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Aitor Gonzalez, Vincent Dubut, Emmanuel Corse, Reda Mekdad, Thomas Dechatre, Emese Meglécz
To cite this version:
Aitor Gonzalez, Vincent Dubut, Emmanuel Corse, Reda Mekdad, Thomas Dechatre, et al.. VTAM:
A robust pipeline for validating metabarcoding data using internal controls. 2021. �hal-03144831�
1
VTAM: A robust pipeline for validating
1
metabarcoding data using internal controls
2
Ai t or Gonzál ez1, Vi nc ent Dubut2, Em m anuel Cor se3 , 4, Reda Mekda d1 , 2, Thom as Dechat r e1 , 2 and Em ese Megl écz2
3
1 Ai x Mar sei l l e Uni v , IN SE RM, T AG C, Tur i ng Cent er f or Li vi ng S yst em s, 13288 4
Mar sei l l e, Fr ance 5
2 Ai x Mar sei l l e Uni v , Avi gnon Uni ver si t é, C NRS , IR D, IMB E, Mar sei l l e, Fr ance 6
3 Cent r e Uni ver si t ai r e de Mayot t e, Rout e N a t i onal e 3, B P 53, 9766 0 Dem beni , 7
Mayot t e, 8
Fr ance 9
4 MAR BE C, CN RS, If r em er , IR D, Uni ver si t y of Mont pel l i er , Mon t pel l i er , Fr ance 10
11
Cor r espondi ng aut hor : Ai t or Go nzál ez ( ai t or .gonzal ez@uni v -am u.f r ) and Em ese 12
Megl écz ( em ese.m egl ecz@i m be.f r ) 13
Runni ng t i t l e: V TAM m et abar codi ng pi pel i ne 14
15 16
2
Abstract
17
1. Met abar codi ng st udi es shoul d be car ef ul l y d esi gned t o m i ni m i ze f al se 18
posi t i ves and f al se ne gat i ve occur r ences . T h e use of i nt er nal cont r ol s, 19
r epl i cat es, and sever al over l appi ng m ar ker s i s expect ed t o i m pr ove t he 20
bi oi nf or m at i c s dat a anal ysi s . 21
2. VTAM i s a t oo l t o per f or m al l st eps of dat a cur at i on f r om r aw f ast q dat a t o 22
t axonom i cal l y assi gned AS V ( Am pl i con Seq uence Var i ant or si m pl y var i ant ) 23
t abl e. It ad dr esses al l known t ech ni cal er r or t ypes and i ncl udes ot h er f eat ur es 24
r ar el y pr esent i n exi st i ng pi pel i nes f or val i da t i ng m et abar codi ng da t a : 25
Fi l t er i ng par am et er s ar e obt ai ned f r om i nt er nal cont r ol sam pl es ; cr oss - 26
sam pl e cont am i nat i on and t ag -j um p ar e cont r ol l ed ; t echni cal r epl i cat es ar e 27
used t o ensur e r epeat a bi l i t y ; i t handl es dat a obt ai ned f r om sever al 28
over l appi ng m ar ker s . 29
3. Two dat aset s wer e ana l ysed by VTAM and t h e r esul t s wer e com par ed t o 30
t hose obt ai ned wi t h a pi pel i ne based on D A DA2. The f al se posi t i ve 31
occur r ences i n sam pl e s wer e consi der abl y hi gher when cur at ed by DA DA2 , 32
whi ch i s l i kel y due t o t he l ack of cont r ol f or t ag -j um p and cr oss -sa m pl e 33
cont am i nat i on.
34
4. VTAM i s a r obust t ool t o val i dat e m et abar codi ng dat a and i m pr ove 35
t r aceabi l i t y, r epr oduci bi l i t y, and com par abi l i t y bet ween r uns and d at aset s.
