Citation
A PCR primer bias free examination of microbial community composition and co-occurrence

Material Information

Title:
A PCR primer bias free examination of microbial community composition and co-occurrence
Creator:
Banks-Rchardson, David Joshua ( author )
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English
Physical Description:
1 electronic file : ;

Subjects

Subjects / Keywords:
Polymerase chain reaction ( lcsh )
Polymerase chain reaction ( fast )
Genre:
Academic theses. ( fast )
Academic theses. ( lcgft )
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )
Academic theses ( fast )
Academic theses ( lcgft )

Notes

Review:
Microbial communities drive biogeochemical cycling, maintain human health, and produce useful compounds. Studies relying on the Polymerase Chain Reaction (PCR) to amplify the conserved small subunit of the Ribosome (SSU) gene have been utilized to study the structure of these communities. The primers used in these studies detect some taxa more readily than others. To overcome PCR primer-bias, we utilized the SSU gene assembler EMRIGE to estimate the community composition of 3110 shotgun sequenced metagenomes. The primer quality evaluator PrimerProspector was used to predict the likelihood that the bacterial primer pair 338f*/1061r and the Earth Microbiome Project primer pair 515f/806 would amplify the reconstructed sequences. While these primer sets are anticipated to amplify many of the taxa they were designed to, the bacterial primer pair 338f*/1061r was not expected to amplify many members of the Verricumicrobia and the Earth Microbiome Project primers were predicted to under amplify some members of the Actinobacteria. Some members of the Candidate Phyla Radiation (CPR) were not predicted to amplify using either primer pair. Beta-Diversity analyses of the communities detected widespread taxonomic similarities between samples collected from similar environments. Co-occurrence analyses were performed using the primer-bias-free estimates of marine and saline water associated microbial communities as well as soil and sediment associated microbial communities. Additionally, a network analysis curriculum was developed for high school students. The methods used to generate this data could be used as a taxonomic composition companion database to the Sequence Read Archive and serve as a valuable resource to the broader scientific community.
Bibliography:
Includes bibliographical references.
System Details:
System requirements: Adobe Reader.
Statement of Responsibility:
by David Joshua Banks-Richardson.

Record Information

Source Institution:
University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
Copyright David Joshua Banks-Richardson. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
on10774 ( NOTIS )
1077479334 ( OCLC )
on1077479334
Classification:
LD1193.L45 2018 m B47 ( lcc )

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PAGE 16

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PAGE 17

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PAGE 20

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PAGE 29

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PAGE 31

! Ia 3C6@;:?6@'36CFECIB9:6'EF'?A6']N]FYWZ[C'3C;I6C'3B;C /L@!SA"N@AB!O@?@QDS@O!EDA!CL@!)PACL!("HAD6"DN@!$AD`@HC! Y9!DE!CL@!++2!U@>@!EADN!P!F"O@!?PA"@C7!DE!6PHC@A"PQ!0/2B!A@HD>BCAMHC@O! ">!CL"B!BCMO7=!PQ6@"C!>DC!PQQ!Y,"UMA@!aZB!DE!BDN@!N@N6@AB!DE!CL@! $ADS"D>"6PHC@A"PH@P@ =! P!UADMS!W>DF>!EDA!"CB!ADQ@!">!PH>@!P>O!>DBDHDN"PQ!">E@HC"D>B=!">!CL@!SL7QMN! #HC">D6PHC@A"P PA@!>DC! @GS@HC@O!CD!PNSQ"E7!PB!@EE"H"@>CQ7!F"CL!CL@!aKaE!SA"N@AB"BC@>C!F"CL!SA@?"DMB!FDAW! @GPN"> ">U!SA"N@A!6"PB!">!BD"Q!N"HAD6"PQ!HDNNM>"C"@B!CLPC!EDM>O!CLPC!CL@!aKaE^VJ_A!SA"N@AB! M>O@AA@SDAC@O!CL@!P6M>OP>H@!DE!N@N6@AB!DE!CL@!SL7QMN! #HC">D6PHC@A"P ">!EP?DA!DE!N@N6@AB!DE!CL@! 8@AAMHDN"HAD6"P Y9M=!RMD=!3@=!-"P=!i!hLP>U=!IJKfZ DC! SA@O"HC@O!CD!LP?@!O"EE"HMQC7!PNSQ"E7">U!MB">U!CL@B@!SA"N@AB!Y,"UMA@!_ZO"OPCMB .DNMAP6PHC@A"P!0/2B!P>O!K\<_m!DE!CL@!II!%P>O"OPCMB (DAP >6PHC@A"P!0/2B!F@A@!SA@O"HC@O!CD!PNSQ"E7 @EE"H"@>CQ7 !6@!PCCA"6MC@O!CD!SA@O"HC@O! N"BNPCHL@B!F"CL! CL@!aKaE!SA"N@A=!6MC!B@?@APQ! A@HD>BCAMHC@O!B@TM@>H@B!PQBD!LPO!N"BNPCHL@B!F"CL!CL@!VJ_A!SA"N@A"6PHC@A "PH@P@!P>O!CL@!("HADU@>DNPC@B!F@A@!SA@O"HC@O!>DC!CD!6">O!@EE"H"@>CQ7!CD!@"CL@A!CL@!aKaE! DA!VJ_A!SA"N@A Q7!fKm!DE!CL@!_ K!("HADU@>DNPC@B!0/2B!"O@>C"E"@O ">!CL"B!BCMO7!F@A@!SA@O"HC@O!CD! PNSQ"E7!@EE"H"@>CQ7!F"CL!CL@B@!SA"N@AB@O!CD!PNSQ"E7!6DCL!6PHC@A"P!P>O!PAHLP@P!CL@!FLDQ@=! CL@B@!SA"N@AB!F@A@!SA@O"HC@O!CD!PNSQ"E7!ed<_m!DE!CL@!_If!A@HD>BCAMHC@O!PAHLP@P!++2B!"O@>C"E"@O!">!CL"B! BCMO7 O">U!"BBM@B!F"CL!BDN@!N@N6@AB!DE!CL@! bD@B@PAHLP@DCP!PB!F@QQ!PB!BDN@!N@N6@AB!DE!CL@!.P>DPAHLP@DCP Y,"UMA@!dZ7!DE!CL@!SADWPA7DC"H!0/2B!F@!A@HD >BCAMHC@O!">!CL"B!FDAW
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! I_ 4@BS"C@!CL@!BCAD>U!SA@O"HC@O!S@AEDANP>H@!DE!CL"B!SA"N@A!SP"A!D>!CL@!8f!A@U"D>B!DE!CL@! SADWPA7DC"H!0/2B!A@HD>BCAMHC@O!">!CL@!FDAW=!CL"B!SA"N@A!SP"A!"B!>DC!SA@O"HC@O!CD!PNSQ"E7!NP>7!DE!CL@! @MWPA7DC"H!B@TM@>H@B!YD>Q7!I'6>?;IB?6@'GJ'3C;I6C3CE>K6:?ECP'' /L@B@! 6ADPO!SADWPA7DC@!SA"N@AB!F@A@!SA@O"HC@O!CD!PNSQ"E7!CL@!8f!A@U"D>!DE!NDBC!A@HD>BCAMHC@O!#AHLP@P!P>O!*PHC@A"P! 0/2BB!Y,"UMA@!_!P>O!,"UMA@!dZH@B!A@PBB@N6Q@O!">! CL"B!FDAW!F@A@!>DC!SA@O"HC@O!CD!PNSQ"E7!@EE"H"@>CQ7!MB">U!CL@B@!SA"N@AB
PAGE 33

