290 Metagenome-Assembled Genomes from the Mediterranean Sea: a Resource for Marine Microbiology

290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology

Benjamin J. Tully1, Rohan Sachdeva2, Elaina D. Graham2 and John F. Heidelberg1,2

1 Center for Dark Energy Biosphere Investigations, University of Southern California, Los Angeles, CA, USA 2 Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States of America

ABSTRACT The Tara Oceans Expedition has provided large, publicly-accessible microbial metagenomic datasets from a circumnavigation of the globe. Utilizing several size fractions from the samples originating in the Mediterranean Sea, we have used current assembly and binning techniques to reconstruct 290 putative draft metagenome-assembled bacterial and archaeal genomes, with an estimated completion of ≥50%, and an additional 2,786 bins, with estimated completion of 0–50%. We have submitted our results, including initial taxonomic and phylogenetic assignments, for the putative draft genomes to open-access repositories for the scientific community to use in ongoing research.

Subjects Bioinformatics, Marine Biology, Microbiology Keywords Metagenomics, Metagenome-assembled genomes, Mediterranean sea, Bacteria, Archaea, Tara oceans

INTRODUCTION Microorganisms are a major constituent of the biology within the world’s oceans Submitted 9 May 2017 and act as important linchpins in all major global biogeochemical cycles (Falkowski, Accepted 19 June 2017 Fenchel & DeLong, 2008). Marine microbiology is among the disciplines at the forefront Published 10 July 2017 in understanding how microorganisms respond to and impact local and large-scale Corresponding author 29 Benjamin J. Tully, [email protected] environments. An estimated 10 Bacteria and Archaea (Whitman, Coleman & Wiebe, 1998) reside in the oceans and represent an immense amount of poorly constrained, and Academic editor Nikos Kyrpides ever evolving genetic diversity. Additional Information and The Tara Oceans Expedition (2003–2010) was a major endeavor to add to the body of Declarations can be found on knowledge collected during previous global ocean surveys to sample the genetic potential page 12 of microorganisms (Karsenti et al., 2011). To accomplish this goal, Tara Oceans sampled DOI 10.7717/peerj.3558 planktonic organisms (viruses to fish larvae) at two major depths, the surface ocean and Copyright the mesopelagic. The amount of data collected was expansive and included 35,000 samples 2017 Tully et al. from 210 ecosystems (Karsenti et al., 2011). The Tara Oceans Expedition generated and Distributed under publically released 7.2 Tbp of metagenomic data from 243 ocean samples from throughout Creative Commons CC-BY 4.0 the global ocean, specifically targeting the smallest members of the ocean biosphere, the OPEN ACCESS viruses, Bacteria and Archaea, and picoeukaryotes (Sunagawa et al., 2015). Initial work

How to cite this article Tully et al. (2017), 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ 5:e3558; DOI 10.7717/peerj.3558 023 Bact 009 Prot Girus Prot 025 007 Bact Girus 018 Prot Bact 030 Bact Prot

Figure 1 Map illustrating the locations and size fractions sampled for the Tara Oceans Mediterranean Sea datasets. Girus, ‘giant virus’ size fraction (0.22–1.6 µm). Bact, ‘bacteria’ size fraction (0.22–1.6 µm). Prot, ‘protist’ size fraction (0.8–5.0 µm). The map in Fig. 1 was modified under a CC BY-SA 3.0 license from ‘Blank Map of South Europe and North Africa’ by historicair (https://upload.wikimedia.org/ wikipedia/commons/d/db/Blank_map_of_South_Europe_and_North_Africa.svg).

on these fractions produced a large protein database, totaling >40 million nonredundant protein sequences and identified >35,000 microbial operational taxonomic units (OTUs) (Sunagawa et al., 2015). Leveraging the publically available metagenomic sequences from the ‘‘girus’’ (giant virus; 0.22–1.6 µm), ‘‘bacteria’’ (0.22–1.6 µm), and ‘‘protist’’ (0.8–5 µm) size fractions, we have performed a new joint assembly of these samples using current sequence assemblers (Megahit (Li et al., 2016)) and methods (combining assemblies from multiple sites using Minimus2 (Treangen et al., 2011)). These metagenomic assemblies were binned using BinSanity (Graham, Heidelberg & Tully, 2017) into 290 draft microbial genomes with an estimated completeness ranging from 50–100%. Environmentally derived genomes are imperative for a number of downstream applications, including comparative genomics, metatranscriptomics, and metaproteomics. This series of genomic data can allow for the recruitment of environmental ‘‘-omic’’ data and provide linkages between functions and phylogenies. This method was initially performed on the seven sites from the Mediterranean Sea containing microbial metagenomic samples (TARA007, −009, −018, −023, −025 and −030), but will continue through the various Longhurst provinces (Longhurst et al., 1995) sampled during the Tara Oceans project (Fig. 1). All of the assembly data is publically available, including the initial Megahit assemblies for each site from the various size fractions and depths along with the recovered putative (minimal quality control) genomes.

MATERIALS AND METHODS A generalized version of the following workflow is presented in Fig. 2.

