Interview with author Chris Chang. User Google group.
If one estimates a user population of ~1000, each saving of order $1000 in CPU/work time per year, then in the next few years PLINK 1.9 and its successors will deliver millions of dollars in value to the scientific community.
Second-generation PLINK: rising to the challenge of larger and richer datasets
Background
PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format.
Findings
To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(n‾√)-time/constant-space Hardy-Weinberg equilibrium and Fisher’s exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0).
Conclusions
The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
2 comments:
"... these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM" :-)
Can someone please set this man up on a date with Miranda Kerr? We need these genes to proliferate in the gene pool.
https://petitions.whitehouse.gov/
Can we petition Obama to make it happen?
Not only is it faster, but it actually works, which is as common as not in bioinformatics.
When I need to do something, I always try to find a way to make plink do it. This isn't just to keep the number of different programs used in pipelines low, but I can be pretty confident it works. The speed is a nice bonus, of course. And Chang has been incredibly quick to come up with patches for those edge cases that are off, less than 24 hours every single time afaicr.
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