An automated pipeline for phylogenomic analysis (AMPHORA) is presented that overcomes existing limits to large-scale protein phyAMPHORA for phylogenomic analysis Abstract The explosive growth of genomic data provides an opportunity to make increased use of protein markers for phylogenetic inference We have developed an automated pipeline for phylogenomic analysis (AMPHORA) that overcomes the existing bottlenecks limiting large-scale protein phylogenetic inference We demonstrated its high throughput capabilities and high quality results by constructing a genome tree of 578 bacterial species and by assigning phylotypes to 18,607 protein markers identified in metagenomic data collected from the Sargasso Sea Background Since the 1970s the use of small subunit (SSU) rRNA (SSU rRNA) sequences has revolutionized microbial classification, systematics, and ecology The SSU rRNA gene has become the most sequenced gene, with hundreds of thousands of its sequences now deposited in public databases It has become the current 'gold standard' in microbial diversity studies, and for good reasons For one, it is present in all microbial organisms For another, the gene sequence is highly conserved at both ends This enables one to obtain nearly full-length SSU rRNA gene sequences by polymerase chain reaction amplification using 'universal' primers and without having to isolate and culture the organism in question Until very recently, the vast majority of microbes were identified and classified only by recovering and sequencing their SSU rRNA genes This single sequence of approximately 1.5 kilobases is often the only information we have about the organism from which it came - the only way we know that it exists in the natural environment Although the SSU rRNA gene has been extremely valuable for phylogenetic studies, it has its limitations For example, it has been well documented that evolutionarily distant SSU rRNA genes that are similar in nucleotide composition have been consistently - but nevertheless incorrectly - placed close together in phylogenetic trees [1,2,1] Furthermore, inferring the phylogeny of organisms from any single gene carries some risks and must be corroborated by the use of other phylogenetic markers Many researchers turned to protein encoding genes such as EF-Tu, rpoB, recA, and HSP70 [3] Because protein sequences are conserved at the amino acid level instead of at the nucleotide level, phylogenetic analyses of protein sequences are in general less prone to the nucleotide compositional bias seen in SSU rRNA [2,4-6] In addition, the less constrained variation at the third codon position allows these genes to be used in studies of more closely related organisms However, because of difficulties in cloning protein encoding genes from diverse species, SSU rRNA remained the gold standard The situation changed with the advent of genomic sequencing Each complete genome sequence brings with it the sequences for all protein encoding genes in that organism Now, not only can one build gene trees based on a favorite Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, protein encoding gene, but also one has the option to concatenate multiple gene sequences to construct trees on the 'genome level' Possessing more phylogenetic signals, such 'genome trees' or 'super-matrix trees' are less susceptible to the stochastic errors than those built from a single gene [7] Recent studies attempting to reconstruct the tree of life have demonstrated the power of this approach [8,9] (for review [10]) Likewise, genome trees have also been used successfully to reassess the phylogenetic positions of individual species [11,12] It is worth pointing out, however, that the genome trees are still susceptible to systematic errors caused by compositional biases, unrealistic evolutionary models, and inadequate taxonomic sampling [7,13,14] Despite its demonstrated usefulness, phylogenetic inference based on protein markers has been limited in application, mainly because of the formidable technical difficulties inherent in this approach Typically, molecular phylogenetic inference involves three steps: retrieval of homologous sequences, creation of multiple sequence alignments, and phylogenetic tree construction Because only characters of common ancestry can be used to infer the evolutionary history, the most critical step is sequence alignment, in which sequences are overlaid horizontally on each other in such a way that, ideally, each column in the alignment would only contain homologous characters (amino acids or nucleotides) To ensure this positional homology, the alignments must be curated - a process that evaluates the probable homology of each column or position in the alignments Positions for which the assignment of homology is uncertain are then excluded from further analysis by masking [15] Judicious masking increases the signal-to-noise ratio and often improves the discriminatory power of the phylogenetic methods [16] Unfortunately, curation requires skilled manual intervention, thus making it impractical to process suitably the massive amount of genome sequence data now available Frequently, masking is simply ignored Automated masking to remove alignment positions that contain gaps or that have a low degree of conservation has not been satisfactory For example, using these criteria and given a set of ad hoc parameters such as the minimum block length, GBLOCKS automatically selects conserved blocks from multiple sequence alignments for phylogenetic analysis However, trees constructed using GBLOCKS-treated alignments have been found to have dramatically weaker support, possibly because of excessive removal of informative sites by the program [17] In addition, although many programs are available to automate the creation of multiple sequence alignments, their use for the de novo alignment of a large protein family is still fairly time consuming To overcome these problems, we have developed an automated pipeline for building concatenated genome trees using multiple protein markers, thus making this powerful method applicable on a larger scale Our pipeline can rapidly and Volume 9, Issue 10, Article R151 Wu and Eisen R151.2 accurately generate highly reproducible multiple sequence alignments for a set of selected phylogenetic markers More importantly, unlike previous automated methods [9] it can mask the alignments with quality equivalent to that of curation by humans The same pipeline can also be applied to metagenomic data analyses In metagenomics or environmental genomics, natural populations of microbes are collected from the environment; their DNAs are cloned and directly sequenced One fundamental goal of metagenomics is to determine who is present in the community and what they are doing Phylogenetic analysis of markers present in these collected samples can be very informative in revealing who is there If the marker happens to be part of a larger assembled sequence fragment, then the entire fragment can be anchored by that marker