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DiBELLA 1.0 Overview

DiBELLA is a distributed-memory scalable long-read to long-read overlap and alignment pipeline.

The code is not only useful as an application-benchmark for high-performance computing research,
but also for direct analysis of long read datasets (producing k-mer histograms, overlaps detected
with frequency-filtered k-mers*, and read-to-read alignments), for inclusion or extension
into full long read de novo assembly pipelines, for extension/modification for similar genomics
applications or application-benchmarks, among other purposes.

The copyright notice is below and in LICENSE.txt, which also contains the license agreement.
The first of DiBELLA's 3-stage pipeline, the k-mer analysis stage, is a modification and
extension of HipMer's k-mer analysis stage [3] - see ThirdPartyLicenses/HipMer_License.txt.

*DiBELLA's methodology for detecting overlaps and computing alignments, at a high-level,
follows BELLA's [2], with user-specified k-mer frequency ranges and alignment parameters
for more general use-cases.

Related Publications (Please cite [1] or [4] if this code is used for publication)

[1] Ellis, Marquita, et al. "diBELLA: Distributed long read to long read alignment."
Proceedings of the 48th International Conference on Parallel Processing. 2019.
[2] Guidi, Giulia, et al. “BELLA:Berkeley Efficient Long-Read to Long-Read Aligner and Overlapper”
bioRxiv, p. 464420, 2020.
[3] Georganas, Evangelos, et al. "HipMer: an extreme-scale de novo genome assembler."
SC'15: Proceedings of the International Conference for High Performance Computing,
Networking, Storage and Analysis. IEEE, 2015.
[4] Ellis, Marquita, et al. "Parallelizing Irregular Applications for Distributed Memory
Scalability: Case Studies from Genomics." Diss. University of California, Berkeley, 2020.
[5] Yelick, Katherine, et al. "The parallelism motifs of genomic data analysis."
Philosophical Transactions of the Royal Society A 378.2166 (2020): 20190394.

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• M.M. Ellis (admin)