ORIGINAL ARTICLE

Quantitative analysis of a deeply sequenced marinemicrobial metatranscriptome

Scott M Gifford, Shalabh Sharma, Johanna M Rinta-Kanto and Mary Ann MoranDepartment of Marine Sciences, University of Georgia, Athens, GA, USA

The potential of metatranscriptomic sequencing to provide insights into the environmental factorsthat regulate microbial activities depends on how fully the sequence libraries capture communityexpression (that is, sample-sequencing depth and coverage depth), and the sensitivity with whichexpression differences between communities can be detected (that is, statistical power forhypothesis testing). In this study, we use an internal standard approach to make absolute (perliter) estimates of transcript numbers, a significant advantage over proportional estimates that canbe biased by expression changes in unrelated genes. Coastal waters of the southeastern UnitedStates contain 1� 1012 bacterioplankton mRNA molecules per liter of seawater (B200 mRNAmolecules per bacterial cell). Even for the large bacterioplankton libraries obtained in this study(B500 000 possible protein-encoding sequences in each of two libraries after discarding rRNAs andsmall RNAs from 41 million 454 FLX pyrosequencing reads), sample-sequencing depth was only0.00001%. Expression levels of 82 genes diagnostic for transformations in the marine nitrogen,phosphorus and sulfur cycles ranged from below detection (o1� 106 transcripts per liter) for36 genes (for example, phosphonate metabolism gene phnH, dissimilatory nitrate reductase subunitnapA) to 42.7� 109 transcripts per liter (ammonia transporter amt and ammonia monooxygenasesubunit amoC). Half of the categories for which expression was detected, however, had too few copynumbers for robust statistical resolution, as would be required for comparative (experimental ortime-series) expression studies. By representing whole community gene abundance and expressionin absolute units (per volume or mass of environment), ‘omics’ data can be better leveraged toimprove understanding of microbially mediated processes in the ocean.The ISME Journal advance online publication, 16 September 2010; doi:10.1038/ismej.2010.141Subject Category: integrated genomics and post-genomics approaches in microbial ecologyKeywords: metatranscriptomics; marine; bacterioplankton; gene expression; biogeochemistry

Introduction

Metatranscriptomics is a powerful tool for capturinggene expression patterns in natural microbialcommunities without previous assumptions as tothe ongoing activities or dominant taxa (Poretskyet al., 2005; Frias-Lopez et al., 2008). In contrast tometagenomics, which provides an inventory of thecommunity gene pool, metatranscriptomics identi-fies which of those genes are being transcribed in agiven ecological context, including under experi-mentally manipulated conditions (Gilbert et al.,2008; Poretsky et al., 2010).

The advent of second-generation sequencinghas increased metatranscriptome library sizes byorders of magnitude (Poretsky et al., 2005, 2009b;Frias-Lopez et al., 2008; Urich et al., 2008; Hewsonet al., 2009a), yet how deeply a communitytranscriptome is ‘covered’ by the sequence library

remains a critical issue. If too shallow, librarieswill be dominated by transcripts from metabolicpathways shared by most cells and poor in thoserepresenting specialized biogeochemical pathways(Hewson et al., 2009b; Poretsky et al., 2009b). As aconsequence, unique expression patterns within acommunity may be missed, and comparative ana-lyses between communities can be insensitive.

Variability in sample-sequencing depth betweencommunity metatranscriptomes, regardless of cover-age level, further limits the power of comparativeanalyses by restricting assessments to relative data(that is, as a proportion of the transcriptome;Figure 1). This is problematic because changesoccurring in the abundance of some mRNAs inresponse to shifting conditions (van de Peppel et al.,2003; Hannah et al., 2008; Robinson and Oshlack,2010) leads to changes in the percent representationof other mRNAs whose absolute abundance hasnot changed (Figure 1). Comparative analyses basedon ratios of mRNA copies to DNA copies (that is,relative abundance in metatranscriptomes vs rela-tive abundance in metagenomes; Frias-Lopez et al.,2008) does not solve this problem, as bothare similarly affected by unknown and possiblyReceived 10 May 2010; revised 1 July 2010; accepted 12 July 2010

Correspondence: MA Moran, Department of Marine Sciences,University of Georgia, Marine Sciences Building, Athens,GA 30602-3636, USA.E-mail: mmoran@uga.edu

The ISME Journal (2010), 1–12& 2010 International Society for Microbial Ecology All rights reserved 1751-7362/10

www.nature.com/ismej

different sample-sequencing depths. Thus as cur-rently obtained, metatranscriptomic data provideinformation on enrichment or depletion of a tran-script category in the community transcriptome, butnot on the absolute abundance of these transcriptcategories per volume or mass of environment(Figure 1), which is the most relevant comparisonfor biogeochemical studies and ecosystem modeling.Metagenomic-, metaproteomic- and environmentalmicroarray-based studies suffer these same propor-tional data constraints.

In this study, we report the deep sequencing oftwo replicate metatranscriptomes from southeasternUnited States coastal seawater to characterizemicrobial gene expression and address three criticalquestions about sequencing effort: (1) What was thesample-sequencing depth for the bacterioplanktoncommunity transcriptome? We used an internalmRNA standard to estimate the number of tran-scripts in the natural sample compared with thenumber of transcripts sequenced. (2) What was theabundance of transcripts representing key bacterialtransformations in the nitrogen (N), phosphorus (P)and sulfur (S) cycles in coastal seawater? We usedBLAST analysis normalized to internal standardrecovery to estimate absolute transcript numbers forover 80 diagnostic steps in marine elemental cycles.(3) Was the sequencing strategy sufficient to detectdifferences in gene transcription between samples?We examined the effect of coverage depth onstatistical comparisons of biogeochemically diag-nostic transcripts in the metatranscriptomes.

Materials and methods

Sample collectionTwo replicate seawater samples (FN56 and FN57)were collected at Marsh Landing, Sapelo Island,Georgia, USA (3112504.08N, 81117043.26W; http://www.simo.marsci.uga.edu) on 6 August 2008 at2330 hours local time, 1 h before high tide and 3 hafter sunset. These samples are part of a multiyeartime series of the Sapelo Island Microbial Observa-tory (http://www.simo.marsci.uga.edu), in whichcollections are made every 3 months and eachcollection set consists of duplicate samples fromfour consecutive high tides (2 days and 2 nights).Surface water (5.75 l from a depth of 0.5 m) waspumped directly through 3 and 0.22 mm filters. The0.22 mm filter was placed in a Whirl-Pak bag (Nasco,Fort Atkinson, WI, USA) and immediately flashfrozen in liquid N2. Total time from the start offiltration to freezing was 10 min. Although thesamples are considered biological replicates withinthe larger time series, we note that there was 8 minbetween the end of the first collection (sampleFN56) and the start of the second (sample FN57).Nutrient data are collected monthly at station GCE6(B3 km from Marsh Landing) as part of the GeorgiaCoastal Ecosystems Long Term Ecological Researchprogram (http://gce-lter.marsci.uga.edu).

