Challenges in Life Sciences data management and cloud enabled collaboration

Barry Wark, Ph.D.Founder and President, Physion

barry@physion.usTwitter @barryjwark

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Barry Wark

PyXG

Voyeur

Symphony

The nature of scientific research has changed, challenging the fundamentals of the scientific method

Life scientists need solutions that help them bridge local needs with global resources

Think globally, act locally

The nature of scientific research has changed fundamentally

‣Data volume• High-content screening: desktop confocal

can image 25,000 samples per day

• Human genome $5000, and falling fast

• IonWorks Barracuda® can perform 6,000 whole-cell patch clamp experiments per hour

‣Data variety• “Coherent” data sets (e.g. Sage, Personal

Genome Project)

• Behavior, anatomy, physiology, genomics experiments on the same subject

‣ Analytical tools• Central computing resources, elastic

provisioning

• Open source software democratizes contribution and distribution

‣Teams• Experimental and analytical specialization

• Research cores and consortia

• Distributed across organizations and institutions

Biology is a context dependent system. Studying context dependence requires lots of data.

Pipelined data flow through computational resources

Researcher dataset Analyst Result/Report

Data that is not easily pipelined doesn’t get incorporated

Researcher dataset Analyst Result/Report

Researcher dataset

Researcher dataset

Not scalable

Analysis provenance that transits individual researchers is hard to track

Researcher dataset Analyst Result/Report

Researcher dataset

Researcher dataset

Researcher dataset Analyst Result/Report

Comprehensive data management must span the entire data lifecycle

Com

plex

ity/C

ost

Paper notebook

ELN

Analytical tools

Enterprise SDMS

Data lifecycle stage

Acquisition Analysis

Figshare

OSF

Comprehensive data management must span the entire data lifecycle

Com

plex

ity/C

ost

Paper notebook

ELN

Analytical tools

Enterprise SDMS

Data lifecycle stage

Acquisition Analysis

Figshare

OSF

Ovation

Ovation’s data model describes science

Ovation is built to represent the language of science. Scientific data, regardless of discipline, fits this model.

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Music, in the language of the domain expert. May include margin notes, etc.

Lab notebook representation

Computer representation in the language of the domain expert (including “margin notes” from composer, conductor, etc.). Any genre

of music is representable.

Ovation representation

Analogous example shows that representing music in the appropriate language of the domain provides an appropriate data model

Ubiquitous data model is the correct granularity for knowledge transfer

Ovation’s data model is more granular than an ELN. Instead of loosing information during conversion to (and from) a report format such as a Word document or PDF, Ovation allows data to be transferred in the natural language and granularity of science.

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Data transferred directly

Information lost in transfer

Seamless collaboration and data transfer removes information bottlenecks

Analogous example shows that transferring data via a “report” (a sound recording) produces an information bottleneck

Common data model enables collaboration

Interoperability across institutional boundaries is easier with Ovation than other solutions. Unlike ad-hoc or customized data management systems, every Ovation customer uses the same data model.

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Data transfer via Ovation data model

Individual researcher Collaborators Global

community

The Ovation data model for subject definition

Project

Experiment

SubjectProtocol

EpochProcedure

Subject{ species : Drosophila melanogaster, father : 79326326-9CC0-4770-8DC6-3695113C7A64, mother : A2D40CFF-3016-41AE-AC67-BB09A7D8D9E1}

The Ovation data model for measurements

Protocol

Subject

MeasurementDataElement

EquipmentSetup

Project

Experiment

EpochProcedure

The Ovation data model for analysis provenance

MeasurementDataElement

MeasurementDataElement

MeasurementDataElement

AnalysisRecord

AnalysisRecord DataElement

Optionally named

Optionally named

Named

Ovation architecture

CouchDBhidden

CouchDBvisible

Objectstorage

Local file cache

http://ovaiton.ioACL ACL

Cloudant

Ovation uses eventual consistency

X2 Y1 X2’ Y1 X1 Y1

Ovation chooses availability and partition tolerance over consistency

(so you can work from the coffee shop)

X1 X1

Client 1 Client 2 Cloud

X2 X2’X1 X1

Ovation uses eventual consistency

Y1 Y1 X1 Y1

Client 1 Client 2 Cloud

This means conflicting edits can be made by disconnected clients

Append-only (mostly) and user-isolated changes at the edge of the object graph minimize these conflicts.

