PRECOG: a tool for automated extraction and visualization of fitness components in microbial ...

15 Pages·2016·2.95 MB·English

Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249
DOI10.1186/s12859-016-1134-2
SOFTWARE Open Access
PRECOG: a tool for automated extraction
and visualization of fitness components in
microbial growth phenomics
Luciano Fernandez-Ricaud1, Olga Kourtchenko2, Martin Zackrisson3, Jonas Warringer3,4 and Anders Blomberg1*
Abstract
Background: Phenomics is a fieldin functional genomics that recordsvariation in organismal phenotypes in the
genetic, epigenetic or environmentalcontext ata massive scale. For microbes, the key phenotype is thegrowth in
population size because it contains informationthat is directly linked to fitness. Dueto technical innovations and
extensive automationour capacity to record complexand dynamic microbial growth data is rapidly outpacing our
capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress inall
fields ofmicrobiology.
Results: Toautomate visualization, analysis and exploration of complex and highly resolved microbialgrowth data
as well as standardized extraction of the fitness components it contains, we developed thesoftware PRECOG
(PREsentation and Characterization Of Growth-data). PRECOG allowsthe user to quality control, interactwith and
evaluate microbial growth data with ease, speed and accuracy, also incases ofnon-standard growth dynamics.
Quality indices filterhigh- from low-quality growth experiments,reducing false positives.The pre-processing filters
in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network
procedures. We provide examples where data calibration, project design and feature extraction methodologies
haveaclearimpactontheestimatedgrowthtraits,emphasisingtheneedforproperstandardizationindataanalysis.
Conclusions:PRECOGisatoolthatstreamlinesgrowthdatapre-processing,phenotypictraitextraction,visualization,
distributionandthecreationofvastandinformativephenomicsdatabases.
Keywords:Phenomics,Yeast,growth,Datapre-processing,Fitnesscomponents,Automation,Datapresentation
Background continuous expansion of data storage, analysis, and visua-
Thanks to recent technological innovations we can now lization capabilities, something that has resulted in the
detect and assess traits on virtually all phenotypic levels, development of an increasing number of specialized
from molecular to population level phenotypes, with un- phenotype databases dedicated to the most important
precedentedspeed.Theseadvancementshavespurredthe model organisms [2–7].
emergenceofphenomics, thefieldinfunctionalgenomics Microbes dominate the biosphere and microbial phe-
that is dedicated the cataloguing of variation in pheno- nomics therefore has a key role in functional genomics
types as a function of variation in genetic, epigenetic and [1]. A wide array of techniques have been introduced to
environmental factors [1]. From a modest origin in docu- addressdifferentaspectsofthemicrobialphenome[8–21],
mentingqualitativetraitswithmoderatethroughput,phe- but the growth of microbial populations and microbial
nomics has evolved to precisely record quantitative traits growth phenomics takes centre stage because of its intim-
with an astounding degree of parallelization. The massive atelinktomicrobialfitnessandevolution.Microcultivation
accumulationof quantitativephenotypic datarequiresthe inliquidmediumoffershighresolution,butonlymoderate
throughput,inthesurveyingofgrowthinmicrobialpopu-
*Correspondence:[email protected] lation size [1]. Recently, we and others have shown that
1DepartmentofMarineSciences,LundbergLaboratory,Universityof
microcultivationofmicrobialcoloniesonsolidmediumof-
Gothenburg,Medicinaregatan9c,41390Göteborg,Sweden
Fulllistofauthorinformationisavailableattheendofthearticle fers both high-resolution and high-throughput estimation
©2016TheAuthor(s).OpenAccessThisarticleisdistributedunderthetermsoftheCreativeCommonsAttribution4.0
InternationalLicense(http://creativecommons.org/licenses/by/4.0/),whichpermitsunrestricteduse,distribution,and
reproductioninanymedium,providedyougiveappropriatecredittotheoriginalauthor(s)andthesource,providealinkto
theCreativeCommonslicense,andindicateifchangesweremade.TheCreativeCommonsPublicDomainDedicationwaiver
(http://creativecommons.org/publicdomain/zero/1.0/)appliestothedatamadeavailableinthisarticle,unlessotherwisestated.