! Id 1;<8C6'[P'$A6'KC6@;:?6@':ER6CB<6'EF'?A6']N]FYWZ[C'KC;I6C'KB;C'E9'@;FF6C69?'GB :?6C;BH'KAJHB'B>'6>?;IB?6@'GJ' 3C;I6C3CE>K6:?ECP' *PAB!A@SA@B@>C!CL@!EAPHC"D>!DE!0/2B!6@QD>U">U!CD!P!U"?@>!SL7QMN!CLPC!PA@!SA@O"HC@O!CD!PNSQ"E7BCAMHC@OHQMO@!BDN@! %$&! Y"<@DNPC@B=!P>O!bb)\Z PB!F@QQ!PB! BDN@!N@N6@AB!DE!CL@!%LQDADEQ@G"=!,"AN"HMC@B=!/L@ANDCDUP@=!%7P>D6PHC@A"P=!P>O!b+_ SL7QP
PAGE 34

! IV 1;<8C6'^P'$A6'KC6@;:?6@':ER6CB<6'EF'?A6']N]FYWZ[C'KC ;I6C'KB;C'E9'@;FF6C69?'!C:AB6B'KAJHB'B>'6>?;IB?6@'GJ' 3C;I6C3CE>K6:?ECP' *PAB!A@SA@B@>C!CL@!EAPHC"D>!DE!0/2B!6@QD>U">U!CD!P!U"?@>!SL7QMN!CLPC!PA@!SA@O"HC@O!CD!PNSQ"E7 < /L"B!SA"N@A!SP"A!"B!@GS@HC@O!CD!@EE"H"@>CQ7!PNSQ"E7!NDBC!DE!CL@!)('&R) 5 A@HD>BCAMHC@O!0/2B! A@SA@B@>C">U!B@?@APQ! SL7QP!DE!#AHLP@PU">U!CD!CL@!$PA?PAHLP@DCP=! .P>DPAHLP@DCP =! bD@B@PAHLP@DCP=!%A@>PAHLP@DCP=!P>O! 4"PSL@ADCA"C"@B!F@A@!SA@O"HC@O!CD!LP?@!O"EE"HMQC7!PNSQ"E7">U!MB">U!CL@B@!SA"N@AB'E9'?A6'%6:E9>?C8:?6@'*$5 > 0?@APQQ=!CL@!CFD!HDNND>Q7 5 @NSQD7@O!SA"N@A!B@CB!@GPN">@O!F@A@!SA@O"HC@O!CD!PNSQ"E7!CL@! NP`DA"C7!DE!CL@!)('&R)!0/2B!A@HD>BCAMHC@OB!DE! eKD>@!DE!CL@! PAHLP@P 8\ 5 8_ !DE eaPQQ7=!CL@!aKaE^VJ_A!SA"N@AB!PA@!SA@O"HC@O!CD!@EE"H"@>CQ7! PNSQ"E7! SDAC"D>B DE!CL@!++2!U@>@!DE! NDA@!N@N6@AB!DE!CL@!8@AAMHDN"HD6"P=!$QP>HCDN7H@C@B=!P>O! %LQDADEQ@G"!C LP>!CL@!\\VE]^KJ_KA!SA"N@A!B@C!FP B!SA@O"HC@O!CDU!SA@O"HC@O!S@AEDANP>H@! DE!CL@B@!SA"N@A!SP"AB=!CL@A@!PA@!CPGP!CLPC!PA@!N"BB@O!67!6DCL!B@CBH@B!PB!F@QQ! PB!++2!B@TM@>H@B!6@QD>U">U!CD!CL@!("HADU@>DN@C@B!P>O!$PAHM6PHC@A"P!PA@!>DC!SA@O"HC@O!CD!PNSQ"E7! aenigmarchaeota aigarchaeota altiarchaeales bathyarchaeota candidate division ynpffa crenarchaeota diapherotrites euryarchaeota hadesarchaea lokiarchaeota miscellaneous euryarchaeotic group(meg) nanoarchaeota nanohaloarchaeota parvarchaeota thaumarchaeota woesearchaeota (dhveg! 6) wsa2 0.00 0.25 0.50 0.75 1.00 Fraction Coverage

PAGE 35

! Ie @EE"H"@>CQ7!MB">U!@"CL@A!B@C!DE!SA"N@AB@O!CD!PNSQ"E7!@MWPA7DC"H!++2! B@TM@>H@B=!P>O!>@"CL@A! SA "N@A!SP"A!"B!SA@O"HC@O!CD!@EE"H"@>CQ7! PNSQ"E7! N P>7!DE!CL@!\VJV!)MWPA7DCP!++2! 0/2B!A@HD>BCAMHC@O!">!CL"B!FDAW!DE!CL@B@!BPNSQ@B!CLPC! N"ULC! LP?@!6@@>!N"BB@O!MB">U!@"CL@A!DE!CL@B@!SA"N@A!B @CBH@!DE!CL@B@!SA"N@A!SP"AB!HP>!6@!PCCA"6MC@O!CD!6">O">U! N"BNPCHL@B!6@CF@@>!CL@!SA"N@A!P>O!CL@!CPAU@C!B@TM@>H@BH@B!F"QQ!BC"QQ!PNSQ"E7!MB">U!CL@B@!SA"N@AB=!6MC!CL@B@!B@TM@>H@B F"QQ!>DC!PNSQ"E7!PB!@EE"H"@>CQ7!PB! CLDB@!DE!DCL@A!HDNNM>"C7!N@N6@AB!F"CL!@GPHC!NPCHL@B!CD!CL@!SA"N@AB@EE"H"@>H"@B!">!SA"N@A! 6">O">U!Q"W@Q7!Q@PO!CD!M>O@A@BC"NPC@B!DE!CL@!P6M>OP>H@B!DE!BDN@!HDNNM>"C7!N@N6@AB! BW@F!@BC"NPC@B!DE!P!H DNNM>"C7jB!HDNSDB"C"D>!">!EP?DA!DE!Q@BB!P6M>OP>C!HDNNM>"C7!N@N6@ABBPQ7B@B!BMUU@BC!CLPC!CL@B@!SA"N@A!SP"AB!HDMQO!6@! 6"PB@O!PUP">BC!CL@!CPGP!Q"BC@O!P6D?@=!CL@B@!6"PB@B!BLDMQO!6@!HD>E"AN@O! ">!C L@! QP6DAPCDA7U! %#&4 5 ,'+:=!LPB!"O@>C"E"@O!$%&!SA"N@A! 6"PB!PUP">BC!N@N6@AB!DE!CL@! $@QPU"6PHC@APQ@B ">!CL@!*PQC"H!+@P! Y4>!@C!PQ<=!IJKKk! 4>=!bD@QW=!1P6A@>[=!i! 9xAU@>B=!IJKfZ < &E9:H8>;E9 %DQQ@HC"?@Q7=!CL@!@?"O@>H@!SA@B@>C@O!P6D?@!BMSSDACB!SA@?"DMB!FDAW!BMUU@BC">U!CLPC!$%& 5 PNSQ"HD> 5 6PB@O!@BC"NPC@B!DE!N"HAD6"PQ!HDNNM>"C7!HDNSDB"C"D>!PA@!BM6`@HC!CD!6"PB@B! Y4>! @C!PQ<=!IJKfk!;Q">OFDACL!@C!PQ<=!IJK\k!(PAC">@[ 5 $DAHLPB=!8"QQPQSP>OD 5 %P>HLDQP=!0AC"[!+MPA@[=!i!8PAUPB 5 #Q6DA@B=!IJKdZ PQQ7=!P!A@ H@>C!P>PQ7B"B!BMUU@BCB!CLPC!P!CAMQ7!M>"?@ABPQ!SA"N@A!B@C!NP7!6@! M>PCCP">P6Q@!OM@!CD!CPGD>DN"H!?PA"PC"D>!">!HD>B@A?@O!A@U"D>B!DE!CL@!++2!A&.#!U@>@! Y(PAC">@[ 5 $DAHLPB! @C!PQ<=!IJKdZ DC"D>!CLPC!EMCMA@!FDAW!@GPN">">U!CL@!HDNSDB"C"D>!DE!N"HAD6"PQ! HDNNM>"C"@B!FDMQO!6@>@E"C!EADN!A@HD?@A">U!SL7QDU@>@C"H!NPAW@A!U@>@B!EADN!BLDCUM>!B@TM@>H@O! N@CPU@>DN@B! Y9M!@C!PQ<=!IJKfZ