to a specific taxonomic clade In this way, environmental shotgun sequences can be sorted into taxon-specific 'bins' in silico, thereby allowing us to determine who is doing what The most striking finding to date from this approach was the discovery of a proteorhodopsin gene in bacteria, a homolog of the bacteriorhodopsin gene previously found only in some archaea In this case, the gene could be anchored within the bacteria because it was found to be associated with a bacterial SSU rRNA gene [18] However, because the SSU rRNA gene constitutes only a tiny fraction of any genome, the probability that any given sequence fragment can be anchored to a specific taxonomic clade by using this one gene is small Thus, phylotyping of metagenomic data can greatly benefit from the use of alternative phylogenetic markers such as the multiple protein markers described below In this paper, we introduce AMPHORA (a pipeline for AutoMated PHylogenOmic infeRence) and demonstrate two significant applications: building a genome tree from 578 complete bacterial genomes that are available at the time of the study and identifying bacterial phylotypes from metagenomic data collected from the Sargasso Sea Results and discussion The AMPHORA pipeline Introduction With the rapid increase in available genomic sequence data, there is an ever-urgent need for automated phylogenetic analyses using protein sequences However, automation is frequently accompanied by reduced quality We introduce here a fully automated method that is not only fast but also is of high quality The main components of our approach are shown in Figure 1, and their implementation is described in detail in the Material and methods section (below) Designed to align and trim protein sequences rapidly, reliably, and reproducibly, AMPHORA eliminates one of the tightest bottlenecks in large-scale protein phylogenetic inference It can Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.3 Query sequences seed seed seed seed seed mask VKVNLDWIESE IFEKED PAPFLEHVNGILVPGGFG VKVNLDWVESE TFEGDEGAAAARLENAHAIMVPGGFG AKVSIRWVDAE NVHDEE AESLLGGVDGILVPGGFG ARVKLAFIDST KLE-EG DLSDLDKVDAILVPGGFG CRVVLTYLDSE RIESEG IGSSFDDIDAILVPGGFG 1111111111100000000000011111111111111 i Phylogenetic i Marker multiple sequence alignment Marker Database HMM model Select markers Align and mask Search against representative genomes Multiple sequence alignment seed seed seed seed seed query query query query mask VKVNLDWIESE -IFEKED PAPFLEHVNGILVPGGFG VKVNLDWVESE -TFEGDEGAAAARLENAHAIMVPGGFG AKVSIRWVDAE -NVHDEE AESLLGGVDGILVPGGFG ARVKLAFIDST -KLE-EG DLSDLDKVDAILVPGGFG CRVVLTYLDSE -RIESEG IGSSFDDIDAILVPGGFG TRVDIHWVDSE -KIEERG AEALLGDCDSVLVAGGFG TRVNIKWIDSE -ILVDN LALLYDVDSLLIPGGFG MKVDIEWIDSEDLEKADDEK LDEIFNEVSGILVAGGFG TKVELKWVDSE -KLENME SSEVFKDVSGILVAGGFG 1111111111100000000000000011111111111111 Build masks Trim query query query query HMM TRVDIHWVDSELGDCDSVLVAGGFG TRVNIKWIDSELYDVDSLLIPGGFG MKVDIEWIDSEFNEVSGILVAGGFG TKVELKWVDSEFKDVSGILVAGGFG Tree inference Steps in building Steps in building a ildi Phylogenetic Marker Database Figure A flowchart illustrating the major components of AMPHORA A flowchart illustrating the major components of AMPHORA The marker protein sequences from representative genomes are retrieved, aligned, and masked Profile hidden Markov models (HMMs) are then built from those 'seed' alignments New sequences of interest are rapidly and accurately aligned to the trusted seed alignments through HMMs Predefined masks embedded within the 'seed' alignment are then applied to trim off regions of ambiguity before phylogenetic inference Alignment columns marked with '1' or '0' were included or excluded, respectively, during further phylogenetic analysis be used for phylogenetic analyses of single genes or whole genomes Protein phylogenetic marker database The core of AMPHORA is a protein phylogenetic marker database that contains curated protein sequence alignments with trimming masks and corresponding profile hidden Markov models (HMMs) Thirty-one protein encoding Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, phylogenetic marker genes (dnaG, frr, infC, nusA, pgk, pyrG, rplA, rplB, rplC, rplD, rplE, rplF, rplK, rplL, rplM, rplN, rplP, rplS, rplT, rpmA, rpoB, rpsB, rpsC, rpsE, rpsI, rpsJ, rpsK, rpsM, rpsS, smpB, and tsf) from representatives of complete bacterial genomes were individually aligned using CLUSTALW The alignments were curated and trimming masks were added manually by visually inspecting the alignments We selected these proteins because they are universally distributed in bacteria; the vast majority of them exist as single copy genes within each genome; and they are housekeeping genes that are involved in information processing (replication, transcription, and translation) or central metabolism, and thus are thought to be relatively recalcitrant to lateral gene transfer [19] High quality and highly reproducible sequence alignments Molecular phylogenetic inference assumes common ancestry, or homology, for every single column of a multiple sequence alignment When this assumption is violated, phylogenetic signal can be obscured by noise It has been shown that alignment quality can have greater impact on the final tree than does the tree building method employed [20] Therefore, preparing high quality sequence alignments is a most critical part of any molecular phylogenetic analysis This preparation typically involves careful but tedious manual editing and trimming of the generated alignments, and thus remains the greatest challenge to automation When scaling up this process, the trimming step is often simply ignored Automated trimming based on the number of gaps in each column or each column's conservation score can be used to select conserved blocks, but this still is not satisfactory when a high quality tree is required [17] Volume 9, Issue 10, Article R151 Wu and Eisen R151.4 comparing them only once each to the HMM model As a result, the computational cost increases linearly with the number of sequences to be aligned In contrast, the computational cost of a pair-wise alignment approach increases polynomially and can soon become prohibitively expensive Application I: Bacterial genome trees Constructing a 'genome' tree We downloaded 578 complete bacterial genomes available at the time of our study from the National Center for Biotechnology Information (NCBI) RefSeq collections (Additional data file 1) Protein marker sequences for 31 proteins were retrieved, aligned, trimmed, and concatenated as described in the Materials and methods section (see below) This resulted in a mega-alignment of 5,591 good amino acid positions (columns) by 578 species (rows) A maximum likelihood genome tree was constructed from this mega-alignment (Additional data file 2) A bootstrapped maximum likelihood genome tree of 310 representatives is shown in Figure We overcame this problem by taking advantage of a unique feature of profile HMM-based multiple sequence alignments When using HMMs to align