A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

Placing a new species on an existing phylogeny has increasing relevance to several applications. Placement can be used to update phylogenies in a scalable fashion and can help identify unknown query samples using (meta-)barcoding, skimming, or metagenomic data. Maximum likelihood (ML) methods of phylogenetic placement exist, but these methods are not scalable to reference trees with many thousands of leaves, limiting their ability to enjoy benefits of dense taxon sampling in modern reference libraries. They also rely on assembled sequences for the reference set and aligned sequences for the query. Thus, ML methods cannot analyze data sets where the reference consists of unassembled reads, a scenario relevant to emerging applications of genome skimming for sample identification. We introduce APPLES, a distance-based method for phylogenetic placement. Compared to ML, APPLES is an order of magnitude faster and more memory efficient, and unlike ML, it is able to place on large backbone trees (tested for up to 200,000 leaves). We show that using dense references improves accuracy substantially so that APPLES on dense trees is more accurate than ML on sparser trees, where it can run. Finally, APPLES can accurately identify samples without assembled reference or aligned queries using kmer-based distances, a scenario that ML cannot handle. APPLES is available publically at github.com/balabanmetin/apples.© The Author(s) 2019. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Rapid environmental change in highly biodiverse tropical regions demands efficient biomonitoring programmes. While existing metrics of species diversity and community composition rely on encounter-based survey data, eDNA recently emerged as alternative approach. Costs and ecological value of eDNA-based methods have rarely been evaluated in tropical regions, where high species richness is accompanied by high functional diversity (e.g., the use of different microhabitats by different species and life stages). We first tested whether estimation of tropical frogs' community structure derived from eDNA data is compatible with expert field assessments. Next, we evaluated whether eDNA is a financially viable solution for biodiversity monitoring in tropical regions. We applied eDNA metabarcoding to investigate frog species occurrence in five ponds in the Chiquitano dry forest region in Bolivia and compared our data with a simultaneous visual and audio encounter survey (VAES). We found that taxon lists and community structure generated with eDNA and VAES correspond closely, and most deviations are attributable to different species' life histories. Cost efficiency of eDNA surveys was mostly influenced by the richness of local fauna and the number of surveyed sites: VAES may be less costly in low-diversity regions, but eDNA quickly becomes more cost-efficient in high-diversity regions with many sites sampled. The results highlight that eDNA is suitable for large-scale biodiversity surveys in high-diversity areas if life history is considered, and certain precautions in sampling, genetic analyses and data interpretation are taken. We anticipate that spatially extensive, standardized eDNA biodiversity surveys will quickly emerge in the future.© 2018 John Wiley & Sons Ltd.

Next generation sequencing (NGS) technologies have led to a ubiquity of molecular sequence data. This data avalanche is particularly challenging in metagenetics, which focuses on taxonomic identification of sequences obtained from diverse microbial environments. Phylogenetic placement methods determine how these sequences fit into an evolutionary context. Previous implementations of phylogenetic placement algorithms, such as the evolutionary placement algorithm (EPA) included in RAxML, or PPLACER, are being increasingly used for this purpose. However, due to the steady progress in NGS technologies, the current implementations face substantial scalability limitations. Herein, we present EPA-NG, a complete reimplementation of the EPA that is substantially faster, offers a distributed memory parallelization, and integrates concepts from both, RAxML-EPA and PPLACER. EPA-NG can be executed on standard shared memory, as well as on distributed memory systems (e.g., computing clusters). To demonstrate the scalability of EPA-NG, we placed $1$ billion metagenetic reads from the Tara Oceans Project onto a reference tree with 3748 taxa in just under $7$ h, using 2048 cores. Our performance assessment shows that EPA-NG outperforms RAxML-EPA and PPLACER by up to a factor of $30$ in sequential execution mode, while attaining comparable parallel efficiency on shared memory systems. We further show that the distributed memory parallelization of EPA-NG scales well up to 2048 cores. EPA-NG is available under the AGPLv3 license: https://github.com/Pbdas/epa-ng.

Background: A fundamental problem in modern genomics is to taxonomically or functionally classify DNA sequence fragments derived from environmental sampling (i.e., metagenomics). Several different methods have been proposed for doing this effectively and efficiently, and many have been implemented in software. In addition to varying their basic algorithmic approach to classification, some methods screen sequence reads for 'barcoding genes' like 16S rRNA, or various types of protein-coding genes. Due to the sheer number and complexity of methods, it can be difficult for a researcher to choose one that is well-suited for a particular analysis.;Results: We divided the very large number of programs that have been released in recent years for solving the sequence classification problem into three main categories based on the general algorithm they use to compare a query sequence against a database of sequences. We also evaluated the performance of the leading programs in each category on data sets whose taxonomic and functional composition is known.;Conclusions: We found significant variability in classification accuracy, precision, and resource consumption of sequence classification programs when used to analyze various metagenomics data sets. However, we observe some general trends and patterns that will be useful to researchers who use sequence classification programs.