PAGE 36

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PAGE 37

! \K 1;<8C6'W P'$A6'C6HB?;E9>A;K'G6?L669'*$5'C;:A96>>'B9@'>67869:;9<'6FFEC?P' +@TM@>H">U!O@SCL!P>O!BS@H"@B!A"HL>@BB!"B! SQDCC@O!EDA!@PHL!BPNSQ@MN6@A!DE!D6B@A?@O!0/2B!"B!PEE@H C@O!67!B@TM@>H">U!@EEDAC!EDA!P!BPNSQ@@! A@SA@B@>CB!CL@!A@UA@BB"D>!Q">@!E"C!CD!CL"B!OPCP!YA I rJ!CL@!CPGD>DN"H!A"HL>@BB!DE!P!BPNSQ@!FPB!@GPN">@OBL"S!6@CF@@>!LP6"CPC!C7S@!P>O!CL@!A"HL>@BB!DE! P!U"?@>!B "C@!"B!>DC!PSSPA@>C!Y#SS@>O"G!,K Z=!PC!Q@PBC! F"CL!CL@! NP>MPQQ7!A@HQPBB"E "@O! LP6"CPC!C7S@B F@!SA@B@>C!L@A@ MN6@A!DE!0/2B! ">!P!BPNSQ@!F@A@!D6B@A?@O!">!P!NPA">@!B"C@!P>O!BDN@!DE!CL@!B"C@B!F"CL!CL@!E@F@BC!>MN6@A!DE!CPGP! SA@B@ >C!F@A@!PQBD!NPA">@!B"C@B7!LP6"CPC!C7S@B!B@@N@O!CD!LP?@!6ADPO!AP>U@B!">!CL@!>MN6@A!DE!0/2B! SA@B@>C!PC!CLDB@!B"C@BC@AB=!P>O!P>"NPQ!LDBC! PBBDH"PC@O!B"C@B!LPO!DE!CL@!L"UL@BC!>MN6@A!DE!0/2B O@C@HC @O!">!CL"B!BCMO7 B! FL@>!@GPN">">U!BS@H"@B!@?@>>@BBDN@B!HDQQ@HC@O!EAD N!P>"NPQ 5 LDBC!PBBDH"PC@O=!6"DUPB! Observed 0 20000 40000 60000 0 100 200 300 400 500 MegaBases Number of OTUs Observed

PAGE 38

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PAGE 39

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PAGE 49

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PAGE 50

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! _\ #:C;K?>'5>6@'?E'! 9BHJD6'? A6'. B?B /L@!BHA"SCB!PA@!FA"CC@>!">!CL@!*PBL!SADUAPNN">U!QP>UMPU@!M>Q@BB!BS@H"E"@O!DCL@AF"B@< #NP'$A6'>:C;K?>'8>6@'?E'@EL9HEB@'?A6'>AE?<89'I6?B<69EI6>'B9@'C6:E9>?C8:?'?A6'# #5'C%)!'<696'FCEI' ?A6>6'@B?B>6?> #!/bin/bash #SBATCH -export=ALL #SBATCH -nodes=1 #SBATCH -ntasks=6 #SBATCH -time=10:0:0 #SBATCH -mem=12G #SBATCH -output=emirge_%A_%a_%j.out module load jdk 8u66 linux x64 gcc 6.1.0 okzjh4b46rmf4sljtche6y4za4vp2k7p m odule load sqlite 3.8.5 gcc 6.1.0 tdo5mfgclca4btwq3yamygdaeioynqh4 module load bowtie2 2.2.9 gcc 6.1.0 mysfnjw module load Python 2.7.12 gcc 6.1.0 wsnpieodpk2dbwgsfsqhfgsrwf5t775u source ${HOME}/venv/EMIRGE_venv/bin/activate cpus=6 ROOT=${SCRATCH}/Bathyarc haeota # CHRIS: how do we set up default SRA directory again? PREFETCH_DIR=${ROOT}/ncbi DB=${HOME}/Bathyarchaeota.db WDIR=${ROOT}/${srr}_prokarys SILVA_BT1=${HOME}/databases/SILVA_123.1_bwt_indices/SILVA_123.1_bwt_in dices SILVA_BT2=${HOME}/databases/SILVA_ 123.1_bwt2_indices/SILVA_123.1_bwt2_ indices SILVA_fna=${HOME}/databases/SILVA_123.1_SSURef_Nr99_tax_trunc.ge1200bp .le2000bp.refixed.97.dna.fasta SOFTWARE=/homelink/banksrid/.local/bin MSOFTWARE=/homelink/millerc5/.local/bin SRASOFTWARE=${HOME}/sratoolkit.2 .7.0 centos_linux64/bin OthSOFTWARE=${HOME}/venv/EMIRGE_venv/bin BATHYIDS=${HOME}/databases/bathyids.txt busyTO="PRAGMA busy_timeout = 60000;" check_exit () { if [[ $rc != 0 ]] then status=$1 # status text to update database with is pass ed to this function as first arg sqlite3 $DB "${busyTO} update SRAsamples set STATUS = '$status',JOBFAILED=(select datetime('now')) where Run_s = '${srr}';" echo "${srr} failed with $status" 1>&2 exit $rc fi } #This will pick an SRR numeb er from the table with the sample metadata

