Applications and Challenges of Real-timeMobile DNA Analysis

Steven Y. KoComputer Science and Engineering

University at Bu�alostevko@bu�alo.edu

Lauren SassoubreCivil, Structural, and Environmental Engineering

University at Bu�alolsassoub@bu�alo.edu

Jaroslaw Zola0

Computer Science and EngineeringBiomedical InformaticsUniversity at Bu�alo

jzola@bu�alo.edu

AbstractThe DNA sequencing is the process of identifying the exact orderof nucleotides within a given DNA molecule. The new portableand relatively inexpensive DNA sequencers, such as OxfordNanopore MinION, have the potential to move DNA sequencingoutside of laboratory, leading to faster and more accessibleDNA-based diagnostics. However, portable DNA sequencing andanalysis are challenging for mobile systems, owing to high datathroughputs and computationally intensive processing performedin environments with unreliable connectivity and power.

In this paper, we provide an analysis of the challengesthat mobile systems and mobile computing must address tomaximize the potential of portable DNA sequencing, and in situDNA analysis. We explain the DNA sequencing process andhighlight the main di�erences between traditional and portableDNA sequencing in the context of the actual and envisionedapplications. We look at the identi�ed challenges from theperspective of both algorithms and systems design, showing theneed for careful co-design.

1 IntroductionDNA, a polymer made from four basic nucleotides (abbreviatedby A, C, G, T), is the main carrier of the genetic information.The DNA sequencing is the process in which this informationis extracted by converting physical DNA molecules into a signalthat describes the exact order and type of the constituentnucleotides. The ability to sequence DNA has revolutionizedmolecular biology, biomedicine and life sciences in general.Among many applications, some of which we review in Section 2,it is recognized as a critical method for diagnosing and improvinghuman health (e.g. dissecting genetic mechanisms of cancer [19]),identifying pathogens and protecting public health (e.g. detectingand tracking spread of infectious diseases [7]) or understandingour environment (e.g. impact of microorganisms on water,air and soil [18]).

The end-to-end DNA sequencing and analysis involves a

0To whom correspondence should be addressed.

combination of laboratory and bioinformatics steps (see Section 3).In a traditional setup, these steps are performed at massivescales by highly trained personnel using expensive benchtop DNAsequencers and supporting computational servers. Consequently,the process has been con�ned to high-end laboratories with�nancial resources and skilled personnel, limiting the access andextending the time it takes from sample collection to results.

The recently introduced portable DNA sequencers, speci�callyOxford Nanopore Technology (ONT) MinION [20] (see Fig. 1),are changing this situation. Compared to the traditional DNAsequencers, these devices are relatively inexpensive and trulymobile: smaller than a cell phone, USB powered, and designed tobe easily operable “in the �eld.” Moreover, they use biochemicalprinciples (i.e. nanopore-based single molecule sequencing [14])that enable near real-time streaming of the raw signal as soon asthe DNA molecules are “sensed,” usually within minutes from theprocess initiation. As a result, portable DNA sequencing emergesas a rapid in situ diagnostic tool, especially when DNA samplesare di�cult or impossible to preserve or transport. Examplesinclude the DNA surveillance of Ebola and Zika during the recentoutbreaks in Africa [17] and Brazil [6], or successful deploymentsin the Arctic [4] and Antarctic [9], in rainforests of Ecuador [15],and even on the International Space Station [3].

However, portable DNA sequencing and analysis arechallenging for mobile systems. This is because the underlyingcomputations, as well as data and communication intensive

46

106

Fig. 1: MiniSeq DNA sequencer from Illumina (left), vs. portableMinION (middle) and forthcoming SmidgION (right). Picturesnot to scale. The approximate size in centimeters is marked forreference. The current price ofMinION is $1,000 compared to $50Kfor an entry-level benchtop sequencer.

1

arX

iv:1

711.

0737

0v1

[cs

.CE

] 1

7 N

ov 2

017

operations, have to be balanced to ensure the desired quality ofthe analysis while running in real-time, typically in the energyand bandwidth constrained environments. Currently, mobileDNA sequencing is driven by the bioinformatics tools designedprimarily for the desktop systems, and organized into mobilework�ows in an ad hoc manner (see Section 4). While thesesolutions have been successful in the initial trials, they cannotbe expected to scale if the underlying problems of processingspeed, energy e�ciency and resilience are not addressed. Asthe technology behind portable DNA sequencing matures andbecomes more accessible, it is reasonable to assume that it will beadopted by individual consumers. Consequently, the underlyingalgorithms and software will have to be able to operate in thewide range of conditions, under di�erent loads, and with varyingresources, while remaining easy to use.