sequences, new sequences can be mapped back, residue by residue, onto the 'seed' alignment from which that HMM originated When the seed alignment includes an accurate human curated mask, the newly generated alignments can be automatically trimmed accordingly, thus producing high quality alignments without requiring further human intervention In addition, the HMM model is the only variable in this automated alignment and trimming When the same model is used, the alignments generated thereby are completely additive and reproducible, thus enabling meaningful comparison of the results from different phylogenetic studies or different researchers As with trees built from SSU rRNA data, all of the major bacterial phyla are well separated into their own monophyletic groups, even though the relationships among some of them remain unclear Strikingly, unlike the SSU rRNA tree, the bushy area (intermediate levels) of our tree is highly resolved In the γ-proteobacteria, for example, the nodes separating taxa into different orders, families, and genera receive generally excellent bootstrapping support, whereas uncertainty is high in the corresponding regions of the SSU rRNA tree (Additional data file 3) Highly robust organismal phylogenies of γ-proteobacteria and α-proteobacteria have been inferred previously using hundreds of commonly shared genes [21,22] and are congruent to our genome tree This reflects the much-reduced stochastic noise present in the concatenated protein sequences compared with that of a single, slowly evolving SSU rRNA gene This uncertainty in the SSU rRNA tree - the backbone of modern microbial systematics often prevents microbial taxonomists from placing new species or genera within higher taxa, particularly at these intermediate levels [23] When such assignments were nevertheless made for these problematic taxa, inconsistency was introduced into the taxonomic nomenclature For example, taxa assigned to the orders Alteromondales, Pseudomonadales, and Oceanospirillales in Bergey's Taxonomic Outline of Prokaryotes [23] are intermingled and paraphyletic in our genome tree It is our view that the taxonomy needs to be revisited and possibly revised in such cases Speed Genome-based microbial taxonomy Another big advantage of using an HMM-based approach is speed For example, AMPHORA needs only 0.5 minutes on an average desktop computer (Intel Pentium CPU 3.2 GHz) to align 340 sequences of the rpoB family In comparison, the same job takes de novo pair-wise alignment methods such as CLUSTALW and MUSCLE 120 and 12 minutes, respectively This is because our HMM-based method aligns sequences by Although use of SSU rRNA was a landmark advancement in molecular microbial systematics, genome sequences provide an important alternative and complement [11,12] Phylogenetic trees built from multiple genes are more robust in resolving taxonomic relationships below the phylum level and hence provide an excellent alternative phylogenetic framework for microbial systematics Until many more Genome Biology 2008, 9:R151 Genome Biology 2008, Prochlorococcus marinus subsp pastoris str CCMP1986 s str MIT 9515 Prochlorococcus marinu elongatus PCC 6301 Synechococcus 7120 Nostoc sp PCC29413 C variabilis ATC IMS101 Anabaena eum ium erythra C 6803 Trichodesm chocystis sp PC ES 843 a NI Syne ruginosMBIC11017 stis ae Microcy loris marina gatus BP ch a 13 us elon Acaryo hococc sp JA 3BCC 7421 fl osynec us sP Therm echococc violaceu iacus J 1041 Syn bacter aurant SM 13 779 G loeo oflexus holzii D CC DB1 T CB 10 Chlor s casten s A tiacu ides sp sis K 55 lo iflexu uran o Rose iphon a lococctubercu MC2 0152 tr a etos Deh sp para atis s IFM 1RHA14 Herp gm ica sp ub m s m sme farcin ccus ns YS3129 1 u aviu o ie um cteri ardia doc ffic TC K4 cteri ycoba Noc Rhoum e e NC eium 233 oba a jeik RL 20 M ri Myc cte eri S R eba diphth rium ea N CN B cI3 la N te a ryn a Co rium bac ythr nico a C sp C 14 c cte ryne a er are pic CN pe eba C o spor ora tro ran lni A AN1 a YXB p o F a E sc 11 ryn ly Co po linis isp sp fu s 68 aro Sa alin an ia a icu 24 S Fr ank bifid lyt MA FBTC1 cch lo Sa Fr mo llu is sp s 20 er ce itil er en 33 B0 is Th us erm act esc C TC ns 16 rm av rob ur TC C ne 02 the es rth r a A str iga 30 12 ido yc A acte rum xyli ich RS 17 Ac om m S A b a pt ro in sp r s P re th on b te an K St Ar alm li su ibac oler nes s xy v ot c la di a m riu nia C ium t e so s r ac eif cu cte L oc a oc nib ne io Ki rop P nib Re B ifid ob ac te riu T m ob rop ad N Fe ac he ol oc rv te ry es ar r x m ce di Th ido yla a nt oid er ba mo ct n w is e sip eriu Aq op hip AT s s T h m uif hil ple C p Th her o m no ex us D i T C JS6 De mo ela ae S W erm ino oto tog nes sumolic M 908 03 De co cc ino Pet ga a le ien R us 94 B us co rot m ttin sis t1 VF Bu uchn B uc ch Th geoth ccus oga aritim gae BI4 B era hne erm e m Buc nera aph rm radio ob a M TM 29 us hne aph id ap the alis du ilis S SB O a idico icola hidic rm DS ran J9 la s str ola p op M s R Wig hidic tr A Bp str hil 11 gle ola us 30 swo str PS A BaizoCc C H cy S n in rt Can Cand hia glog Sch rthos gia p ara c B8 dida idatu ss izap iph ista edr inid on cia i tus Bloc s Bloc ia e his gra pis e um nd hm hm m Bau annia annia osym inum Sod b m p Esch alis glos annia ennsyflorida iont sinid cica lvan nus erich ic Erwin ia coli O ius str dellinic us ola mo ia 15 Serra carotovo7 H7 st rsitans tia pr S r Saka Y ot C Photor ersinia en eamacul RI1043 i teroco ans habdus Mannhe Actinobacillu lumines litica 80568 81 cens s imia su ccinicip succinogen TTO1 es Haem op roducens MB 130Z hil EL55 Haemop us influenzae Pit E hilus somn tEE Pasteurella us 129P mul T Haemophilus tocida str Pm70 Actinobacillus pleur ducreyi 35000HP opneumoniae L20 Vibrio fischeri ES114 Photobacterium profundum SS9 Aeromonas salmonicida subsp A449 Aeromonas hydrophila ATCC 7966 Psychrom onas ingrahamii 37 ATCC 700345 Shewanella pealeana raea 34H Colwellia psychreryth TAC125 as haloplanktis is L2TR udoalteromon Pse loihiens Idiomarina s atlantica T6c romona MWYL1 Pseudoalte nomonas sp M 3043 DS M ari lexigensKCTC 2396 acter sa is VT8 mohalob lla chejuensaquaeolei 40 Chro r s Hahe adan 01 obacte Marin gus degrutzeri A15 73 us f aropha as st Sacch udomon er arctic PRw 78 P se robact acter spC 179 ch C ADP Psy sychrob nii AT sp P uman cter sis SK2 A en ii H a r ba toba acte Acine borkum kutan nific g o etob ius ma CL rax Acin nivo osoc thia na X 11 Alca icomytus Ru noge ida U 493 y ru ovic SA orb es dida c V tus Can spira bsp n etii Rstr C ula1 dida c u ro rn a Can mic sis s a bu phil eme 85 L1 Thio laren oxiell umo sa T str ila S E tu C pne tidio torialoph LH 07 lla ise ella a fas esicaa hei M 19 03A h nc C t ion ll Fra eg Xyle pv v ospir hrlic TC S1 r Ba L e i A VC st PH 42 tris od es rh ola an s us S S mp H alo m nic oce dosu ulat ans sp J 00 C ca r s li s as ali ccu r no ap vo rax AA F0 CJ on Alk oco acte us c cidoovo rulli e E ns S66 18 om ros lob cc ia a cid cit nia J T1 nth Xa Nit he loco elft A sp eise nivo s sp ns PM b e Dic thy D su cter hale