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! _f #srr=$(sqlite3 ${DB} "${busyTO} begin exclusive; select Run_s from SRAsamples where STATUS='UNPROCESSED' and Assay_Type_s = 'WGS' and Platform_s = 'ILLUMINA' and MBases_l >= 1000 and LibraryLayout_s = 'PAIRED' lim it 1; update SRAsamples set STATUS='SCREENING', JOBSTARTED=(select datetime('now')) where Run_s = (select Run_s from SRAsamples where STATUS = 'UNPROCESSED' and Assay_Type_s = 'WGS' and Platform_s = 'ILLUMINA' and MBases_l >= 1000 and LibraryLayout_s = 'PA IRED' limit 1); commit;" | tail 1) #This inserts the job id into the JOBID column# #sqlite3 $DB "${busyTO} update SRAsamples set JOBID='${SLURM_ARRAY_JOB_ID}_${SLURM_ARRAY_TASK_ID}' where Run_s='${srr}';" #this makes a directory for the files generate d by the filtering process #mkdir ${ROOT}/${srr}_prokarys #this moves us into that directory #cd ${ROOT}/${srr}_prokarys #This grabs the metagenome from the SRA #${SRASOFTWARE}/prefetch X 10000G ${srr} #rc=$?; check_exit "FailedPrefetch" srr=$(sqlite3 $ {DB} "${busyTO} begin exclusive; select Run_s from SRAsamples where STATUS='SCREENING' limit 1; update SRAsamples set STATUS='FASTQ DUMP is working', JOBSTARTED=(select datetime('now')) where Run_s = (select Run_s from SRAsamples where STATUS = 'SCREENING' limit 1); commit;" | tail 1) #This step creates a gzipped fastq file from the sra files# cd ${ROOT}/${srr}_prokarys ${SRASOFTWARE}/fastq dump -skip technical -readids -dumpbase -split files -clip -gzip ${srr} rc=$?; check_exit "Failed FASTQ dump" # This collects the read_length before quality trimming# read_length1=$(zcat ${srr}_1.fastq.gz | head 40000 | grep "length=" | cut d'=' f 2 | sort n r | head 1) read_length2=$(zcat ${srr}_2.fastq.gz | head 40000 | grep "length=" | cut d'=' f 2 | sort n r | head 1) [[ $read_length2 gt $read_length1 ]] && read_length=$read_length2 || read_length=$read_length1 #This updates the database with the read length# sqlite3 $DB "${busyTO} update SRAsamples set ReadLength=${read_length} where Run_s='${s rr}';" #This uses a modified version of Xianjun Dong's script to detect quality encoding from a fastq file see:http://onetipperday.sterding.com/2012/10/code snip to decide phred encoding of.html# quality_encoding=$(zcat ${srr}_1.fastq.gz | head n 40000 | awk '{if(NR%4==0) printf("%s",$0);}' | od A n t u1 | awk 'BEGIN{min=100;max=0;}{for(i=1;i<=NF;i++) {if($i>max) max=$i;

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! _a if($i73 && min>=64) print "phred64"; else if(min>=59 && min<64 && max>73) print "solexa64"; else print "Unknown score encoding";}') Squality_encoding=$(zcat ${srr}_1.fastq.gz | head n 40000 | awk '{if(NR%4==0) printf("%s",$0);}' | od A n t u1 | awk 'BEGIN{min=100;max=0;}{for(i=1;i<=NF;i++) {if($i>max) max=$i; if( $i73 && min>=64) print "illumina"; else if(min>=59 && min<64 && max>73) print "solexa"; else print "Unknown score encoding";}') #quality trimming the files. Leave as uncompressed for n ow -current version #of EMIRGE will need at least one uncompressed# sickle pe l 35 f ${srr}_1.fastq.gz r ${srr}_2.fastq.gz t ${Squality_encoding} o qtrimmed.${srr}_1.fastq \ p qtrimmed.${srr}_2.fastq s qtrimmed.${srr}_unpaired.fastq rc=$?; check_e xit "Failed Quality Trim" #This gets the mean read length from reformat.sh's output# number_of_qtrimmedbasesreads1=$(reformat.sh in=qtrimmed.${srr}_1.fastq |& egrep 'Input:' | egrep o '[0 9]+') number_of_qtrimmedreads1=$(echo ${number_of_qtrimmedbasesre ads1} | cut d f 1) number_of_qtrimmedbases1=$(echo ${number_of_qtrimmedbasesreads1} | cut d f 2) qtrimmed_read1_length=$(echo "scale=4;${number_of_qtrimmedbases1}/${number_of_qtrimmedreads1}" | bc) rc=$?; check_exit "Failed All Forward Bases an d Reads Trimmed Out" number_of_qtrimmedbasesreads2=$(reformat.sh in=qtrimmed.${srr}_2.fastq |& egrep 'Input:' | egrep o '[0 9]+') number_of_qtrimmedreads2=$(echo ${number_of_qtrimmedbasesreads2} | cut d f 1) number_of_qtrimmedbases2=$(echo ${number_ of_qtrimmedbasesreads2} | cut d f 2) qtrimmed_read2_length=$(echo "scale=4;${number_of_qtrimmedbases2}/${number_of_qtrimmedreads2}" | bc) rc=$?; check_exit "Failed All Reverse bases and reads Trimmed out" if [[ ${number_of_qtrimmedbases2} == 0 ]]; th en sqlite3 ${DB} "${busyTO} update SRAsamples set STATUS='Failed All Reverse bases and reads Trimmed out' where Run_s='${srr}';" cd ${ROOT} rm rf ${ROOT}/${srr}_prokarys rm ${ROOT}/ncbi/sra/sra/${srr}.sra exit elif [[ ${number_of_qtrim medbasesreads1} == 0 ]]; then sqlite3 ${DB} "${busyTO} update SRAsamples set STATUS='Failed All Forward bases and reads Trimmed out' where Run_s='${srr}';" cd ${ROOT} rm rf ${ROOT}/${srr}_prokarys rm ${ROOT}/ncbi/sra/sra/${srr}.sra exi t else

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! __ echo "Qtrimming successful" fi sum=$(echo "scale=2;${qtrimmed_read2_length} + ${qtrimmed_read1_length}" | bc) mean_qtrimmed_read_length=$(echo "scale=2;${sum}/2" | bc) sqlite3 $DB "${busyTO} update SRAsamples set QTrimmedReadLength=${mean_qtrimm ed_read_length} where Run_s='${srr}';" #Screen for potential 16S reads using the latest SILVA database# bowtie2 x $SILVA_BT2 U qtrimmed.${srr}_1.fastq,qtrimmed.${srr}_2.fastq -very sensitive local \ -time -threads ${cpus} k 1 -no unal S ${srr}.SS Uhits.sam rc=$?; check_exit "Failed Premapping" #Storing the number of Bathyarchaeota 16S hits into variable "bathy_count"# bathy_count=$(Python "${SOFTWARE}/find_bathys.py" ${srr}.SSUhits.sam | cut f 2) rc=$?; check_exit "Failed To Count Bathy Reads" #Storing the number of total number of 16S hits into variable "ssu_screening_hits"# ssu_screening_hits=$(samtools view c ${srr}.SSUhits.sam) rc=$?; check_exit "Failed to Count SSUhits" #This checks for samples with greater than 10% rRNA reads# rpercent 16s=$(egrep "overall alignment rate" ${HOME}/slurmoutputs/emirge_${SLURM_ARRAY_JOB_ID}_${SLURM_ARRAY_TASK_I D}_${SLURM_JOB_ID}.out | egrep o "[0 9 \ .]+") percent16s=$(printf "%.0f" $(echo "scale=2;${rpercent16s}" | bc)) if [[ ${percent16s} gt 20 ]]; then echo "Failed EMIRGE not a metagenome `echo ${percent16s}` % 16s" cd ${ROOT} rm rf ${ROOT}/${srr}_prokarys rm ${ROOT}/ncbi/sra/sra/${srr}.sra exit else echo "Probably a metagenome" fi # This updates the table containing the sampl e metadata# sqlite3 $DB "${busyTO} update SRAsamples set number16s=${ssu_screening_hits}, numberBathy16s=${bathy_count}, STATUS='RunningEMIRGE' where Run_s='${srr}';" # Getting the IDs to all reads that mapped to a 16S# samtools view ${srr}.SSUhits.sam | cut f 1 > ${srr}.SSUhits.ids.txt rc=$?; check_exit "Failed to make a list of reads that hit to an SSU" #compressing the sam file# gzip ${srr}.SSUhits.sam