RNA processing and sequencingRNA processing in preparation for pyrosequencingwas carried out as previously described (Poretskyet al., 2009a, b), with the exception of the addition ofan in vitro transcribed standard to the extractiontube before beginning the extraction. The standardwas constructed by linearizing a pGem-3Z plasmid(Promega, Madison, WI, USA) with ScaI restrictionenzyme (Roche, Penzberg, Germany) and cleanedwith a phenol/chloroform/isoamyl alcohol extrac-tion. Complete digestion of the plasmid was con-firmed with a 1% agarose gel. The DNA fragmentwas then in vitro transcribed using the Riboprobein vitro Transcription System (Promega) accordingto the manufacturer’s protocol; an SP6 RNA poly-merase was used to create a 994 nt long RNAfragment. The pGem plasmid had another internalT7 promoter region, but it was present in the reversecomplement sequence during in vitro transcriptionand aRNA amplification (see below), and did notinterfere. Residual DNA was removed with RQ1RNase-Free DNase, and the RNA was cleaned witha phenol/chloroform/isoamyl alcohol extraction.The RNA standard was quantified with a NanodropSpectrophotometer (Thermo Scientific, Wilmington,DE, USA), and correct fragment size was confirmedwith an Experion automated electrophoresis system(Bio-Rad, Hercules, CA, USA).

RNA standard (25 ng; 4.7� 1010 copies) was addedto a 50 ml conical tube containing 8 ml RLT lysisbuffer (Qiagen, Valencia, CA, USA) and 3 g of RNA

Sample 1 Sample 2

Equal EffortRelative2:1 2:1

1:1? Absolute

Known Depth

Figure 1 Effect of sample-sequencing depth on quantification oftranscripts (or genes) in environmental samples. ‘Equal-effort’sequences the same number of reads per sample volume,regardless of the size of the mRNA pool, and therefore conveysonly relative abundance. ‘Known-depth’ sequences a knownproportion of the transcript pool (50% for both, in this example),and therefore also conveys absolute copy numbers per samplevolume. The latter is more relevant to biogeochemical ratemeasurements, as mRNAs of biogeochemical interest (gray dots)can make up different proportions in community transcriptomesyet have identical numbers in the environment.

Microbial metatranscriptome analysisSM Gifford et al

2

The ISME Journal

PowerSoil beads (Mo-Bio, Carlsbad, CA, USA). Thesample filters were removed from �80 1C storage,shattered and added to the extraction tubes. RNAwas then extracted using an RNEasy kit (Qiagen),and any residual DNA was removed using the TurboDNA-free kit (Applied Biosystems, Austin, TX,USA). To reduce the number of rRNAs in thepyrosequencing reads, total RNA was treated intwo ways to enrich for mRNA. Epicentre’s mRNA-Only isolation kit (Madison, WI, USA) was first usedto decrease rRNA contamination enzymatically. Thesamples were then treated with MICROBExpressand MICROBEnrich kits (both from Applied Biosys-tems), which couple an oligonucleotide rRNA probewith magnetic separation to enrich for mRNA.Successful reduction of rRNA was confirmed byrunning both pre- and post-treated samples onan Experion automated electrophoresis system(Bio-Rad). To obtain enough mRNA for pyrosequen-cing, the samples were linearly amplified using theMessageAmp II-Bacteria kit (Applied Biosystems).The amplified RNA was then converted to cDNAusing the Universal RioboClone cDNA synthesissystem with random primers (Promega), whichproduced cDNAs primarily in the size range of200–600 bp. Residual reactants and nucleotidesfrom cDNA synthesis were removed from thesample using the QIAquick PCR purification kit(Qiagen), and gel-based size selection was used toselect fragments in the 250–500 bp range. cDNAsfrom each replicate sample were loaded into 1/2 ofeach of four GS-FLX plates for 454 pyrosequencing.Sequences are deposited in the CAMERA database(http://camera.calit2.net/about-camera/full-datasets)under accession name ‘CAM_PROJ_Sapelo2008’.

Read annotationDuplicate clusters were identified using an onlineprogram (Gomez-Alvarez et al., 2009). RibosomalRNA sequences were identified with a BLASTnsearch against the small and large subunit SILVAdatabase (http://www.arb-silva.de) with a bit scorecutoff X50; sequences identified as rRNA were thenremoved from further consideration. To identifysmall, nonprotein encoding RNAs (Shi et al.,2009), all nonribosomal reads were compared withthe RFam database (http://rfam.janelia.org) usingBLASTn with a bitscore X40, and hits wereconsidered putative small RNAs if the best hit inthe RefSeq database was a hypothetical protein or ifthe RFam alignment was X95 nt (SupplementaryFigure S1).

Remaining reads were annotated using BLASTxsearches against the NCBI RefSeq and Clusters ofOrthologous Genes (COG) databases (Tatusov et al.,2003) with a bit score cutoff X40. Taxonomicbinning was based on RefSeq hit. Collector’s curveswere produced from a custom script in the Renvironment (R Development Core Team, 2009).Read coverage of proteorhodopsin PU1002_03206

gene bin and the internal standard was assessed byassembling reads against the reference sequenceusing Geneious version 4.8 (Biomatters, Auckland,New Zealand) with gaps and default scoring(word length¼ 18, maximum gap size¼ 1, maximumgaps per read¼ 20, maximum mismatches¼ 20and maximum ambiguities¼ 4) and the consensussequence representing the majority nucleotide ateach position. Sequence variation at each nucleotideposition was determined using a custom script in Rwith the BioStrings package (Pages et al., 2009).

Elemental cycle transcriptsReference diagnostic genes representing transforma-tions in the N, P and S cycles were selected frommarine alphaproteobacteria, gammaproteobacteriaand bacteriodetes genomes (the three most commontaxa in marine metagenomic libraries), or from othertaxa if these three groups did not contain an orthologto the gene of interest. These reference sequenceswere used as query sequences in BLASTX analysisagainst the metatranscriptomic data (bitscore X40,E-value o10�3) and redundant hits were removed.Remaining hits were manually checked withBLASTX against the RefSeq database and discardedif the top three hits were not to a similar annotationas the original reference gene.