ona duc ilum Me ae a ht m e ph en ob ap ro rrir lei av phr s n ola fe tro ax ine na P rax pe o or fe m ov erm rom iu id V la ho lib Ac Po R ethy M Ru Po 51 81 12 A 16 018 SM BA 11 37 M4 ns D CC i 40 BC R a T 21 n N eri rific is A 12 jeju us p t sp tzl nit in RM s fe 62 m bu de om ni ub sp 92 10 vu r s r h ju i s ub 26 CC ro cte na cte r je jun s 525 38 HT lfu ba imo ba cte r je tus us s ue Su rco lfur ylo ba acte er fe urv cisu r c n biq ng t A u p lo aste i S am py ylob bac cter r co MC ter u oryo og y C am p ylo ba te sp ac i B lan ala E C am p ylo bac cus gib sh 38 drid ama a me ia m g C am p lo oc ela mu 85 Ma ay hil C am py toc s P ga SU str Miy osop of Bru C am ne atu tsu ii O kii str f Dr RS T u o C g id tsu ell ze Maand ntia ia b rowa nnets biont strain aries C rie etts ia p se sym iont t M O ick tts ttsia ndo mb str S HZ nden R icke icke ia e dosy ale hilum evo R eor ach en argin ytop Welg N olb hia m oc str W olbac sm a phag ntium e sas W apla sma mina Jak rkan MB An pla ru is str str A um A 70 Anarlichia can ffeensisagnetic C 111 Eh rlichia cha um m m ATC Eh rlichia spirill rubru NIH1 Eh gneto irillum tum JF nsis CGD PAl Ma odosp m cryp thesde rophicus Rh iphiliu er be diazot Acidnulibacttobacter ans 621H Gra conace cter oxyd i RW1 ZM4 Glu conoba mobilis wittichi G lu ngomonas obilis subsp 2256 12444 Sphi onas m ensis RB ns DSM Zymom pyxis alaskarom aticivora m Sphingo CC2594 hingobiu Novosp acter litoralis HT Erythrob maris MCS10 Maricaulis crescentus CB15 Caulobacter nium ATCC 1544 Hyphomonas neptuicans PD1222 Paracoccus denitrif Rhodobacter sphaeroides Dinoroseobacter shibae DFL 12 Jannaschia sp CCS1 Gammaproteobacteria Betaproteobacteria Alphaproteobacteria Epsilonproteobacteria Deltaproteobacteria Wu and Eisen R151.5 70 P 09 TI 70 C C AB TC U is 53 PG str A on ae e lm vi tia HF ar pu no ac s ov R 29 a sy al an er m a ag etr s um M 26 68 as m a en m tic e pl las sm a p arvu isep onia G3 TA4 str o p AB yc o la m p all m um s H is M yc cop plas sma a g neu itali hilu ubtil KB 31 M y co la sm a p en top p s nsis E8 30 M y ap la m a g us bs ne HT SA M re op las m ka su sis sU U yc op las s ilis tep en reu M yc op cillu bt ns ey 12 au M yc ba su ihe s ih s C 16 sp M eo illus we cillu ran K sub CH G ac lus ba du KSM eus 62 str B acil ano halo sii aur 112 sub B ce lus clau us Clip V58 hallis 403 O acil lus cocc ua alis str C s Il1 B acil ylo noc faec onii lacti i 23K B taph ia in cus gord ubsp sake S te r coc cus tis s bsp 34 365 Lis tero coc s lac ei su CC C BAA En epto occu s sak i AT ATC Str ctoc cillu case eckii 457 C11 La ctoba cillus delbru us DPC 533 s UC La ctoba cillus elvetic NCC salivariu La ctoba illus h nsonii subsp 8293 La ctobac llus joh arius es ATCC La obaci s saliv teroid Lact obacillu c mesen SU Lact onosto s oeni P F275 25745 Leuc ococcu reuteri eus ATCC O enobacillus pentosac CFS1 Lact ococcus rum W Pedi bacillus planta ATCC 367 Lacto bacillus brevis a SH ila UWE2 Lacto irellula baltic amoeboph Rhodop us Protochlamydia 13 Candidat trachomatis A HAR Chlamydia la abortus S26 ophi Chlamyd oniae J138 Chlamydophila pneum Leptospira borgpetersenii JB197 Borrelia garinii PBi Treponema denticola ATCC 35405 Treponema pallidum subsp pallidum str Nichols Chlorobium tepidum TLS Chlorobium Chlorobium phaeobacteroides DSM 266 Pelodictyon chlorochromatii CaD Prostheco luteolum DSM 273 Saliniba chloris vibriof Cytophacter ruber DS ormis DSM 265 Bactero ga hutchins M 13855 Paraba ides fragil onii ATCC Porph cteroide is NCTC 93 33406 43 C an yromon s distas Gramdidatus as ging onis ATC Flavo ella fo Sulcia ivalis W83 C 8503 Flavo bacte rsetii K muelleri G WSS Acid bacte rium jo T0803 rium hnso So oba n Bd libactecteria psych iae UW So ellov r us bacte rophilum 101 JIP0 My rang ibrio itatus rium 86 Anaxoco ium c bacteri Ellin60Ellin34 ccu ellu ovo 76 A ero P naero my s xa losum rus H Pe eloba myxxobac nthus So c D100 lob cte ob ter e5 DK G Geeoba acte r ca acter deha 1622 log sp G ob cte r p rb S eo ac r u rop inoli Fw ena S ynt bac ter ran ionic cus 109 ns 2C PC D yn rop ter me iire us DS D es trop hu su tallir duc DS M 23 L es ulf ho s a lfur ed en M Deaws ulfo ococ bac cidit redu ucen s Rf4 2379 D s o ta c te ro c s D es ulf nia le us r f ph en GS N es ulfo ovib in a ps ole uma icus s PC 15 tr y o W i u S A r H o trati lfov vibr rio d ace chr vora oxid B el lin ru ib io e llu op n an ic e p rio v su la hil s H s ob lla to u M a lf r ac s r s vulg lgar uric is P LS xd3 PO B te ucc p S a is an HE v5 r a in B ris su s M ci og 15 su bsp G2 N1 no e b 00 ny ne sp vulg vu a ch s D lg ris is S ar st M is str H rS DP il he 740 den eb bo a ro ug h ly n R ucle a o B lsto bac u He rkh nia ter Ja rm old sol sp nt inii er an Q hin m ia ac LW ob on xe ea P ac as no ru te ar vo m DM De Nit riu s G W ch e M ros lor Nit A B m nic ns I A ros omo Nitro om A zoa ord sp M ox LB 100 os na so ona zo rc et a yd 40 Th pir s e m s arc us ell rs an iob o a a e a ac Meth m u urop nas romus s sp E pe ille s Ch illu ylo ltif ae eu a p b tr rom ob Neis s den bac orm a A troptica BH7N1 ii acte se il is TC h R it riu ria g rifica lus fl ATC C a C CB B m v on ns ag C 97 91 Ba arton rton ella iolac orrho ATCellatu251 18 Bart ell e e C s q one a he uinta um A ae F 252 KT Bru Bart lla trib nsela na strTCC A 10 59 cell a Och meliteonella ocoru e str H Tou 1247 roba nsis bac m C ou lous Mes ctrum biovailliformIP 10ston 1e orhiz anth r Ab is K 547 ortu C5 obiu ropi Mes m loti ATCC s 23083 Rhizo Agroba MA orh ct bium legu erium tu izobiu FF30 9188 S inor minosar mefaci m sp B 3099 um bv ens N hizobi st C1 Brady um medic viciae r C58 ae W 3841 rhizob Bradyr ium sp SM419 O hi Rhodop Nitrobacter zobium sp RS278 hambu seudom BTAi1 rg Xanthob onas palustrisensis X14 CG Azorhizobi acter autotrophic A009 um caulino us dans ORS Py2 Methylobac 571 terium exto rqu Parvibaculum lavamentivor ens PA1 Silicibacter pome ans DS royi DSS Silicibacter sp TM104 Roseobacter denitrificans OCh 114 br Volume 9, Issue 10, Article R151 Prochlorococcus marinus str NATL1A Prochlorococcus marinus str MIT 9211 Prochlorococcus marinus subsp m arinus str CCMP1375 Synechococcus Prochlorococcussp RCC307 marinus str MIT Prochloroco 9303 Synechoco ccus marinus str Synechoc ccus sp WH 780 MIT 9313 Synech occus sp CC Synech ococcus sp CC 9311 ococcu 9902 Syne s Synt chococcu sp CC9605 S ymrophomon s sp WH 81 as wol 02 Moo biobac fe Desurella th terium th i subsp wol fei Carb lfitoba ermoace ermophi lum IA str Goetting tica A Pelo oxyd cteriu en Des toma otherm m hafn TCC 39 M 14863 Cald ulfoto culum us hyd iense Y5 073 The icell macu therm rogeno opro form Th rmo ulos lum Clo ermo anae iruptor reduce pionicu ans Z ns M m S 901 C strid ana roba sa I C lostr ium eroba cter ccharo I A lostr idium therm cter sp X5 lyticu 14 ten sD ph Alklkali idiu o SM Clo ali phil m d yto cellu gco 890 C lo str philuus or ifficile ferme m AT ngens C s idiu s em 63 ntan CC is M s IS 274 B4 C los trid m me lan Dg 05 C los trid ium pe tallir dii C los trid ium b rfrin ed Oh C los trid ium