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page2of15
of microbial population size growth [22–24]. Expectations extracts fitness components, and presents both processed
are that this will lead to an explosion in microbial growth dataseries,i.e.thegrowthcurves,andtheextractedfitness
phenomics data and make activities in bioinformatics like componentsinamannerthatallowseasyuser-interaction,
storage,analysisandvisualizationoftheaccumulatedinfor- exploration, and evaluation. PRECOG is designed to han-
mation a critical issue. Various algorithms for analysing dle data generated by microcultivation in liquid cultures.
and extracting fitness components from microbial growth However, PRECOG is essentially agnostic with regards to
datahavebeendevelopedforuseinmicrobialgrowthphe- instrumental platform and requires no prior knowledge
nomics[25]. oftheexperimentaldesign.Thisallowstheusertoiniti-
Quantifying the growth in microbial population size at ate data-analysis immediately after the data acquisition
both high resolution and high throughput has technic- has been completed. To enable proper comparison be-
ally two key components. First, an instrument that auto- tween research groups of experimentally generated
matically and accurately records proliferation of a large traits it is essential to standardize procedures between
number of microbial populations in parallel is needed. laboratories at many levels, e.g. analytical procedures
Several microcultivation instruments currently on the for data pre-processing and feature extraction [36].
market achieve both a reasonable accuracy and a reason- PRECOG is a first step in our goal to create an exten-
able throughput [25–30]. The output from these instru- sive microbial phenomics framework that streamlines
ments is files where the measured proxy for population dataacquisition andstandardizesfitnesscomponentex-
size,e.g.opticaldensity(OD),islistedforeachexperimen- traction and datavisualization, storage and distribution.
tal position as a function of time. Second, an analytical
framework is needed toautomaticallyhandle, analyse and
PRECOG implementation
extractfitnesscomponentsfromeachseriesofpopulation
The PRECOG analysis pipeline consists of four steps
size estimates and report the results in a convenient for-
(Fig. 1a): i) data import, ii) data processing, iii) data
mat. There is currently no generally accepted and widely
visualization, and iv) data export. PRECOG was initially
used standard tool for the conversion of raw growth data
developed to process time series of optical density data
into fitness components. On the contrary, most research
from the Bioscreen C instrument, but similar proxies of
groups develop their own ad hoc methods, leading to in-
population size from other types of instruments can be
accurate,incomparableandincompletedata.
usedasinput.
Algorithms to calculate growth rates, lag times and
yield have been presented but are either not publically
available [31, 32], or only available as Perl scripts [29] or Systemoverview
Excel macros [25] upon request. GrowthRates, is a re- PRECOG was implemented using C#, targeting the
cently published, freely available software for analysing Microsoft.Net framework [37], and is accessible as three
growth data [33] but does not contain visualization op- different platforms. A webserver hosts a website that
tions for scrutiny of obtained curves in parallel to ex- operates as a gateway to PRECOG [38] and it can also
tracted traits. GATHODE, is a semi-automated open can be reached via a link from ourPROPHECYdatabase
source software [34] that contains a graphical interface, [39]. The website hosts the three PRECOG platforms: a
but it is currently only available as source code and thus desktop application to be downloaded, installed and used
programming skills are essential for its installation. locally, an online tool at the website and an Application
YODA, is mainly designed for chronological life span Program Interface (API)(web service). A table compar-
analysis and requires the establishment of a web-server ing the functionalities of the different versions of PRE-
[35].Severaloftheabove-mentionedtoolslacktheability COG can be found in supplementary material
to correct for the non-linearity of the recorded optical (Additional file 1: Table S1). Documentation, a video-
densityathighercelldensities,andnoneofthemprovide tutorial, access to publications linked to PRECOG, and
a quality measure of growth curves to speed-up and contact details are also available at the website.
standardizethedownstreamanalyzesoflargedata-sets. ThedesktopapplicationcanbeinstalledonanyWindows
To simplify analysis and standardize comparisons computer (Windows 7 or higher) from the website via the
between different laboratories in the conversion of raw link“DesktopApplication”onthemainmenu,orfromthe
estimates of microbial population size into accurate fit- desktop install page [40]. Note that the program works
ness components, we developed the open source soft- without problems when ran on another operating system
ware PRECOG (PREsentation and Characterization Of using a virtual machine with Windows installed (e.g.
Growth-data)thatisreadytobedownloadedinadesktop Oracle’s VirtualBox running on a Mac). The desktop
version, accessed via a web-site or utilized as a web- platform offers the richest user experience by providing
service(API).PRECOGconvertstherawODdataintoac- the most features, including an intuitive user interface,
curate population size estimates, provides quality indices, and rich graphics allowing the user to visualize, explore
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page3of15
Fig.1PRECOG’soveralldesign.aThefunctionalitythatPRECOGprovidesisorganizedasapipelinethatfollowsfourbasicsteps:step1-data
import,step2-dataprocessing,step3-datavisualization,andstep4-dataexport.bScreenshotsfromthePRECOGdesktopapplication.PRECOG’s
userinterfaceisdividedintotwozones:actionsandviews.Theactionzonecontrolstheprogram’sfunctions:i)uploaddatafiles,ii)settingparameters,
iii)experimentselection,iv)graphcontrols,andv)savedata.Theviewszonepresentsthedataindifferentdisplays:vi)tableview,vii)thumbnailview
andviii)detailedcurveview
and evaluate the processed data. This publication is fo- the code underlying raw data processing and fitness
cused on the desktop application. component extraction. This allows more advanced users
The online PRECOG platform, called PRECOG-lite, is to integrate the code employed into their own programs,
a slimmed-down version of the desktop application that regardless of operating system or programing language.
