HACSim: an R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves

Phillips, Jarrett D.; French, Steven H.; Hanner, Robert; Gillis, Daniel

doi:10.7717/peerj-cs.243

Cited by 22 publications

(17 citation statements)

References 75 publications

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“…To estimate the extent to which all haplotypes were captured by our sampling, 10,000 permutations of our haplotype frequency distribution were used to calculate haplotype accumulation curves using the iterative extrapolation simulation algorithm, HACSim, available through the R package HACSim (v.1.0.5) [39]. HACSim calculates the number of samples needed to recover 95% of all haplotypes.…”

Section: Discussionmentioning

confidence: 99%

Novel Post-Glacial Haplotype Evolution in Birch—A Case for Conserving Local Adaptation

Belton¹,

Cubry

Fox³

et al. 2021

Forests

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Despite constituting the western-most edge of the population distributions for several native European plants, Ireland has largely been left out of key Europe-wide phylogeographic studies. This is true for birch (Betula pubescens Ehrh. and Betula pendula Roth), for which the genetic diversity has yet to be mapped for Ireland. Here we used eight cpDNA markers (two Restriction Fragment Length Polymorphism (RFLP) and six Simple Sequence Repeat (SSR)) to map the genetic diversity of B. pubescens, B. pendula, and putative hybrid individuals sampled from 19 populations spread cross most of the island of Ireland. Within Ireland, 11 distinct haplotypes were detected, the most common of which (H1) was also detected in England, Scotland, France, and Norway. A moderate level of population structuring (GST = 0.282) was found across Ireland and the genetic diversity of its northern populations was twice that of its southern populations. This indicates that, unlike other native Irish trees, such as oak and alder, post-glacial recolonization by birch did not begin in the south (i.e., from Iberia). Rather, and in agreement with palynological data, birch most likely migrated in from eastern populations in Britain. Finally, we highlight Irish populations with comparatively unique genetic structure which may be included as part of European genetic conservation networks.

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Section: Discussionmentioning

confidence: 99%

Novel Post-Glacial Haplotype Evolution in Birch—A Case for Conserving Local Adaptation

Belton¹,

Cubry

Fox³

et al. 2021

Forests

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“…However, only a few DNA barcode reference libraries have been developed for wild bee national faunas, including Ireland 38 ; Germany 27 , Canada 25 , 39 and Chile 28 as well as some regional faunas 26 , 40 , 41 . In addition, identification accuracy depends also on the complete characterization of intraspecific variability 42 – 44 .…”

Section: Introductionmentioning

confidence: 99%

A DNA barcode-based survey of wild urban bees in the Loire Valley, France

Villalta

Ledet

Baude

et al. 2021

Sci Rep

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The current decline of wild bees puts important ecosystem services such as pollination at risk. Both inventory and monitoring programs are needed to understand the causes of wild bee decline. Effective insect monitoring relies on both mass-trapping methods coupled with rapid and accurate identifications. Identifying wild bees using only morphology can be challenging, in particular, specimens from mass-trapped samples which are often in poor condition. We generated DNA barcodes for 2931 specimens representing 157 species (156 named and one unnamed species) and 28 genera. Automated cluster delineation reveals 172 BINs (Barcodes Index Numbers). A total of 36 species (22.93%) were found in highly urbanized areas. The majority of specimens, representing 96.17% of the species barcoded form reciprocally exclusive groups, allowing their unambiguous identification. This includes several closely related species notoriously difficult to identify. A total of 137 species (87.26%) show a “one-to-one” match between a named species and the BIN assignment. Fourteen species (8.92%) show deep conspecific lineages with no apparent morphological differentiation. Only two species pairs shared the same BIN making their identification with DNA barcodes alone uncertain. Therefore, our DNA barcoding reference library allows reliable identification by non-experts for the vast majority of wild bee species in the Loire Valley.

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“…As stated in literature (Agbolade et al, 2020;Phillips et al, 2020), we conducted statistical analysis to investigate the significant performance of different AE variants for intrusion detection. In this line, various statistics are computed on AUC, AUPR and F1-measures illustrated in Figs.…”

Section: Discussionmentioning

confidence: 99%

Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation

Vaiyapuri¹,

Binbusayyis²

2020

PeerJ Computer Science

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The ever-increasing use of internet has opened a new avenue for cybercriminals, alarming the online businesses and organization to stay ahead of evolving thread landscape. To this end, intrusion detection system (IDS) is deemed as a promising defensive mechanism to ensure network security. Recently, deep learning has gained ground in the field of intrusion detection but majority of progress has been witnessed on supervised learning which requires adequate labeled data for training. In real practice, labeling the high volume of network traffic is laborious and error prone. Intuitively, unsupervised deep learning approaches has received gaining momentum. Specifically, the advances in deep learning has endowed autoencoder (AE) with greater ability for data reconstruction to learn the robust feature representation from massive amount of data. Notwithstanding, there is no study that evaluates the potential of different AE variants as one-class classifier for intrusion detection. This study fills this gap of knowledge presenting a comparative evaluation of different AE variants for one-class unsupervised intrusion detection. For this research, the evaluation includes five different variants of AE such as Stacked AE, Sparse AE, Denoising AE, Contractive AE and Convolutional AE. Further, the study intents to conduct a fair comparison establishing a unified network configuration and training scheme for all variants over the common benchmark datasets, NSL-KDD and UNSW-NB15. The comparative evaluation study provides a valuable insight on how different AE variants can be used as one-class classifier to build an effective unsupervised IDS. The outcome of this study will be of great interest to the network security community as it provides a promising path for building effective IDS based on deep learning approaches alleviating the need for adequate and diverse intrusion network traffic behavior.

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HACSim: an R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves

Cited by 22 publications

References 75 publications

Novel Post-Glacial Haplotype Evolution in Birch—A Case for Conserving Local Adaptation

Novel Post-Glacial Haplotype Evolution in Birch—A Case for Conserving Local Adaptation

A DNA barcode-based survey of wild urban bees in the Loire Valley, France

Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation

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