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 2/15 Multiple Sites - Mediterranean Sea EMBL Tara Oceans Metagenomic TARA007-GIRUS-SRF, reads TARA023-PROT-DCM, etc megahit --preset meta-sensitive -1 tara_read1 -2 tara_read1 Assemble each site, individually, -o tara.megahit_asm with Megahit

Collect all contigs ≥2kb in length

cd-hit-est -M 500000 -c 0.99 Combine assemblies 99% identical -n 10 with CD-HIT-EST

minimus2 -D OVERLAP=100 MINID=95 Assemble with Minimus2

new Minimus2 contigs + data-rich-contigs unincorpoarted Megahit contigs

bowtie2 -D 15 -R 2 -N 0 -L 20 Recruit reads to contigs with -i S,1,0.75 Bowtie2

SAM file for each site

Determine number of reads featureCounts assigned with featureCounts

Convert to coverage (reads/bp)

Coverage matrix for contigs ≥7.5kb binned-contigs

binSanity -d 0.95 -m 4000 Bin contigs based on transformed -v 400 -p -3 coverage with BinSanity

binSanity -d 0.95 -m 4000 checkm lineage_wf Assess bins with CheckM Repeat BinSanity with raw coverage values -v 400 -p -3

Putative High-quality genomes High contamination bins

BinSanity refinement, Repeat twice Assess with CheckM

High-quality Low completion High contamination

Downstream analysis

Taxonomy, Phylogeny, Relaltive abundance

Storage of low completion bins

Figure 2 Workflow used to process Tara Oceans Mediterranean Sea metagenomic datasets.

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 3/15 Sequence retrieval and assembly All sequences for the reverse and forward reads from each sampled site and depth within the Mediterranean Sea were accessed from European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) utilizing their FTP service (Table 1). Paired-end reads from different filter sizes from each site and depth (e.g., TARA0007, girus filter fraction, sampled at the deep chlorophyll maximum) were assembled using Megahit (Li et al., 2016) (v1.0.3; parameters: -preset, meta-sensitive). To keep consistent with TARA sample nomenclature, ‘‘bacteria’’ or ‘‘BACT’’ will be used to encompass the size fraction 0.22–1.6 µm. Megahit assemblies ≥2 kb in length from all samples were pooled and combined based on ≥99% semi-global identity using CD-HIT-EST (Fu et al., 2012) (v4.6; -c 0.99) in order to reduce the number of redundant contigs for the downstream assembly step. The reduced set of contiguous DNA fragments (contigs) was then cross-assembled based on a minimum of 100 bp overlaps with 95% nucleotide identity using Minimus2 (Treangen et al., 2011) (AMOS v3.1.0; parameters: -D OVERLAP = 100 MINID = 95). This assembly method is available on Protocols.io at https://dx.doi.org/10.17504/protocols.io.hfqb3mw.

Metagenome-assembled genomes Due to memory limitations during the binning step, contigs ≥7.5 kb in length generated during the two-step assembly process (includes Minimus2 contigs and unincorporated Megahit contigs) were used to recruit sequence reads from each of the Tara samples using Bowtie2 (Langmead & Salzberg, 2012) (v4.1.2; default parameters). Read counts for each contig were determined using featureCounts (Liao, Smyth & Shi, 2014) (v1.5.0; default parameters). Coverage was determined for all contigs by dividing the number of recruited reads by the length of the contig (reads/bp). Due to the low coverage nature of the samples, in order to effectively delineate between contig coverage patterns, the coverage values were transformed by multiplying by five (determined through manual tuning). Transformed coverage values were then utilized to cluster contigs into bins utilizing BinSanity (Graham, Heidelberg & Tully, 2017) based on a preference value of −3 to be run for a maximum of 4,000 iterations with completion if convergence is achieved for 400 consecutive iterations and a damping value of 0.9 (parameters: -p -3, -m 4,000, -v 400, -d 0.9). Bins were assessed for the presence of putative microbial genomes using the lineage workflow in CheckM (Parks et al., 2015) (v1.0.3; parameters: lineage_wf). Bins were split in to three categories: (1) putative draft genomes (≥50% complete and ≤10% cumulative redundancy (% contamination—(% contamination × % strain heterogeneity ÷ 100))); (2) draft genomes with high contamination (≥50% complete and ≥10% cumulative redundancy); and (3) low completion bins (<50% complete). The high contamination bins containing approximately two genomes, three genomes, or ≥4 genomes used BinSanity (Binsanity; -m 2000, -v 200, -d 0.9) with variable preference values (-p) of −1,000, −500, and −100, respectively. The resulting bins were added to one of the three categories: putative draft genomes, high contamination bins, and low completion bins. The high contamination bins were processed for a third time with the Binsanity-refine utilizing a preference of −100 (-p −100). These bins were given final assignments as either putative draft genomes or low completion bins.

