FN Archimer Export Format
PT J
TI Data, time and money: evaluating the best compromise for inferring molecular phylogenies of non-model animal taxa
BT 
AF ZAHARIAS, Paul
   PANTE, Eric
   GEY, Delphine
   FEDOSOV, Alexander E.
   PUILLANDRE, Nicolas
AS 1:1;2:2;3:3;4:4;5:1;
FF 1:;2:;3:;4:;5:;
C1 Univ Antilles, Sorbonne Univ, Inst Systemat Evolut Biodiversite ISYEB, Museum Natl Hist Nat,CNRS,EPHE, 43 Rue Cuvier,CP 26, F-75005 Paris, France.
   La Rochelle Univ, UMR 7266 CNRS, Littoral Environm & Soc LIENSs, 2 Rue Olympe de Gouges, F-17042 La Rochelle, France.
   Museum Natl Hist Nat, Acquisit & Anal Donnees Hist Nat 2AD UMS 2700, Paris, France.
   Russian Acad Sci, AN Severtsov Inst Ecol & Evolut, Leninsky Prospect 33, Moscow 119071, Russia.
C2 MNHN, FRANCE
   UNIV LA ROCHELLE, FRANCE
   MNHN, FRANCE
   RUSSIAN ACAD SCI, RUSSIA
IF 4.286
TC 14
UR https://archimer.ifremer.fr/doc/00756/86802/92301.pdf
LA English
DT Article
CR KANACONO
BO Alis
DE ;Phylogenomics;Transcriptomics;High throughput sequencing;Sanger sequencing;Non-model taxa;Turridae
AB For over a decade now, High Throughput sequencing (HTS) approaches have revolutionized phylogenetics, both in terms of data production and methodology. While transcriptomes and (reduced) genomes are increasingly used, generating and analyzing HTS datasets remain expensive, time consuming and complex for most nonmodel taxa. Indeed, a literature survey revealed that 74% of the molecular phylogenetics trees published in 2018 are based on data obtained through Sanger sequencing. In this context, our goal was to identify the strategy that would represent the best compromise among costs, time and robustness of the resulting tree. We sequenced and assembled 32 transcriptomes of the marine mollusk family Turridae, considered as a typical non-model animal taxon. From these data, we extracted the loci most commonly used in gastropod phylogenies (cox1, 12S, 16S, 28S, h3 and 18S), full mitogenomes, and a reduced nuclear transcriptome representation. With each dataset, we reconstructed phylogenies and compared their robustness and accuracy. We discuss the impact of missing data and the use of statistical tests, tree metrics, and supertree and supermatrix methods to further improve phylogenetic data acquisition pipelines. We evaluated the overall costs (time and money) in order to identify the best compromise for phylogenetic data sampling in non-model animal taxa. Although sequencing full mitogenomes seems to constitute the best compromise both in terms of costs and node support, they are known to induce biases in phylogenetic reconstructions. Rather, we recommend to systematically include loci commonly used for phylogenetics and taxonomy (i.e. DNA barcodes, rRNA genes, full mitogenomes, etc.) among the other loci when designing baits for capture.
PY 2020
PD JAN
SO Molecular Phylogenetics And Evolution
SN 1055-7903
PU Academic Press Inc Elsevier Science
VL 142
UT 000498650200012
DI 10.1016/j.ympev.2019.106660
ID 86802
ER

EF