In this paper, we discuss the challenges that mobile systemsand mobile computing must address to maximize the potentialof portable DNA sequencing, and in situ DNA processing. Ouranalysis is guided by the current and envisioned applications ofDNA sequencing. We look at the identi�ed challenges from theperspective of both algorithms and systems design, and argue forcareful co-design and functionality separation. We note that thepaper is written from the systems perspective, for readers with noprior background in genomics or bioinformatics.

2 Seqencing ApplicationsWe begin with a discussion of general applications that highlightthe enabling nature of the portable DNA sequencing.

Sequencing When Time is Critical: Rapid diagnosis ofinfectious diseases is critical for protecting human health [7].The DNA sequencing can be used to identify the infectiousagent, assess its responsiveness to vaccines or antibiotics, andprescribe the best treatment. In some diseases, starting the righttreatment within hours is vital [2, 8], and when responding toepidemics, real-time genomic surveillance increases situationalawareness (e.g. by tracking evolution rate and transmissionpatterns), helping with planning and resource allocation [6, 17].Importantly, the same principles apply in detection and mitigationof biological threats [22]. However, the current culture-basedlaboratory methods for identifying pathogens take days, and infact some pathogens are di�cult or impossible to grow in culture.

Because emerging portable DNA sequencers are free ofthese limitations, they o�er signi�cant advantage when time iscritical. One excellent example is the response to the recentEbola epidemics, where mobile laboratory based on MinIONs,transported in a standard airplane luggage, was deployed inGuinea [17]. Despite logistic di�culties, such as lack ofcontinuous electric power and poor Internet connectivity, thelaboratory became operational within two days, was generatingresults in less than 24 hours from receiving a sample, and providedvaluable insights into disease dynamics.

Sequencing When Location is Critical: The samples weknow the least about, and would like to study by DNA sequencing,are usually located far from established sequencing facilities. Thisis especially true for metagenomic studies in which communities

of microbial organisms are sampled directly from their nativeenvironments, to characterize their structure and function [18].However, many types of samples cannot be transported due tothe legal and international export barriers, or other practicalconsiderations (e.g. cost e�ectiveness). Furthermore, sequencingin laboratory does not allow for on-site iterative surveillance, inwhich sampling decisions are made in real-time. Such approachesare important when studying rapidly changing environments.

Portable DNA sequencers reached the level where they canbe operated in some of the most demanding environments.For example, in one recent study, a battery-only poweredlaboratory, consisting of MinION and ad hoc cluster of two laptopswithout Internet connection, was harnessed for in situ analysisof microorganisms found in the 100 meters deep South WalesCoal�eld [5]. Although the entire process was far from simple,the study demonstrated that DNA sequencing in remote locationsis currently feasible. Other studies [3, 9, 15] serve as furtherproof of principle.

Future: With the continuing improvements to the sequencingtechnology, and simpli�cation and automation of DNA extractionand preparation protocols (see next section), we may expectthat portable DNA analysis will become a ubiquitous tool.Rapid medical diagnostics, forensics, agriculture, and generalexploration of microbial diversity on Earth and in outer space,are just some domains that will bene�t. However, the mostexciting opportunities are in consumer genomics. ONT has beenpromoting the idea of Internet of Living Things (IoLT) [13, 23],where anyone will be able to sequence anything anywhere,opening endless possibilities for DNA-driven discoveries. Yet,because even short DNA sequencing runs can easily delivergigabytes of data, which may require hours to analyze, the successof IoLT will depend on the ability of mobile and cloud computingto provide adequate support.

3 Portable vs. BenchtopIn order to understand computational challenges in portableDNA sequencing, it helps to �rst look at the end-to-endDNA sequencing process, and how this process di�ers betweenthe current benchtop sequencing platforms and the emergingportable sequencers.