runs on a web-server. By connecting to the webpage, the The API service provides maximum flexibility but
usercanuploadagrowth-datafileandperformlimitedex- minimal visualization capabilities. The API service can
ploration of the extracted fitness components and the be found at [41].
underlying population size growth curves. Finally, the user PRECOG will read an input file with several series of
candownloadresultsastextfilesforfurtherlocalanalyses. opticaldensitydata,pre-processeachdataseriestoremove
The API is a classic web service (responds to HTTP technicalnoiseandsometechnicalbias,calibrateandcon-
requests using the SOAP protocol) providing access to verttherawopticaldensitydatatoactualpopulationsizes,
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page4of15
flag low-quality curves, extract fitness components and Data series are organized column-wise in the input file,
output both fitness components and the underlying data with column headers being integer numbers, typically
series(growthcurves)forvisualexplorationandevaluation denoting position on the experimental plate, beginning
bytheuser. from 1. Each data series is identified in the display by its
All the core functions of PRECOG are stored in a vir- columnheader(Fig.1b).Thetableviewsupportsfullsort-
tual library referred to asthe PRECOG engine. This core ing and copy functionality, the latter allowing the user to
library issharedbyallthree versions ofPRECOG. copy from PRECOG and paste into other programs, e.g.
MSExcel,forfurtheranalysis.Thisviewshowsthequality
PRECOGdesktopapplication indices of each data series (data series and growth curve
Using the Microsoft’s Windows Presentations Foundation will here be used interchangeably) of fully processed
(WPF) framework [42–44], we created a rich user inter- populationsizeestimates,supportingtheuserindecisions
face for the PRECOG engine that functions as a stand- on to what degree the data series should be trusted. The
alone Windows client and facilitates the exploration and usercanthenmarkthedata seriesforexclusion.Itis also
visualizationofmicrobialpopulationsizeestimates. possible to merge multiple rows into a single graph. This
The user interface is divided into two zones: actions feature is useful when wanting to visualize the effect of
and views (Fig. 1b). The action zone organizes all the multiple samples at once. The user can change this view
system operationsunder an “ActionsTab”. Here the user to a more compact display with many individual data
canfindalltheoperationsavailableinPRECOGascollaps- series included in the same run as individual thumbnails,
iblesections.Whentheuserhasimported datafilesinthe togetherwiththefitnesscomponentsextractedfromeach
“Upload experimental files” window, another tab on the series(Fig.1b).
actions zone, “Experimental Runs”, shows the uploaded Adouble clickonagraphthumbnailcreatesaseparate
growth-data files as individual experiments. Experiments tab in the view zone with a detailed display of the
can now be explored individually or in batch by selecting sample’s growth curve (Fig. 1b). The detailed graph view
the relevant files. Exploration is performed in the views allows the user to interact with the data in detail; the
zone. usercanperformzoomandpanoperations,uselogarithmic
PRECOG has three basic user operations: Upload data or non-logarithmic y-axis, and simultaneously visualize
files, Save data, and Parameters. The upload data section boththerawopticaldensitiesandthefullyprocessedpopu-
supports xl~files and CSV exports generated by Bioscreen lationsizeestimates.Thesystemalsoshowsmarkersinthe
C instruments, and generic tab-delimited files. Unlike the region of each data series where the fitness components
Web and API PRECOG platforms, the desktop application were extracted as metadata (Additional file 2: Figure S1).
allows processing and comparison of multiple data sets. This allows the user to visually detect and avoid the intro-
The upload data section also allows the user to set special ductionofanalytical biasthat couldconfoundfitnesscom-
options,liketheselectionoftheratetraitextractionmethod ponent extraction. The detailed graph view has another
or the use of the monotonic filter. The parameters section usefulfunction;itcanshowthefirstderivativeofthecurve.
lets the user customize the calibration function that The first derivative maximum should overlap with the
converts the recorded optical densities into actual popula- growthratemetadatamarkers(Additionalfile2:FigureS2).
tion size estimates. It also allows the user to specify the Ifnot,thisisaclearindicationofanalyticalerrorsinthefit-
blank values that subtracts the background signal. At a ness component extraction. The detailed graph view, its
minimum,thecalibrationfunctionshouldbecustomizedto underlying data and the extracted fitness components can
each type of instrument and each species of microbes, and becopy-pastedintootherapplications.