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 4/15 ul ta.(2017), al. et Tully PeerJ Table 1 Statistics for Megahit contigs, recruitment to data-rich-contigs, and relative abundance of draft genome results for each sample. 10.7717/peerj.3558 DOI , TARA Size Depth No. of reads No. of N50c (bp; Longest Recruitment Relative Relative sample fraction (Surface initial initial Megahit initial (% data-rich- abundancec of abundancec site (Girus, or Megahit assembly) Megahit contigs) draft genomes of ten most Bacteria, DCMa) assembly assembly (bp) (%) abundant or Protist) genomes (% ) TARA007 Girus DCM 178,519,830 1,318,470 828 220,754 72.84 14.64 6.35 TARA007 Girus Surface 221,166,612 1,308,847 861 211,946 81.74 14.83 6.12 TARA007 Protist DCM 744,458,992 4,667,618 654 188,635 19.45 8.60 3.18 TARA007 Protist Surface 265,432,098 2,590,120 564 18,444 25.58 1.57 0.61 TARA009 Girus DCM 416,553,274 2,796,841 831 1,643,839 69.48 14.16 6.32 TARA009 Girus Surface 489,617,426 1,787,467 929 1,142,851 68.85 12.29 4.76 TARA009 Protist DCM 329,036,110 1,938,636 613 95,724 22.07 13.35 4.20 TARA009 Protist Surface 370,813,078 1,700,350 588 292,050 22.53 15.97 6.17 TARA018 Bacteria DCM 408,021,182 2,520,645 840 1,573,060 76.22 11.49 3.18 TARA018 Bacteria Surface 414,976,308 2,604,031 816 2,086,508 75.80 11.03 3.02 TARA023 Bacteria DCM 147,400,552 1,273,576 830 213,456 76.08 13.29 4.09 TARA023 Bacteria Surface 149,566,010 1,237,617 825 134,179 75.98 13.82 4.01 TARA023 Protist DCM 508,610,652 2,707,801 734 336,689 28.23 25.07 7.83 TARA023 Protist Surface 397,044,232 2,246,571 593 397,140 23.00 25.16 10.31 TARA025 Bacteria DCM 386,627,816 2,516,865 806 388,546 69.77 14.55 5.35 TARA025 Bacteria Surface 457,560,422 2,326,838 857 330,773 75.57 10.99 3.18 TARA030 Bacteria DCM 346,837,034 1,968,945 1,097 508,775 80.16 10.31 2.57 TARA030 Bacteria Surface 478,785,582 1,639,697 1,194 204,976 77.70 7.26 2.64 TARA030 Protist DCM 426,896,616 1,620,343 616 478,892 15.12 17.83 5.13 TARA030 Protist Surface 430,029,974 1,838,588 628 287,782 22.36 17.60 6.73

Notes. aDCM—deep chlorophyll maximum. bN50—length of DNA sequence above which 50% of the total is contained. cRelative abundance—determined using the reads recruited data-rich-contigs. 5/15 Any contigs not assigned to putative draft genomes were assessed using BinSanity using raw coverage values. Two additional rounds of refinement were performed with the first round of refinement using preference values based on the estimated number of contaminating genomes (as above) and the second round using a set preference of −10 (-p −10). Following this binning phase, contigs were assigned to draft genome bins (e.g., Tara Mediterranean genome 1, referred to as TMED1, etc.), low completion bins with at least five contigs (0–50% complete; TMEDlc1, etc. lc, low completion), or were not placed in a bin (Supplemental Informations S1 and S2). Taxonomic and phylogenetic assignment of draft genomes The bins representing the draft genomes were assessed for taxonomy and phylogeny using multiple methods to provide a quick reference for selecting genomes of interest. Taxonomy was assigned using the putative placement provided via CheckM during the pplacer (Matsen, Kodner & Armbrust, 2010) step of the analysis to the lowest taxonomic placement (parameters: tree_qa -o 2). This step was also performed for all low completion bins. Two separate attempts were made to assign the draft genomes a phylogenetic assignment. Draft genomes were searched for the presence of the full-length 16S rRNA gene sequence using RNAmmer (Lagesen et al., 2007) (v1.2; parameters: -S bac -m ssu). All full-length sequences were aligned to the SILVA SSU reference database (Ref123) using the SINA web portal aligner (Pruesse, Peplies & Glöckner, 2012)(https://www.arb-silva.de/aligner/). These alignments were loaded in to ARB (Ludwig et al., 2004) (v6.0.3), manually assessed, and added to the non-redundant 16S rRNA gene database (SSURef123 NR99) using ARB Parsimony (Quick) tool (parameters: default). A selection of the nearest neighbors to the Tara genome sequences were selected and used to construct a 16S rRNA phylogenetic tree. Genome-identified 16S rRNA sequences and SILVA reference sequences were aligned using the SINA web portal aligner (Supplemental Information 4). An approximately-maximum- likelihood tree with Shimodaira–Hasegawa local support values (Shimodaira & Hasegawa, 1999) was constructed using FastTree (Price, Dehal & Arkin, 2010) using the generalized time reversible and discrete gamma models (Yang) (v2.1.3; parameters: -nt -gtr -gamma; Fig. 3; Supplemental Information 5). Draft genomes were assessed for the presence of the 16 ribosomal markers genes used in Hug et al. (2016). Putative CDSs were determined using Prodigal (v2.6.3; parameters: -m -p meta) and were searched using HMMs for each marker using HMMER (Finn, Clements & Eddy, 2011) with matches based on an e-value cutoff of 1 × 10−3 (v3.1b2; parameters: hmmsearch -E 1E −10). If a genome had multiple copies of any single marker gene, neither was considered, and only genomes with ≥8 markers were used to construct a phylogenetic tree. Markers identified from the draft genomes were combined with markers from 6,080 reference genomes accessed from NCBI GenBank (Supplemental Information 6) that represent the major bacterial phylogenetic groups. Each marker gene was aligned using MUSCLE (Edgar, 2004) (parameters: -maxiters 8) and automatically trimmed using trimAL (Capella-Gutiérrez, Silla-Martínez & Gabaldón, 2009) (v1.2rev59; parameters: -automated1). Automated trimming results were manually curated in Geneious (Kearse

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 6/15 AF418950

0.1 GT305807

TMED14 TMED231 AB698042

Actinobacteria JN532725 ET919817 Deltaproteobacteria DQ071098 KF786935 TMED115 HQ674351

HQ673510 GT940759 JX530444 Firmicutes EF574227 KF786951 ET801426 ET803717 TMED24 HQ674443 GT940767 ET802220 EF575009 ET799463 FJ820442 AACY020259437 TMED123 ET802019