36 37
Keywor ds: m et abar codi ng, m ock sam pl e, neg at i ve cont r ol , r epl i cat es, t axonom i c 38
assi gnat i on , f al se posi t i ves, f al se negat i ves 39
40
1 Introduction
41
Met abar codi ng has be com e a power f ul ap pr o ach t o st udy bi o di ver si t y f r om 42
envi r onm ent al sam pl es ( i ncl udi ng gut cont en t o r f aecal sam pl es ) . Met abar codi ng , 43
however , i s pr one t o s om e pi t f al l s , and cons equent l y, ever y m et ab ar codi ng st udy 44
shoul d be desi g ned i n a f r om -bencht op -t o -de skt op way ( f r om sam pl i ng t o dat a 45
anal ys i s) t o m i ni m i ze t he bi as of each st ep o n t he out com e ( Al ber d i , Ai zpur ua, 46
Gi l ber t , & Bohm ann, 2 018; Cr i st escu & He be r t , 2018; Zi nger et al ., 2019) . Sever al 47
paper s have cal l ed f or good pr act i ce i n st udy desi gn, dat a pr oduct i on and anal yses 48
t o ensur e r epeat abi l i t y and com par abi l i t y bet ween st udi es . Not abl y , t he i m por t ance 49
of m ock com m uni t y sam pl es , negat i ve cont r o l s, and r epl i cat es i s f r equent l y 50
hi ghl i ght ed ( Al ber di e t al ., 2018; Bakker , 20 18; Cr i st escu & Heber t , 2018;
51
3 O’Rourke, Bokulich, Jusino, MacManes, & Foster, 2020) . However, their use in 52
bi oi nf or m at i cs pi pel i nes i s of t en l i m i t ed t o t he ver i f i cat i on of expe ct at i on s.
53
In t hi s st ud y, we pr ese nt t he bi oi nf or m at i cs pi pel i ne, VT AM ( Val i dat i on and 54
Taxonom i c Assi gnat i o n of Met abar codi ng da t a) t hat ef f ect i vel y i nt egr at es negat i ve 55
cont r ol s, m ock com m uni t i es and t echni cal r epl i cat es t o cont r ol exp er i m ent al 56
f l uct uat i ons ( e.g. sequ enci ng dept h, PC R st o chast i c i t y) and val i dat e m et abar codi ng 57
dat a . 58
A r ecent st udy on t he ef f ect of di f f er ent st eps of dat a cur at i on on s pat i al 59
par t i t i oni ng of bi odi ve r si t y l i st ed t he f ol l owi ng pot ent i al pr obl em s: Sequenci ng and 60
PCR er r or s, pr esence of hi ghl y spur i ous se q uences , chi m er as, i nt e r nal or ext er nal 61
cont am i nat i on and dys f unct i onal PC Rs ( Cal d er ón‐ Sanou, Mü nkem ü l l er , Boyer , 62
Zi nger , & Thui l l er , 2 0 20) . They sho wed t hat exhaust i ve cur at i on a nd ensur i ng 63
r epeat abi l i t y by t echni cal r epl i cat es ar e necessar y , especi al l y f or b i odi ver si t y 64
m easur em ent s . Ideal l y , a m et abar codi ng wor kf l ow shoul d addr ess al l of t hese 65
t echni cal er r or s. Exi st i ng t ool s , ho wever , ar e ei t her hi ghl y f l exi bl e but t oo com pl ex 66
or t hey do n ot i ncl ude t he cur at i on of al l pot ent i al bi ases ( Mahé, R ognes, Qui nce, 67
de Var gas, & Dunt hor n, 2014; Boyer et al ., 2016; Cal l ahan et al ., 2016; Edgar , 68
2016b; R ognes, Fl our i , Ni chol s, Qui nce , & Mahé, 2016; Bol yen et al ., 201 9) . The 69
f i l t er i ng st eps of VT A M ai m t o addr ess t hes e poi nt s and i ncl ude s ever al addi t i onal 70