X2’X2

X2’X2 X3X2 X2’X3 X3

Ovation uses eventual consistency

Ovation requires users to resolve conflicts that they have authority to decide during sync.

Y1 Y1 Y1

Client 1 Client 2 Cloud

Ovation Scientific Data Management System®

• Comprehensive data management

• Multi-modality

• Multi-user annotation

• Analysis provenance

• Seamless user experience

• Double-click installation

• Integration with existing tools: Matlab, Python, R, Java

• Guide to success

• Effective collaboration

• Distributed and co-located experts

• Data ownership maintained

• Cloud-based replication and archiving

Integrated analysis workflow

OrganizeAcquire Search Analyze

%% Run a simple queryiterator = context.query('Epoch', ' ...criteria... ');

while(iterator.hasNext()) currEpoch = itrator.next(); ...analyze currEpoch...end

Analysis pipelines that begin with a search, facilitate automatic incorporation of new results

Integrated analysis workflow

OrganizeAcquire

Search Analyze

Acquire Organize

Replication technology allows Ovation to replicate a subset of the database for data locality within a computational cluster.

Execute workflows on a local or cloud cluster

context = NewDataStoreCoordinator('username', password).getContext();epochs = context.query(context.getQuery('query-name'));

%% analysis parametersparams = struct();params.MaxLag = 1000; % time window for cross-correlation functionparams.ResponseDelayPts = 0; % exclude at end of modulated lightparams.MinAnalysisEpochs = 3;params.FrequencyCutoff = 500;params.FlushData = 1;

%% ANALYZE AND COLLECT RESULTS

====> ORIGINAL ANALYSIS CODE HERE <====

%% save analysis record for this figure ar = project.insertAnalysisRecord('Figure 1’, epochs, 'AnalysisFunction.m', params, svnRevision, svnURL); ar.setUserDescription('Manuscript - Figure 1');ar.addTag(<manuscript>);ar.addOutput('Figure 1a’, './Figure1a.pdf');ar.addResource('Figure 1b’, './Figure1b.pdf');

Share data in context

Trial

Stimulus Response

DerivedResponse

name: spikesparameters: {…}code: spikes.m

ovation:///f694d05a-131b-4644-aa7c-f6e8934e60c0/

Trial

Stimulus Response

DerivedResponse

name: spikesparameters: {…}code: spikes.m

Share data in context

Project

Experiment

Source

Trial Group

Trial Trial

Stimulus Response

DeviceExperiment

Trial

Stimulus Response

DerivedResponse

name: spikesparameters: {…}code: spikes.m

Ovation enables researchers to extract more knowledge from existing data

• Lab’s lifetime work was enough data to answer fundamental questions about signal and noise in the early visual system

• Data was locked in individual’s ad-hoc data management• Ovation enabled meta-analysis of this existing data• New graduate students start with the old data, not new experiments

“Ovation has changed the way we do science…” —Fred Rieke

if the active lifetime of rhodopsin is pro-portional to the time required for eachphosphorylation event, and the transduc-tion cascade acts linearly to convert theactivity of rhodopsin to a change in cur-rent. Based on the above assumptions, wedetermined the ratios of !0/" and #0/$that best fit the integrals of the single-photon responses in Arr1!/", GRK1!/",and GRK1!/"Arr1!/" rods (Table 2). Be-cause of the third assumption, the esti-mated ratios represent average valuesacross different phosphorylation events(i.e., unphosphorylated rhodopsin, singlyphosphorylated rhodopsin, etc). The fit-ting procedure is not ensured of providinga close correspondence between modeland experiment because the model hastwo free parameters (!0/" and #0/$) andis fit to experimentally determined val-ues (the response areas) of each of thethree mutants relative to wild type.Nonetheless, the model accounted forthe measured response areas within theexperimental accuracy (Table 2).

