Alachiotis, Nikolaos

Low, Tze Meng

Pavlidis, Pavlos

English

PubMed

University of Twente Research Information

Editorial: Scalable Bioinformatics:Methods, Software Tools, andHardware ArchitecturesNikolaos Alachiotis1*, Tze Meng Low2 and Pavlos Pavlidis31Faculty of EEMCS, University of Twente, Enschede, Netherlands, 2Department of Electrical and Computer Engineering, CarnegieMellon University, Pittsburgh, PA, United States, 3Foundation for Research and Technology-Hellas, Heraklion, GreeceKeywords: autism spectrum disorder, basic local-alignment search tool, BLAST, SMUFIN, copy number variations,SARS–CoV–2Editorial on the Research TopicScalable Bioinformatics: Methods, Software Tools, and Hardware ArchitecturesAdvances in DNA sequencing technology have contributed to the accumulation of molecularsequence data at an unprecedented pace, since whole genomes can now be sequenced rapidly,accurately, and cost effectively. When methods and tools are not specifically designed to handle bigvolumes of data efficiently, large-scale analyses practically become infeasible due to the explosion inprocessing and memory requirements. Bioinformatics algorithms frequently rely on approximationsand heuristics to yield computationally tractable implementations, at the cost of performing lessthorough analyses. This Research Topic presents a series of works that connect computationalproblems in the fields of Bioinformatics and Computational Biology with software and hardwaresolutions from the fields of Computer Science and Computer Engineering to address scalability issuesacross a variety of Bioinformatics problems.The Basic Local-Alignment Search Tool, BLAST (Altschul et al., 1990), is one of the mostwidely used algorithms to search for sequence similarities in Bioinformatics. Gálvez et al.introduce BLVector, a heuristic algorithm that adapts high-level concepts of BLAST+ tomany-core x86 architectures with Single-Instruction Multiple Data (SIMD) vectorinstructions of the Advanced Vector eXtensions (AVX)-512 instruction set. BLVectoroutperforms BLAST+ for mid-size protein sequences (∼750 amino acids), and retrieves amuch larger set of results than BLAST+ when applied to longer proteins, at the cost of a longerexecution time. BLVector can be up to an order of magnitude faster than BLAST+ on variousmany-core processor architectures, and the authors suggest that BLVector and BLAST+ can beconsidered as complementary tools.Autism spectrum disorder (ASD) is a neurodevelopmental disorder that has been extensivelystudied over the past decades (Cox et al., 1999; Marshall et al., 2008; Ozonoff and Iosif, 2019).Garbulowski et al. developed an analysis pipeline to construct interpretable machine learningmodels and performed an analysis of multiple cohorts of control-case studies of Autism SpectrumDisorder (ASD). The analysis revealed that autism is the most severe subtype of ASD, while pervasivedevelopmental disorder-not otherwise specified (PDD-NOS) and Asperger syndrome are closelyrelated and milder subtypes of ASD. Additionally, the authors analyzed the most important ASD-related features described in terms of gene co-predictors, finding a strong co-predictive mechanismand possible co-regulation between genes EMC4 and TMEM30A. This study showcases one moreapplication of Machine Learning and outlines its potential in providing insights into importantmedical and biological questions while encouraging the deployment of other techniques such as deeplearning (LeCun et al., 2015) as well.Edited and reviewed by:Richard D. Emes,University of Nottingham,United Kingdom*Correspondence:Nikolaos Alachiotisn.alachiotis@utwente.nlcomSpecialty section:This article was submitted toComputational Genomics,a section of the journalFrontiers in GeneticsReceived: 26 November 2021Accepted: 10 December 2021Published: 05 January 2022Citation:Alachiotis N, Low TM and Pavlidis P(2022) Editorial: ScalableBioinformatics: Methods, SoftwareTools, and Hardware Architectures.Front. Genet. 12:822986.doi: 10.3389/fgene.2021.822986Frontiers in Genetics | www.frontiersin.org January 2022 | Volume 12 | Article 8229861EDITORIALpublished: 05 January 2022doi: 10.3389/fgene.2021.822986Analyzing a patient’s genetic data is the first step toward precisionmedicine. Cadenelli et al. present SMUFIN-F, a memory-efficientapproach to perform mutation detection on commodity personalcomputers with a small amount of DRAM. The analysis of somaticmutations is essential to the study and treatment of cancer. Theauthors modified SMUFIN (Moncunill et al., 2014; Cadenelli et al.,2017), the leading algorithm for detecting somatic mutations, tooperate on big data structures that reside in secondary storageinstead of DRAM. The authors show that when SMUFIN-Fallocates 16 times less memory than SMUFIN, for the sameproblem size, it is only 1.24x slower than SMUFIN. Based on theobservation that two commodity PCs running SMUFIN-F deliverthe same throughput as an enterprise server running SMUFIN, theauthors estimate that SMUFIN-F achieves the same performance atonly 36% of the capital cost and 45% of the operational cost (energy).The exponential growth of genome sequences available hasspurred research on pattern detection with the aim to extractevolutionary signals. Pechlivanis et al. describe a novelcomputational framework for identifying potentially meaningfulfeatures based on k-mers retrieved from unaligned sequence