f eat ur es t hat ar e uni qu e or r ar el y f ound i n ex i st i ng pi pel i nes: ( i ) t he use of i nt er nal 71
cont r ol s and ( i i ) r epl i cat es t o opt i m i ze f i l t er ing par am et er val ues ; ( i i i ) t he 72
i nt egr at i on of m ul t i pl e over l appi ng m ar ker s and ( i v) f i l t r at i on t o addr ess cr oss - 73
sam pl e cont am i nat i on , i ncl udi ng t ag -j um ps . Fi nal l y, VT AM i s a va r i ant -based 74
f i l t er i ng pi pel i ne ( such as ot her den oi si ng m et hods: Cal l ah an et al . , 2016; Edgar , 75
2016b) t hat deal s wi t h am pl i con sequence va r i ant s ( ASVs) . 76
2 Features
77
2.1 Implementation
78
VTAM i s based o n t he m et hod descr i bed i n Cor se et al . 2 017. It i s a com m and -l i ne 79
appl i cat i on t hat r uns o n Li nux, MacOS or Wi ndows Subsyst em f or Li nux ( WSL) . 80
VTAM i s i m pl em ent ed i n Pyt hon 3 , usi ng a C onda envi r onm ent t o e nsur e 81
r epeat abi l i t y and easy i nst al l at i on of VTAM and t hese t hi r d -par t y a ppl i cat i ons : 82
WopMar s (ht t ps: / / wopm ar s.r eadt hedocs.i o ) , NC B I B LA ST , Vsear c h ( Rognes et al ., 83
2016) , Cut adapt ( Mar t i n, 2011) . Dat a i s st or ed i n a n S QLi t e dat aba se t hat ensur es 84
t r aceabi l i t y.
85 86
4
2.2 Workflow
87
Tabl e 1 sum m ar i zes t he di f fer ent com m ands and st eps of VT AM, t h ei r pur pose and 88
t he r el at ed er r or t ype s.
89
2.2.1 Pr e - pr ocessi ng ( opt i onal ) 90
An exam pl e of t he dat a st r uct ur e i s i l l ust r at ed i n Fi g. 1.
91
Pai r ed -end F AST Q f i l es ar e m er ged , r eads a r e t r i m m ed and d em ul t i pl ex ed 92
accor di ng t o f or war d a nd r ever se t ag com bi n at i on s.
93
2.2.2 Fi l t er i ng 94
Dem ul t i pl exed r eads a r e der epl i cat ed and A S Vs ar e st or ed i n a n S Q Li t e dat abase.
95
Al l occur r ences ar e ch ar act er i zed by t hei r r ead count . 96
Fi l t erLFN: el i m i nat es occur r ences l i kel y due t o Lo w Fr eque ncy N o i se . Occur r ences 97
ar e f i l t er ed out i f t hey have l ow r ead cou nt s ( i ) i n absol ut e t er m s ( Ni j k i s sm al l , 98
wher e Ni j k i s t he r ead count of var i ant i i n sa m pl e j and r epl i cat e k) , ( i i ) com par ed 99
t o t he t ot al num ber of r eads of t he sam pl e -r e pl i cat e ( Ni j k/ Nj k) or ( i i i ) com par ed t o 100
t he t ot al num ber of r e ads of t he var i ant ( Ni j k/ Ni) . 101
Fi l t erM i nRepl i cat eNumber: Occ ur r ences ar e r et ai ned onl y i f t he A SV i s pr e sent i n 102
at l east a user -def i ned num ber of r epl i cat es.
103
Fi l t erPCRerror: AS Vs wi t h one di f f er ence f r om anot her AS V of t h e sam e sam pl e 104
ar e f i l t er ed out i f t he pr opor t i on of t hei r r ea d count s i s bel o w a u s er -def i ned 105
t hr eshol d val ue . 106
Fi l t erChi mera r uns t h e uchi m e3_denovo chi m er a f i l t er i ng i m pl ement ed i n vsearch.
107
Fi l t erRenkonen r em ov es whol e r epl i cat es t h at ar e t oo di f f er ent co m par ed t o ot her 108
r epl i cat es i n t he sam e sam pl e.