the blank values should be customized to each type of in-
strument and each medium. PRECOG allows the user to PRECOG-lite–onlineversion
save and output data as tab delimited text files. Four types The online PRECOG-lite platform is a slimmed-down
ofdatacanbeexported,asindividualrunsor inbatchesof version of PRECOG that allows the user to upload a sin-
more than one run. These are: raw unprocessed data, fully gle experimental file. The raw optical densities will be
processedpopulationsizeestimates,firstderivatives(slopes) pre-processed and calibrated as in the desktop applica-
of the fully processed series of population size estimates tion and the fully processed population size estimates
and the fitness components extracted from each growth willbepresentedas growthcurves.Thesecanbesavedin
curve. Growth curves that were manually marked as ex- a tab-delimited text file. During the upload and process
cluded(seebelow)willnotbeincludedintheexportfiles. steps, the user can set several parameters like the ones
Once an imported data file is selected, data will be pre- determining the calibration function, the blank value, the
sented in the views zone. By default, it opens in the table growth rate trait extraction method and if they want to
view. This shows the fitness components extracted from skip the monotonic filter (Additional file 2: Figure S3).
each fully processed series of population size estimates. Once the data has been processed, it can be accessed in
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page5of15
thesamethreeformatsasinthedesktopapplication:table is avoided by padding (filling the gap by cloning the end-
view, thumbnail view and detailed graphview (Additional points). Median filters are standard to minimize the im-
file 2: Figure S3). In the online version, potential low- pact from high amplitude noise at single time points,
quality curves are flagged for visual inspection by one or “spikes”, while preserving the overall data characteristics
moreofthefourquality-indices.However,theusercannot [46–49]. Optical density spikes originate from lamp fail-
in PRECOG-lite mark the curves for exclusion as can be ure, heterogeneity in the cell population, electronic inter-
doneinthedesktopversion. ference, dust particles or sudden change in light influx
from the environment. In the evaluated test data series,
PRECOGwebservice–API spikes affected about 4 % of 90,000 growth curves.
The API version of PRECOG offers access to some of Whereas the median filter effectively removed spikes at
the basic functions of the core library (see [45] for an single time points (Fig. 2; upper two graphs), it failed to
overview),allowingotherdeveloperstousethemintheir remove the rare “wide spikes” that extended over mul-
own programs. The current API implementation is basic tiple consecutive time-points (Fig. 2; lower two graphs).
andisintendedtoevolveorganicallyfollowinguserinput. Low amplitude but high frequency noise is challenging
to remove. Standard signal analysis instead seeks to
Pre-processingfiltersreducingnoiseandbias minimize the influence of such noise by smoothing
To remove or reduce random noise and technical bias, series of signals, accepting that also data that is close to
PRECOG filters the raw optical densities in a three-step the truth are subject to minor adjustments [47–50].
procedure.Thisisdonebeforethebackgroundsubtraction After the median filtering, PRECOG therefore employs a
and the calibration that transform raw optical densities one-dimensional mean filter in a sliding window that
into actual population size estimates. First, a sliding win- considers three consecutive data values. The middle
dow one-dimensional median filter considers each three value in each window is replaced by the mean over the
consecutive data values in the data series, replacing the three values in the series. The smoothing is among
middle value with the median of all three. Edge cropping the lightest employed in signal analysis, accepting that
Fig.2Effectsofdatapre-processing.Effectsofdifferenttypesofnoiseintherawdata(redline)onthefully(blackline)orpartially(greenline)
processeddata,thelatterwithoutthemeanfilterthatremovesspikes.Ifspikesarenotremoved,asinthecaseofwidespikesconsistingofmore
thanonedatapoint,theprocesseddatawillbedistorted(asseeninthelowertwographs).FiguresarescreenshotsfromPRECOG.
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page6of15
some noise may penetrate in order not to distort the increase in cell number is positive and constant and a sta-
truedata trends. tionary phase of no net increase. These phases are sepa-
Neither the median nor the mean filter is capable of rated by acceleration and deceleration phases that typically
accounting for the strongly confounding noise and bias aredisregardedintheanalysis.Theinevitablefinalphaseof
associated with collapsing growth curves. Curve collapses netdeath,whichrarelyisinitiatedwithinthetime-frameof
followfromthesuddenemergenceofdramaticheterogen- a typical experiment, also tends to be overlooked. In this
eity in microbial populations, typically due to bubble traditional view, the three fundamental fitness components
formation,cell-cellcohesionleadingtoaggregates,orwell isthelength ofthelag phase, therateofgrowthintheex-
- wall adhesion leading to deprivation of cells in the cen- ponential growth phase and the total gain in population
tral well area. Curve collapses are associated with drastic sizeupuntilgrowthendsinthestationaryphase.Thelatter
drops in cell density, often followed by equally drastic in- reflects the efficiency with which the limiting resource has
creases when bubbles and cell aggregates drift stochastic- been converted into population size growth. PRECOG ex-
ally in and out of light beams. PRECOG, as a third tractsthesethreefitnesscomponents,using thealgorithms
filtering step, sweeps each data series and enforces data earlierpresented[25],inthefollowingway:
monotonicitybyreplacinganyvalueslowerthanitsprede- The growth lag is extracted from population size esti-
cessor with the predecessor itself (Fig. 2). Given that the mates,presentedonthelog-scaleastheinterceptbetween
enforcement of monotonicity has been preceded by both the initial population size and the line extrapolated from
median and mean filtering, its effects are restricted to re- maximum growth. PRECOG calculates the mean of the
moving or reducing abnormalities in collapsing curves. A first five population-size estimates, providing a robust
relevant note of caution is that there might be naturally measure of the initial OD. PRECOG then calculates the
occurring biological phenomena that result in a negative timefor all the intercepts between the initialmean values
net growth (curve collapses), e.g. bacterial cultures being and every slope. Slopes are obtained in a moving window
treated with cell lysing agents like certain antibiotics, or of eight time-points, departing from the beginning of the
beingexposedtochangingosmoticpressureorstarvation, curve and proceeding to the end. The fitness component
withassociatedcelllysisand/orautophagocytosis.Tomake lag timeisestimatedas themeanofthetwo largestinter-
possiblestudiesofthesephenomenausingPRECOG,there ceptsandisexpressedinhours.