StatisticsPairwise statistical comparisons were carried outwith Xipe, a bootstrapped difference of meanscalculation developed by Rodriguez-Brito et al.(2006), using 20 000 bootstrap iterations and 95%confidence intervals, or with 2� 2 contingencytables and the Fisher’s exact test (White et al.,2009) using Po0.05. Subsampled libraries for Xipeanalyses were created by sampling without replace-ment using R (R Development Core Team, 2009). TheBenjamini–Hochberg correction was used to adjustthe Fisher’s exact test P-values as a control for thefalse discovery rate using the R package ‘multtest’(Strimmer, 2008), and only those genes with anadjusted P-value o0.05 were considered significant.A simulation analysis of Fisher’s exact test signifi-cance threshold as a function of count numberwas carried out using an R script that ran 2� 2contingency tables at incrementing count values forlibrary sizes of 125 000 reads.

Results and discussion

Sequence librariescDNAs derived from two replicate coastal bacterio-plankton samples (samples FN56 and FN57 inthe Sapelo Island Microbial Observatory series;http://simo.marsci.uga.edu) were sequenced in fourGS-FLX 454 runs (Margulies et al., 2005), withfour technical sequencing replicates per biologicalreplicate. Over a million reads averaging 210 nt

Microbial metatranscriptome analysisSM Gifford et al

3

The ISME Journal

in length were obtained per sample (Table 1).After removal of rRNAs and putative small RNAs(Shi et al., 2009), there were B500 000 possibleprotein encoding reads in each library (Table 1).

Sample-sequencing depthSample-sequencing depth is defined here as thepercent of mRNA molecules present in a sample thatis represented in the sequence library. The greaterthe sequencing depth of an mRNA pool, the morethorough the representation of microbial genetranscription. Further, if the volume or weight ofthe sample is also known, information on thesample-sequencing depth allows absolute transcriptabundance to be calculated for a given quantity ofthe environment, not just proportional abundance inthe community transcriptome. To estimate sample-sequencing depth, a known number of artificialRNA sequences serving as an internal standard wasadded immediately before cell lysis at the initiationof nucleic acid extraction. This approach may havesome biases, for example if the internal standard ismore susceptible to degradation than natural mRNAor if the efficiency of release of natural mRNA fromcells is o100%, but it provides a consistentaccounting across samples through extraction, pro-cessing and sequencing steps. Similar approacheshave been successfully applied to qPCR (Coyneet al., 2005) and microarray studies (Hannah et al.,2008).

A total of 4014 internal standards were identifiedin the FN56 sequence library out of 4.7� 1010 copiesadded before cell lysis, leading to an estimate of1.0� 1012 bacterioplankton mRNA molecules perliter of coastal seawater (Table 2). Two other esti-mates of the size of the community transcriptomewere derived using literature values for mRNAcontent of marine bacterioplankton (Table 2), andthese were in reasonable agreement with the

internal standard method. The sample-sequencingdepth was therefore B0.00001%, or 1 in 107

transcripts, with FN57 sequenced slightly deeperthan FN56 (Table 2).

Direct cell counts indicated 4.2� 109 bacterio-plankton cells per liter in the seawater samples, andtherefore an average of 190 mRNA transcripts percell (Table 2). Laboratory cultures of Escherichia coliin exponential growth phase have B1400 trans-cripts per cell (Neidhardt and Umbarger, 1996). Thesevenfold lower estimate for coastal bacterioplank-ton was not unexpected, however, because the cellsare considerably smaller in size (Azam and Hodson,1977) and have much lower growth rates (Ducklow,2000) than laboratory-grown E. coli. On the basis ofthis per cell abundance estimate, it can be deducedthat transcript copy number was lower than genecopy number for most of the bacterial and archaealgenes present in this coastal ocean.

Coverage depthCoverage depth is defined here as the percent of theunique mRNAs present in a sample that is repre-sented in the sequence library. Sample-sequencingdepth and coverage depth are not strictly coupled,as a low richness/high evenness community tran-scriptome will be well covered even with shallowsample sequencing.

We evaluated coverage depth for the coastalmetatranscriptomes in terms of taxa, functional genecategories and genes. Taxonomic coverage, asassessed by a collector’s curve of NCBI taxonomybins at the species or strain level, was approachingsaturation for the library (Figure 2, inset); indeed,75% of the total taxonomic richness emerging fromthis analysis would have been discovered witho15% of the sequencing effort. Saturating coveragewas also found for functional gene assignmentsbased on best hits to the COGs database (Table 1);75% of the total richness would have been foundwith o10% of the sequencing effort (Figure 2,inset). However, these coverage assessments areconstrained by the composition of the referencedatabase, as apparent richness can be no higher thanthe number of reference bins available for transcriptassignment. We found 1909 taxon bins representedin the metatranscriptomic libraries out of 8054entries in the NCBI taxonomy database, and 3298COGs out of 5666 entries in the COG database.

When coverage was assessed based on geneassignments in the RefSeq database (46 millionaccession numbers), transcripts binned to over168 000 genes (Table 1), and the collector’s curveindicated that the metatranscriptome library was farfrom saturating (Figure 2). Singletons made up 59%of the sequences (Supplementary Figure S2), andabundant transcripts (410 hits accession number�1)and highly abundant transcripts (4100 hits acces-sion per number) composed only 3% and 0.5% ofthe library, respectively. Although RefSeq binning

Table 1 Summary statistics for coastal ocean metatranscriptomedatasets

FN56 FN57 Combined

Total reads 1 067 363 1 114 536 2 181 899rRNA 466 834 (44%) 623 804 (56%) 1 090 638 (50%)psRNA 100 437 (9%) 25 213 (2%) 125 650 (6%)

Possible proteins 500 092 (47%) 465 519 (42%) 965 611 (44%)RefSeq Hits 255 280 260 739 516 019RefSeq Genes 96 573 109 395 168 669RefSeq Taxa 1707 1761 1909COG hits 162 925 170 593 333 518Unassigned 244 812 204 780 449 592

Abbreviation: COG, Clusters of Orthologous Groups.Percentages are of total reads.RefSeq Hits, number of reads with significant homology to the RefSeqdatabase; RefSeq Genes, number of unique accession numbers withinthose hits; Unassigned, ¼number of possible proteins that did nothave a significant hit to either the RefSeq or COG databases (47% ofpossible proteins).

Microbial metatranscriptome analysisSM Gifford et al

4

The ISME Journal

could overestimate transcript richness (if identicaltranscripts bin to different reference genes becauseof differences in the region sequenced or because ofsequencing errors), it also underestimates richness(if a variety of sequence variants bin to the samereference gene; Figure 3). In any event, despiteefforts to sequence more deeply than typical, ourlibraries exhibited the same low coverage that hasbeen reported in previous metatranscriptomic ana-lyses of marine bacterioplankton communities(Frias-Lopez et al., 2008; Poretsky et al., 2010;Stewart et al., 2010).