ET703418 AB355058 ET801251FN433355 FJ628310 ET801285ET703392 ET799413 FJ205243

KF596556 TMED270 AM997823 ET801403 FJ516780 AM997703 CT926479 ET801225 68 JX227042 ET800535JN119209 TMED15 JN487393 FJ612256 FJ205240 TMED114 FJ626884 KC896682 AY627529 TMED154 ET592353 GQ340303 ET802089 AY948359

ET8028 ET703446 JN515584 ET703390HM129619 HQ672089 GT061506 JN535993 ET104254 CAOS01000004

AY948357 GQ390232

EF471577 KC000309 KC836759 EF471579 JN536125 EF575268GT230412 JN531871 EF574855 KJ480297 JQ515046 KF500027 JN523715 KF427966

GQ350624 EF573463 ET805394 JN537880 JF747624 JQ844338 JQ844348 KP005026

ET491735

JQ515083 CT918341

JX016284 GQ111893 AF382139 HE817802 JX016122 KJ589766 JQ844337 JN517084 JX015984 APGO01000272KF286195 ET373999 JX016221 AQPC01000005 FJ416101 HQ673908 JN986299JX016051 ET802816 FJ626886 JN177961 EF016485 JX504295 KJ589921 JX015701 JN166345 Bacteroidetes GQ348788 JN166319 GQ850555 ET795217

EF572850EF574581 FR683798 KJ465940 KC682604 AM279197AB476256FQ032816 AM279204AM279161 DQ396110 KC00035 FN435254 GQ349699JQ3474209 ET236363 GT451479 TMED138 Planctomycetes DQ372846EF573103 TMED39 KJ094286 HM127539 JN874328 ET934238 ET283511 FN822205 JX524993 GQ452882 GT362997 KF786441 KC000876 HQ153932 ET249952 KJ754636EF061974 DQ396077 DQ660388ET570843 ET048667 ET919760 EF572020 HM161878 TMED10 EF573122 EF573486 M58792 HM798666 TMED32GT235095 DQ396265 HQ673330 FQ032830 GT362996 JN177656 KC631451 ET491341 GQ246416 KJ754664JQ800731 GQ263758 JN672626 GT363004 HM445938 FJ152887 JQ428103 HQ163355 JQ426667 AF468236 HQ692020 FJ973596 JN441968 TMED105 KF234354 AACY020525321 TMED11 HQ672203 JN452804 ET799766 JN428149 HQ242253 EF589351 EF589352 HQ672036 TMED23 GQ348045 HQ845500 AACY020443294FJ154966 ANOH01000401 GT567970 FJ624355 JN166296 HQ153836 DQ351810 HQ673159 GT119490 HQ673657 EF591885 GQ346796 FN822131 FJ615110 JQ579975 ET236306 TMED283 AF406520 ET617890 AB694399 HM057812 TMED25 JN166133 DQ811902 KC682754 JF748734 FJ873319 JN591805JQ753183 JN832946 GQ348014 ET802577FJ745150 HQ671908 FN822207 KF786393 GT180185 GQ349915 AY534087 GT119 HQ673421 FJ545502 AY907820GQ337221 223 TMED53 FJ802297 HQ163711 GQ346535 HQ242356AF406538 DQ396079JX529012 JX016834 KC677615 GQ348836 JX525642HQ673145 JN493226 JQ855519 HQ163262 TMED181 CAGM02001594 ET799242 TMED230 HQ163771 HQ672489 AACY020068177TMED70 AGWX01000001EF205444 KP142320 TMED218 HQ672119 ET802629 GQ157433 AB33 TMED4 KP142352 AB331890 AB294932 KF786824 1889 HQ132385 KF786937 AY820751 KF786948 KC872852 AY820749 HQ674365 AY820744 AB476180 ET802747 JN986504GT567992 Chloroflexi KC682829 KJ589913 HQ671966 JN443694 AB981859AB981835GT369924 KP005036 HQ729995 AY102325 HQ673270 KF756202KF756255 TMED76 HQ154002 HQ721336 KJ589945HQ673196 TMED18 AY102327 AB193918 GT369888 KJ589871 GT940987 KC122613 ET805003

KJ589710 KP214628

AACY020553637 Alphaproteobacteria GQ348022 TMED6 HF968439 KF799062 EF573580 ET695218 ET695219

TMED190 HQ672108 JX945378 AM747382 JX016791 Verrucomicrobia

GQ350073 JN447496 FJ825999 EF646119

EF506945

TMED144 JX526223 JN470252 DQ395844 GCIB01034586 FJ203515 ET686594 HG004192 JN245720 FJ744842 HQ692000 KM200021 JN448080 Marinimicrobia KC873417 AACY020222384 FR683981

EF573230 JQ269287

AY907765 TMED67

JN433154

TMED58 Gammaproteobacteria

TMED27 CP003730 Chloroplast TMED88 Deltaproteobacteria

FX534292

FX533220

FX532111 ATZY01000070

FX527310

GT941026

HQ672230

HQ418451 GQ222227

GT940773

FJ745099 FJ612269

FJ744890

FJ744961

TMED223

KJ742377 TMED32 TMED66 TMED32

EF409832

X15435 Mitochondria APMI01014340

JPID01000014

APJO01001223

GQ339576

FJ999600

FJ873264

FJ999595

Figure 3 FastTree approximate maximum-likelihood phylogenetic tree constructed with 37 and 406 16S rRNA genes from putative draft genomes and references, respectively. Sequence alignment is available in Supplemental Information 4. Phylogenetic tree with Shimodaira–Hasegawa local support values as available in Newick format in Supplemental Information 5.

et al., 2012)(Supplemental Information 7). Final alignments were concatenated and used to construct an approximately-maximum-likelihood tree using the LG (Le & Gascuel, 2008) and Gamma models with Shimodaira–Hasegawa local support values with FastTree (v2.1.10; parameters: -lg -gamma; Fig. 4; Supplemental Information 8). A separate tree was constructed using the same concatenated alignments and tree-building parameters for the 210 draft genomes, without the reference genomes (Fig. 4).