n eije ge ige ILA Fu los trid ium ac ovyi rinck ns S ns Q s trid iu YM e A s N ii M1 As cho oba iumm b tetan tobu T NCIM 01 F t M t le c o i B8 M e er p te k tu E88 ylicu 05 m M y so ye la riu luy lin AT M yc co pla llowsma m n ver um yc op pla sm s la uc i D A CC op la sm a w id le SM str 82 la sm a flo itch law atu AT sm a c ru e ii m 55 CC a hy apr m L s b PG sub 19 m op ic ro sp 39 om A ob n ol nu ile eu um ph cle 16 mo su yt at op um 3K nia bs la e pc sm AT 74 a CC a 48 pri AY co 25 W lu 58 B m AT CC 27 34 http://genomebiology.com/2008/9/10/R151 Acidobacteria Bacteroidetes/Chlorobi Spirochaetes Chlamydiae/Planctomycetes Firmicutes Cyanobacteria Chloroflexi Actinobacteria Aquificae Thermotogae Deinococcus/Thermus Figure An unrooted maximum likelihood bacterial genome tree An unrooted maximum likelihood bacterial genome tree The tree was constructed from concatenated protein sequence alignments derived from 31 housekeeping genes All major phyla are separated into their monophyletic groups and are highlighted by color The branches with bootstrap support of over 80 (out of 100 replicates) are indicated with black dots Although the relationships among the phyla are not strongly supported, those below the phylum level show very respectable support The radial tree was generated using iTOL [42] genomes have been sequenced, however, a hybrid approach may be most fruitful A genome tree built from sequenced genomes can be used as a scaffold; species for which we lack full genome sequences can be placed by comparing their SSU rRNA sequences with those of sequenced species Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Average rate of protein evolution in bacterial genomes The average rates of protein evolution are proportional to the branch lengths of our genome tree The branch length varies widely among different lineages For example, as has been previously reported, bacteria that have adopted an intracellular lifestyle have, in general, evolved more rapidly [24], with Wigglesworthia glossinidia (the endosymbiont of Glossina brevipalpis) and Neorickettsia sennetsu str Miyayama evolving at the fastest pace The slowest rates are found in a group of spore forming bacteria such as Carboxydothermus hydrogenoformans, Moorella thermoacetica, Clostridium spp., and Bacillus spp These slow rates are of particular interest because it has been suggested that they might be related to the longer generation times for organisms that spend a significant fraction of their time as dormant spores Our data for spore forming bacteria are consistent with that hypothesis and differ strikingly from the findings of a recent study [25], which identified no generation time effect in these organisms Application II: metagenomic phylotyping Reanalysis of the phylotypes reported in the Sargasso Sea We used our automated pipeline to reanalyze the environmental shotgun sequencing data collected from the Sargasso Sea and phylotyped in a previous study [26] The approximately 1.1 million predicted genes yielded a total of 18,607 genes that corresponded to our 31 protein markers and that were long enough for phylogenetic analysis Figure illustrates the distribution of each of the 31 protein markers among the major phylotypes Our analysis identifies the αproteobacteria as the most abundant group, because more than half of the marker sequences were assigned to this group Notably, the various individual protein markers present remarkably consistent microbial diversity profiles, thus suggesting that results for different markers may be additive Volume 9, Issue 10, Article R151 Wu and Eisen R151.6 be anchored The genome of one SAR11 member, namely Candidatus Pelagibacter ubique, was subsequently sequenced, which allows for much finer phylotyping now In our phylotyping analyses, 8,656 marker sequences (46.5% of the total) form outgroups to only P ubique We have assigned them to the SAR11 clade because their closest neighbor in the tree is P ubique and their dominance in the population is consistent with previous quantitative estimations by fluoresence in situ hybridization that, on average, members of the SAR11 clade account for one-third of the ocean surface bacterioplankton communities [27] Strategically located reference genomes The use of metagenomics to phylotype communities has been limited by the lack of sequenced genomes from many taxonomic groups To help fill some of these gaps, we sequenced representatives of several phyla for which genome sequences were not previously available For example, we recently sequenced the genomes of Dictyoglomus thermophilum and Thermomicrobium roseum as part of a US National Science Foundation (NSF) funded 'Tree of life' project (Eisen JA and coworkers, unpublished data) To demonstrate the usefulness of these additional genomes for improved phylotyping, we analyzed metagenomic data from a Yellowstone hot spring community From the 8,341 Sanger sequence reads obtained, we identified 59 reads that match the marker sequences present in our database For 20 of these reads, their closest neighbors by phylogenetic analysis are D thermophilum or T roseum (ten reads each), thus demonstrating the usefulness of these genomes for phylotyping their close relatives in the Yellowstone community (see Additional data file for one such example) This highlights the need to increase taxonomic sampling by selecting bacteria for sequencing based on their phylogenetic positions Selecting reference sequences It was noted that SSU rRNA gives significantly different estimates of microbial composition than those by protein markers [26] This is believed to be caused by large variations in rRNA gene copy numbers among different species The protein markers used in our study are nearly all single-copy genes and thus should, theoretically, give a more accurate estimation of the microbial composition One factor affecting our analysis is that the peptides are based on assemblies rather than sequence reads Therefore, our method will underestimate those organisms that have deep coverage in assemblies This is one major reason why depth of coverage should be provided with metagenomics assemblies and annotation For best phylotyping results, the more reference sequences the better Therefore, theoretically, the greater number of marker sequences identifiable from a more comprehensive database such as the NCBI nonredundant protein sequence (nr) database would be preferable to the lesser number obtainable from complete genomes However, taxonomic sampling bias of the reference sequences has a great impact on the resulting phylotype assignments (see below) To be able to make meaningful comparisons among the results obtained using different markers, the taxonomic sampling must be controlled In this regard, a complete genome database, in which every marker was sampled equally, would be preferable to the NCBI nr database, in which each marker was sampled to a different extent Members of the α-proteobacterial SAR11 clade are the most dominant micro-organisms