3.1 How DNA is StudiedA typical DNA sequencing work�ow involves both laboratory andcomputational steps (see Fig. 2 and Tab. 1). While the speci�csof the protocols executed in every step may vary, the main stepsremain the same irrespective of the DNA sequencing platform.

Sample Collection: The �rst step is to obtain the materialfrom which DNA will be extracted. The choice and quantity ofmaterial to sample, and the actual number of samples, are dictatedby the particular application. Sample types include patient’s blood(e.g. in epidemiology), feces (e.g. when studying gut microbial�ora), water or soil (e.g. when tracking biological contaminants),etc. The samples are preserved, e.g. by freezing or addinga chemical bu�er, to minimize degradation or contaminationuntil they can be further processed. As we mentioned earlier,

2

DNA Reads ProcessingDNA SequencingLibrary PreparationDNA IsolationSample Collection

Laboratory Computational

Fig. 2: The major steps in a typical DNA sequencing work�ow.

Table 1: General characteristics of di�erent steps in DNA sequencing.

Library Preparation DNA Sequencing DNA Reads ProcessingMinION Illumina MinION Illumina MinION Illumina

Time Scale Minutes-hours Hours-days Real-time,Minutes-hours

Days-weeks Real-time,Minutes-hours

Batch-mode,Minutes-days

Equipment Basic, portable laboratory equipment(e.g. pipe�es, centrifuge)

USB-stick-sizeportable device

Large benchtopmachine

Laptop to data center

Energy 1-2W 1W Up to 10KW No reliable analysis available,sustained high load

So�ware N/A Proprietary firmware, drivers,and control so�ware

Usually open source, complex stringand statistical algorithms

Advantages Fast and easyprotocol

Protocols for verylow DNA mass

Portable, real-time,long reads,inexpensive device

Low cost per base,low error rate

Streamingalgorithms and interactivesequencing

Many testedworkflowsavailable

Challenges High massof input DNA

Time and laborintensive

High error rate,high cost per base

Short reads,expensive device

High error rate Short read length,high data volume

sometimes samples may be impossible to adequately preserve,necessitating immediate processing.

DNA Isolation: In this step, the collected samples aresubjected to chemical, mechanical or thermal processing to extractand purify the DNA molecules. DNA extractions performed ina laboratory with the standard commercially available kits takefrom minutes to hours, and in addition to the basic tools likepipettes, involve heating/cooling and specialized equipment, e.g.a centrifuge. Consequently, this step requires the access to powersupply, or the use of improvised solutions.

Library Preparation: The puri�ed DNA is further processed,to make it compatible with the sensing machinery of thesequencer. This usually involves basic biochemical processingthat nevertheless may require complex protocols. The stepbecomes even more di�cult, when, to lower the cost ofsequencing, instead of whole genome DNA, only speci�c DNAregions (e.g. corresponding to known marker genes) are tobe sequenced, or if multiple samples are to be sequencedconcurrently in a single run. Overall, the entire librarypreparation takes from several minutes to several days, and withthe exception of targeted DNA sequencing, does not requireadditional equipment beyond the portable laboratory tools.

DNA Sequencing: Once the DNA library is ready, the actualsequencing can be performed. Currently, several sequencingplatforms are available, for example Illumina, PacBio, Ion Torrentor Oxford Nanopore. They di�er in how DNA is detected andread, which translates into di�erences in: sequencing speed andthroughput (i.e. the number of nucleotides detected per unit oftime), length of the output reads (i.e. how long DNA fragmentssequencer can sense), and error rate (i.e. how many incorrectly

detected nucleotides one may expect in the output). Thesedi�erences are crucial, since they directly a�ect the downstreamprocessing and analysis (see e.g. [16]). The current sequencers arecontrolled by computers, which alse receive and store output data.With the exception of the MinION, they are not portable, takingdays to complete a single sequencing run in laboratory. Finally,the sequencing process involves additional consumable resources,such as biochemical reagents and �ow cells – devices in which theactual DNA sequencing happens.

DNA Reads Processing: The �nal step is purelycomputational, and its goal is to �rst convert signal producedby a sequencer into DNA reads, and then analyze these readsfor insights. Because of the volatility of DNA, and the technicallimitations of the sequencing platforms, DNA is hard to sequenceas a single large molecule (e.g. a chromosome). Instead, it issequenced in fragments that the sequencer is able to sense. Theraw signal produced for each detected DNA fragment is runthrough base calling algorithms to generate DNA reads – theactual strings where each detected nucleotide is represented byits corresponding letter, commonly referred as base (A – adenine,C – cytosine, G – guanine, T – thymine).