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 7/15 Verrucomicrobia Kiritimatiellaeota

TMED149

TMED175 TMED73 TMED40 TMED60 TMED44 TMED158 TMED56 Planctomycetes TMED137 TMED71 TMED86 TMED18 TMED138TMED76

TMED10 TMED67 TMED102 TMED266 TMED6 TMED11 TMED198 TMED274 TMED46 TMED24 TMED217 TMED151 TMED80 TMED108 TMED144 NC10 TMED105 TMED53TMED9 TMED214 TMED152 Bacteroidetes TMED15 TMED32 TMED130 TMED39 TMED114 TMED77 TMED224 TMED45 Actinobacteria TMED189 TMED209 TMED244 TMED16 TMED135 TMED210 TMED84 TMED123 TMED98 TMED113 TMED115 TMED191 TMED223 TMED14 TMED TMED121 TMED212 TMED20 TMED187205 TMED42 TMED120 TMED66 TMED48 Cyanobacteria TMED90 TMED68 TMED19 TMED179 TMED155 TMED147 TMED169 TMED208 TMED116 TMED185 TMED220 TMED70 TMED206 TMED230 TMED96 TMED97 TMED238 TMED129 TMED81 TMED164 TMED204 TMED192 TMED12 TMED145 TMED255 TMED58 Euryarchaeota TMED132 TMED126 Deltaproteobacteria TMED248 TMED88 TMED99 TMED281 TMED252 TMED218 TMED103 TMED22 TMED141 TMED82 TMED279 TMED156 TMED41 Betaproteobacteria TMED85 TMED100 TMED29 TMED139 TMED83 TMED107 TMED143 TMED260 TMED162 TMED47 TMED94 TMED49 TMED168 TMED148 TMED122 TMED227 TMED21 TMED25 TMED157 TMED28 TMED79 TMED74 TMED33 TMED4 TMED59 TMED17 TMED91 TMED31 TMED243 TMED5 TMED30 TMED92 TMED111 TMED34 TMED38 TMED3 TMED50 TMED182 TMED271 TMED95 TMED87 TMED133 TMED2 TMED180 TMED27 TMED119 TMED69 TMED54 TMED282 TMED199 TMED61 TMED257 TMED245 TMED78 TMED176 TMED36 Gammaproteobacteria TMED213 TMED159 TMED52 TMED222 TMED55 TMED219 TMED37 TMED112 TMED236 Alphaproteobacteria TMED26 TMED186 TMED8 TMED225 TMED234 TMED63 TMED226 TMED278 TMED242 TMED194 TMED237 TMED62 TMED259 TMED258 TMED142 TMED93 TMED166 TMED196 TMED203

TMED128 TMED263 TMED197 TMED109TMED13 TMED65

TMED195 TMED275 TMED118 TMED273 TMED239 TMED64 TMED106

TMED202 TMED165 TMED272

TMED286

TMED253 TMED153 Pelagibacteraceae

Figure 4 Cladogram of a FastTree approximate maximum-likelihood phylogenetic tree constructed using 16 syntenic, single-copy marker genes for 210 draft genomes. Support for internal nodes were determined based on the Shimodaira–Hasegawa test (white ≥0.500, gray ≥0.750, black ≥0.950). Sequence alignment for the draft genomes and the reference genomes used for phylogenetic assignment is available in Supplemental Information 7. Phylogenetic tree of the draft genomes and the reference genomes used for phylogenetic assignment is available in Newick format in Supplemental Information 8.

Relative abundance of draft genomes To set-up a baseline that could approximate the ‘‘microbial’’ community (Bacteria, Archaea and viruses) present in the various Tara metagenomes, which included filter sizes specifically targeting both protists and giruses, reads were recruited against all contigs generated from the Minimus2 and Megahit assemblies ≥2 kb using Bowtie2 (default parameters). The contigs <2 kb in length likely constitute low abundance bacteria and archaea, bacteria and archaea with high degrees of repeats resulting in poor assembly, fragmented picoeukaryotic genomes, and problematic read sequences (low quality, sequencing artefacts, etc.) and were not included in further analysis. All relative abundance measures are relative to the number of reads recruited to the assemblies ≥2 kb. Read counts were determined using featureCounts (as above). Length-normalized relative abundance values were determined for each draft genome for each sample: Reads P bp per genome Recruited reads to genomes × ×100. P Reads PRecruited reads to all contigs(≥2 kb) bp all genomes

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 8/15 Table 2 Assembly statistics at various steps during processing.