109
Fi l t erI ndel and Fi l t erCodonSt op ar e i nt ende d t o det ect pseudo gene s and shoul d 110
onl y be used f or codi n g m ar ker s. Fi l t erI ndel el i m i nat es al l var i ant s, wi t h aber r ant 111
l engt h , wher e t he m od ul o t hr ee of t he l engt h i s di f f er ent f r om t he m aj or i t y . 112
Fi l t erCodonSt op el i m i nat es al l var i ant s t hat have codon ST OP i n a l l r eadi ng f r am es 113
of t he di r ect st r and.
114
The out put of t he f i l t er s i s an AS V t abl e wi t h val i dat ed var i ant s i n l i nes, sam pl e s i n 115
col um ns and t he sum o f r ead count s over r epl i cat es i n t he cel l s.
116
2.2.3 Taxonom i c assi g nat i on 117
Taxonom i c assi gnat i o n i s based on t he Lo w est Taxonom i c Gr o up m et hod descr i bed 118
i n det ai l i n Suppor t i ng Inf or m at i on 1. The t axonom i c r ef er ence dat abase has a 119
BLA ST f or m at wi t h t a xonom i c i dent i f i er s so t hat cust om dat abase s or t he com pl et e 120
NCB I nucl eot i de dat ab ase can be use d by VT AM . A cust om t axono m i c r ef er ence 121
5 dat abase of C O I seque nces m i ned f r om NC B I nucl eot i de and BOL D
122
(https://www.boldsystems.org/) dat abases i s avai l abl e wi t h t he pr ogr am . 123
2.2.4 Par am et er o pt i m iz at i on 124
User s shoul d f i r st i den t i f y expect ed and u nex pect ed occur r ences ba sed on t he f i r st 125
f i l t r at i on wi t h def aul t par am et er s . The opt i m i zat i on st ep wi l l gui de user s t o choose 126
par am et er val ue s t hat m axi m i ze t he num ber of expect ed occur r enc es i n t he dat aset 127
and m i ni m i ze t he num ber of unexpect ed o cc ur r ences ( f al se posi t i ves) . Par am et er s 128
ar e opt i m i zed f or t he t hr ee LF N f i l t er s and t he Fi l t er PCRer r or . Op t i m i zed 129
par am et er s can t hen b e used t o r epeat t he f i l t er i ng st eps.
130
2.2.5 Pool r uns/ m ar ke r s 131
A r un i s FAS TQ dat a f r om a sequenci ng r un and a m ar ker i s a r egi on of a l ocus 132
am pl i f i ed by a pr i m er pai r . The po ol com m and pr oduces a n A SV t abl e wi t h any 133
num ber of r un -m ar ker com bi nat i ons. When m or e t han one over l appi ng m ar ker i s 134
used, A SVs i dent i cal t o t hei r over l appi ng pa r t s are pool ed t o t he s am e l i ne.
135
3 Benchmarking
136
VTAM was t est ed wi t h t wo p ubl i shed m et ab ar codi ng dat aset s: a f i sh dat aset 137
obt ai ned f r om f i sh f aecal sam pl es ( Cor se et al ., 2017) , and a bat d at aset obt ai ned 138
f r om bat guano sam pl e s ( Gal an et al ., 2018) . Bot h dat aset s i ncl ude d negat i ve 139
cont r ol s , m ock sam pl es and t hr ee P CR r epl i cat es. A f r agm ent of t h e CO I gene was 140
am pl i f i ed usi ng t wo o ver l appi ng m ar ker s i n t he f i sh dat aset , and o ne i n t he bat 141
dat aset ( See det ai l s i n t he or i gi nal st udi es ) . 142
Bot h dat aset s wer e an al ysed by VTAM . The f i sh dat aset was anal y sed separ at el y f or 143
t he t wo m ar ker s and t h e r esul t s of bot h m ar k er s wer e pool ed t oget h er . 144
Bot h dat aset s wer e al s o anal ysed wi t h t he D AD A2 den oi si ng al gor i t hm ( Cal l ahan et 145