The collected DNA reads are the input to bioinformaticsanalysis. Here di�erent work�ows can be applied, dependingon how samples had been prepared for sequencing, andwhat are the questions of interest. For example, de novoDNA assembly aims at reconstructing genome from inputreads [16], while metagenomic analysis uses DNA reads todetect, classify, and functionally annotate microorganisms presentin the sequenced samples [18]. However, irrespective ofthe applied analysis, the common denominator is the reliance

3

on compute and memory intensive string, combinatorial andstatistical algorithms, ranging from massive graphs constructionand traversal [16], through clustering [25], to large databasesquerying [11]. Consequently, this step requires access tonon-trivial computational resources, often exceeding capabilitiesof a single laptop or even a desktop computer.

3.2 Comparison

To compare portable and benchtop sequencing we concentrateon the MinION, currently the only portable DNA sequencingtechnology, and Illumina platform, the dominating benchtopsolution. We make our comparison in the context of LibraryPreparation, DNA Sequencing, and DNA Reads Processing, sincethese steps vary between platforms. Table 1 summarizesour comparison.

Library Preparation: The attractiveness of the MinION isin rapid sequencing protocol that can be executed in the �eldfor genomic DNA sequencing. The protocol takes roughly10 min, and can be automated by using portable hands-o� samplepreparation hardware [21]. However, compared to more timedemanding protocols, it has limitations: it usually leads to lowerquality sequencing output (i.e. with higher error rates), andit requires signi�cant amount of input DNA (∼240 ng). Incomparison, protocols for the Illumina platform, while much morecomplex and labor intensive (fastest take ∼90 min, and most takehours), can be performed with an order of magnitude less DNAthan MinION, without impacting sequencing quality.

DNA Sequencing: The MinION is based on the idea ofnanopore sequencing, where DNA molecules pass throughorganic nanoscale sensors in a �ow cell [14]. This approachhas three main advantages: �rst, it permits portable and easyto use design with a minimal power consumption, second, itenables real-time sequencing – the signal gathered by hundredsof nanopores in a sequencer becomes immediately available fordownstream processing, third, it can produce long reads (currentaverage is ∼7K bases compared to ∼250 bases for Illumina), andtheoretically this length is limited by the physical size of DNAfragments. All this comes at the price of high error rate (anywherebetween 10%-30% compared to ∼0.1% for Illumina), which maylead to poor sequencing yield and complicates downstreamanalysis. Finally, because of the lower throughput, the costof sequencing a single base is higher compared to benchtopsequencers (if we exclude the required upfront investments). Thisis in part because the MinION �ow cells last for at most 48h ofcontinuous sequencing.

DNA Reads Processing: The DNA reads produced inreal-time by MinION allow for �exible approach to bioinformaticsanalysis, with the emphasis on streaming and one-pass strategiesexecuted outside of data centers. This makes “interactive”sequencing possible, where the process can be terminated as soonas su�cient DNA reads have been collected to answer questions athand. Long reads simplify tasks such as querying of databases orDNA assembly, but the high error rate makes certain analyses (e.g.variants detection) extremely challenging, or requires more inputreads to resolve uncertainties. In contrast, the Illumina platform

is high-throughput and high-volume oriented. The data from asingle batch run is typically processed by well established, andto some extent standardized, work�ows executed in a data centerclose to the sequencing facility. Low error rate combined withhigh DNA reads volume, makes tasks such as variants detectionpossible. However, short reads force complex algorithms onother tasks, e.g. assembly.

4 Mobile Computing PerspectiveWe are now ready to highlight some of the open problems inportable DNA analysis as pertaining to mobile computing. Tohelp understand the problems, we �rst provide overview thestate of the art in mobile DNA analysis software. Then wediscuss the limitations and open problems. While we base ourdiscussion on the current MinION platform, we believe that thepoints we make are equally applicable to the future generationof portable sequencers.