isanoptionwherethemonotonicityfiltercanbeexcluded. Thegrowthrate,expressedaspopulationsizedoubling
time, is extracted from the highest of many short slopes
Background/blanksubtractionanddatacalibration in the exponential growth phase and converted into
The last pre-processing steps before the fitness compo- population doubling time. The first three hours of the
nents are extracted are the blank subtraction and the curve is excluded to avoid obtaining slopes corresponding
calibration adjustment. The blank value, provided by the to cell size increases when cells exit from the starved G0
user, is subtracted from every data point in the data stateandrecaptureasizethatispermissiveforcelldivision.
series. Thiseffectively removes the background signal on Thenittakesthelogarithm(base10)oftheremainingdata
the assumption that the background is constant across points and calculates the slopes in a moving window of
all experimental positions. Recorded optical densities do three time-points. Slopes are ranked based on their values
not increase linearly with cell densities because of an ac- and the two highest slopes are discarded in order to
celerating cell shielding effect against light at higher minimizetheeffectofanyremainingartefactoutliers.From
densities.ThisistypicallydetectableatOD>0.3[25].Sam- t(cid:1)heremainin(cid:3)gslopes,themeanofthefivehighestisformed
ple dilution is not logistically feasible in high-throughput (cid:1)x andconvertedintodoublingtime(inhours).
highestslopes
growthphenomics;thus,recordedopticaldensitieshaveto
The growth efficiency is extracted as the total increase
beanalyticallytransformedintoactualpopulationsizeesti-
inpopulationsize,fromthesmallesttothelargestpopula-
mates. The transformation uses an empirically established
tion sizes, given that growth has actually ceased when
calibration function that should be separately established
populationsizeismaximal. Themean ofthetwo smallest
for each species and instrument type. For the Bioscreen C measurements ð(cid:1)x Þ and the mean of the six highest
instrument, a calibration function has been calculated and (cid:1) ba(cid:3)se
measurements (cid:1)x definestheintervalsuchthattheef-
applied to correct for this effect. The function is valid for top
ficiency is estimated as (cid:1)x − (cid:1)x . No estimate of effi-
most lineages and physiological states of the model yeast top base
Saccharomycescerevisiae[25]andisofferedasdefault. ciency is given if the standard d(cid:1)evia(cid:3)tion of the six highest
OD measurements divided by (cid:1)x is greater than 0.02,
top
Thedefaultprocedureforfitnesscomponentextraction corresponding to a rough distinction between curves that
The traditional view of the microbial population size haveandhavenotreachedastationaryphase.
growth curve postulates an initial lag phase of no net in- PRECOG also provides an alternative algorithm for
crease in cell number, an exponential phase where net growth rate extraction that is less prone to overestimate
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page7of15
the growth rate when growth curves are noisy and more series, overalltime-points.Large correction values reflect
robust when data are sparse. The algorithm performs curvesthathavecollapsedinlateexponentialorstationary
linear regressions in a window of five time points that phase.Thesearerarelycapturedbyotherqualityindices.
slides along the curve, extracting slopes from the regres-
sion models and converting the highest slope into doub-
Methods
lingtimes.