Ratios of !0/" #6 and #0/$ #8 mini-mized the mean-square error betweenmodel and experiment. These rate con-stants make two predictions about thephosphorylation process in wild-typerods (! $ !0 and # $ #0) under the con-ditions of our experiments. First, activerhodopsin spends #85% of its timebound to arrestin1 (Fig. 1B, reaction 1),and only #15% of the time is available forGRK1 binding. The large fraction of timerhodopsin spends interacting with arrestin1is a requirement for arrestin competition tocontrol the effective GRK1 binding rate.Second, GRK1 binding is rapid comparedwith phosphate attachment (#0 % $).This latter observation can explain whyarrestin competition was revealed more ro-bustly when GRK1 binding was slowed by re-ducing the GRK1 concentration.

Implications of arrestin competition for single-photonresponse variabilityThe single-photon responses of rod photoreceptors showmuch less trial-to-trial variability than other signals generatedby single molecules (Baylor et al., 1979), such as the chargeflowing through an ion channel during a single opening or thesignal generated by the binding of an odorant molecule to itscognate GPCR (Bhandawat et al., 2005). Several results indi-cate that variability in rhodopsin shutoff rather than down-stream components of the phototransduction cascadedominates variability in the single-photon response (Riekeand Baylor, 1998; Doan et al., 2006). The model most consis-tent with experimental observations is that Rh* shuts offthrough a series of steps (Rieke and Baylor, 1998; Field andRieke, 2002a; Hamer et al., 2003; Doan et al., 2006; Bisegna etal., 2008). One salient aspect of the measured responses is thatmost of the variability in the single-photon response occurs

well after the response reaches peak (Rieke and Baylor, 1998;Field and Rieke, 2002a; Hamer et al., 2003). This late varianceis inconsistent with a short Rh* lifetime (Rieke and Baylor,1998; Hamer et al., 2003; Krispel et al., 2006), which shouldcause the responses to vary in amplitude but not in shape.

The low and late variability of the single-photon responses aresignatures of the underlying molecular events regulating Rh* ac-tivity (Field and Rieke, 2002a; Hamer et al., 2003). We used thesecharacteristics in the context of the arrestin competition hypoth-esis to test how altering the time constants of known events in Rh*shutoff affects reproducibility and to resolve the apparent conflictbetween the late time-dependent variance and the short Rh* life-time reported previously (Krispel et al., 2006). The experiments andanalyses described below indicate that, under the conditions of ourexperiments, arrestin competition tunes the kinetics of rhodopsinshutoff to minimize variability and that the active lifetime of rho-dopsin persists through much of the single-photon response.

Figure 5. The time-dependent variance of the single-photon responses in wild-type, Arr1!/", GRK1!/", andGRK1!/"Arr1!/" rods. Left column superimposes 10 isolated single-photon responses from a wild-type and a Arr1!/"

rod (A), a GRK1!/" rod (B), and a GRK1!/"Arr1!/" rod (C). Right column compares the squared mean (thin trace) andthe time-dependent variance (thick trace) of wild-type rods (gray; n $ 29) with Arr1!/" (red; n $ 41) rods (A), GRK1!/"

(blue; n $ 30) rods (B), and GRK1!/"Arr1!/" (green; n $ 40) rods (C). The responses in each cell were normalized by theamplitude and time-to-peak of the average single-photon response of the cell to facilitate comparison of the time courseof the variance.

11874 • J. Neurosci., September 23, 2009 • 29(38):11867–11879 Doan et al. • Arrestin Competition and Rhodopsin Inactivation

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Our vision: living data sets

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et al., 2001; Smirnakis et al., 1997; Baccus and Meister, 2002;Kim and Rieke, 2001). Here we focus on the dynamics of theslow component of adaptation.

Contrast and Luminance AdaptationExhibit Multiple TimescalesDynamics of Adaptation to Temporal ContrastTo determine if the dynamics of contrast adaptation depend onstimulus history, we measured responses to a periodic switchbetween low- and high-contrast stimuli. As described below,the dynamics of adaptation following an increase in contrastdepended on the stimulus switching period.