data.The framework employs unsupervised machine learning to identifycharacteristic k-mers of the input dataset across a range of differentk-values and within a reasonable time frame. The authors appliedtheir approach on 8,693 SARS-CoV-2 genomes and identifiedk-mers at the nucleotide level, from which they constructed anevolutionary tree. Furthermore, integration with populationdemographic and chronological metadata led to the identificationof unique clusters and time correlated features among the availablesequences and k-mers. The results can be beneficial for a betterunderstanding of the genetic diversity of SARS-CoV-2.Copy number variations (CNVs) (Freeman et al., 2006; Redonet al., 2006) are important genomic structural variations that arewidespread in the human genome and cause a variety of complexdiseases. Huang et al. developed an improved method calledCNV-MEANN (CNV detection of neural network based on mindevolutionary algorithm). CNV-MEANN introduces a new featurecalled mapping quality to evaluate whether the mapping positioncan be trusted. In addition, it considers the influence of the losscategories of CNV on disease prediction and deploys a mindevolutionary algorithm to optimize the backpropagation (neuralnetwork) neural network model while calculating individualscores for each genome bin to predict CNVs. CNV-MEANwas tested with both simulated and real datasets, andoutperformed other methods with respect to sensitivity,precision, and F1-score. Furthermore, CNV-MEAN was ableto detect CNVs that were not detected by other approaches.Scalable solutions and high performance are imperative inthe fields of Bioinformatics, and the COVID-19 pandemicbrought Bioinformatics into the spotlight, revealing thatseveral existing methods, algorithms, and tools were notwell prepared to handle large amounts of genomic dataefficiently. Performant and memory-aware solutions arerequired to ensure that future computing systems will beable to keep up with the molecular data avalanche. ThisResearch Topic highlights the need for an interdisciplinaryapproach to address scalability issues in Bioinformatics withthe hope of paving the way for fruitful future collaborationsbetween researchers from these fields.AUTHOR CONTRIBUTIONSAll authors listed have made a substantial, direct, and intellectualcontribution to the work and approved it for publication.REFERENCESAltschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990). Basic LocalAlignment SearchTool. J.Mol. Biol.215, 403–410. doi:10.1016/s0022-2836(05)80360-2Cadenelli, N., Polo, J., and Carrera, D. (2017). Accelerating K-Mer FrequencyCounting with GPU and Non-volatile Memory in 2017 IEEE 19th InternationalConference on High Performance Computing (HPCC). Bangkok. 18-20 Dec.2017. IEEE, 434–441. doi:10.1109/HPCC-SmartCity-DSS.2017.57Cox, A., Klein, K., Charman, T., Baird, G., Baron-Cohen, S., Swettenham, J., et al.(1999). Autism Spectrum Disorders at 20 and 42 Months of Age: Stability ofClinical and ADI-R Diagnosis. J. Child Psychol. Psychiatry 40, 719–732.doi:10.1111/1469-7610.00488Freeman, J. L., Perry, G. H., Feuk, L., Redon, R., McCarroll, S. A., Altshuler, D. M.,et al. (2006). Copy Number Variation: New Insights in Genome Diversity.Genome Res. 16, 949–961. doi:10.1101/gr.3677206LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep Learning.Nature 521, 436–444.doi:10.1038/nature14539Marshall, C. R., Noor, A., Vincent, J. B., Lionel, A. C., Feuk, L., Skaug, J., et al.(2008). Structural Variation of Chromosomes in Autism Spectrum Disorder.Am. J. Hum. Genet. 82, 477–488. doi:10.1016/j.ajhg.2007.12.009Moncunill, V., Gonzalez, S., Beà, S., Andrieux, L. O., Salaverria, I., Royo, C., et al.(2014). Comprehensive Characterization of Complex Structural Variations inCancer by Directly Comparing Genome Sequence Reads. Nat. Biotechnol. 32,1106–1112. doi:10.1038/nbt.3027Ozonoff, S., and Iosif, A.-M. (2019). Changing Conceptualizations of Regression:what Prospective Studies Reveal about the Onset of Autism Spectrum Disorder.Neurosci. Biobehavioral Rev. 100, 296–304. doi:10.1016/j.neubiorev.2019.03.012Redon, R., Ishikawa, S., Fitch, K. R., Feuk, L., Perry, G. H., Andrews, T. D., et al.(2006). Global Variation in Copy Number in the Human Genome. Nature 444,444–454. doi:10.1038/nature05329Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.Publisher’s Note: All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations, or those ofthe publisher, the editors and the reviewers. Any product that may be evaluated inthis article, or claim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Copyright © 2022 Alachiotis, Low and Pavlidis. This is an open-access articledistributed under the terms of the Creative Commons Attribution License (CC BY).The use, distribution or reproduction in other forums is permitted, provided theoriginal author(s) and the copyright owner(s) are credited and that the originalpublication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply withthese terms.Frontiers in Genetics | www.frontiersin.org January 2022 | Volume 12 | Article 8229862Alachiotis et al. Editorial: Scalable Bioinformatics

Editorial: Scalable Bioinformatics:Methods, Software Tools, and Hardware Architectures

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Editorial: Scalable Bioinformatics:Methods, Software Tools, and Hardware Architectures

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