al ., 2016) , one of t he m ost wi de l y used m et h od s f or m et abar codi ng dat a cur at i on . 146
The out put of D AD A2 was f i l t er ed by L UL U ( Fr øsl ev et al ., 20 17) t o f ur t her 147
el i m i nat e pr obabl e f al se posi t i ve occur r ence s . Then t he t hr ee r epl i cat es of each 148
sam pl e wer e pool ed ( a s i n VT AM ) , onl y acce pt i ng t he occur r ence i f i t was pr esent 149
i n at l east t wo r epl i cat es ( Suppor t i ng i nf or m at i on 2) . 150
We com par ed t he α -di ver si t y and β -di ver si t y obt ai ned f or t he en vi r onm ent al 151
sam pl es t o addr ess t he ef f ect of t he cur at i on pi pel i nes on di ver si t y est i m at i ons . α- 152
di ver si t y was est i m at ed usi ng bot h AS V r i ch ness and cl ust er r i chn ess ( cl ust er s 153
aggr egat e AS Vs wi t h < 3% di ver gence) , and β-di ver si t y was sum m ar i zed usi ng t he 154
Br ay- Cur t i s pai r wi se d i ssi m i l arit y i ndex . ( Su ppor t i ng i nf or m at i on 3 ) . 155
In t he f i sh dat aset , al l expect ed var i ant s i n t h e m ock sam pl es wer e val i dat ed by 156
bot h pi pel i nes. Howev er , i n t he bat dat aset , t wo expect ed var i ant s had ver y l o w 157
6 r ead abundance ( 2 - 18 r eads/ r epl i cat e ) , whi ch wer e i n t he r ange of t he num ber of 158
r eads i n t he negat i ve c ont r ol s ( t en out of t he 19 negat i ve cont r ol s h ad at l east one 159
r ead count gr eat er t ha n 18 ) . T her ef or e, we i gnor e d t hese t w o expe ct ed var i ant s i n 160
t he Bul k Fr a nce m ock sam pl e, and we opt i m i zed t he V TAM par am e t er s t o r et ai n al l 161
ot her expect ed occur r e nces.
162
Af t er f i l t er i ng wi t h V TAM , t he num ber of f al se posi t i ves i n t he m ock sam pl es was 163
m ar kedl y l ower t han w i t h DA DA2 ( Tabl e 2) . Si m i l ar l y , ASV a nd cl ust er r i chness 164
wer e on av er age t wo t i m es l ower wi t h VT AM t han wi t h DA DA 2 i n envi r onm ent al 165
sam pl es ( Fi g. 2A and B) . In cont r ast , di ssi m i l ar i t i es bet ween sam pl es wer e hi gher 166
wi t h VT AM ( Fi g. 2D) . In bot h pi pel i nes , m ost cl ust er s cont ai ned a si ngl e AS V 167
( Suppor t i ng i nf or m at i on 3; Fi g. 2C) . 168
4 Discussion
169
Met abar codi ng i s kno wn t o be pr one t o t wo t ypes of er r or s: f al se n egat i ve s and 170
f al se posi t i ves . Based on cont r ol s ( negat i ve and m ock sam pl es) , V TAM ai m s t o f i nd 171
a com pr om i se bet ween t he se t wo er r or t yp es by m i ni m i z i ng f al se posi t i ve 172
occur r ences whi l e r et a i ni ng expect ed var i ant s i n m ock sam pl es t o a voi d f al se 173
negat i ves. Ther ef or e , t he m ock sam pl es shou l d cont ai n bot h wel l a nd weakl y 174
am pl i f i ed t axa , wher e t he abundance , i .e. t he num ber of r eads , of w eakl y am pl i f i ed 175
t axa i s m ar gi nal l y hi gher t han t hose f ou nd i n negat i ve sam pl es. T hi s shoul d ensur e 176
f i ndi ng f i l t er i ng par am et er val ues t hat si m ul t aneousl y m i ni m i ze f al se posi t i ves and 177
f al se negat i ves. Ad di t i onal l y, i n l ar ge -scal e st udi es wi t h m or e t han one sequenci n g 178