Metatranscriptomes might be expected to havelower richness compared with metagenomes ifexpression is limited to a small fraction of thebacterial genome at any one time. In this case, theywould also have higher coverage than metagenomesfor the same size sequence library (Gilbert et al.,2008). However, for this coastal metatranscriptome,the distribution of hits per gene (SupplementaryFigure S2) did not indicate dominance by a limitednumber of highly transcribed genes (Figure 2).

Similarly, a synchronized clonal population ofBacillus anthracis expressed 40–80% of genes underall growth conditions tested (Passalacqua et al.,2009), suggesting that the population’s transcrip-tome was only slightly less rich than its genome.Even allowing for significant advances in sequen-cing technology, the extremely low sample-sequen-cing depth found in this study suggests that mostnatural community transcriptomes will continue tobe undersampled.

MicrodiversityAssembly of transcripts from the most highlyexpressed genes (41000 reads for some) revealedsignificant variation within reference bins (Hollibaughet al., in press). For example, the 2259 reads thatbinned to the Pelagibacter ubique HTCC1002 pro-teorhodopsin gene (PU1002_03206; 1 of 28 proteor-hodopsin bins in the libraries) had high sequencediversity (Figure 3). That this observed diversity wasin fact real biological variation was substantiated by

Table 2 Estimation of the number of bacterioplankton mRNA molecules in coastal seawater and sequencing depth of themetatranscriptomic libraries

Calculation method Sample mRNA molecules per liter mRNA molecules per cellb Sequencing depth (%)

Internal standarda FN56 1.0�1012 238 0.000009FN57 0.6�1012 142 0.000015

Extracted RNA massc FN56 0.2�1012 48 0.000043FN57 0.4�1012 95 0.000020

Per cell RNA contentd FN56 2.6�1012 619 0.000003FN57 2.6�1012 619 0.000003

aThe libraries contained 4014 (FN56) and 6865 (FN57) copies of the internal standard out of a total of 500 092 (FN56) and 465 519 (FN57) potentialprotein encoding sequences. The standard was added at 4.7� 1010 copies per 5.75 liter of seawater just before cell lysis for total RNA extraction(see Materials and methods for details).bCell numbers in the 3 mM filtrate averaged 4.2� 109 per liter based on epifluorescence microscopy.cExtraction yields were 14.4 (FN56) and 32.9 (FN57) mg total RNA from 5.75 liter of seawater. Total RNA is assumed to contain 4% mRNA by mass(Neidhardt and Umbarger, 1996) and bacterial mRNAs are assumed to average 924 nt (Xu et al. (2006)).dMarine bacterial cells are assumed to contain 5.7 fg total RNA per cell (mid point of 1.9–9.5 fg range reported by Simon and Azam (1989)).See footnote c for estimate of percent mRNA by mass and footnote b for cell counts per litre.

0

42100

84300

126500

168700

25%

50%

75%

100%

taxa

COGs

0 516000333518

0 64500 129000 193500 258000 322500 387000 451500 516000

0

0

1900

3300

acce

ssio

n nu

mbe

rs

reads

Figure 2 Collector’s curve of gene richness as a function of reads analyzed. Light gray: FN56; dark gray: FN57; medium gray: combinedlibraries. Dashed lines indicate the number of reads needed to reach quarter percentiles of the total richness of the combined library.Inset: collector’s curves for taxonomic and functional gene category (COG) richness, with the y axis corresponding to the number ofunique reference organisms or COG numbers.

Microbial metatranscriptome analysisSM Gifford et al

5

The ISME Journal

an assembly of the internal standard reads (Figure 3),which indicated a mean sequencing error rate in thisstudy of 3.7±7.4 per 1000 bp compared with a meansequence variation rate of 97.6 (±28.0) per 1000 bpfor transcripts binning to PU1002_03206. Althoughhigh diversity in proteorhodopsin genes has beenfound previously in the ocean (Rusch et al., 2007;Campbell et al., 2008), the metatranscriptomicdata revealed simultaneous expression of scores ofmicrodiverse sequence variants. Transcriptomecoverage estimates based on gene binning to theRefSeq database are considerable underestimates ofthe true sequence richness.

Detection of biogeochemically informative transcriptsWe determined the absolute abundance of tran-scripts for key genes representing the P cycle(25 diagnostic genes), N cycle (50 genes) and Scycle (7 genes) (Figure 4). Transcripts were found for56% of genes surveyed. Most P and sulfur cycletransformations were represented by at least onetranscript. N cycle expression was dominated byammonia transporter and ammonia monooxygenasetranscripts, which had the highest copy numbers of

any gene category (2.7� 109 transcripts per liter;Figure 4); many other N cycle genes were notdetected at all.

To examine detection of biogeochemically diag-nostic mRNAs in theoretically smaller libraries, thefull metagenomic libraries were randomly sub-sampled in silico to generate subsets. The majorityof the elemental cycle transcripts detected in the fulllibraries were still evident in smaller libraries. Forexample, 480% of the P-cycle related genes wouldhave had at least one hit in a library one-fourth thesize (Supplementary Figure S3).

Statistical resolutionComparative metatranscriptomics seeks to differ-entiate transcript abundance between samples, forexample, across natural environmental gradients orin response to experimental manipulations. Weexamined the statistical power of comparativeanalyses as a function of library size, starting firstwith broad categories of gene function as repre-sented by COG assignments. Subsets of each of thereplicate libraries were generated in silico and the

0 700 1400 21000 350 700 1050 1400nt positionnt position

consensusdivergence (%)

referencedivergence (%)

coverage

readdistribution

100

50

100

50

P. ubique bacteriorhodopsin internal standard

1,825 reads 10,879 reads

Figure 3 Assembly of 1825 reads (out of 2259 total) binning to the P. ubique HTCC1002 proteorhodopsin gene PU1002_03206 (left), andof 10 879 reads (out of 10 879 total) binning to the internal transcript standard (right). (a) Percent nucleotide divergence from theconsensus sequence. (b) Percent nucleotide divergence from the reference sequence. (c) Coverage by nucleotide position. (d) Readassembly to the reference gene (shown in red), with dashed lines indicating start and end positions of the reference. Note that thereference gene lengths are extended by assembly gaps. Divergence from the consensus sequence (that is, the majority nucleotide at agiven position) is indicated as follows: A¼ red, T¼ green, C¼ blue and G¼ yellow. Insets show close-up regions of assemblies.