Contig grouping No. of contigs N50a Total sequence (bp) Megahit assemblies 200–499 bp 24,999,285 n.d. 9,293,098,676 Megahit assemblies 500–1,999 bp 16,103,221 n.d. 13,382,057,993 Megahit assemblies ≥2 kb 1,517,360 4,658 6,691,877,664 Megahit assemblies ≥2 kb (post-CD-HIT-EST) 1,126,975 4,520 4,894,479,496 Minimus2 contigs 158,414 15,394 1,727,079,865 Minimus2 + unincorporated Megahit contigs ≥2 kb (data-rich-contigs) 660,937 5,466 3,612,405,904 Minimus2 + unincorporated Megahit contigs ≥7.5 kb (binned-contigs) 95,506 20,556 1,725,063,313 Notes. aN50—length of DNA sequence above which 50% of the total is contained. RESULTS Assembly The initial Megahit assembly was performed on the publicly available reads for Tara stations 007, 009, 018, 023, 025, 030. Starting with 147–744 million reads per sample, the Megahit assembly process generated 1.2–4.6 million contigs with a mean N 50 and longest contig of 785 bp and 537 kb, respectively (Table 1). In general, the contigs generated from the Tara samples targeting the protist size fraction (0.8–5 µm) had a shorter N 50 value than the bacteria size fractions (mean: 554 bp vs 892 bp, respectively). Contigs from the Megahit assembly process were pooled and separated by length. Of the 42.6 million contigs generated during the first assembly, 1.5 million were ≥2 kb in length (Table 2). Several attempts were made to assemble the shorter contigs, but publicly available overlap-consensus assemblers (Newbler (454 Life Sciences), cap3 (Huang & Madan, 1999), and MIRA (Chevreux et al., 2004)) failed on multiple attempts. Processing the ≥2 kb contigs from all of the samples through CD-HIT-EST reduced the total to 1.1 million contigs. This group of contigs was subjected to the secondary assembly through Minimus2, generating 158,414 new contigs (all ≥2 kb). The secondary contigs were combined with the Megahit contigs that were not assembled by Minimus2. This provided a contig dataset consisting of 660,937 contigs, all ≥2 kb in length (Table 2; further referred to as data-rich-contigs). Binning The set of data-rich-contigs was used to recruit the metagenomic reads from each sample using Bowtie2. The data-rich-contigs recruited 15–81% of the reads depending on the sample. In general, the protist size fraction recruited substantially fewer reads than the girus and bacteria size fractions (mean: 19.8% vs 75.0%, respectively) (Table 1). For the protist size fraction, the ‘‘missing’’ data for these recruitments likely results from the poor assembly of more complex and larger eukaryotic genomes. The fraction of the reads that do not recruit in the girus and bacterial size fraction samples could be accounted for by the large number of low quality assemblies (200–500 bp) and reads that could not be assembled due to low abundance or high complexity (Table 2). Unsupervised binning was performed using both transformed and raw coverage values for a subset of 95,506 contigs from the data-rich-contigs that were ≥7.5 kb (referred to

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 9/15 further as binned-contigs). Binning using the transformed coverage data generated 237 putative draft genomes containing 15,032 contigs (Supplemental Information 1). Contigs not in putative genomes were re-binned based on raw coverage values, generating 53 additional putative draft genomes encompassing 3,348 contigs. In total, 290 putative draft genomes were generated with 50–100% completion (mean: 69%) with a mean length and number of putative CDS of 1.7Mbp and 1,699, respectively (Supplemental Information 1). In analyzing the quality of generated draft genomes, 31 of the genomes had a contamination value >10%, but had a cumulative redundancy value <10%, while an additional 12 genomes had a cumulative redundancy value >10% (genomes highlighted in Supplemental Information 1). For instances where the predicted strain heterogeneity is high and sharply reduces the calculated cumulative redundancy value (e.g., TMED1), downstream analysis of these genomes may offer opportunities to examine within strain variation, if the gene content varies between contigs, or identify duplicate contigs that can be removed to rectify contamination issues. For instances where cumulative redundancy values remain high (e.g., TMED20 or TMED106), downstream analysis utilizing composition signatures, for example within Anvi’o (Eren et al., 2015), should be able to identify problematic contigs and reduce the overall contamination reported for the genome. All other contigs were grouped into bins with at least five contigs, but with estimated completion of 0–50% (2,786 low completion bins; 74,358 contigs; Supplemental Information 2) or did not bin (2,732 contigs). Nearly a quarter of the 2,786 low completion bins (24.7%) have an estimated completion of 0%. These bins may be good candidates for exploring small double-stranded viral genomes or episomal genetic elements. Taxonomy, phylogeny, & potential organisms of interest The 290 putative draft genomes had a taxonomy assigned to it via CheckM during the pplacer step. All of the genomes, except for 20, had an assignment to at least the Phylum level, and 83% of the genomes had an assignment to at least the Class level (Supplemental Information 1). Phylogenetic information was determined for as many genomes as possible. Genomes were assessed for the presence of full-length 16S rRNA genes. In total, 37 16S rRNA genes were detected in 35 genomes. 16S rRNA genes can prove to be problematic during the assembly steps due to the high level of conservation that can break contigs (Miller, Koren & Sutton, 2010)(Fig. 3). The conserved regions of the 16S rRNA, depending on the situation, can over- or under-recruit reads, resulting in coverage variations that can misplace contigs into the incorrect genome. As such, most of the 16S rRNA phylogenetic placements support the taxonomic assignments, while eight of the assignments were contradictory in nature (denoted in Supplemental Information 1). An example of this nonconformity of 16S rRNA assignment would be TMED32. A Bacteroidetes, TMED32 is assigned to the Order Cytophagales via CheckM and the ribosomal marker tree and contains three 16S rRNA sequences. One of the 16S rRNA sequences is conformational, with placement in the Family Flammeovirgaceae, while the remaining two 16S rRNA are assigned to the Mitochondria. For future research purposes, contigs with contradictory 16S rRNA or incongruent phylogenetic/taxonomic signatures should be removed. Downstream analysis