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! _d rc=$?; check_exit "Failed Compression of Alignment File" # Moving the compressed sam file to t he SAM file directory# mv ${srr}.SSUhits.sam.gz /gpfs/scratch/banksrid/Bathyarchaeota/bathyecodata/filt_hits #Cleaning the IDs so that I can grab a read that matched from the read 1 or read 2 file# cat ${srr}.SSUhits.ids.txt | cut d. f 1 2 > ${srr}.cle an.SSUhits.ids.txt # Adding a .1 to the IDs so that I can use fastq_pull_sequence.py to gather reads that mapped to a 16S sequence in SILVA# sed 's/$/.1/' ${srr}.clean.SSUhits.ids.txt > ${srr}_1.SSUhits.ids.txt # Adding a .2 to the IDs so that I can use fa stq_pull_sequence.py to gather reads that mapped to a 16S sequence in SILVA# sed 's/$/.2/' ${srr}.clean.SSUhits.ids.txt > ${srr}_2.SSUhits.ids.txt # This grabs the reads that mapped to a 16S and stores the hits into a new fastq file# filterbyname.sh in=q trimmed.${srr}_1.fastq out=${srr}_1.SSUfiltered.fastq names=${srr}_1.SSUhits.ids.txt include=t rc=$?; check_exit "Failed to Create File Containing Forward SSU Reads" filterbyname.sh in=qtrimmed.${srr}_2.fastq out=${srr}_2.SSUfiltered.fastq names=${srr}_2.S SUhits.ids.txt include=t rc=$?; check_exit "Failed to Create File Containing Reverse SSU Reads" #Using EMIRGE to reconstruct the 16S sequences# emirge_amplicon.py ${srr}_EMIRGE 1 ${srr}_1.SSUfiltered.fastq 2 ${srr}_2.SSUfiltered.fastq f ${SILVA_fna} b $SILVA_BT1 l ${read_length} i 500 s 1000 j 1.0 n 80 a ${cpus} -${quality_encoding} >& ${srr}.emirge.log rc=$?; check_exit "Failed EMIRGE" sqlite3 ${DB} "${busyTO} update SRAsamples set STATUS='ranEMIRGE' where Run_s='${srr}';" cd ${ROOT}/${srr}_ prokarys/${srr}_EMIRGE # Labeling the reconstructed 16S sequences with their estimated relative abundance in the sample# emirge_rename_fasta.py iter.80 > ${srr}.iter.80.fasta rc=$?; check_exit "Failed EMIRGE Rename Fasta" #Putting the fasta file from the 80th iteration of EMIRGE into the 80th iteration directory# mv ${srr}.iter.80.fasta /gpfs/scratch/banksrid/Bathyarchaeota/bathyecodata/iter80s rc=$?; check_exit "Failed to Move FASTA File from 80th Iteration to Appropriate Location" #This is Compressing the 80th iteration directory# tar zcvf ${srr}.iter.80.tar.gz iter.80 rc=$?; check_exit "Failed to Compress 80th Iteration Directory"

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! _V #This moves the compressed 80th iteratin directory to a directory with all the other 80th iteration directories# mv ${srr }.iter.80.tar.gz /gpfs/scratch/banksrid/Bathyarchaeota/bathyecodata/iter80dirs rc=$?; check_exit "Failed to Move 80th iteration Directory to Appropriate Location" cd ${ROOT}/${srr}_prokarys #Collecting the number of fragments that mapped from the EMIRGE log file# FRAGSMAPPED="$(tail ${srr}.emirge.log | grep "# reads with at least one reported alignment" | cut d f 9)" sqlite3 ${DB} "${busyTO} update SRAsamples set FRAGSMAPPED='${FRAGSMAPPED}' where Run_s='${srr}';" #Moving the EMIRGE log file to th e log file directory# mv ${srr}.emirge.log /gpfs/scratch/banksrid/Bathyarchaeota/bathyecodata/emirge_logs rc=$?; check_exit "Failed to Move The EMIRGE Log File to Appropriate Location" cd ${ROOT} #Deleting the main working directory to save space# rm rf ${srr}_prokarys rm ${ROOT}/ncbi/sra/sra/${srr}.sra #Updating the database to show that we finished with this sample and when we finished with it# sqlite3 ${DB} "${busyTO} update SRAsamples set STATUS='Finished' where Run_s='${srr}'; update SRAsamples se t JOBENDED=(select datetime('now')) where Run_s='${srr}';" rsync avz e "ssh o StrictHostKeyChecking=no o UserKnownHostsFile=/dev/null" -progress /gpfs/scratch/banksrid/Bathyarchaeota/bathyecodata/emirge_logs/ dbrichardson@132.194.123.8:/home/dbrichardson/Bathyarchaeota/bathyecod ata/emirge_logs/ rsync avz e "ssh o StrictHostKeyChecking=no o UserKnownHostsFile=/dev/null" -progress /gpfs /scratch/banksrid/Bathyarchaeota/bathyecodata/filt_hits/ dbrichardson@132.194.123.8:/home/dbrichardson/Bathyarchaeota/bathyecod ata/filt_hits/ rsy nc avz e "ssh o StrictHostKeyChecking=no o UserKnownHostsFile=/dev/null" -progress /gpfs/scratch/banksrid/Bathyarchaeota/bathyecodata/iter80s/ dbrichardson@132.194.123.8:/home/dbrichardson/Bathyarchaeota/bathyecod ata/iter80s/ rsync avz e "ssh o StrictHostKeyChecking=no o UserKnownHostsFile=/dev/null" -progress /gpfs/scratch/banksrid/Bathyarchaeota/bathyecodata/iter80dirs/ dbrichardson@132.194.123.8:/home/dbrichardson/Bathyarchaeota/bathyecod ata/iter80dirs/

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! _e #SP'$A6'>:C;K?>'8>6@'?E'K;:M' *$5> j=bathyecowhole cpus=12 otu_pct=97 map _pct=97 #remove sequences with N's from the all_seqs file# /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/bin/vsearch -fastx_filter ${j}_emirge_all_seqs.fasta -fastq_maxns 1 -fastaout ${j}_nfiltered_emirge_all_seqs.fasta /home/dbrichardson/Bath yarchaeota/bathyecodata/otutable/bin/vsearch -fastx_filter bathyecowhole_emirge_thresholded_seqs.fasta -fastq_maxns 1 -fastaout ${j}_nfiltered_emirge_thresholded.fasta #Open QIIME# source /home/data/software/opt/qiime 1.9.1/activate.sh # add the read counts to the EMIRGE all seqs file# python /home/dbrichardson/Bathyarchaeota/scripts/label_fasta_with_emirge_read _counts.py ${j}_nfiltered_emirge_all_seqs.fasta ${j}_frags_mapped.txt > ${j}_all_seqs_counts_vsearch_labels.fasta # Label the EMIRGE thresheld seqs with their counts# ###TODO: THIS WILL FAIL HERE### python /home/dbrichardson/Bathyarchaeota/scripts/label_fasta_with_emirge_read _counts.py ${j}_nfiltered_emirge_thresholded.fasta ${j}_frags_mapped.txt > ${j}_counts_vsearch_labels.fasta #sort the se quences by size# python /home/dbrichardson/Bathyarchaeota/scripts/sort_fasta_for_uparse.py ${j}_counts_vsearch_labels.fasta > ${j}_sorted_counts_vsearch_labels.fasta #dereplicate the sequences# /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/bin/v search -threads ${cpus} -derep_fulllength ${j}_sorted_counts_vsearch_labels.fasta -output ${j}_derep.fasta -uc ${j}_derep.uc #merging the cluster sizes# python /home/dbrichardson/Bathyarchaeota/scripts/merge_cluster_abunds_uparse. py f ${j}_derep.fast a u ${j}_derep.uc > ${j}_derep_merged.fasta # sort the sequences by size once more# python /home/dbrichardson/Bathyarchaeota/scripts/sort_fasta_for_uparse.py s ${j}_derep_merged.fasta > ${j}_sorted_derep_merged.fasta #pick OTUs#