in the Sargasso Sea [27] At the time of the Sargasso Sea metagenomics study, there were no complete genome sequences available for members of the SAR11 clade, and thus many of the SAR11 sequences could not With very few exceptions such as gyrB [28], the protein marker sequences with species information in the nr database were mostly derived from genome sequencing projects This is because it is very difficult to obtain protein encoding genes by polymerase chain reaction amplification because their Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 0.8 0.7 Relative abundance 0.6 0.5 0.4 0.3 0.2 0.1 t am es yd ia no e b Ac ac te id ob ria Th act e er m ria ot Fu o so gae Ac bac te tin ob ria ac te r Pl Aq ia ui an fi ct om cae Sp yce te iro ch s ae t Fi rm es ic C ute hl or s U of nc le la ss Ch xi l ifi ed oro ba bi ct er ia hl C ya C ct er oi de er ia ria ct ba Ba ot eo ac pr ed dnaG frr infC nusA pgk pyrG rplA rplB rplC rplD rplE rplF rplK rplL rplM rplN rplP rplS rplT rpmA rpoB rpsB rpsC rpsE rpsI rpsJ rpsK rpsM rpsS smpB tsf U nc la ss ifi si lo np ro te ob ba eo ot te er ia ria ct te er ob te Ep D el ta pr ro ap m am ac ct ba eo pr ta Be G Al ph ap ro ot te ob ac te ria ia Wu and Eisen R151.7 Figure Major phylotypes identified in Sargasso Sea metagenomic data Major phylotypes identified in Sargasso Sea metagenomic data The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using AMPHORA and the 31 protein phylogenetic markers The microbial diversity profiles obtained from individual markers are remarkably consistent The breakdown of the phylotyping assignments by markers and major taxonomic groups is listed in Additional data file sequences are not conserved at the nucleotide level [29] As a result, the nr database does not actually contain many more protein marker sequences that can be used as references than those available from complete genome sequences value to define the top hits Because individual bacterial genomes and proteins can evolve at very different rates, a universal cut-off that works under all conditions does not exist As a result, the final results can be very subjective Comparison of phylogeny-based and similarity-based phylotyping In contrast, our tree-based bracketing algorithm places the query sequence within the context of a phylogenetic tree and only assigns it to a taxonomic level if that level has adequate sampling (see Materials and methods [below] for details of the algorithm) With the well sampled species Prochlorococcus marinus, for example, our method can distinguish closely related organisms and make taxonomic identifications at the species level Our reanalysis of the Sargasso Sea data placed 672 sequences (3.6% of the total) within a P marinus clade On the other hand, for sparsely sampled clades such as Aquifex, assignments will be made only at the phylum level Thus, our phylogeny-based analysis is less susceptible to data sampling bias than a similarity based approach, and it makes Although our phylogeny-based phylotyping is fully automated, it still requires many more steps than, and is slower than, similarity based phylotyping methods such as a MEGAN [30] Is it worth the trouble? Similarity based phylotyping works by searching a query sequence against a reference database such as NCBI nr and deriving taxonomic information from the best matches or 'hits' When species that are closely related to the query sequence exist in the reference database, similarity-based phylotyping can work well However, if the reference database is a biased sample or if it contains no closely related species to the query, then the top hits returned could be misleading [31] Furthermore, similarity-based methods require an arbitrary similarity cut-off Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, 0.8 0.7 Specificity 0.7 Wu and Eisen R151.8 0.9 0.8 Sensitivity 0.9 Volume 9, Issue 10, Article R151 0.6 0.5 0.4 0.6 AMPHORA MEGAN 0.5 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 phylum class order family genus species phylum class order family genus species Figure Comparison of the phylotyping performance by AMPHORA and MEGAN Comparison of the phylotyping performance by AMPHORA and MEGAN The sensitivity and specificity of the phylotyping methods were measured across taxonomic ranks using simulated Sanger shotgun sequences of 31 genes from 100 representative bacterial genomes The figure shows that AMPHORA significantly outperforms MEGAN in sensitivity without sacrificing specificity sequence assignments only at the taxonomic levels that are supported by the available data To compare quantitatively the performance of the phylogeny based and the similarity based phylotyping, we carried out a simulation study We determined the sensitivity and specificity of the taxonomic assignments made by AMPHORA and MEGAN using 3,088 simulated shotgun sequences of 31 phylogenetic marker genes identified from 100 known bacterial genomes as benchmarks The 100 genomes were chosen in such a way that maximizes their representation of the phylogenetic diversity and thus decreases the impact of the data sampling bias of current genome sequencing efforts on our results Figure compares the sensitivity and specificity of the phylotyping assignments at the phylum, class, order, family, and genus level using AMPHORA and MEGAN The general trend toward decreasing sensitivity seen in the figure from the phylum to the species level simply reflects the fact that the amount of reference data available for taxonomic assignment is decreasing However, AMPHORA significantly outperformed MEGAN in sensitivity at all taxonomic ranks Both methods performed extremely well in specificity at all levels (>0.97) except at the species level, where AMPHORA (0.63) outperformed MEGAN (0.43) by a large margin Future issues Additional markers We are in the process of adding more proteins to our initial database of 31 markers, including the commonly used protein markers RecA, HSP70, and EF-Tu Ideally, a probability based method that evaluates the positional homology of the multiple sequence alignment could be developed to automate fully the process of masking Major expansion will also require systematic assessment of many other protein families for their suitability as phylogenetic markers For metagen- omic phylotyping, the marker genes not have to be singlecopy or universal, but they must have been reasonably well sampled, have sufficient phylogenetic signal, and not be frequently exchanged between distantly related lineages Until we learn more about the extent of lateral gene transfer in natural microbial communities, we caution against using every protein sequence collected in metagenomics studies for microbial diversity study More reference genomes We have shown that adding representatives of novel phyla can facilitate metagenomic phylotyping More reference genomes are needed for optimal performance Although the sequencing of thousands of microbial genomes is underway, the organisms chosen are a biased sample and thus are not