Microbial metatranscriptome analysisSM Gifford et al

6

The ISME Journal

NOxNH4

0.0

2.0

4.0

J-08 M-08 M-08 J-08 S-08 N-08 J-09 M-09 M-09 J-09 S-09 N-09 J-10

0.0

0.5

1.0

1.5 PiDOP

Con

cent

ratio

n (µ

m)

Pi high affinity transport

Pi low affinity transport

polyphosphate

alkaline phosphatase

phosphonate uptake

phosphonate lyase pathway

phosphonate2-aminoethylphsophonic acid pathway

phosphonate phosphonoacetic acid pathway

phosphonate phosphonoalanine pathway

dissimilatory nitrate reductionmembrane/cytoplasmic

dissimilatory nitrate reductionperiplasmic

nitrite reduction

nitric-oxide reduction

nitrous-oxide reduction

assimilatory nitrate reduction

hydroxylamine oxidoreductase

urease

nitrogen fixation

ammonia oxidation

ammonium transport

nitrogen regulation

DMSP cleavage

DMSP demethylation

sulfide metabolism

sulfate assimilation cysH

soxBsoxC

sulP

dmdA

dddPdddD

ntcA

amoC

hao

amoBamoA

amt

nifDnifHnifK

ureAureC

nasFnasD

nasA/narB

nirB

nosXnosLnosYnosFnosDnosZnosR

norFnorEnorDnorQnorBnorC

nirKnirNnirJnirHnirGnirDnirFnirCnirEnirS

napFnapDnapGnapHnapCnapBnapA

narInarJnarHnarGnarK

palCpalEpalA

phnB

phnZphnYphnXphnW

phnMphnNphnJphnI

phnHphnG

phnEphnD

phoAphoDphoX

ppk

pitA

phoUpstC/pstA

pstSphoR

N

P

S

log10 (transcripts L-1)

FN56FN57

9.46 7 8 9

low statistical power

sequencing depth

Figure 4 Copy numbers of phosphorus, nitrogen and sulfur cycle transcripts in a coastal ocean microbial community. The left linerepresents the limit of detection for this study, and together with the right line defines the region where copy numbers are too low forrobust statistical analysis (that is, where the fold-difference requirement is 42). Symbols indicate copy numbers in biological duplicates.Bottom graphs show monthly nutrient concentrations for GCE-LTER station six. The arrows mark the date of sample collection.

Microbial metatranscriptome analysisSM Gifford et al

7

The ISME Journal

fold-difference criteria (high abundance count/lowabundance count) needed for statistically significantdifferences were compared using a resamplingmethod based on difference of medians (Xipe;Rodriguez-Brito et al., 2006). Even for librariesone-fourth of the original size, there was little effecton the fold-difference threshold required for a COGcategory to be considered significantly differentbetween samples (Figure 5). This was true as wellfor an alternate statistical approach using contin-gency tables and Fisher’s exact test (White et al.,2009) (Figure 5, inset b), and also when analyzinglibraries much smaller than the original (forexample, the average fold-difference threshold forsignificance in a 10 000 read library was o1%greater than in a 500 000 read library).

Library size, however, had a direct impact on thenumber of counts in a transcript category, therebyaffecting the power of statistical comparisons.Transcript categories with low copy numbers(defined here as p15 hits in the lower abundancesample) required from 2- to 8-fold differencebetween the two samples for statistical signifi-cance (Figure 5). For smaller in silico subsetsof the libraries or for more specific transcriptannotation categories (for example, RefSeq gene

bins), both of which result in lower counts percategory, the power to detect statistical differencesbetween two samples decreased. For example, 17out of the 25 genes that mediate key steps in themarine P cycle fell into a low-count category evenwith the full-size library (Figure 4), and nearly allwould do so if the library was one-fourth of theoriginal size. For metatranscriptomic libraries of themagnitude obtained in this study (41 000 000 454FLX reads), only those transcripts present atconcentrations 41� 106 per liter had a good prob-ability of being detected, and only those present atconcentrations 41.5� 107 per liter (which wouldexclude all singletons and other low-count tran-script categories) could be compared across sampleswith good statistical power.

ReplicationThe need to improve sample-sequencing depthcompetes with the need for replication in compara-tive metatranscriptomic analyses. Two importantsources of variability that can be quantified throughreplication include technical variation during sam-ple processing/sequencing, and natural biologicalvariation within the environment sampled.

100%50%

25%

100%50%25%

0

2

4

6

820

18

16

14

12

10

8

6

4

2

0 5 10 15 20 25 30 35 40 45 50low abundance count

fold

cha

nge

(hig

h / l

ow)

XipeSignificant

XipeNon-Significant

fold

cha

nge

low abundance count

Fisher’s exactXipe

0 5045403530252015105

Figure 5 Minimum fold difference required for statistical significance (Xipe, Po0.05) as a function of both the count in the lowerabundance sample and the library size. Samples and subsamples were from the combined libraries (FN56 and FN57). Marker color isbased on the statistical outcome (significant or nonsignificant) and library size (percent of full library). (a) Zoom of region in the mainfigure. Note that the minimum fold-difference for significance is independent of the three library sizes analyzed. (b) An alternativeanalysis of the significance threshold using contingency tables and Fisher’s exact test. The minimum fold-difference threshold at which alow abundance count is significant by the Fisher’s exact test is plotted as a dotted black line. The results from the Xipe analysis (mainfigure) at the 100% library size are also shown in inset B for direct comparison with the Fisher’s exact test.

Microbial metatranscriptome analysisSM Gifford et al

8

The ISME Journal

For the first type, 454 pyrosequencing is proneto artifacts in which single DNA fragments aresequenced more than once (‘duplicate sequences’).Although artifactual duplicates are recognized inmetagenomes as sequences with identical 50-seq-uence and high identity throughout (Dinsdale et al.,2008; Gomez-Alvarez et al., 2009), true duplicatesequences can arise in metatranscriptomes fromdiscrete mRNAs from highly expressed genes. Inthis study, 24% of RefSeq reads were ‘duplicates’(same start site and X90% identity). As eachreplicate biological sample was sequenced as fourtechnical replicates (independent emulsion PCRsand sequencing runs), and assuming that artifactualduplicates arise during the emulsion PCR step(Gomez-Alvarez et al., 2009; Stewart et al., 2010),artifactual duplicates should have uneven distribu-tions across the four 454 runs, whereas naturalduplicates should be evenly distributed. We foundthat most duplicate clusters averaged B25% pertechnical sequencing replicate (SupplementaryFigure S4), and a statistical comparison of COGassignments for all six within-sample pairwisecombinations of the technical sequencing replicatesindicated that only 0.2% fit the pattern for artifac-tual duplicates (significantly higher in one technicalreplicate compared to the other three). For thetranscript with the highest copy number in thecombined library (Rac prophage; ZP_03400590),removal of duplicate reads would have decreasedthe count by 98% (from 6235 to 111 hits) despiteevidence from technical replicates that many ofthese are natural (Supplementary Figure S5). Dupli-cate removal from metatranscriptomic librariesbased on sequence start position and percentidentity (Gomez-Alvarez et al., 2009; Stewart et al.,2010) may therefore produce systematic underesti-mates of abundance for the most highly transcribedgenes in the community, and statistical analysis oftechnical replicates is a recommended alternative.