4.1 Current Software OverviewFigure 3 shows the general MinION software architecture. The�rst component is MinKNOW – the proprietary control andsignal acquisition software suite. MinKNOW is responsiblefor the con�guration and supervision, including initiation andtermination, of sequencing runs. It also receives and stores DNAsignals generated by the ASIC in the sequencer’s �ow cell.

The second component is base calling software. Here multipleoptions are available [24], including execution on the host deviceor delegation to a specialized cloud service (e.g. Metrichor [12]).The current basecallers are very compute intensive, e.g. theytypically involve Recurrent Neural Nets (RNNs), and most of thetime are recommended to run in the cloud.

The �nal software component consists of the applicationspeci�c bioinformatics tools selected by end-user. Here,bioinformatics and computational biology deliver very broadrange of open source solutions to choose from, and new solutionsare constantly being developed. Usually, these tools are organizedinto a pipeline of their own, and may involve multiple processingstages (e.g. preprocessing to remove low-quality data, querying adatabase to �nd sequences matching given DNA read, building atree of generic relationship, etc.). Depending on the complexity,this DNA analytics may be deployed on the host machine, butmore frequently it will be running in the cloud.

4.2 LimitationsThe software architecture discussed above has been alreadyused with success to showcase the promise of portable DNAsequencing. However, the approaches thus far focused onmanual and ad hoc organization of the existing bioinformaticstools to perform mobile DNA analysis, without addressing manyimportant concerns, including a systematic approach to energy,data and network management. While this is a great �rst step, inthe long run this strategy has too many limitations to be scalable.

Energy Management: Energy is one of the most criticalresources in mobile environments. It is especially important formobile DNA analysis, since a DNA sequencing device is directly

4

Proprietary MinKNOWRunning on local device

Device ControlOpen source base callersRunning on local deviceor in cloud

Base CallingOpen source workflowsRunning on local deviceor in cloud

Downstream ProcessingBased on real-time inputs from downstream processing

End-user AnalysisRaw signal DNA reads Processing report

stored inlocal FS

stored or send back to local FS

GUI or CLI

Terminate or continue sequencing, or move to the next experiment

Fig. 3: Current MinION software work�ow.

attached to, and draws energy from, a host device. Moreover,computational tasks executed at di�erent stages of the sequencingwork�ow can run for tens of minutes to hours. However,the current software tools do not have any mechanisms toconsider energy as a manageable resource. In fact, bioinformaticssoftware tools are routinely designed under the assumption thatthey will be executing either on computational servers or indata centers, with abundant main memory and storage, parallelexecution capabilities, and stable power supply.

Data Management: DNA sequencer generates large volumesof data, which �ows through various processing stages. Forexample, a 48h continuous run may produce up to ∼250 GB ofoutput and ∼20 GB of temporary data, scattered across millions of�les. Furthermore, if a cloud backend is used for the analysis, dataneeds to be transferred back and forth between a mobile deviceand the backend.

Unfortunately, data management is currently doneindependently by each software component. This puts anunnecessary burden on software designers – they need toimplement not only core functionality (e.g. a base callingalgorithm) but also data management logic (e.g. sending databetween a mobile device and a cloud, ensuring interoperabilitywith other software, etc.). In addition, this makes it di�cult todeploy new data management mechanisms rapidly, as they haveto be integrated into multiple and disjoint software elements.

Network Management: Some of the most promising andanticipated applications of mobile DNA analysis are in situprocessing scenarios, where DNA has to be completely handledat a remote location. In such scenarios, network connectivitycould be sporadic and bandwidth could highly �uctuate. However,the current software is not designed to be adaptive to changingnetwork conditions. It assumes either no network connectivity,and hence runs locally, or depends on full network connectivity,and hence assumes the always-on availability of a cloud service.Moreover, the decision to run locally or remotely is left to the enduser, who must decide before executing the experiment.

Consumables Management: As we mentioned earlier, a �owcell is the workhorse of a sequencer. It is a consumable that can beused to analyze only a limited number of DNA samples, and withina limited time. Moreover, a �ow cell degrades over time, andthat translates into progressively lower sequencing throughputand potentially growing error rate. Consequently, in truly mobilesetups it is necessary to manage �ow cells as a scarce resource.While this problem has been recognized, currently no systematicsolution exists that would o�er the necessary functionality.