truly representative of the total microbial diversity We see a need to select microbes systematically for sequencing based mainly on their phylogenetic positions, thus maximizing their value for comparative genomics and phylogenomic studies Conclusion Currently, SSU rRNA is still the most powerful phylogenetic marker because of the number of sequences available and the scope of taxonomic coverage However, the imminent arrival of thousands of microbial genome sequences will vastly expand the amount of data available for alternative protein phylogenetic markers, thus presenting us with both a challenge and an opportunity We have developed AMPHORA, a fully automated method for phylogenetic inference using multiple protein markers AMPHORA offers speed, reliability, and high quality analyses By eliminating the need for time consuming manual curation of sequence alignments, it removes one of the tightest bottlenecks in large-scale protein phylogenetic inference We demonstrated its usefulness for Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, automating both the construction of genome trees and the assignment of phylotypes to environmental metagenomic data We believe such a phylogenomic approach will be valuable in helping us to make sense of rapidly accumulating microbial genomic data Volume 9, Issue 10, Article R151 Wu and Eisen R151.9 marker and treated it as 'missing' in that particular genome It has been shown that as long as there is sufficient data, a few 'holes' in the dataset will not compromise the resulting tree [36] Phylotyping by phylogenetic analyses (AMPHORA) Materials and methods Protein phylogenetic marker database For each marker, we first identified their protein sequences from representative bacterial genomes The amino acid sequences were aligned using CLUSTALW [32] and then manually edited and masked using the GDE package [33] The mask is a text string of '1' and '0', where reliably aligned columns were labeled '1' and ambiguous columns were labeled '0' Next, we used HMMer [34] to make local profile HMMs from these 'seed' alignments (Figure 1) Automated sequence alignment and trimming Subsequent steps are carried out by a Perl script joining multiple automated processes (Figure 1) First, HMMer efficiently aligns the query amino acid sequences onto the trusted and fixed seed alignments The Perl script then reads the masks embedded in the seed alignments and automatically trims the query alignments accordingly Bacterial genome tree construction Homologs of each of the 31 phylogenetic marker genes were identified from the 578 complete bacterial genomes by BLASTP searches (using marker sequences of Escherichia coli as query sequences and a cut-off E-value of 0.1) followed by HMMer searches (cut-off E-value × e-10) The corresponding protein sequences were retrieved, aligned, and trimmed as described above, and then concatenated by species into a mega-alignment A maximum likelihood tree was then constructed from the mega-alignment using PHYML [35] The model selected based on the likelihood ratio test was the WAG model of amino acid substitution with γ-distributed rate variation (five categories) and a proportion of invariable sites The shape of the γ-distribution and the proportion of the invariable sites were estimated by the program To speed up bootstrapping analyses, very closely related taxa were removed from the original mega-alignment, which left us with 310 taxa Maximum likelihood trees were made from 100 bootstrapped replicates of this reduced dataset using PHYML with the same parameters described above The protein markers used to construct the bacterial genome tree (see above) and the resultant genome tree were used as the reference sequences and the reference tree for phylotyping metagenomic data from the Sargasso Sea or the simulated sequences described below Each marker sequence identified from the metagenomic data or simulated sequences was individually aligned to its corresponding reference sequences and trimmed using the method described above Then it was inserted into the reference tree using a maximum parsimony method of RAXML [37], constraining the topology of the tree to that of the genome tree This tree construction procedure was extremely fast, and 100 bootstrap replicates were run for each query sequence to assess the confidence of the branching orders The trees were rooted arbitrarily using Deinococcus radiodurans as the outgroup Tree branch lengths were calculated using the neighbor joining algorithm with a fixed tree topology A tree-based bracketing algorithm was then employed to assign a phylotype to the query sequence (Figure 5), as follows Starting from the immediate ancestor n0 of the query sequence and moving toward the root of the tree, the first internal node n1 whose bootstrap support exceeded a cut-off (for example, 70%) was identified The common NCBI taxonomy t1 that was shared by all descendants of the node n1 represented the most conservative taxonomic prediction for the query sequence Using the branch length information, finer scale phylotyping was carried out by comparing the normalized branch length from n0 to n1 with these between taxonomic ranks that had been tallied from the bacterial genome tree Based on this comparison, a taxonomic rank below or equal to t1 was assigned to the node n0 The taxonomy of the sister node of the query at this rank was then assigned to the query All tree branch lengths were normalized by dividing them by the lengths of the root-to-tip branches of their particular lineages This was done to make the tree more clock-like, and therefore the branch lengths would be much more informative in inferring the time of evolution In the simulation study, the query sequence itself was removed from the reference dataset before the analyses Phylotyping by similarity-based analyses (MEGAN) With very few exceptions, the marker genes are single-copy genes in all of the bacterial genomes analyzed In those rare cases in which two or more homologs were identified within a single species, a tree-guided approach was used to resolve the redundancy If the redundancy resulted from a species-specific duplication event, then one homolog was randomly chosen as the representative In all other cases, to avoid potential complications such as lateral gene transfer, we excluded that A total of 3,088 simulated phylogenetic marker gene sequences described below were searched against a database of complete bacterial genomes using BLASTX The query sequence itself was discarded from the BLAST hits before feeding the BLAST results into the software MEGAN [30] for similarity-based phylotyping A top per cent cut-off of 20% was used to retain only those hits whose matching scores are at least 80% of the best matching score This cut-off was Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Order Family Genome Biology 2008, I H G F E n1 Wu and Eisen R151.10 SSU rRNA tree construction Genus n0 Volume 9, Issue 10, Article R151 D C query * B A SSU rDNA sequences