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 10/15 should allow for the determination of the most parsimonious result in any draft genome with contradictory phylogenetic assignments. Beyond the 16S rRNA gene, genomes were searched for 16 conserved, syntenic ribosomal markers. Sufficient markers (≥8) were identified in 210 of the genomes (72%) and placed on a tree with 6,080 reference sequences and used to assign a putative phylogeny (Supplemental Information 1). Phylogenies were then assigned to the lowest taxonomic level that could be confidently determined (Fig. 4). These putative results reveal a number of genomes were generated that represent multiple clades for which environmental genomic information remains limited, including: Planctomycetes, Verrucomicrobia, Cyanobacteria, and uncultured groups within the Alpha- and Gammaproteobacteria. Relative abundance A length-normalized relative abundance value was determined for each genome in each sample based on the number of reads recruited to the data-rich-contigs. The relative abundance for the individual genomes was determined based on this portion of the dataset (Supplemental Information 3). In general, the genomes had low relative abundance (maximum relative abundance = 1.9% for TMED155 a putative Cyanobacteria at site TARA023 from the protistan size fraction sampled at the surface; Supplemental Information 1). The draft genomes accounted for 1.57–25.16% of the approximate microbial community as determined by the data-rich-contigs (mean = 13.69%), with the ten most abundant genomes in a sample representing 0.61–10.31% (Table 1).

CONCLUDING STATEMENT The goal of this project was to provided preliminary putative genomes from the Tara Oceans microbial metagenomic datasets. The 290 putative draft genomes and 2,786 low completion bins were created using the 20 samples and six stations from the Mediterranean Sea. Initial assessment of the phylogeny of these metagenomic-assembled genomes based on concatenated ribosomal markers indicates several new genomes from environmentally relevant organisms, including approximately 10 new Cyanobacteria genomes within the genera Prochlorococcus and Synechococcus and 22 new SAR11 genomes. Additionally, there are putative genomes from the marine Euryarchaeota (n = 13), Verrucomicrobia (n = 15), and Planctomycetes (n = 7). Additionally, the low completion bins may house distinct viral genomes. Of particular interest may be the 40 bins with 0% completion (based on single-copy marker genes), but that contain >500 kb of genetic material (including 3 bins with >1 Mb). These large bins lacking markers may be good candidates for research in to the marine ‘‘giant viruses’’ and episomal DNA sources (plasmids, etc.). It should be noted, researchers using this dataset should be aware that all of the genomes generated from these samples should be used as a resource with some skepticism towards the results being an absolute. Like all results for metagenome-assembled genomes, these genomes represent a best-guess approximation of a taxon from the environment (Sharon & Banfield, 2013). Researchers are encouraged to confirm all claims through various genomic analyses and accuracy may require the removal of conflicting sequences.

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 11/15 ACKNOWLEDGEMENTS We are indebted to the Tara Oceans project and team for their commitment to open-access data that allows data aficionados to indulge in the data and attempt to add to the body of science contained within. This is C-DEBI Contribution 333.

ADDITIONAL INFORMATION AND DECLARATIONS

Funding Funding was provided by the Center for Dark Energy Biosphere Investigations (C-DEBI) to BJT and JFH (OCE-0939654). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant Disclosures The following grant information was disclosed by the authors: Center for Dark Energy Biosphere Investigations (C-DEBI): OCE-0939654. Competing Interests The authors declare there are no competing interests. Author Contributions • Benjamin J. Tully conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper. • Rohan Sachdeva reviewed drafts of the paper, provided origins of assembly workflow. • Elaina D. Graham performed the experiments, reviewed drafts of the paper. • John F. Heidelberg contributed reagents/materials/analysis tools, reviewed drafts of the paper. Data Availability The following information was supplied regarding data availability: This project has been deposited at DDBJ/ENA/GenBank under the BioProject accession no. PRJNA385857 and drafts of genomes are available with accession no. NHBG00000000– NHMJ00000000. All contigs generated using Megahit from each sample are available through iMicrobe (http://data.imicrobe.us/project/view/261). TARA Oceans has deposited the reads in the NCBI Sequence Read Archive (SRA) with accessions ERS488346, ERS488330, ERS477998, ERS477979, ERS488509, ERS488486, ERS478040, ERS477953, ERS477931, ERS488147, ERS488119, and ERS478017. Additional files have been provided and are available through FigShare, such as: all contigs from Minimus2 + Megahit output used for binning and community assessment; contig read counts per sample; the putative genome contigs and Prodigal-predicted nucleotide and protein putative CDS FASTA files; the ribosomal marker HMM profiles; reference genome markers; draft genome markers; low completion bins, and contigs without a bin.

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 12/15 Tully, Benjamin; Sachdeva, Rohan; Graham, Elaina; Heidelberg, John (2017): 290 Genomes from the Mediterranean Sea: Supplemental Data. figshare. https://doi.org/10. 6084/m9.figshare.3545330.v3. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.3558#supplemental-information.