For the second type of variation, within-treatmentbiological variability sets the false-positive rateagainst which differences in gene expressionpatterns across treatments or environments can beevaluated (Poretsky et al., 2010). In this study,patchiness in community gene transcriptionpatterns was detectable in paired coastal seawatersamples separated by B300 m (based on tidalflushing rates past a fixed collection point). Atthe level of functional gene categories, pairwisecomparisons indicated significant differencesbetween the samples for 461 of 3298 COGs (14%)(Xipe, Po0.05). Only nine significant COGscontained sequences from a putative artifactualduplicate cluster (see above), highlighting thebenefit of technical replicate averaging for reducingspurious differences from sequencing artifacts. Inaccordance with other studies of environmentalsequence libraries (Rodriguez-Brito et al., 2006), aswell as our observations above, decreasing thelibrary size had a major influence on the number

of significant differences that were detectable(Supplementary Table S1).

Differences between replicate samples at theindividual gene level (that is, transcripts binned byRefSeq hits) were also examined, using Fisher’sexact test coupled with a correction for the falsediscovery rate (Strimmer, 2008) to control for type Ierrors arising when simultaneously conductinglarge numbers of statistical tests (in this case, for4186 000 different RefSeq bins). Eighty-three(0.05%) of the gene bins were statistically differentbetween the two samples (Po0.05 with Benjamini–Hochberg correction), including those representingphage genes, ammonia oxidation genes and variousgenes for light-driven energy acquisition (Supple-mentary Figure S6). The replicate samples thereforeestablished within-treatment variability (Figure 4)for future between-treatment comparisons.

Microbial gene expression in a coastal oceanTranscripts from the combined library binned togenes from 1909 reference organisms. Thirty-threepercent of the sequences had best hits to alphapro-teobacteria genes (with roseobacters accounting for11% and P. ubique for 7%) and 27% had best hitsto gammaproteobacteria genes (Figure 6). Unexpect-edly, 4% of the transcripts binned to the twoarchaeal genomes of Nitrosopumilus maritimus(3.3%) and Cenarchaeum symbiosum (0.1%).Although Archaea are often abundant and active indeep ocean environments, they were not expected tocontribute significantly to gene expression in thisshallow coastal water system; however, theN. maritimus taxonomic bin was the second largestin the metatranscriptome (Hollibaugh et al., in press).The oligotrophic marine gammaproteobacteriaclades, which are usually in low abundancein oceanic 16S rRNA libraries (o3%; Cho andGiovannoni, 2004), were also unexpectedly wellrepresented in the metatranscriptome (Figure 6; 8%of transcripts), and transcripts binning to threegenes from a gammaproteobacteria prophage (3%of transcripts) may indicate an ongoing infectionof these oligotrophic marine gammaproteobacteriapopulations. Eukaryotic transcripts composed 6%of the total, with those binning to Ostreococcus spp.particularly well represented (20% of eukaryotic hits).

Copy numbers of transcripts representing 82genes diagnostic for P, N and S cycling weredetermined simultaneously from the metatranscrip-tomic data (Figure 4). For P transformations,annotations suggest bacterioplankton were trans-porting phosphate by both high- and low-affinitytransporters. The expression of low-affinity trans-porters, along with polyphosphate storage genes, isconsistent with elevated phosphate concentrations(1.2 mM) at the time of sampling, which is typical oflate summer in this coastal ocean (Figure 4).Expression patterns also indicated ongoing use oforganic P, including phosphoesters (by phoX, phoD

Microbial metatranscriptome analysisSM Gifford et al

9

The ISME Journal

and phoA) and phosphonates (although transcriptsfor the canonical C–P lyase pathway were near thelimit of detection). For N transformations, samplingoccurred during a local ammonia peak (2.6 mM;Figure 4), and transcripts related to the uptake andoxidation of ammonia (amt, amoA, B and C) wereorders of magnitude higher in abundance than genesmediating nitrate or nitrite processing (for example,

nar, nap and nir genes) (Figure 4). Transcriptsfor urea metabolism, the only representative ofdissolved organic N use included in the analysis,made up the second most abundant group ofN-related sequences (Figure 4). Nitrogen is oftenthe limiting nutrient (or co-limiting with carbon;Pomeroy et al., 2000) to microbial activity in thisecosystem, and dissolved organic N is 2- to 200-fold

combined

FN56

FN57

0 0005 00001 00051 00002 25000

1127CCTH .ps retcabigaleP sutadidnaC1MCS sumitiram sulimuposortiN0802CCTH muiretcaboetorp ammag eniram991LAB muiretcaboetorp ahpla

2001CCTH euqibu retcabigaleP sutadidnaCB−15RON muiretcaboetorp ammag3412CCTH muiretcaboetorp ammag eniram415nillE muiretcab8412CCTH muiretcaboetorp ammag eniramA2−420SM muiretcab airetcabovalF2601CCTH euqibu retcabigaleP sutadidnaC5522CCTH muiretcab selaretcabodohR7022CCTH muiretcaboetorp ammag1099ECC suniramicul succocoertsO3−5RON muiretcaboetorp ammag73−E atallets aluttigaS

WS munetnec mulliripsodohR3802CCTH muiretcab selaretcabodohR17TK silarotil retcabigergnoCb3−KwzA .ps retcaboesoR21 LFD eabihs retcaboesoroniD1−SD snarovitnemaval mulucabivraP11HLK muiretcab eaecaretcabodohR5321GD muiretcab eaiborcimocurreV701adeU sucinopaj oirbivlleC11−LFD iirdnaxela aiznerbaL7978 MSD siram secymotcnalP3−SSD iyoremop airegeuRsisneiraseac retcabiinutpeN7RGbE−LH .ps oirbivilaklaoihT04−2 snadarged sugahporahccaS0512CCTH muiretcab selaretcabodohR07111 CCTA murbur mulliripsodohR31BK muiretcaboetorp ateb6932 CTCK sisneujehc allehaH5069CC .ps succocohcenyS11R .ps airegeuR5301MT .ps suiravoesoRC3−420SM muiretcab airetcabovalF941 hcO silarotil retcaboesoRirbursiram allenamreB

2SCC .ps retcaboesoRMSI snebihnibun suiravoesoR1062CCTH .ps suiravoesoR792DEM .ps aekenieRiitdrahnier sanomodymalhC

1−TO adicigla aidroK41621 MAI atagergga aiznerbaL398GD alocigla retcaboniraM

Gammaproteobacteria bacteriophage

hits per organism

hits per organism

rank

ord

er

1800

200

800

1400

25000150005000

Figure 6 Rank-order abundance of taxonomic bins (species or strain level). Main figure: top 50 taxonomic annotation bins; inset:all 1909 taxonomic annotation bins.