were extracted from complete genomes, aligned, and trimmed using an online tool MyRDP [40] When multiple copies of SSU rRNA genes were present within a single genome, one representative was randomly chosen Maximum likelihood tree was constructed using PHYML [35], applying the GTR model of substitution, with a γ-distribution (α estimated by the program) of rates of five categories of variable sites and a proportion of invariable sites (proportion estimated by the program) Availability Figure A tree based bracketing algorithm for phylotyping a query sequence A tree based bracketing algorithm for phylotyping a query sequence To assign a phylotype to the query sequence, its immediate ancestor n0 and the first internal node n1 with ≥70% bootstrapping support were identified The known descendant leaf nodes of n1, namely A through D, are used to infer the taxonomy of the query, in conjunction with the normalized branch length information The dashed timelines delimiting various taxonomic ranks were inferred from a clock that had been calibrated from the bacterial genome tree chosen to match the one used in a similar phylotyping simulation study described in the original MEGAN report [30] All other parameters of MEGAN were set as default values except that the min-support (the minimum number of sequence reads that must be assigned to a taxon) is set to 1, because in our simulation study each query sequence was assigned a phylotype independently The AMPHORA package and the simulation study data can be downloaded from [41] Abbreviations AMPHORA: AutoMated PHylogenOmic infeRence; HMM: hidden Markov model; NCBI: National Center for Biotechnology Information; nr: nonredundant protein sequence; SSU: small subunit NSF: US National Science Foundation Authors' contributions MW designed the study, developed the method, and performed the analyses JAE advised on method design and testing MW and JAE wrote the paper Additional data files Phylotyping simulation study To assess the performance of the phylotyping methods, a simulation study was carried out One hundred representative genomes maximizing the phylogenetic diversity of the 578 complete bacterial genomes were selected using the genome tree and an algorithm described in the report by Steel [38] From each of the 31 phylogenetic marker genes identified from the 100 bacterial genomes, a DNA sequence fragment of 300 to 900 base pairs in length was randomly chosen, which resulted in a total of 3,088 simulated shotgun sequences that were used as benchmark query sequences in phylotyping (some markers are missing in some of the genomes) By comparing the predicted taxa with the known taxa, the sensitivity and specificity of phylotyping methods were calculated as described in the report by Krause and coworkers [39] Briefly, for a taxon i, let Pi be the number of query sequences from i, TPi be the number of sequences that are correctly assigned to i, and FPi be the number of sequences that are incorrectly assigned to i The sensitivity TPi/Pi measures the proportion of query sequences that are correctly classified The specificity TPi/(TPi + FPi) measures the reliability of the phylotyping assignments The following additional data are available with the online version of this paper Additional data file is a table listing the 578 complete bacterial genomes downloaded from the NCBI RefSeq database for this study Additional data file is a figure of a maximum likelihood genome tree of 578 bacterial species; major taxonomic groups are highlighted by color Additional data file provides a figure that compares γ-proteobacterial phylogenetic trees made from a super-matrix of 31 protein phylogenetic markers and from the SSU rDNA; bootstrap support values are shown along their corresponding branches Additional data file is a figure of a maximum likelihood tree of rpoB; adding a novel genome (Thermomicrobium roseum) to the reference tree helped anchor a sequence read (ZAVAM73TF) from a Yellowstone hotspring metagenomic study Additional data file is a table listing phylotypes breakdown of the Sargasso Sea metagenomic sequence data by phylogenetic markers and major taxonomic groups a novelalong figure2of genomesofbranches.support Yellowstone showntaxonomic thegenomeSargassobreakdownmarkers of (A) database file sequence (B) Bootstrapmetagenomic sequence trees here dataγ-proteobacterial by database genome NCBI RefComparison oftable 1thatmaximum γ-proteobacterialrpoB are bacterial species.a super-matrix(ZAVAM73TF) treesthegenomes Maximum likelihoodofrDNA RefSeq likelihood highlighted and tree Click madefortheirgroups ofdata downloaded tree of the Sargasso downloaded fromMajorof therpoBphylogeneticto thisaphylogenetic Presented is athesequencecomparescomplete phylogenetic by color Seq metagenomicSSUtreemaximumgroups arefor the valuesmarkers 578 andanchorbacterial ataxonomicof protein bacterialstudy.Adding Additionalmetagenomicastudy.57831likelihoodfromofreference 578 major from a corresponding Sea datacomplete figurelisting read Phylotypes breakdown hotspring from(Thermomicrobium roseum) from tree helped genome file NCBI tree phylogenetic species phylotypes Sea the 578 bacterial Acknowledgements The initial development of this work was supported in part by NSF grant DEB-0228651 to JAE The final development and testing was funded by the Gordon and Betty Moore Foundation (grant #1660 to JAE) Genome Biology 2008, 9:R151 http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, References 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Woese CR, Achenbach L, Rouviere P, Mandelco L: Archaeal phylogeny: reexamination of the phylogenetic position of Archaeoglobus fulgidus in light of certain composition-induced artifacts Syst Appl Microbiol 1991, 14:364-371 Hasegawa M, Hashimoto T: Ribosomal RNA trees misleading? 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NN, Goesmann A, Kelley S, Nattkemper TW, Rohwer F, Edwards RA, Stoye J: Phylogenetic classification of short environmental DNA fragments Nucleic Acids Res 2008, 36:2230-2239 Cole JR, Chai B, Farris RJ, Wang Q, Kulam SA, McGarrell DM, Garrity GM, Tiedje JM: The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis Nucleic Acids Res 2005, 33:D294-D296 AMPHORA [http://bobcat.genomecenter.ucdavis.edu/ AMPHORA] Letunic I, Bork P: Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation Bioinformatics 2007, 23:127-128 Genome Biology 2008, 9:R151 ... performance of the phylogeny based and the similarity based phylotyping, we carried out a simulation study We determined the sensitivity and specificity of the taxonomic assignments made by AMPHORA and. .. 0.1 0.1 0 phylum class order family genus species phylum class order family genus species Figure Comparison of the phylotyping performance by AMPHORA and MEGAN Comparison of the phylotyping performance... the sensitivity and specificity of phylotyping methods were calculated as described in the report by Krause and coworkers [39] Briefly, for a taxon i, let Pi be the number of query sequences from