Establishingthecalibrationfunction
To make the species-specific calibration functions to
Qualityindices
correct higher OD values, cells were grown overnight to
Automatic evaluation of the quality of the data series of
stationaryphaseinsyntheticdefinedmedia;YNBwith2%
population size estimates is challenging because both
(w/v)glucoseforthevariousyeastspeciesandLBmedium
high-quality data series, that should be retained, and
for Escherichia coli. The stationary phase cultures were
low-quality data series, that should be rejected, vary
x1.5seriallydilutedinthecorrespondinggrowthmediato
greatly in their growth dynamics and types of technical
finally reach 15 dilution-steps, and the whole series of
errors(Additional file 2: Figure S4, Figure S5). To distin-
dilutions measured in the Bioscreen instrument. The
guish high from low quality growth curves given this
blank-correcteddilutedOD valuesandtheblank-corrected
variability, PRECOG establishes four quality indices
undiluted OD values are for practical purposes indistin-
(QI), based on the raw data series, that together esti-
guishableuptoOD≈0.3,andabovethisvaluethetrueOD
mates curve quality. The indices are QI1, “overall noisi-
valueswerecalculatedusingthecorrespondingdilutionfac-
ness” in the form of the average fit of regression lines to
tor and the diluted samples (OD<0.3). A blank value was
the data along the curve, QI2, “local noisiness” based on
estimated on only media without cells, and this value was
local regions with poor fit of regression lines to the data
subtracted from all other measured OD values. Using the
along the curve, QI3, “number of spikes” that identifies
previously established formula for calibration, y=x+cx3
short, dramatic increases in data values and QI4, “curve
[51],whichassumesanear1:1linearityforlowOD,thefol-
collapses” that identifies curves with sudden and sus-
lowing values for c were found: Candida albicans: c=
tained drops in data values. Conceptually, these phe-
0.5790256635480614, Pichia pastoris: c=0.56532843458
nomena are all incompatible with accepted models of
04932, Schizosaccharomyces pombe: c=0.6467246377423
true growth curve dynamics. Highvalues in one or more
4579, and E. coli: c=0.75389848795692815. For S. cerevi-
of the quality indices therefore suggest technical prob-
siae we obtained c=0.82673123484708266 which is very
lems and identify data series that should be visually
close to the earlier established and since long used c value
inspected bytheuserfor possible exclusion.
for this species,0.8324057[19,51]; for consistencyreasons
For the QI1, “overall noisiness”, we exclusively consider
wewillstaywiththeoldcvalue.
the noise in the growth phase of the curve, by calculating
linear regression over a five-point sliding window with
slopek>0.07.QI1isdefinedas1-theaveragecoefficients Results
of determination, r2, of the data in all such windows to Flagginglow-qualitydataseriesformanualinspection
their respective linear regression, and provides a measure andexclusion
oftheoverallnoisinessinadataseries. ToevaluatetheperformanceofPRECOG,wecreatedtwo
The QI2, “local noisiness” uses the same principle as benchmarking sets of 100 high- and 100 low-quality
QI1butonlyconsidershighlocalvariationofthegrowth growth curves. Curves in the high-quality set are technic-
data. QI2 is defined as 1 - the average of the three worst allyofgoodstandardandcaptureawiderangeoftruebio-
coefficient of determination, r2, of the linear regressions. logical properties, including individual and combined
Thus,itindicatestheexistenceof very noisy regions. effectsoneachofthe classicalfitnesscomponentsgrowth
The QI3, “number of spikes”, estimates the number of lag, rate and efficiency, as well as multimodal growth.
spikesinthegrowthcurve.TheQI3indexisinitiallycal- Multimodalgrowthcorrespondstomultiplephasesofele-
culated in the same way as the QI2. However, here PRE- vated growth (Additional file 2: Figure S4 and data S1).
COG counts the number of r2 values that are less than Growthcurvesinthelow-qualitysetrepresentavarietyof
0.5. From practical experience we have seen that this amplitudes and frequencies of technical noise (Additional
index shows how many sharp bends the curve has, thus file2:FigureS5anddataS2);roughlyhalfofthecurvesin
beinganindicationofthe number ofspikes. this benchmarkingset are completely dominated by tech-
The QI4, “curve collapses”, measures the magnitude of nical error and should be rejected. We believe that these
the corrections done to the raw growth curve. The QI4 two benchmarkingsets could begenerallyvaluable in mi-
index is calculated by comparing data series before and crobial phenomics for performance tests of growth algo-
after filtering with the monotonic filter. We obtain QI4 rithms and software. The two benchmarking sets can be
as the sum of the absolute differences between the data downloadedfrom[52].
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page8of15
Growth curves are quality filtered by PRECOG in a series results in an excessive workload on the operator, in
semi-automated procedure. The first step is the auto- termsofmanualinspection.