Figures 1A and 1B show the inhibitory postsynaptic currents inan OFF-transient RGC elicited by a single cycle of a stimulus thatswitched between low and high contrast with period of 16 s(Figure 1A) or 32 s (Figure 1B). When averaged across trialswith different instantiations of the random contrast stimulus,both the mean (Figures 1C and 1D) and r.m.s synaptic input(Figure S1 available online) decreased over the course of severalseconds following the increase in contrast. The slow relaxationof the mean and r.m.s. current following an increase in contrastindicates a change in the gain with which light inputs are con-verted to RGC synaptic inputs—i.e., variations in the light inputshortly after the step produce larger responses than thoseseveral seconds later. This slow adaptation caused the meanresponse to decline to 64% ± 6% (mean ± SEM, n = 41) of itsinitial peak.

The trajectories of the mean responses in Figures 1C and 1Dfollowing an increase in contrast appear different; this suggeststhat the dynamics of adaptation depended on stimulus switchingperiod. To quantify this dependence, we fit the mean inputcurrent with an exponential, I(t) = Ae!(t!D)/t + c, where t is theeffective adaptation time constant, c is an offset, and D allowsfor the delay in the cell’s response (red lines in Figures 1Cand 1D). Response delay was typically 250–500 ms under theconditions tested. For cells in which the input currents were

nonrectified, the r.m.s. current was fit with the same function.The exponential amplitude A and baseline c did not changesignificantly as a function of the switching period (not shown).

Figure 1E shows the population average time constant asa function of period. The average effective time constant ofadaptation scales approximately linearly across a broad rangeof switching periods ("8–32 s). The observed scaling fails forshort periods but extends to the longest period (T = 32 s) thatwe could measure reliably. A similar relationship was observedwhen comparing the time constant of an exponential fit to onlythe first 8 s of 8, 16, and 32 s periods (not shown). Thus the effectis not simply the result of fitting an exponential to a nonexponen-tial response over varying time windows. These results indicatethat a fixed first-order process does not govern the dynamicsof contrast adaptation in mouse retina. Instead, the adaptingmachinery has access to multiple timescales.Dynamics of Adaptation to LuminanceTo test the generality of multiple-timescale dynamics of adapta-tion, we measured responses to periodic changes in mean lightintensity (luminance). As for contrast adaptation, the dynamics ofadaptation following an increase in luminance depended on thestimulus switching period.

Figures 2A and 2B show responses to a single presentation ofa periodic luminance step lasting 3.2 or 6.4 s. Figures 2C and 2Dshow average responses to many repetitions of the luminancestep with different instantiations of the random additive noise.The mean synaptic current following a change in luminanceshows an initial rapid transient component followed by a slowersecond component. The r.m.s. current had a similar trajectory,indicating an adaptive change in response properties(Figure S1). The first component of the mean response is pre-dicted by the (biphasic) linear impulse response function of thecell (not shown) and is thus unrelated to adaptation; the kineticsof this component did not depend on the switching period. Wetherefore focused on the slow component of the response.During this part of the response, the mean current declined to

Figure 1. The Time Course of Adaptation following an Increase in Temporal Contrast Depends on the Period between Contrast Switches(A and B) Inhibitory synaptic current to an OFF-transient RGC (holding potential 10 mV) in response to a single switch in stimulus contrast (6%–36%,

mean "400 R*/rod/s; red). The switching period was 16 s in (A) and 32 s in (B).

(C and D) Mean synaptic currents from approximately 100 trials as in (A) and (B). Exponential fits to the response following an increase in contrast are shown in red.

(E) Population-averaged (n z 10 for each period) time constant (mean ± SEM) of the exponential fit to the response following an increase in contrast (6%–36%) for

all RGC types (ON, OFF-sustained, OFF-transient, and ON-OFF) as a function of stimulus switching period.

Neuron

Inference in Visual Adaptation

Neuron 61, 750–761, March 12, 2009 ª2009 Elsevier Inc. 751

et al., 2001; Smirnakis et al., 1997; Baccus and Meister, 2002;Kim and Rieke, 2001). Here we focus on the dynamics of theslow component of adaptation.

Contrast and Luminance AdaptationExhibit Multiple TimescalesDynamics of Adaptation to Temporal ContrastTo determine if the dynamics of contrast adaptation depend onstimulus history, we measured responses to a periodic switchbetween low- and high-contrast stimuli. As described below,the dynamics of adaptation following an increase in contrastdepended on the stimulus switching period.