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! dJ /home/dbrichardson/Bath yarchaeota/bathyecodata/otutable/bin/vsearch -threads ${cpus} -cluster_smallmem ${j}_sorted_derep_merged.fasta -id 0.${otu_pct} -centroids ${j}_vsearch_otus_${otu_pct}.fasta -usersort # Label OTUs: python /home/data/software/opt/drive5_py/fasta_numbe r.py ${j}_vsearch_otus_${otu_pct}.fasta OTU > ${j}_vsearch_otus_${otu_pct}_labeled.fasta #map the full set back onto picked otus and make the otutable# /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/bin/vsearch -threads ${cpus} -usearch_global ${j}_sorted_counts_vsearch_labels.fasta -db ${j}_vsearch_otus_${otu_pct}_labeled.fasta -strand plus -id 0.${map_pct} -top_hits_only -otutabout ${j}_otu_table_${otu_pct}.dvsearch.txt -uc ${j}_otu_${otu_pct}_readmap_${map_pct}.uc -biomout ${j}_otu_ta ble_${otu_pct}.dvsearch.biom #Align the filtered sequences with pynast# align_seqs.py i ${j}_vsearch_otus_${otu_pct}_labeled.fasta m pynast t /home/dbrichardson/Bathyarchaeota/SILVA_128_QIIME_release/rep_set_alig ned/97/97_otus_aligned.fasta o ${j}_vse arch_otus_${otu_pct}_labeled_pynast #Find the chimeric seqeunces using ChimeraSlayer# identify_chimeric_seqs.py m ChimeraSlayer i ${j}_vsearch_otus_${otu_pct}_labeled_pynast/${j}_vsearch_otus_${otu_pc t}_labeled_aligned.fasta a /home/data/software/opt/q iime 1.9.1/lib/python2.7/site packages/qiime_default_reference/gg_13_8_otus/rep_set_aligned/97_otus. fasta o ${j}_vsearch_otus_${otu_pct}_labeled_chimeric_seqs_cs.txt #Filter out the chimeric sequences# filter_fasta.py f ${j}_vsearch_otus_${otu_pct}_labe led.fasta o ${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta s ${j}_vsearch_otus_${otu_pct}_labeled_chimeric_seqs_cs.txt n #map the full set back onto picked otus and make the otutable# /home/dbrichardson/Bathyarchaeota/bathyecodata/otutabl e/bin/vsearch -threads ${cpus} -usearch_global ${j}_sorted_counts_vsearch_labels.fasta -db ${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta -strand plus -id 0.${map_pct} -top_hits_only -otutabout ${j}_otu_table_${otu_pct}_cleaned_dvsear ch.txt -uc ${j}_otu_${otu_pct}_readmap_${map_pct}_cleaned.uc -biomout ${j}_otu_table_${otu_pct}_cleaned_dvsearch.biom #VP'$A6'>:C;K?>'8>6@'?E'C6FECIB?'?A6'@B?B'B9@'B@@'I6?B@B?B'?E'?A6'*$5'?BGH6P' j=bathyecowhole cpus=1

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! dK otu_pct=97 map_pct=97 #remove l owercase nucleotides sed 's/a/A/g' ${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta > A_${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta sed 's/g/G/g' A_${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta > G_A_${j}_vsearch _otus_${otu_pct}_labeled_non_chimeric_seqs.fasta sed 's/t/T/g' G_A_${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta > T_G_A_${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta sed 's/c/C/g' T_G_A_${j}_vsearch_otus_${otu_pct}_labeled_no n_chimeric_seqs.fasta > ${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs_reformated.fast a #clean up intermediate files# rm A_${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta rm G_A_${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.f asta rm T_G_A_${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs.fasta j=bathyecowhole otu_pct=97 map_pct=97 cpus=1 #Assign taxonomy to the sequences # source /home/data/software/opt/qiime 1.9.1/activate.sh assign_taxonomy.py m rdp -rdp_max_memory 1000000 c 0.8 i ${j}_vsearch_otus_${otu_pct}_labeled_non_chimeric_seqs_reformated.fast a r /home/data/databases/SILVA_128_QIIME_release/rep_set/rep_set_all/97/97 _otus.fasta t /home/data/databases/SILVA_128_QIIME_release/taxonomy/taxonomy_all/97/ taxonom y_7_levels.txt #Add the taxonomies to the OTU table# biom add metadata -sc separated taxonomy -observation header OTUID,taxonomy -observation metadata fp rdp_assigned_taxonomy/${j}_vsearch_otus_${otu_pct}_labeled_non_chimeri c_seqs_reformated_tax_assign ments.txt i ${j}_otu_table_${otu_pct}_cleaned_dvsearch.biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_cleaned_table_w_rdp_tax.biom #Add other metadata to the biom file# sqlite3 header csv ~/Bathyarchaeota/Bathyarchaeota.db "select run_s,manually_combin ed_sample_categories from cleaned_sample_data;" > run_num_and_organism_sam_metadata.txt biom add metadata -sample metadata fp run_num_and_organism_sam_metadata.txt -sample header Run_s,manually_combined_sample_categories i

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! dI ${j}_vsearch_otu_${otu_pct}_${ map_pct}_cleaned_table_w_rdp_tax.biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_w_rdp_tax_sam_metadata.bi om biom add metadata -sc separated network_status -observation header OTU.ID,Network_Status -observation metadata fp otu_network_status.txt i bathyecowhole_vsearch_otu_97_97_table_w_rdp_tax_sam_metadata.biom o bathyecowhole_vsearch_otu_97_97_table_w_rdp_tax_sam_metadata_otu_net_s tatus.biom #\P'$A6'>:C;K?>'8>6@'?E'6RBH8B?6'KC;I6C'G;B>P' j=bathyecowhole otu_pct=97 map_pct=97 cpus=1 #getting into the QIIME environment# source /home/data/software/opt/qiime 1.9.1/activate.sh #There was a problem with primer PrimerProspector accessing cogent, but this fixes that in a Python environment # from import cogent #Run PrimerProspector# analyze_prime rs.py P /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/primers.txt f /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/${j}_vsearch_o tus_${otu_pct}_labeled_non_chimeric_seqs_reformated.fasta #Use taxa_coverage.py to predict which OTUs wil l amplify using these primers taxa_coverage.py i 338f_bathyecowhole_vsearch_otus_97_labeled_non_chimeric_seqs_reformate d_hits.txt:1061r_bathyecowhole_vsearch_otus_97_labeled_non_chimeric_se qs_reformated_hits.txt T ${j}_vsearch_otus_${otu_pct}_tax_assignm ents_pprospector.txt p s overall_mismatches taxa_coverage.py i 515f_bathyecowhole_vsearch_otus_97_labeled_non_chimeric_seqs_reformate d_hits.txt:806r_bathyecowhole_vsearch_otus_97_labeled_non_chimeric_seq s_reformated_hits.txt T ${j}_vsearch_otus_${otu_p ct}_tax_assignments_pprospector.txt p s overall_mismatches taxa_coverage.py i #Using ggplot to produce figures 3 7 using the R programming language # library(ggplot2) kingdom_coverage_338f_1061 < read.table("~/Desktop/Primer Bias Figures/338f_1061r_ domain_coverage.txt", header=T, sep=",") ggplot(data=kingdom_coverage_338f_1061, aes(x=Kingdom_Name, y=Percent_Coverage)) + geom_bar(stat="identity", width=0.4) +

