Abstract
BackgroundThe identification of all matches of a large set of position weight matrices (PWMs) in long DNA sequences requires significant computational resources for which a number of efficient yet complex algorithms have been proposed.ResultsWe propose BLAMM, a simple and efficient tool inspired by high performance computing techniques. The workload is expressed in terms of matrix-matrix products that are evaluated with high efficiency using optimized BLAS library implementations. The algorithm is easy to parallelize and implement on CPUs and GPUs and has a runtime that is independent of the selected p-value. In terms of single-core performance, it is competitive with state-of-the-art software for PWM matching while being much more efficient when using multithreading. Additionally, BLAMM requires negligible memory. For example, both strands of the entire human genome can be scanned for 1404 PWMs in the JASPAR database in 13 min with a p-value of 10−4 using a 36-core machine. On a dual GPU system, the same task can be performed in under 5 min.ConclusionsBLAMM is an efficient tool for identifying PWM matches in large DNA sequences. Its C++ source code is available under the GNU General Public License Version 3 at https://github.com/biointec/blamm.
Highlights
The identification of all matches of a large set of position weight matrices (PWMs) in long DNA sequences requires significant computational resources for which a number of efficient yet complex algorithms have been proposed
The Basic linear algebra subprograms (BLAS)-accelerated motif matching (BLAMM) algorithm is based on the brute-force algorithm in the sense that it exhaustively computes all PWM scores for all PWMs at each starting position of the input sequence(s)
It does so with much higher efficiency by expressing all computations through matrix-matrix products (MMPs). It is well-known that MMPs are among a select class of algorithms that can be evaluated on modern, cache-based CPUs with a performance that approaches the theoretical peak performance of the CPU
Summary
We propose BLAMM, a simple and efficient tool inspired by high performance computing techniques. The workload is expressed in terms of matrix-matrix products that are evaluated with high efficiency using optimized BLAS library implementations. The algorithm is easy to parallelize and implement on CPUs and GPUs and has a runtime that is independent of the selected p-value. In terms of single-core performance, it is competitive with state-of-the-art software for PWM matching while being much more efficient when using multithreading. Both strands of the entire human genome can be scanned for 1404 PWMs in the JASPAR database in 13 min with a p-value of 10−4 using a 36-core machine. On a dual GPU system, the same task can be performed in under 5 min
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