r un, t he use of i dent i c al m ock sam pl es i n al l r uns can ens ur e com p ar abi l i t y am ong 179
r uns i f t hey consi st ent l y yi el d t he sam e r esul t s . 180
The use of t echni cal r epl i cat e s i s anot her i m por t ant t ool t o l i m i t f al se posi t i ve s and 181
f al se negat i ve s ( Al ber di et al . 2018 , Cor se e t al . 2017) . Fal se posi t i ves can be 182
st r ongl y r educed by o nl y accept i ng var i ant s i n a sam pl e i f t hey ar e pr esent i n at 183
l east a cer t ai n num ber of r epl i cat es. Thi s st r a t egy i s st r ongl y advi se d t o r educe 184
exper i m ent al st ochast i ci t y and val i dat e A SV occur r ences . Fur t her m or e, r em ovi ng 185
r epl i cat es wi t h r adi cal l y di f f er ent com posi t i on s ( Renko nen f i l t er ) f ur t her r educes 186
t he ef f ect of exper i m ent al st ochast i ci t y ( De Bar ba et al ., 2014) . A ddi t i onal l y, f al se 187
negat i ves can be f ur t h er reduced by am pl i f yi ng sever al m ar ker s ( C or se et al ., 188
2019) . If t he di f f er ent m ar ker s over l ap , VT A M can pool seq uences t hat ar e 189
i dent i cal i n t hei r over l appi ng r egi ons . Thi s i nt egr at es t he r esul t s o f di f f er ent 190
m ar ker s unam bi guousl y.
191
Whi l e f al se posi t i ve occur r ences du e t o seq u enci ng and PC R er r or s ar e gener al l y 192
wel l det ect e d by d enoi si ng pi pel i nes such as DA DA2 , t ag -j um p and cr oss -sam pl e 193
7 cont am i nat i on ar e r ar el y t ak en i nt o account ( but see Bo yer et al ., 2 016; Edgar , 194
2016a) . Howe ver , f ai l i ng t o f i l t er out t hese a r t ef act s i s l i kel y t o i nf l at e f al se 195
posi t i ve occur r ences a nd ar t i f i ci al l y i ncr ease i nt er -sam pl e si m i l ar i ti es. In f act , t he 196
DA DA2 based pi pel i n e pr oduced AS V and c l ust er r i chness per sa m pl e t hat was on 197
aver age t wi ce as hi g h as wi t h V TAM a nd ev en hi gher f or som e sa m pl es ( Fi g. 2 A, 198
B) . O n t he ot her hand, di ssi m i l ar i t i es bet ween sam pl es wer e l o wer af t er DAD A2 199
f i l t r at i on . Addi t i onal l y , t he near 1: 1 cor r el at i on bet ween AS V and cl ust er r i chness 200
i n bot h pi pel i nes i ndi c at ed t hat m ost cl ust er s cont ai ned j ust one AS V per sam pl e.
201
Thi s suppor t s t he not i on t hat di ver si t y i nf l at i on i n D AD A2 r esul t e d f r om unf i l t er ed 202
t ag-j um p cont am i nat i o n s r at her t han P CR or sequenci ng er r or s as t hi s woul d have 203
pr oduced m or e ASVs t hat bel ong t o t he sam e cl ust er . Our VT AM pi pel i ne , 204
t her ef or e , appear s m or e appr opr i at e f or com p ar i ng t he di ver si t y bet ween sam pl es 205
and f or i nvest i gat i ng t he bi ol ogi cal r esponse s t o envi r onm ent al cha nge . 206
5 Conclusions
207
The V TAM m et abar co di ng pi pel i ne ai m s t o addr ess kno wn t echni c al er r or s dur i ng 208
dat a anal ysi s ( Tabl e 1 ) t o val i dat e m et abar codi ng dat a . It i s a com pl et e pi pel i ne 209
f r om r aw FA ST Q dat a t o cur at ed AS V t abl e s wi t h t axonom i c assi gn m ent s.