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! d\ coord_flip() + ylab("Fraction of OTUs Predicted to Amplify") + xlab("Domain Name") + theme(ax is.text.x = element_text(size=16.0), axis.text.y = element_text(size=16.0), title = element_text(size=16.0), axis.title.x = element_text(size=16.0), axis.title.y = element_text(size=16.0)) bacteria_phyla_coverage_338f_1061r < read.table("~/Desktop/Primer Bias Figures/338f_1061r_bacteria_phyla_coverage.txt", header=T, sep=",") ggplot(data=bacteria_phyla_coverage_338f_1061r, aes(x=Phylum_Name, y=Percent_Coverage)) + geom_bar(stat="identity") + coord_flip() + ylab("Fraction of OTUs Predicted to Amplify") + x lab("Phylum Name") + theme(axis.text.x = element_text(size=12.0), axis.text.y = element_text(size=12.0), title = element_text(size=12.0), axis.title.x = element_text(size=12.0), axis.title.y = element_text(size=12.0)) kingdom_coverage_515f_806r < read.ta ble("~/Desktop/Primer Bias Figures/515f_806r_domain_coverage.txt", header=T, sep=",") ggplot(data=kingdom_coverage_515f_806r, aes(x=Kingdom_Name, y=Percent_Coverage)) + geom_bar(stat="identity", width=0.4) + coord_flip() + xlab("") + ylab("Fraction of OTUs Predicted to Amplify") + theme(axis.text.x = element_text(size=16.0), axis.text.y = element_text(size=16.0), title = element_text(size=16.0), axis.title.x = element_text(size=16.0), axis.title.y = element_text(size=16.0)) bacteria_phyla_coverage_515f_806 r < read.table("~/Desktop/Primer Bias Figures/515f_806r_bacteria_phyla_coverage.txt", header=T, sep=",") ggplot(data=bacteria_phyla_coverage_515f_806r, aes(x=Phylum_Name, y=Percent_Coverage)) + geom_bar(stat="identity") + coord_flip() + xlab("") + ylab("F raction of OTUs Predicted to Amplify") + theme(axis.text.x = element_text(size=12.0), axis.text.y = element_text(size=12.0), title = element_text(size=12.0), axis.title.x = element_text(size=12.0), axis.title.y = element_text(size=12.0)) archaea_phyla_cov erage_515f_806r < read.table("~/Desktop/Primer Bias Figures/515f_806r_archaea_phyla_coverage.txt", header=T, sep=",") ggplot(data=archaea_phyla_coverage_515f_806r, aes(x=Phylum_Name, y=Percent_Coverage)) + geom_bar(stat="identity") + coord_flip() + ylab( "Fraction of OTUs Predicted to Amplify") + xlab("")+ theme(axis.text.x = element_text(size=16.0), axis.text.y = element_text(size=16.0), title = element_text(size=16.0), axis.title.x = element_text(size=16.0), axis.title.y = element_text(size=16.0)) #]P'$ A6'>:C;K?>'8>6@'?E'<696CB?6'?A6' BHKAB' @;R6C>;?J' KHE?> ;9'?A6'%'KCE;9<'T440P' library(phyloseq) library(ggplot2) #importing the full OTU table bathyecowhole_97 < import_biom("~/Desktop/bathyecowhole_vsearch_o tu_97_97_table_w_rdp_tax

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! df _sam_metadata.biom", "~/Desktop/ITOL tree labeling data/bathyecowhole_sorted_derep_merged_pseudo_c_relabeled_w_phyla_reps _treeFTMP") #Adding the sample data that didn't import properly# sam_data_bathyecowhole_97 < read.csv("~/Desk top/run_num_and_organism_sam_metadata.txt", header=TRUE, row.names=1) #Attaching it to the phyloseq object# sam_data_bathyecowhole_97_phyloseq < sample_data(sam_data_bathyecowhole_97) microeco_phyloseq < merge_phyloseq(sam_data_bathyecowhole_97_phyloseq bathyecowhole_97) #plotting richness# #making the observed taxa richness plot# plot_richness(microeco_phyloseq, x="manually_combined_sample_categories", measures="Observed") + geom_point(aes(color=manually_combined_sample_categories)) + theme(legend.p osition = "none") + xlab("Habitat Category") + ylab("Number of OTUs Observed") #making the Shannon index plot# plot_richness(bew_97_otus_phyloseq, x="manually_combined_sample_categories", measures="Shannon") + geom_point(aes(color=manually_combined_sampl e_categories)) + theme(legend.position = "none") + xlab("Habitat Category") + ylab("Shannon Index") #Getting into a QIIME environment source /home/data/software/opt/qiime 1.9.1/activate.sh #Pruning samples from OTU table with fewer than 10 observations f ilter_samples_from_otu_table.py i /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/bathyecowhole_ vsearch_otu_97_97_table_w_rdp_tax_sam_metadata.biom o /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/bathyecowhole_ vsearch_otu_97_97_table_w_ rdp_tax_sam_metadata_10_obs_filtered.biom n 10 #making the PCoA plot# beta_diversity_through_plots.py i /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/bathyecowhole_ vsearch_otu_97_97_table_w_rdp_tax_sam_metadata_10_obs_filtered.biom m /home/db richardson/Bathyarchaeota/bathyecodata/otutable/mapping_file_q iime.txt o microeco_beta_div/ t /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/bathyecowhole_ otus_97_w_phyla_reps_treeFTMP #[ P'$A6'>:C;K?>'8>6@'FEC'?A6':E = E::8CC69:6'B9BHJ>;>P'

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! da otu_p ct=97 map_pct=97 cpus=1 DataB=/home/dbrichardson/Bathyarchaeota/Bathyarchaeota.db source /home/data/software/opt/qiime 1.9.1/activate.sh #Selecting Marine and Saline Water Associated Metagenomes# sqlite3 ${DataB} "select run_s from cleaned_sample_data wher e manually_combined_sample_categories='Soils and Sediment Associated Metagenomes';" > soils_sediemnts_samples.txt filter_samples_from_otu_table.py i ${j}_otu_table_${otu_pct}_cleaned_dvsearch.biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_so ils_sediment_sa mples.biom -sample_id_fp soils_sediemnts_samples.txt #Removing OTUs found in 25 or fewer sites# filter_otus_from_otu_table.py i ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_soils_sediment_sa mples.biom o ${j}_vsearch_otu_${otu_pct }_${map_pct}_table_labeled_soils_sediment_sa mples_for_net.biom s 25 #producing a txt file for SparCC.py to use for the correlation calculations# biom convert -to tsv i ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_soils_sediment_sa mples_for_net. biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_soils_sediment_sa mples_for_net.txt #getting a biom file with sediment samples# filter_samples_from_otu_table.py i ${j}_vsearch_otu_${otu_pct}_${map_pct}_cleaned_table_w_rdp_tax.biom o ${j}_vse arch_otu_${otu_pct}_${map_pct}_table_labeled_soils_sediment_sa mples_w_rdp_tax.biom -sample_id_fp soils_sediemnts_samples.txt #Removing OTUs found in samples removed from the OTU Table# filter_otus_from_otu_table.py i ${j}_vsearch_otu_${otu_pct}_${map_pc t}_table_labeled_soils_sediment_sa mples_w_rdp_tax.biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_soils_sediment_sa mples_cleaned_w_rdp_tax.biom s 1 #Selecting Marine and Saline Water Associated Metagenomes# sqlite3 ${DataB} "select run_s fro m cleaned_sample_data where manually_combined_sample_categories='Marine and Saline Water Associated Metagenomes';" > marine_saltw_samples.txt filter_samples_from_otu_table.py i ${j}_otu_table_${otu_pct}_cleaned_dvsearch.biom o ${j}_vsearch_otu_${otu_pct} _${map_pct}_table_labeled_marine_saltw_samp les.biom -sample_id_fp marine_saltw_samples.txt #Removing OTUs found in 25 or fewer sites#