4.3 Open Problems and Proposed Approach

The majority of the limitations we identi�ed above, arecross-cutting issues that involve multiple software componentsin the current work�ow design. For example, all softwarecomponents should have some form of data management, energymanagement, and network management; in order to implement anew solution that addresses a limitation for any one of these, weneed to work with multiple software components and apply thesolution across all of the components. This is time consuming anderror prone, and thus hinders rapid innovation.

To address this challenge, we envision a new softwarearchitecture that identi�es all necessary functions and separatesthem into di�erent software components with clean interfaces toensure interoperability. Speci�cally, we propose the architecturebased on three elements: the data management layer, the DNAanalytics layer, and the work�ow manager. This architecturalseparation has the bene�t of allowing di�erent components toinnovate independently from other components. It also has anadvantage of simplifying software development, by allowing eachcomponent to focus on its core functionality. At the same time, itallows to accommodate the existing software, especially rich andgrowing set of bioinformatics tools.

Data Management Layer: The goal of having a separatedata management layer is to free other software componentsfrom the burden of managing data on their own. Thus, thedata management layer should provide all functionality relatedto managing DNA sequencing and analysis data. This includes1) an interface for other components to read and store data,including the back compatibility support for the POSIX interfacethat the existing tools use for �at �les, 2) e�cient algorithmsand mechanisms for data management, including discovery,monitoring and delivery, and 3) integration with cloud servicesfor processing delegation and data backup.

Interesting questions arise for the design of a data managementlayer in all three aspects. First, for the clean slate interface design,the primary question is what kind of abstractions make the mostsense for DNA sequencing and analysis. As mentioned earlier,there are mainly three types of data – raw signals generatedby a sequencer, DNA reads (strings), and analysis results (e.g.stored as data tables). Thus, perhaps the most natural interfacedesign is to have an abstraction for each data type. Such designwould allow other components to easily search, access, and whenneeded join data without dealing with low-level details such as�le management.

Second, once the interface is �xed, the underlyingimplementation can freely employ various mechanisms to

5

manage data. For example, it is well known that DNA sequencingand analysis produce a large amount of data. It is also known thatDNA has much inherent redundancy due to small alphabet (onlyfour letters) and its repetitive nature. Thus, the data managementlayer can employ some compression strategies. However, it is anopen question as to how best to compress this data consideringthe trade-o�s between computation cost, computation precisionand constrained storage inherent to mobile systems. The existinggeneral strategies, as well as many DNA-speci�c methods thattake into account DNA quality and prior knowledge of sequencedgenomes [1], may work well, but it remains to be seen whichstrategy is practical in a mobile setup.

Lastly, often times DNA sequencing and analysis data needs tobe shipped back and forth between a mobile device and a cloudservice, for further analysis. As discussed earlier, base callingrequires extensive RNNs and it is recommended to run it in thecloud. Similarly, DNA analysis is often times computationallyintensive and requires much computational power and access tolarge reference databases. Thus, the data management layer needsalgorithms and mechanisms to optimize data transfer. Here again,the existing techniques, such as similarity detection and dynamicchunking, may or may not work well.

DNA Analytics Layer: Ideally, the DNA analytics layershould have multiple sub-components, each implementing onealgorithm relevant to DNA sequencing and analysis. This includesbase calling algorithms, as well as the DNA reads processingalgorithms. The goal of this layer is to allow algorithm designersto solely focus on algorithms and their implementations withoutworrying about other orthogonal issues, such as data or energymanagement.

Interesting questions arise if we consider that DNA processingcan leverage both mobile devices and cloud services at the sametime. This provides an opportunity to revisit current solutionsand redesign them such that they become amenable to runningin both domains, or in either one of the domains. In fact, wecan envision a programming model to simplify implementationof such strategies. The model could provide primitives toencapsulate alternative realizations of the same DNA processingtask as small migratable entities, allowing them to move acrossmobile and cloud domains or run in parallel if necessary. Itcould be further extended to account for the fact that certainDNA processing problems can be answered with di�erent quality,trading speci�city or sensitivity for computational, memory orenergy performance. For instance, one of the most generalquestions a user may wish to ask is “what’s in my pot” [10, 11],which is to report all known organisms whose DNA has beenfound in a metagenomic sample, and hence sequenced. Thequestion can be answered by classifying detected organisms atthe species level (i.e. �ne-grain assignment) or just at the familylevel (i.e. coarse-grain assignment). The �ne-grain assignmentwill typically require large reference databases and computeintensive sequence comparison algorithms. However, the processcan be accelerated by using e.g. data abbreviation techniquesand clever indexing schemes at the cost of lower sensitivity andprecision. By supporting such multiple task realizations of the

same DNA analysis problem, the model would permit for more�exible execution paths. For example, in cases where resourcesare scarce, “approximate” tasks could provide less precise butpotentially useful information, instead of waiting until resourcesare su�cient to deliver the detailed answer.