210
The i m pl em ent at i on of VT AM pr ovi des se ver al advant ages such as usi ng a C onda 211
envi r onm ent t o f aci l i t at e t he i nst al l at i on , dat a s t or age i n SQ Li t e d at abase f or 212
t r aceabi l i t y and t he po ssi bi l i t y t o r un one or sever al sequenci ng r un -m ar ker 213
com bi nat i ons usi ng t h e sam e com m and. VT AM i ncl udes f eat ur es r ar el y consi der ed 214
i n m ost m et abar codi ng pi pel i nes, and w e bel i eve i t pr ovi des a usef u l t ool f or t he 215
anal ysi s and val i dat i o n of m et abar codi ng da t a f or conduct i ng r ob u st anal yses of 216
bi odi ver si t y . 217
Acknowledgements
218
We t hank Di ane Zar zo so -Lac ost e and Sam ant a Or t uno Mi guel f or v al uabl e 219
com m ent s on t he use o f VT AM , Luc Gi f f on a nd Li onel S pi nel l i f or t he devel opm ent 220
of Wopm ar s and K ur t Vi l l sen f or Engl i sh ed i t i ng. Cent r e de Cal cul Int ensi f d’ Ai x - 221
Mar sei l l e i s acknowl edged f or gr ant i ng acce ss t o i t s hi gh per f or m a nce com put i ng 222
r esour ces. Thi s wor k i s a cont r i but i on t o t he Eur opean pr oj ect SEA MoBB, f u nded 223
by ER A -Net Mar -T ER A and m anage d by A N R ( num ber A NR _17_ MART -00 01_01) . 224
Authors’ contributions
225
EM, EC , VD c oncei ve d t he i deas and desi gn ed t he m et hodol ogy . E M and A G 226
concei ved t he sof t war e ar chi t ect ur e and t est ed t he V TAM . A G, T D and RM 227
devel oped t h e VT AM sof t war e ; AG cont r i bu t ed t o t he WopMar s so f t war e 228
8 devel opm ent . EM, AG , V D and EC wr ot e t he m anuscr i pt . Al l aut ho r s cont r i but ed 229
cr i t i cal l y t o t he dr af t and appr oved t he f i nal ver si on of t he m anuscr i pt . 230
231
Data Availability
232
VTAM i s avai l abl e at ht t ps: / / gi t hub.com / ai tgon/ vt am. A det ai l ed u ser m anual i s 233
f ound at ht t ps: / / vt am .r eadt hedocs.i o. 234
Em pi r i cal dat a used i n t hi s paper ar e avai l ab l e f r om t he Dr yad Di gi t al Reposi t or y 235
ht t ps: / / dat adr yad.or g/ st ash/ dat aset / doi : 10.5061/ dr yad.f 40v5 and 236
ht t ps: / / dat adr yad.or g/ st ash/ dat aset / doi : 10.5061/ dr yad.kv02 g . 237
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303 304
10
Figures and tables
305 306
307
F i g u r e 1. A n e x a m p l e o f a d a t a s t r u c t u r e w i t h o n e r u n , t w o m a r k e r s a n d 308
t h r e e r e p l i c a t e s f o r e a c h s a m p l e . S 1 - R 1 : R e p l i c a t e 1 o f S a m p l e 1 . R e p l i c a t e s 309
a r e n o t e s s e n t i a l b u t s t r o n g l y r e c o m m e n d e d . S a m p l e s s h o u l d i n c l u d e a t l e a s t 310
o n e m o c k s a m p l e a n d o n e n e g a t i v e c o n t r o l . 311
312
11 313
314
F i g u r e 2. D i v e r s i t y e s t i m a t e s f r o m t h e f i s h a n d b a t d a t a s e t s , b a s e d o n t h e 315
V T A M a n d D A D A 2 - b a s e d p i p e l i n e s . A ) A S V r i c h n e s s p e r s a m p l e B ) c l u s t e r 316
r i c h n e s s p e r s a m p l e C ) T h e c o r r e l a t i o n b e t w e e n A S V a n d c l u s t e r r i c h n e s s . P - 317