Microbial metatranscriptome analysisSM Gifford et al

10

The ISME Journal

higher in concentration than inorganic N. For S trans-formations, gene expression suggested substantial useof reduced S compounds typically found in highconcentrations in marsh-dominated coastal systems(Kiene and Capone, 1988; Pakulski and Kiene, 1992).Transcripts were found for metabolism of dimethyl-sulfoniopropionate (dmdA, dddP and dddD), aswell as oxidation of sulfide/thiosulfate (sox genes)(Figure 4). This broad inventory of P, N and S cycletranscripts represents an absolute benchmark againstwhich time-series and experimentally manipulatedtranscriptomes in this ecosystem can be compared.

Conclusions

Addition of an internal mRNA standard provides asignificant advantage in metatranscriptomics proto-cols as it allows estimation of the fraction of themicrobial transcriptome captured in the sequencelibrary, as well as the absolute quantification oftranscript copy number in the environment (Figures1 and 4). Although RT-qPCR approaches canalso provide absolute transcript numbers, often withgreater sensitivity (Church et al., 2010), they arecurrently limited to a handful of functional genes ata time. Furthermore, the high microdiversity foundin many natural gene populations (Figure 3) makesprimer design challenging (Varaljay et al., 2010), andlikely results in RT-qPCR only quantifying a subsetof the total functional gene population. Multipleinternal standards that vary in length and concen-tration (van de Peppel et al., 2003) will allow formore robust calculations of sequencing depth infuture studies, and better position ‘omics’ data forintegration with biogeochemical rate measurements.

As low-count transcript categories are difficult toresolve statistically, library size had a critical effecton comparative metatranscriptomic analyses. Manyof the biogeochemically diagnostic transcriptsdetected in our libraries would have been detectedin ones that were one-fourth or one-tenth the size,but these theoretically smaller libraries resulted ina decreased ability to statistically differentiatebetween samples. Typical library sizes for metatran-scriptomes (105–106 sequence reads) are thereforesufficient for descriptive studies, but significantgains in comparative analyses of biogeochemicallyinformative gene expression patterns will require agreater sequencing investment. Indeed, 54% of the46 detected steps in the marine N, P and S cycleswould require at least a twofold difference in copynumber between samples to meet statistical criteriafor hypothesis testing (that is, Po0.05). Although atwofold change in transcript abundance is anappropriate minimum criterion for expression stu-dies of clonal bacterial cultures in synchronizedgrowth (Burgmann et al., 2007), it may fail to catchsmaller expression differences among complex micro-bial communities that are ecologically relevant.

Despite a sample-sequencing depth of only 1 in107 transcripts, the libraries provided remarkable

insights into gene expression in a marine microbialcommunity, including evidence for active microbesnot known previously to have a major role in theecosystem and quantification of transcripts forscores of steps in marine elemental cycles. Thesedata establish the foundation for comparativeassessments of diel, seasonal and annual changesin microbial gene expression that will provideinsights into the regulation of biogeochemicalprocesses in the coastal ocean.

Acknowledgements

We thank R Newton for assistance with sample collectionand bioinformatics analysis, L Tomsho and S Schusterat Penn State University for 454 sequencing expertise,JT Hollibaugh for comments and discussion on the articleand S Rathbun for helpful discussions on statisticalmethods. Nutrient data were provided by K Hunter andS Joye through the Georgia Coastal Ecosystems Long TermEcological Research program (OCE-0620959). This projectwas supported by funding from the Gordon and BettyMoore Foundation and the National Science FoundationMicrobial Observatories Program (MCB-0702125).

References

Azam F, Hodson RE. (1977). Size distribution and activityof marine microheterotrophs. Limnol Oceanogr 22:492–501.

Burgmann H, Howard EC, Ye WY, Sun F, Sun SL,Napierala S et al. (2007). Transcriptional response ofSilicibacter pomeroyi DSS-3 to dimethylsulfoniopro-pionate (DMSP). Environ Microbiol 9: 2742–2755.

Campbell BJ, Waidner LA, Cottrell MT, Kirchman DL.(2008). Abundant proteorhodopsin genes in the NorthAtlantic Ocean. Environ Microbiol 10: 99–109.

Cho J-C, Giovannoni SJ. (2004). Cultivation and growthcharacteristics of a diverse group of oligotrophicmarine gammaproteobacteria. Appl Environ Microbiol70: 432–440.

Church MJ, Wai B, Karl DM, DeLong EF. (2010). Abun-dances of crenarchaeal amoA genes and transcripts inthe Pacific Ocean. Environ Microbiol 12: 679–688.

Coyne KJ, Handy SM, Demir E, Whereat EB, Hutchins DA,Portune KJ et al. (2005). Improved quantitative real-time PCR assays for enumeration of harmful algalspecies in field samples using an exogenous DNAreference standard. Limnol Oceanogr Meth 3: 381–391.

Dinsdale EA, Edwards RA, Hall D, Angly F, Breitbart M,Brulc JM et al. (2008). Functional metagenomicprofiling of nine biomes. Nature 452: 629–632.

Ducklow HW. (2000). Bacterial production and biomass inthe oceans. In: Kirchman (ed). Microbial Ecology of theOcean , 1st edn. Wiley-Liss: New York, NY.

Frias-Lopez J, Shi Y, Tyson GW, Coleman ML, SchusterSC, Chisholm SW et al. (2008). Microbial communitygene expression in ocean surface waters. Proc NatlAcad Sci 105: 3805–3810.

Gilbert JA, Field D, Huang Y, Edwards R, Li W, Gilna Pet al. (2008). Detection of large numbers of novelsequences in the metatranscriptomes of complexmarine microbial communities. PLoS ONE 3: e3042.

