concern
  - dekhn 6y ago
    What good is a paper if their methods are a single sentence? Ugh. Reads mapping is a massively embarassingly parallel computation, again not something you would need or want a supercomputer for. You mainly need disk IO to/from the source reads and the mapping table you produce.
    - dnhz 6y ago
      The description of RNA-seq analysis spans nearly the entire paragraph, by my interpretation and limited understanding of the methods. > RNA-Seq analysis was performed using the latest version of the human transcriptome (GRCh38_latest_rna.fna, 160,062 transcripts to which we appended the SARS-CoV-2 reference genome, MN908947). Mapping parameters were set with a mismatch cost of two, insertion and deletion cost of three, and both length and similarity fraction were set to 0.985. TPMs were generated for all 160,063 transcripts for the nine COVID-19 samples and the 40 controls (Supplementary file 2). The resulting transcript mappings for genes of interest were manually inspected to account for any expression artifacts, such as reads mapping solely to repetitive elements such as the Alu transposable element or all reads mapping to a UTR or pseudogene therein. Transcripts whose counts came solely from (or were dominated by) reads at repetitive elements were removed from the analysis. For the controls cases we ran an outlier analysis using the prcomp function in the R package factoextra. Input data were TPM for transcripts that averaged greater than one across all samples (30,102, Supplementary file 2).
    - mimd 6y ago
      If it's simple alignment like blast, they don't need the interconnect. But supercomputers provide all the other necessary needs: fast processors, connected nodes, accelerators, massive I/O bandwidth. Why build a cluster without a fast interconnect to complement those features? That's just turning down research customers. And 15% on top of the other hardware is nothing. The main costs is all the power, custom software, and support staff. And not all bioinformatics is ops on strings with minimal message passing. For these guys, it's likely their best and only option. They probably weren't given the money to build a cluster optimized for their needs or to maintain a cloud instance. Why? Its oakridge, their main priority is HPC physics. It's hard to argue when you have access to such a HPC center. And HPC sites need all the customers they can get, lest their clusters get shoved into the cloud. It's a real fear. They'd end up with hidden costs, data lockin, and poor interconnects. To help pay for those peak massive simulations, traditional HPC need to fill up that last 10-15% and bioinformatics needs most of what they offer. Perhaps all that hard won knowledge will rub off on the burgeoning field too. :) A vision of bio-oriented HPC is IU's clusters. Though they have a shiny new cray shasta with ampere and slingshot, several of their other clusters are 10 gigs with high mem nodes. All connected to the same storage too. The hospital is the main customer and dictates their designs. Ditto on the paper though. It's what I disliked about Bioinformatics. All the glory to the researchers designing the experiments and they can't even bother to mention what software they used.

by btilly 6y ago

R handles statistics. It does not simulate complex biochemical interactions in different parts of the body. From the description, they did something that requires a lot more horsepower.

dekhn 6y ago

I read the underlying article (https://elifesciences.org/articles/59177 https://elifesciences.org/articles/59177) and was unable to find any evidence of that. In fact the paper doesn't mention anything about SUmmit or details on the computations :( There certainly wasn't any "heavy biochemical calculations"; this work is entirely comparative genomics, so just operating on DNA strings. I see this fluff article https://www.ornl.gov/blog/genomics-code-exceeds-exaops-summit-supercomputer https://www.ornl.gov/blog/genomics-code-exceeds-exaops-summi... and there may be more detail here: https://www.hpcuserforum.com/presentations/april2019/JoubertORNL.pdf https://www.hpcuserforum.com/presentations/april2019/Joubert... which shows near-linear performance they ascribe to "Made possible by aggressive communication overlap and low-congestion Mellanox Infiniband fat tree network with adaptive routing" So there may actually be an HPC/supercomputer story in there, but I'm having trouble figuring out what they did in this most recent work.

rrss 6y ago
I can't find details either, but I think they just used R for the post-processing, and there's a lot of computation behind this sentence: > RNA-Seq analysis was performed using the latest version of the human transcriptome I found this article discussing read mapping for RNA sequence analysis: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833417/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833417/ > In particular, RNA sequencing (RNA-seq) technology,1 which provides a comprehensive profile of a transcriptome, is increasingly replacing conventional expression microarrays.2 Primary data processing in RNA-seq (as well as in other massive sequencing experiments, including genome resequencing) involves mapping reads onto a reference genome. This step constitutes a computationally expensive process in which, in addition, sensitivity is a serious concern
- dekhn 6y ago
  What good is a paper if their methods are a single sentence? Ugh. Reads mapping is a massively embarassingly parallel computation, again not something you would need or want a supercomputer for. You mainly need disk IO to/from the source reads and the mapping table you produce.
  - dnhz 6y ago
    The description of RNA-seq analysis spans nearly the entire paragraph, by my interpretation and limited understanding of the methods. > RNA-Seq analysis was performed using the latest version of the human transcriptome (GRCh38_latest_rna.fna, 160,062 transcripts to which we appended the SARS-CoV-2 reference genome, MN908947). Mapping parameters were set with a mismatch cost of two, insertion and deletion cost of three, and both length and similarity fraction were set to 0.985. TPMs were generated for all 160,063 transcripts for the nine COVID-19 samples and the 40 controls (Supplementary file 2). The resulting transcript mappings for genes of interest were manually inspected to account for any expression artifacts, such as reads mapping solely to repetitive elements such as the Alu transposable element or all reads mapping to a UTR or pseudogene therein. Transcripts whose counts came solely from (or were dominated by) reads at repetitive elements were removed from the analysis. For the controls cases we ran an outlier analysis using the prcomp function in the R package factoextra. Input data were TPM for transcripts that averaged greater than one across all samples (30,102, Supplementary file 2).
  - mimd 6y ago
    If it's simple alignment like blast, they don't need the interconnect. But supercomputers provide all the other necessary needs: fast processors, connected nodes, accelerators, massive I/O bandwidth. Why build a cluster without a fast interconnect to complement those features? That's just turning down research customers. And 15% on top of the other hardware is nothing. The main costs is all the power, custom software, and support staff. And not all bioinformatics is ops on strings with minimal message passing. For these guys, it's likely their best and only option. They probably weren't given the money to build a cluster optimized for their needs or to maintain a cloud instance. Why? Its oakridge, their main priority is HPC physics. It's hard to argue when you have access to such a HPC center. And HPC sites need all the customers they can get, lest their clusters get shoved into the cloud. It's a real fear. They'd end up with hidden costs, data lockin, and poor interconnects. To help pay for those peak massive simulations, traditional HPC need to fill up that last 10-15% and bioinformatics needs most of what they offer. Perhaps all that hard won knowledge will rub off on the burgeoning field too. :) A vision of bio-oriented HPC is IU's clusters. Though they have a shiny new cray shasta with ampere and slingshot, several of their other clusters are 10 gigs with high mem nodes. All connected to the same storage too. The hospital is the main customer and dictates their designs. Ditto on the paper though. It's what I disliked about Bioinformatics. All the glory to the researchers designing the experiments and they can't even bother to mention what software they used.