REFERENCES Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. 2009. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25:1972–1973 DOI 10.1093/bioinformatics/btp348. Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Müller WEG, Wetter T, Suhai S. 2004. Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Research 14:1147–1159 DOI 10.1101/gr.1917404. Edgar RC. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32:1792–1797 DOI 10.1093/nar/gkh340. Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO. 2015. Anvio: an advanced analysis and visualization platform for omics data. PeerJ 3:e1319 DOI 10.7717/peerj.1319. Falkowski PG, Fenchel T, DeLong EF. 2008. The microbial engines that drive earth’s biogeochemical cycles. Science 320:1034–1039 DOI 10.1126/science.1153213. Finn RD, Clements J, Eddy SR. 2011. HMMER web server: interactive sequence similarity searching. Nucleic Acids Research 39:W29–W37 DOI 10.1093/nar/gkr367. Fu L, Niu B, Zhu Z, Wu S, Li W. 2012. CD-HIT: accelerated for clustering the next- generation sequencing data. Bioinformatics 28:3150–3152 DOI 10.1093/bioinformatics/bts565. Graham ED, Heidelberg JF, Tully BJ. 2017. BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation. PeerJ 5:e3035–19 DOI 10.7717/peerj.3035. Huang X, Madan A. 1999. CAP3: a DNA sequence assembly program. Genome Research 9:868–877 DOI 10.1101/gr.9.9.868. Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, Butterfield CN, Hernsdorf AW, Amano Y, Ise K, Suzuki Y, Dudek N, Relman DA, Finstad KM, Amundson R, Thomas BC, Banfield JF. 2016. A new view of the tree of life. Nature Microbiology 1(5):16048 DOI 10.1038/nmicrobiol.2016.48. Karsenti E, Acinas SG, Bork P, Bowler C, De Vargas C, Raes J, Sullivan M, Arendt D, Benzoni F, Claverie J-M, Follows M, Gorsky G, Hingamp P, Iudicone D, Jaillon O, Kandels-Lewis S, Krzic U, Not F, Ogata H, Pesant S, Reynaud EG, Sardet C, Sieracki ME, Speich S, Velayoudon D, Weissenbach J, Wincker P. 2011. A holistic approach to marine eco-systems biology. PLOS Biology 9:e1001177–5 DOI 10.1371/journal.pbio.1001177.

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 13/15 Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Mentjies P, Drummond A. 2012. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649 DOI 10.1093/bioinformatics/bts199. Lagesen K, Hallin P, Rødland EA, Staerfeldt H-H, Rognes T, Ussery DW. 2007. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Research 35:3100–3108 DOI 10.1093/nar/gkm160. Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nature Methods 9:357–359 DOI 10.1038/nmeth.1923. Le SQ, Gascuel O. 2008. An improved general amino acid replacement matrix. Molecular Biology and Evolution 25:1307–1320 DOI 10.1093/molbev/msn067. Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, Yamashita H, Lam T- W. 2016. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102:3–11 DOI 10.1016/j.ymeth.2016.02.020. Liao Y, Smyth GK, Shi W. 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930 DOI 10.1093/bioinformatics/btt656. Longhurst A, Sathyendranath S, Platt T, Caverhill C. 1995. An estimate of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research 17:1245–1271 DOI 10.1093/plankt/17.6.1245. Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, Buchner A, Lai T, Steppi S, Jobb G, Förster W, Brettske I, Gerber S, Ginhart AW, Gross O, Grumann S, Hermann S, Jost R, König A, Liss T, Lüssmann R, May M, Nonhoff B, Reichel B, Strehlow R, Stamatakis A, Stuckmann N, Vilbig A, Lenke M, Ludwig T, Bode A, Schleifer K-H. 2004. ARB: a software environment for sequence data. Nucleic Acids Research 32:1363–1371 DOI 10.1093/nar/gkh293. Matsen FA, Kodner RB, Armbrust EV. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11:538 DOI 10.1186/1471-2105-11-538. Miller JR, Koren S, Sutton G. 2010. Assembly algorithms for next-generation sequencing data. Genomics 95:315–327 DOI 10.1016/j.ygeno.2010.03.001. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research 25:1043–1055 DOI 10.1101/gr.186072.114. Price MN, Dehal PS, Arkin AP. 2010. FastTree 2—approximately maximum-likelihood trees for large alignments. PLOS ONE 5:e9490 DOI 10.1371/journal.pone.0009490. Pruesse E, Peplies J, Glöckner FO. 2012. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28:1823–1829 DOI 10.1093/bioinformatics/bts252. Sharon I, Banfield JF. 2013. Microbiology. Genomes from metagenomics. Science 342:1057–1058 DOI 10.1126/science.1247023.

Tully et al. (2017), PeerJ, DOI 10.7717/peerj.3558 14/15 Shimodaira H, Hasegawa M. 1999. Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Molecular Biology and Evolution 16:1114–1116 DOI 10.1093/oxfordjournals.molbev.a026201. Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, Cornejo-Castillo FM, Costea PI, Cruaud C, D’Ovidio F, Engelen S, Ferrera I, Gasol JM, Guidi L, Hildebrand F, Kokoszka F, Lepoivre C, Lima-Mendez G, Poulain J, Poulos BT, Royo-Llonch M, Sarmento H, Vieira-Silva S, Dimier C, Picheral M, Searson S, Kandels-Lewis S, Tara Oceans Coordinators, Bowler C, De Vargas C, Gorsky G, Grimsley N, Hingamp P, Iudicone D, Jaillon O, Not F, Ogata H, Pesant S, Speich S, Stemmann L, Sullivan MB, Weissenbach J, Wincker P, Karsenti E, Raes J, Acinas SG, Bork P. 2015. Ocean plankton. Structure and function of the global ocean microbiome. Science 348:1261359–1261359 DOI 10.1126/science.1261359. Treangen TJ, Sommer DD, Angly FE, Koren S, Pop M. 2011. Next generation sequence assembly with AMOS. Current Protocols in Bioinformatics Chapter: Unit 11.8 DOI 10.1002/0471250953.bi1108s33. Whitman WB, Coleman DC, Wiebe WJ. 1998. Prokaryotes: the unseen majority. Proceedings of the National Academy of Sciences of the United States of America 95:6578–6583 DOI 10.1073/pnas.95.12.6578. Yang Z. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. Journal of Molecular Evolution 39:306–314 DOI 10.1007/BF00160154.

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