Microbial metatranscriptome analysisSM Gifford et al

11

The ISME Journal

Gomez-Alvarez V, Teal TK, Schmidt TM. (2009). Syste-matic artifacts in metagenomes from complex micro-bial communities. ISME J 3: 1314–1317.

Hannah MA, Redestig H, Leisse A, Willmitzer L. (2008).Global mRNA changes in microarray experiments. NatBiotechnol 26: 741–742.

Hewson I, Poretsky RS, Beinart RA, White AE, Shi T,Bench SR et al. (2009a). In situ transcriptomic analysisof the globally important keystone N2-fixing taxonCrocosphaera watsonii. ISME J 3: 618–631.

Hewson I, Poretsky RS, Dyhrman ST, Zielinski B, WhiteAE, Tripp HJ et al. (2009b). Microbial community geneexpression within colonies of the diazotroph, Tricho-desmium, from the Southwest Pacific Ocean. ISME J 3:1286–1300.

Hollibaugh JT, Gifford SM, Sharma S, Bano N, Moran MA.(2010). Metatranscriptomic analysis of ammonia-oxidizing organisms in an estuarine bacterioplanktonassemblage. ISME J. In press.

Kiene RP, Capone DG. (1988). Microbial transformations ofmethylated sulfur-compounds in anoxic salt-marshsediments. Microb Ecol 15: 275–291.

Margulies M, Egholm M, Altman WE, Attiya S, Bader JS,Bemben LA et al. (2005). Genome sequencing inmicrofabricated high-density picolitre reactors. Nature437: 376–380.

Neidhardt FC, Umbarger HE. (1996). Chemical composi-tion of Escherichia coli. In: Bock A, Curtiss III R, KaperJB, Karp PD, Neidhardt FC, Nystrom T et al (eds).EcoSal—Escherichia coli and Salmonella: Cellularand Molecular Biology, 2nd edn. ASM Press:Washington, DC.

Pages H, Aboyoun P, Gentleman R, DebRoy S. (2009).Biostrings: string objects representing biological seq-uences, and matching algorithms. R package version2.14.8.

Pakulski JD, Kiene RP. (1992). Foliar release of dimethyl-sulfoniopropionate from Spartina alterniflora. MarEcol-Prog Ser 81: 277–287.

Passalacqua KD, Varadarajan A, Ondov BD, Okou DT,Zwick ME, Bergman NH. (2009). Structure andcomplexity of a bacterial transcriptome. J Bacteriol191: 3203–3211.

Pomeroy LR, Sheldon JE, Sheldon WM, Blanton JO, Amft J,Peters F. (2000). Seasonal changes in microbial pro-cesses in estuarine and continental shelf waters of thesouth-eastern USA. Estuar Coast Shelf S 51: 415–428.

Poretsky RS, Bano N, Buchan A, LeCleir G, Kleikemper J,Pickering M et al. (2005). Analysis of microbial genetranscripts in environmental samples. Appl EnvironMicrobiol 71: 4121–4126.

Poretsky RS, Gifford SM, Rinta-Kanto J, Vila-Costa M,Moran MA. (2009a). Analyzing gene expression frommarine microbial communities using environmentaltranscriptomics. JoVE 2. (http://www.jove.com/index/details.stp?ID¼ 1086).

Poretsky RS, Hewson I, Sun SL, Allen AE, Zehr JP, MoranMA. (2009b). Comparative day/night metatranscrip-tomic analysis of microbial communities in the North

Pacific subtropical gyre. Environ Microbiol 11:1358–1375.

Poretsky RS, Sun S, Mou X, Moran MA. (2010). Transpor-ter genes expressed by coastal bacterioplankton inresponse to dissolved organic carbon. Environ Micro-biol 12: 616–627.

R Development Core Team (2009). R: A Language andEnvironment for Statistical Computing v210.0.R Foundation for Statistical Computing: Vienna,Austria, (http://www.R-project.org).

Robinson M, Oshlack A. (2010). A scaling normalizationmethod for differential expression analysis ofRNA-seq data. Genome Biol 11: R25.

Rodriguez-Brito B, Rohwer F, Edwards RA. (2006). Anapplication of statistics to comparative metagenomics.BMC Bioinformatics 7: 162.

Rusch DB, Halpern AL, Sutton G, Heidelberg KB,Williamson S, Yooseph S et al. (2007). The SorcererII Global Ocean Sampling expedition: northwestatlantic through eastern tropical pacific. PLoS Biol 5:398–431.

Shi YM, Tyson GW, DeLong EF. (2009). Metatranscrip-tomics reveals unique microbial small RNAs in theocean’s water column. Nature 459: 266–269.

Simon M, Azam F. (1989). Protein-content and protein-synthesis rates of planktonic marine bacteria. Mar EcolProg Ser 51: 201–213.

Stewart FJ, Ottesen EA, DeLong EF. (2010). Developmentand quantitative analyses of a universal rRNA-sub-traction protocol for microbial metatranscriptomics.ISME J 4: 896–907.

Strimmer K. (2008). A unified approach to false discoveryrate estimation. BMC Bioinformatics 9: 303.

Tatusov R, Fedorova N, Jackson J, Jacobs A, Kiryutin B,Koonin E et al. (2003). The COG database: anupdated version includes eukaryotes. BMC Bio-informatics 4: 41.

Urich TA, Lanzen J, Qi DH, Huson DH, Schleper C,Schuster SC. (2008). Simultaneous assessment of soilmicrobial community structure and function throughanalysis of the meta-transcriptome. PLoS ONE 3:e2527.

van de Peppel J, Kemmeren P, van Bakel H, Radonjic M,van Leenen D, Holstege FCP. (2003). Monitoring globalmessenger RNA changes in externally controlledmicroarray experiments. EMBO Rep 4: 387–393.

Varaljay VA, Howard EC, Sun SL, Moran MA. (2010). Deepsequencing of a dimethylsulfoniopropionate-degrad-ing gene (dmdA) by using PCR primer pairs designedon the basis of marine metagenomic data. ApplEnviron Microbiol 76: 609–617.

White JR, Nagarajan N, Pop M. (2009). Statistical methodsfor detecting differentially abundant features inclinical metagenomic samples. PLoS Comput Biol 5:e1000352.

Xu L, Chen H, Hu XH, Zhang RM, Zhang Z, Luo ZW.(2006). Average gene length is highly conserved inprokaryotes and eukaryotes and diverges only betweenthe two kingdoms. Mol Biol Evol 23: 1107–1108.

Supplementary Information accompanies the paper on The ISME Journal website (http://www.nature.com/ismej)

Microbial metatranscriptome analysisSM Gifford et al

12

The ISME Journal