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! d_ filter_otus_from_otu_table.py i ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_marine_saltw_samp les.biom o ${j}_ vsearch_otu_${otu_pct}_${map_pct}_table_labeled_marine_saltw_samp les_for_net.biom s 25 #producing a txt file for SparCC.py to use for the correlation calculations# biom convert -to tsv i ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_marine_saltw _samp les_for_net.biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_marine_saltw_samp les_for_net.txt #getting a biom file for the marine and salt water associated samples# filter_samples_from_otu_table.py i ${j}_vsearch_otu_${otu_pct}_${map_pct }_cleaned_table_w_rdp_tax.biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_marine_saltw_samp les_w_rdp_tax.biom -sample_id_fp marine_saltw_samples.txt #Removing OTUs found in samples removed from the OTU Table# filter_otus_from_otu_table.py i ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_marine_saltw_samp les_w_rdp_tax.biom o ${j}_vsearch_otu_${otu_pct}_${map_pct}_table_labeled_marine_saltw_clea ned_w_rdp_tax.biom s 1 python ~/Bathyarchaeota/bathyecodata/otutable/sparcc/SparCC.py /hom e/dbrichardson/Bathyarchaeota/bathyecodata/otutable/${j}_vsearch_o tu_${otu_pct}_${map_pct}_table_labeled_marine_saltw_samples_for_net.tx t c /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/${j}_vsearch_o tu_${otu_pct}_${map_pct}_table_labeled_marine _saltw_samples_for_net_sp arccmxt.txt python ~/Bathyarchaeota/bathyecodata/otutable/sparcc/SparCC.py /home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/${j}_vsearch_o tu_${otu_pct}_${map_pct}_table_labeled_soils_sediment_samples_for_net. txt c /home/db richardson/Bathyarchaeota/bathyecodata/otutable/${j}_vsearch_o tu_${otu_pct}_${map_pct}_table_labeled_soils_sediment_samples_sparccmx t.txt #a modified version of get_significant_pairs.py was used to make _selected_cor.txt # Using Python's igraph to complete the co occurrence analyses # from igraph import #Read the correlation matrix# igmicroeco = Graph.Read_Ncol("/home/dbrichardson/Bathyarchaeota/bathyecodata/otutab le/soils_selected_cor.txt", directed=False)

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! dd #Calculating the average clu stering coefficient# clustering_coeff = Graph.transitivity_avglocal_undirected(igmicroeco) #Placing this value into a file# a = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/sed_clus tering_coeff.txt', 'w') a.write(str(clustering_coeff)) a. close() #Calculating the degree of each node# degree_all = igmicroeco.degree() # Placing these values into a file b = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/sed_degr ee_all.txt', 'w') b.write(str(degree_all)) b.close() #Calculatin g the average shortest path# avg_shortest_path = igmicroeco.average_path_length(directed=False) #Placing this value into a file# f = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/sed_avg_ shortest_path.txt', 'w') f.write(str(avg_shortest_pa th)) f.close() #Calculating the modularity# modularity = igmicroeco.modularity(igmicroeco.community_multilevel()) #Placing this value into a file# g = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/sed_modu larity.txt','w') g.write(str(mod ularity)) g.close() #Read the correlation matrix# igmicroeco = Graph.Read_Ncol("/home/dbrichardson/Bathyarchaeota/bathyecodata/otutab le/marine_selected_cor.txt", directed=False) #Calculating the average clustering coefficient# clustering_coeff = Graph.tr ansitivity_avglocal_undirected(igmicroeco) #Placing this value into a file#

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! dV a = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/marine_c lustering_coeff.txt', 'w') a.write(str(clustering_coeff)) a.close() #Calculating the degree of each node # degree_all = igmicroeco.degree() #Placing these values into a file# b = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/marine_d egree_all.txt', 'w') b.write(str(degree_all)) b.close() #Calculating the average shortest path# avg_shortest_p ath = igmicroeco.average_path_length(directed=False) #Placing this value into a file# f = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/marine_a vg_shortest_path.txt', 'w') f.write(str(avg_shortest_path)) f.close() #Calculating the modular ity# modularity = igmicroeco.modularity(igmicroeco.community_multilevel()) #Placing this value into a file# g = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/marine_m odularity.txt','w') g.write(str(modularity)) g.close() layout=igmicroeco .layout_fruchterman_reingold_3d plot(igmicroeco, "marine_network.pdf") #This constructs the random graphs, calculates the properties of those graphs, and stores the values into a file# #calculating the network properties of the permuted marine and saline water associated metagenome network# from igraph import import os i = os.environ['SGE_TASK_ID'] igmicroeco = Graph.Read_Ncol("/home/dbrichardson/Bathyarchaeota/bathyecodata/otutab le/marine_selected_cor.txt", directed=False) igmicroeco.rewire_edges(prob= 1) rnd_graph = igmicroeco clustering_coeff = Graph.transitivity_avglocal_undirected(rnd_graph)

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! de a = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/Permuted _G_boot_data/Permuted_G_boot_data_marine/clustering_coeffs/clustering_ coeff_%s.txt' % i 'w') a.write(str(clustering_coeff)) a.close() degree_all = rnd_graph.degree() b = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/Permuted _G_boot_data/Permuted_G_boot_data_marine/degrees/degree_all_%s.txt' % i, 'w') b.write(str(degree_all)) b.close() avg_shortest_path = rnd_graph.average_path_length(directed=False) f = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/Permuted _G_boot_data/Permuted_G_boot_data_marine/avg_s_path/avg_shortest_path_ %s.txt' % i, 'w') f.write(str(avg_s hortest_path)) f.close() modularity = rnd_graph.modularity(rnd_graph.community_multilevel()) g = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/Permuted _G_boot_data/Permuted_G_boot_data_marine/modularity/modularity_%s.txt' % i,'w') g.write( str(modularity)) g.close() #calculating the network properties of the permuted soils and sediment associated metagenome network# from igraph import import os i = os.environ['SGE_TASK_ID'] igmicroeco = Graph.Read_Ncol("/home/dbrichardson/Bathyarchaeota/b athyecodata/otutab le/soils_selected_cor.txt", directed=False) igmicroeco.rewire_edges(prob=1) rnd_graph = igmicroeco clustering_coeff = Graph.transitivity_avglocal_undirected(rnd_graph) a = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/Perm uted _G_boot_data/Permuted_G_boot_data_sediments/clustering_coeffs/clusteri ng_coeff_%s.txt' % i, 'w') a.write(str(clustering_coeff)) a.close() degree_all = rnd_graph.degree() b = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/Permuted _G_boot_ data/Permuted_G_boot_data_sediments/degrees/degree_all_%s.txt' % i, 'w') b.write(str(degree_all)) b.close() avg_shortest_path = rnd_graph.average_path_length(directed=False)

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! VJ f = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otutable/Permuted _G_boot_ data/Permuted_G_boot_data_sediments/avg_s_path/avg_shortest_pa th_%s.txt' % i, 'w') f.write(str(avg_shortest_path)) f.close() modularity = rnd_graph.modularity(rnd_graph.community_multilevel()) g = open('/home/dbrichardson/Bathyarchaeota/bathyecodata/otuta ble/Permuted _G_boot_data/Permuted_G_boot_data_sediments/modularity/modularity_%s.t xt' % i,'w') g.write(str(modularity)) g.close() ' ' ' '

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