One additional advantage of having tasks-based analytics layeris the ability to easily deploy work�ows with stream processingand speculative execution capabilities (the techniques known toimprove resource utilization). We note that many of the existingalgorithms, especially involving querying of reference databases,can be cast into this model with little or no e�ort, some other(e.g. construction of DNA assembly graphs, clustering, etc.) wouldrequire additional research and reformulation.

Work�ow Manager: The goal of the work�ow manager isto orchestrate all aspects of the DNA sequencing and analysiswork�ow, taking into account multiple static and dynamic factorsthat a user might encounter during a sequencing experiment.Many of the limitations that we discussed earlier fall intothis category. Energy consumption, network connectivity,bandwidth variation, �ow cell degradation, etc. all contribute todynamically changing conditions of an experiment. Hence, thework�ow manager should carefully monitor these variables andcontinuously make decisions on what the best course of actionsis. This leads to several design questions. For example, how tomonitor an experiment including not only the basic propertiesof a mobile device, e.g. how much energy is left or what is thecurrent network condition, but also status of a �ow cell and thequality of reads it is producing. Currently, very limited resourcesare available regarding �ow cell monitoring, which we believe isan interesting research opportunity.

Once we have monitoring capabilities, the second questionis how best to utilize available resources to get a desiredoutcome. This is especially important since a user conductinga DNA analysis in the �eld often has to make critical decisionsbased on limited information. For example, suppose a user isconducting DNA analysis in a remote area where there is nonetwork connectivity. The user might wonder if she has enoughenergy to �nish pending DNA analysis on her laptop and usethe resulting data to adjust her experiment (e.g. collect moresamples, etc.), or if she should move to an area where thereis network connectivity to o�oad the analysis to a cloud andthen continue experiment. The work�ow manager should eitherassist users to make well-informed decisions, or be intelligentenough to make decisions on its own, without requiring anyuser intervention. In cases where DNA analysis algorithms areamenable for partitioning or migrating across di�erent domains,the work�ow manager could make �ne-grained decisions ofmoving various computational tasks across domains directlyleveraging capabilities exposed by the Data Management Layerand DNA Analytics Layer.

5 Final RemarksThe proposed architecture is our attempt to introduce a moresystematic and scalable approach to mobile DNA analytics,by tapping into concepts known from edge computing. One

6

potential caveat is that the proposed architecture would requirereimplementation of some of the existing bioinformatics tools tofully leverage facilities in the DNA Analytics Layer. However, webelieve this is feasible considering that portable sequencers arerelatively new technology, with almost no algorithms designedfor mobile systems. Currently, our team uses the latest release ofMinION to investigate the questions we pose in this paper in thecontext of environmental DNA analysis.

References[1] M.C. Brandon, D.C. Wallace, and P. Baldi. 2009. Data

Structures and Compression Algorithms for Genomic SequenceData. Bioinformatics 25, 14 (2009), 1731–1738.

[2] M.D. Cao, D. Ganesamoorthy, A.G. Elliott, et al. 2016. StreamingAlgorithms for Identi�cation of Pathogens and AntibioticResistance Potential from Real-time MinION sequencing.GigaScience 5, 1 (2016).