matic calculation of quality indices based on four curve To find the optimal breakpoint for what to flag and
features.Thesecond step is manual scrutinyof indicated what not to flag, we evaluated six thresholds for each
problematiccurvesforpossibleexclusion.Thefourquality qualityindex:flaggingtheworst0.5,1,2.5,5,10and15%
indices are QI1; “overall noisiness”, QI2; “local noisiness”, of data series on almost 90,000 growth curves, the aggre-
QI3;“numberofspikes”andQI4;“curvecollapses”.Thus, gated 90 k set, generated over many years and by several
the four indices capture different types of technical prob- different experimentalists in our lab (Fig. 3). Each thresh-
lems encountered, and one should not expect all indices old was then tested on the two benchmarking sets to
to flag for low quality of a particular problematic curve. evaluate false positives and false negatives. We found the
Low-quality data series evading the four quality indices numbers of false positives, in the high-quality set, and
may result in extractions of incorrect fitness components. false negatives, in the low-quality set, to be lowest at
On the other hand, false flagging of high-quality data the 5 % flagging level for each QI in the aggregated 90 k
Fig.3FilteringdatausingPRECOG’squalityindices.aDataisfilteredusingfourqualityindices,QI1-“overallnoisiness”,QI2-“localnoisiness”,
QI3-“numberofspikes”,andQI4-“curvecollapses”.Upperpanels:performanceofeachqualityfilteronthe“aggregated90kset”,includingalmost
90,000growthcurves.x-axisshowsQIscore,y-axisshowsnumberofgrowthcurvesflaggedateachQIscoresetting.Bluebars=non-cumulative
flagging,redline=cumulativeflagging,dashedblackline=selectedQIscoresettingthatflagsacumulative5%ofcurves.Lowerpanels:performance
oftheQIfilter(QIscore,y-axis)onthetwoselectedbenchmarkingsetsof100high-and100low-qualitycurves(x-axis).Dashedhorizontallines:
performanceatthe5%rejectionthresholdselectedbasedonthe“aggregated90kset”growthcurves.bSummaryperformanceofallqualityindices.
Numberofcurvesthatobtain0,1,2,3and4flagsinthe“aggregated90kset”attheselectedthreshold,whereeachqualityindexflagstheworst5%
ofgrowthcurves,i.e.90%ofallcurveswerenotscoredbyanyofthequalityindiceswhile2%werescoredbyallfour.cSummaryperformanceofall
qualityindices.NumberofQIflagsinthehigh-andlow-qualitybenchmarkingsets.Coloursindicatequalityindexresponsiblefortheflagging,with
blue=QI1,red=QI2,green=QI3andpurple=QI4.dNumberoffalsepositivesandnegativesinthetwobenchmarkingsets,asafunctionofusing
variousthresholdsfromthe“aggregated90kset”
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page9of15
set(Fig.3d).Atthisthresholdthelow-qualitycurveswere datafilteringiscomputationallyinexpensiveandtherefore
easily distinguished by the quality indices; only one curve possibletoemployonaverylargescale,andaddressesthe
escaped all four quality-indices. None of the high-quality vastmajorityoftechnicalerrorsmanifestingindataseries.
curveswereflaggedatthe5 %threshold.Itwasalsoclear We tested PREGOG’s data filtering by comparing its per-
that different quality indices captured different curve formancetoamulti-layered neuralnetworks noise reduc-
problems (Fig. 3c); only 39 % of the low quality curves tionfilter.Neuralnetworkshavebeenusedformanyyears
wereflaggedbyallfourqualityindiceswhileroughly11% asareliabletooltosolvenoisereductionproblems[53,54].
were captured by only one quality index. The flagged A multi-layered neural network, iterated 500x to exclude
curves can be visually inspected in PRECOG in a semi- outliers (remove noise), did not perform better than the
automated manner, allowing fast operator decisions on three-step PRECOG data filtering process (Fig. 4). Fitness
which flagged curves to accept and reject. Non-flagged components,extractedfollowingthedataprocessingbythe
curves are automatically accepted and included in the two methods, were nearly identical for high-quality curves
downstreamanalysis. (r2, lag=0.9938, rate=0.9976, efficiency=0.9997) and cor-
related well also for low-quality curves (r2, lag= 0.7868,
BenchmarkingPRECOG’spre-processingdatafiltersfor rate= 0.7465, efficiency=0.991); if the really bad curves
minimizingnoise flaggedforallfourQI:swereexcludedthecorrelationswere
To remove or reduce noise and bias, PRECOG filters the even better (r2, lag = 0.9716, rate = 0.9762, efficiency =
rawopticaldensitiesinathree-stepprocedure.PRECOG’s 0.9933). Visual inspection of low-quality data series where
Fig.4BenchmarkingofPRECOG’sdefaultdatacleaningalgorithm.WecomparedPRECOG’spre-processingfiltersagainstacomputationally
demandingneuralnetworkpre-processingprocedure.Afterthepre-processing,thefitnesscomponentsgrowthlag,growthrateandgrowth
efficiencywereextractedbyPRECOG’sstandardprocedurefromthehigh-(leftpanel)andlow-quality(rightpanel)benchmarkingsets,and
compared.Low-qualitycurvesareintherightpanelmarked(greenwitharedmark)ifflaggedbyallfourquality-indices(thus,indicating
curvesofreallylowquality)
Fernandez-Ricaudetal.BMCBioinformatics (2016) 17:249 Page10of15
noticeably different outcomes were observed, failed to es- more detailed description of experimental and analytical
tablish a pattern ofconsistent superior performanceof any proceduresforestablishingvalidcalibrationfunctions).