Figures 1A and 1B show the inhibitory postsynaptic currents inan OFF-transient RGC elicited by a single cycle of a stimulus thatswitched between low and high contrast with period of 16 s(Figure 1A) or 32 s (Figure 1B). When averaged across trialswith different instantiations of the random contrast stimulus,both the mean (Figures 1C and 1D) and r.m.s synaptic input(Figure S1 available online) decreased over the course of severalseconds following the increase in contrast. The slow relaxationof the mean and r.m.s. current following an increase in contrastindicates a change in the gain with which light inputs are con-verted to RGC synaptic inputs—i.e., variations in the light inputshortly after the step produce larger responses than thoseseveral seconds later. This slow adaptation caused the meanresponse to decline to 64% ± 6% (mean ± SEM, n = 41) of itsinitial peak.

The trajectories of the mean responses in Figures 1C and 1Dfollowing an increase in contrast appear different; this suggeststhat the dynamics of adaptation depended on stimulus switchingperiod. To quantify this dependence, we fit the mean inputcurrent with an exponential, I(t) = Ae!(t!D)/t + c, where t is theeffective adaptation time constant, c is an offset, and D allowsfor the delay in the cell’s response (red lines in Figures 1Cand 1D). Response delay was typically 250–500 ms under theconditions tested. For cells in which the input currents were

nonrectified, the r.m.s. current was fit with the same function.The exponential amplitude A and baseline c did not changesignificantly as a function of the switching period (not shown).

Figure 1E shows the population average time constant asa function of period. The average effective time constant ofadaptation scales approximately linearly across a broad rangeof switching periods ("8–32 s). The observed scaling fails forshort periods but extends to the longest period (T = 32 s) thatwe could measure reliably. A similar relationship was observedwhen comparing the time constant of an exponential fit to onlythe first 8 s of 8, 16, and 32 s periods (not shown). Thus the effectis not simply the result of fitting an exponential to a nonexponen-tial response over varying time windows. These results indicatethat a fixed first-order process does not govern the dynamicsof contrast adaptation in mouse retina. Instead, the adaptingmachinery has access to multiple timescales.Dynamics of Adaptation to LuminanceTo test the generality of multiple-timescale dynamics of adapta-tion, we measured responses to periodic changes in mean lightintensity (luminance). As for contrast adaptation, the dynamics ofadaptation following an increase in luminance depended on thestimulus switching period.

Figures 2A and 2B show responses to a single presentation ofa periodic luminance step lasting 3.2 or 6.4 s. Figures 2C and 2Dshow average responses to many repetitions of the luminancestep with different instantiations of the random additive noise.The mean synaptic current following a change in luminanceshows an initial rapid transient component followed by a slowersecond component. The r.m.s. current had a similar trajectory,indicating an adaptive change in response properties(Figure S1). The first component of the mean response is pre-dicted by the (biphasic) linear impulse response function of thecell (not shown) and is thus unrelated to adaptation; the kineticsof this component did not depend on the switching period. Wetherefore focused on the slow component of the response.During this part of the response, the mean current declined to

Figure 1. The Time Course of Adaptation following an Increase in Temporal Contrast Depends on the Period between Contrast Switches(A and B) Inhibitory synaptic current to an OFF-transient RGC (holding potential 10 mV) in response to a single switch in stimulus contrast (6%–36%,

mean "400 R*/rod/s; red). The switching period was 16 s in (A) and 32 s in (B).

(C and D) Mean synaptic currents from approximately 100 trials as in (A) and (B). Exponential fits to the response following an increase in contrast are shown in red.

(E) Population-averaged (n z 10 for each period) time constant (mean ± SEM) of the exponential fit to the response following an increase in contrast (6%–36%) for

all RGC types (ON, OFF-sustained, OFF-transient, and ON-OFF) as a function of stimulus switching period.

Neuron

Inference in Visual Adaptation

Neuron 61, 750–761, March 12, 2009 ª2009 Elsevier Inc. 751

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Analysis provenance that transits individual researchers is hard to track

Researcher dataset Analyst Result/report

Researcher dataset

Researcher dataset

Researcher dataset Analyst Result/report

Ovation enables integration of non-pipeline data, and comprehensive analysis provenance

Researcher dataset Analyst Result/report

Researcher dataset

Researcher dataset

Researcher dataset Analyst Result/report

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