[3] S.L. Castro-Wallace, C.Y. Chiu, K.K. John, et al. 2016. NanoporeDNA Sequencing and Genome Assembly on the International SpaceStation. bioRxiv (2016). h�ps://doi.org/10.1101/077651

[4] A. Edwards, A.R. Debbonaire, B. Sattler, L.AJ. Mur, and A.J. Hodson.2017. Extreme Metagenomics Using Nanopore DNA Sequencing: AField Report From Svalbard, 78 N. bioRxiv (2017). h�ps://doi.org/10.1101/073965

[5] A. Edwards, A. Soares, S. Rassner, et al. 2017. Deep sequencing:Intra-terrestrial Metagenomics Illustrates the Potential of O�-gridNanopore DNA Sequencing. bioRxiv (2017). h�ps://doi.org/10.1101/133413

[6] N.R. Faria, E.C. Sabino, M.R.T. Nunes, et al. 2016. Mobile Real-timeSurveillance of Zika Virus in Brazil. Genome Medicine 8, 1 (2016).

[7] J.L. Gardy and N.J. Loman. 2017. Towards a Genomics-informed,Real-time, Global Pathogen Surveillance System. Nature ReviewsGenetics (2017), nrg.2017.88.

[8] F.C. Hewitt, S.L. Guertin, K.L. Ternus, et al. 2017. Toward RapidSequenced-based Detection and Characterization of CausativeAgents of Bacteremia. bioRxiv (2017). h�ps://doi.org/10.1101/162735

[9] S.S. Johnson, E. Zaikova, D.S. Goerlitz, et al. 2017. Real-Time DNASequencing in the Antarctic Dry Valleys Using the Oxford NanoporeSequencer. Journal of Biomolecular Techniques 28, 1 (2017).

[10] S. Juul, F. Izquierdo, A. Hurst, et al. 2015. What’s in My Pot?Real-time Species Identi�cation on the MinION. bioRxiv (2015).h�ps://doi.org/10.1101/030742

[11] D. Kim, L. Song, F.P. Breitwieser, and S.L. Salzberg. 2016. Centrifuge:Rapid and Sensitive Classi�cation of Metagenomic Sequences.Genome Research 26, 12 (2016).

[12] Metrichor Ltd. 2017. Metrichor, an Oxford Nanopore Company.h�ps://metrichor.com/. (2017).

[13] Metrichor Ltd. 2017. Metrichor Ltd: For an Internet of Living Things.h�ps://youtu.be/xFPWlAwTQ8w. (2017).

[14] H. Lu, F. Giordano, and Z. Ning. 2016. Oxford Nanopore MinIONSequencing and Genome Assembly. Genomics, Proteomics &Bioinformatics 14, 5 (2016).

[15] A. Pomerantz, N. Pena�el, A. Arteaga, et al. 2017. Real-time DNABarcoding in a Remote Rainforest Using Nanopore Sequencing.bioRxiv (2017). h�ps://doi.org/10.1101/189159

[16] M. Pop. 2009. Genome Assembly Reborn: Recent ComputationalChallenges. Brie�ngs in Bioinformatics 10, 4 (2009).

[17] J. Quick, N.J. Loman, S. Dura�our, et al. 2016. Real-time, PortableGenome Sequencing for Ebola Surveillance. Nature 530, 7589 (2016).

[18] C. Quince, A.W. Walker, J.T. Simpson, et al. 2017. ShotgunMetagenomics, From Sampling to Analysis. Nature Biotechnology35, 9 (2017).

[19] M.R. Stratton, P.J. Campbell, and P.A. Futreal. 2009. The CancerGenome. Nature 458, 7239 (2009).

[20] Oxford Nanopore Technologies. 2017. Oxford Nanopore.h�ps://nanoporetech.com. (2017).

[21] Oxford Nanopore Technologies. 2017. VolTRAX.h�ps://nanoporetech.com/products/voltrax. (2017).

[22] M.C. Walter, K. Zwirglmaier, P. Vette, et al. 2017. MinION as Part of aBiomedical Rapidly Deployable Laboratory. Journal of Biotechnology250 (2017).

[23] E. Waltz. 2017. Portable DNA Sequencer MinION Helps Build theInternet of Living Things. IEEE Spectrum (2017).

[24] R.R. Wick, L.M. Judd, and K.E. Holt. 2017. Comparison ofOxford Nanopore Basecalling Tools. h�ps://github.com/rrwick/Basecalling-comparison. (2017).

[25] X. Yang, J. Zola, and S. Aluru. 2011. Parallel Metagenomic SequenceClustering via Sketching and Maximal Quasi-clique Enumeration onMap-Reduce Clouds. In IEEE International Parallel and DistributedProcessing Symposium.

7