one filtering method. The computational inexpensiveness
of PRECOG’s three-step data pre-processing therefore Fitnesscomponentextractionandfrequencyofsampling
speaksemphaticallyinitsfavor. PRECOG extracts the three canonical fitness components
associatedwithnetgrowthofmicrobialpopulations;length
Theimportanceofcalibrationtoconvertopticaldensities of the initial lag phase, in which no net growth occurs,
intopopulationsizeestimates maximal rate of growth (specifically: minimum population
The optical density of cultures is easy to measure and can doubling time) in the phase when net growth is positive,
be implemented in automated procedures, which is why it andthetotalgaininpopulationsize,giventhatastationary
has become a standard way of estimating changes in high- phase of no further net growth has been reached at the
throughputmicrobialpopulationsizestudies.Calibrationof maximum population size. Stationary phase is almost ex-
recorded optical densities into population size estimates clusively entered because one resource, typically energy or
drasticallyadjuststheshapeofrecordedgrowthcurves,sug- nitrogen, has been depleted, and the population size yield
gesting a potentially profound distortion of fitness compo- thereforerepresentstheefficiencywithwhichthisresource
nents extracted by softwares failing to employ this hasbeenconvertedintopopulationgrowth.PRECOGalso
procedure (Fig. 5). To evaluate to what degreefitness com- tracks the data points used to extract each fitness compo-
ponents are confounded by failure to calibrate, we com- nents as associated metadata, allowing stringent user
pared fitness components extracted from the high-quality evaluation of the reliability of each extracted fitness com-
benchmarkingset,beforeandaftercalibration(Fig.5b).Dis- ponent(Additionalfile2:FiguresS1,S2).
tortions in the form of a systematic underestimation of lag The primary experimental settings that operators need
phases, corresponded to 15 % shorter lags than reality, and to decide on prior to experiment start are frequency of
systematic overestimation of population doubling times, data measurements and total number of data measure-
correspondedto25%slowergrowththanreality,weresub- ments. Together, these parameters define the length of an
stantial. In the case of growth efficiency, the systematic un- experiment.Weevaluatedtheinfluenceofvaryingthedata
derestimations from non-calibrated data were outright measurement frequency on extracted fitness components
catastrophic, with a mean of 61 % lower estimates than inastandard72hexperiment,usingthetwobenchmarking
reality (Fig. 5b). Thus, calibration of recorded optical dens- sets. While growth efficiency and lag were largely un-
itiesisabsolutelyessentialandshouldneverbeoverlooked. affected by realistic frequency changes (Additional file 2:
PRECOG allows the user to choose between calibration FigureS7), minimum population doubling times were sys-
functions stored centrally or to enter functions established tematically and continuously overestimated when data
empiricallybytheindividuallaboratory.Itisclearfromour measurements were less frequent (Fig. 6a, upper panel).
experimentaldatathatdifferentorganismsrequiredifferent Minimum population doubling times typically occur early
calibrationfunctions(Fig.5c).Forexample,therelationbe- in the net growth phase. As data measurement frequency
tweenobservedopticaldensityandactualpopulationsizeis decreases, minimum population doubling times are in-
verydifferentinthefissionyeastS.pombethaninthebud- creasingly based on data points from later in the net
ding yeast S. cerevisiae. In this context it should be men- growthphase,whereslopesarelesssteepandtheexponen-
tioned that OD is influenced by other factors besides cell tial growth assumption increasingly incorrect (Additional
number, like the volume and biomass of each cell, absorp- file2:FigureS6).
tion from internal molecules, the shape of individual cells We also tested an alternative method for rate extrac-
and cell aggregation. This means that OD measurements tion, based on a single linear regression over five con-
are most effective in comparing microbial cultures for secutive time-points, expecting it to have a lower degree
which other properties that affect light transmission are of dependency for alterations in frequency of sampling.
identical. Thus, OD should always be seen as a proxy for Indeed,wefoundthatthetendencytowardsoverestimating
the microbial population size, and interesting phenomena true minimum population doubling times at lower meas-
should ideally be confirmed by alternative cell-counting urementfrequencieswassomewhatlessseverewhenlinear
methods, like microscopy or FACS, for which the biases regressions over five consecutive time-points were consid-
are different. The impact from the calibration function ered (Fig 6b). Comparing the two growth rate extraction
will especially be great for the extraction of growth effi- methods on high frequency sampled data, we found near
ciency,ascalibrationfunctionsdeviatemorefromlinearity perfect correlation (r2=0.9966) over the whole range of
athigheropticaldensities.PRECOGenablesuserstoenter growth rates in the high-quality data set (Fig. 6b), but a
calibration functions established by comparing optical slight, systematic tendency for extraction of shorter mini-
densities of diluted and non-diluted microbial samples mum population doubling times using linear regression.
over a range of densities [25] (see Methods section for a Thistendencywassubstantiallystrongerinthelow-quality