v a l u e i n d i c a t e s a s i g n i f i c a n t s l o p e d i f f e r e n c e b e t w e e n t h e t w o p i p e l i n e s . 318
D ) β - d i v e r s i t y w a s e s t i m a t e d u s i n g t h e B r a y - C u r t i s d i s s i m i l a r i t y i n d e x 319
c a l c u l a t e d f o r e a c h p a i r w i s e s a m p l e c o m p a r i s o n . S o l i d l i n e s i n d i c a t e l i n e a r 320
r e g r e s s i o n l i n e s , h a t c h e d l i n e s a r e t h e 1 : 1 r e f e r e n c e l i n e s . 321
322
12 323
T a b l e 1 . L i s t o f V T A M c o m m a n d s a n d t h e i r r o l e s . 324
325
VTAM command
VTAM step
(Name in Corse et al. 2017) Role Error Type
merge
Merges paired-end reads
and quality filtering Sequencing errors
sortreads Assigns reads to samples Sequencing errors
filter Dereplicate Dereplicates
filter Delete singletons Deletes singletons
Sequencing errors, highly spurious se- quences
filter
LFN_variant filter (LFNtag)
Deletes low frequency er- rors
Tug jump, inter sam- ple contamination filter
LFN_read_count filter (LFNneg)
Deletes low frequency er- rors
Sequencing error, light contamination filter
LFN_sample_replicate filter (LFNpos)
Deletes low frequency er- rors
Sequencing error, light contamination
filter FilterMinReplicateNumber
Ensures consistency be-
tween replicates PCR heterogeneity filter
FilterPCRerror (Obliclean)
Eliminates PCR errors (even
if frequent) PCR errors
filter FilterChimera Eliminates chimeras Chimeras
filter FilterRenkonen
Eliminates aberrant repli-
cates Dysfunctional PCRs
filter
FilterIndel
(Pseudogene filter) Eliminates pseudogenes
Pseudogenes, spuri- ous sequences filter
FilterCondonStop
(Pseudogene filter) Eliminates pseudogenes
Pseudogenes, spuri- ous sequences
taxassign (LTG) Assigns variants to taxa
Highly spurious sequences
optimize OptimizeLFNsampleReplicate
Finds the optimal parameter for the LFN-sample-replicate
filter
optimize OptimizePCRerror
Finds the optimal parameter for FilterPCRerror
optimize
OptimizeLFNreadCountAndLFN- variant
Finds the optimal value for LFN-read-count and LFN-
variant filters
pool
Pools the results from differ-
ent runs/markers
326 327 328
13 T a b l e 2 . N u m b e r o f f a l s e p o s i t i v e o c c u r r e n c e s c o m p a r e d t o t h e t o t a l n u m b e r 329
o f o c c u r r e n c e s . I n n e g a t i v e c o n t r o l a n d m o c k s a m p l e s , t h e c o u n t o f f a l s e 330
p o s i t i v e s i s p r e c i s e , s i n c e t h e s a m p l e c o m p o s i t i o n i s k n o w n . 331
VTAM Fish DADA Fish VTAM Bat DADA Bat Negative controls 0/0 (0%) 32/32 (100%) 2/2 (100%) 19/19 (100%) Mock samples 5/17 (29%) 37/49 (75%) 22/61 (36%) 73/114 (65%) 332
333
14
Supporting Information
334 335
S u p p I n f o 1 . p d f 336
D e s c r i p t i o n o f t h e t a x o n o m i c a s s i g n a t i o n a n d i t s c u s t o m d a t a b a s e . 337
S u p p I n f o 2 . p d f 338
C o m m a n d s , u s e r i n p u t f i l e s , a n d t h e f i n a l A S V t a b l e s p r o d u c e d b y V T A M 339
a n d t h e D A D A b a s e d p i p e l i n e f o r t h e f i s h a n d t h e b a t d a t a s e t s . 340
341
S u p p I n f o 3 . p d f 342
D i v e r s i t y e s t i m a t i o n p r o t o c o l 343