We present an evolutionary placement algorithm (EPA) and a Web server for the rapid assignment of sequence fragments (short reads) to edges of a given phylogenetic tree under the maximum-likelihood model. The accuracy of the algorithm is evaluated on several real-world data sets and compared with placement by pair-wise sequence comparison, using edit distances and BLAST. We introduce a slow and accurate as well as a fast and less accurate placement algorithm. For the slow algorithm, we develop additional heuristic techniques that yield almost the same run times as the fast version with only a small loss of accuracy. When those additional heuristics are employed, the run time of the more accurate algorithm is comparable with that of a simple BLAST search for data sets with a high number of short query sequences. Moreover, the accuracy of the EPA is significantly higher, in particular when the sample of taxa in the reference topology is sparse or inadequate. Our algorithm, which has been integrated into RAxML, therefore provides an equally fast but more accurate alternative to BLAST for tree-based inference of the evolutionary origin and composition of short sequence reads. We are also actively developing a Web server that offers a freely available service for computing read placements on trees using the EPA.

Likelihood-based methods for placing short read sequences from metagenomic samples into reference phylogenies have been recently introduced. At present, it is unclear how to align those reads with respect to the reference alignment that was deployed to infer the reference phylogeny. Moreover, the adaptability of such alignment methods with respect to the underlying reference alignment strategies/philosophies has not been explored. It has also not been assessed if the reference phylogeny can be deployed in conjunction with the reference alignment to improve alignment accuracy in this context.We assess different strategies for short read alignment and propose a novel phylogeny-aware alignment procedure. Our alignment method can improve the accuracy of subsequent phylogenetic placement of the reads into a reference phylogeny by up to 5.8 times compared with phylogeny-agnostic methods. It can be deployed to align reads to alignments generated by using fundamentally different alignment strategies (e.g. PRANK(+F) versus MUSCLE).http://www.exelixis-lab.org/software.html

How do you put a name on an unknown piece of DNA? From microbes to mammals, high-throughput metabarcoding studies provide a more objective view of natural communities, overcoming many of the inherent limitations of traditional field surveys and microscopy-based observations (Deiner et al. 2017). Taxonomy assignment is one of the most critical aspects of any metabarcoding study, yet this important bioinformatics task is routinely overlooked. Biodiversity surveys and conservation efforts often depend on formal species inventories: the presence (or absence) of species, and the number of individuals reported across space and time. However, computational workflows applied in eukaryotic metabarcoding studies were originally developed for use with bacterial/archaeal datasets, where microbial researchers rely on one conserved locus (nuclear 16S rRNA) and have access to vast databases with good coverage across most prokaryotic lineages - a situation not mirrored in most multicellular taxa. In this issue of Molecular Ecology Resources, Hleap et al. (2021) carry out an extensive benchmarking exercise focused on taxonomy assignment strategies for eukaryotic metabarcoding studies utilizing the mitochondrial Cytochrome C Oxidase I marker gene (COI). They assess the performance and accuracy of software tools representing diverse methodological approaches: from "simple" strategies based on sequence similarity and composition, to model-based phylogenetic and probabilistic classification tools. Contrary to popular assumptions, less complex approaches (BLAST and the QIIME2 feature classifier) consistently outperformed more sophisticated mathematical algorithms and were highly accurate for assigning taxonomy at higher levels (e.g. family). Lower-level assignments at the genus and species level still pose significant challenge for most existing algorithms, and sparse eukaryotic reference databases further limit software performance. This study illuminates current best practices for metabarcoding taxonomy assignments, and underscores the need for community-driven efforts to expand taxonomic and geographic representation in reference DNA barcode databases.This article is protected by copyright. All rights reserved.

Extraction and identification of DNA from an environmental sample has proven noteworthy recently in detecting and monitoring not only common species, but also those that are endangered, invasive, or elusive. Particular attributes of so-called environmental DNA (eDNA) analysis render it a potent tool for elucidating mechanistic insights in ecological and evolutionary processes. Foremost among these is an improved ability to explore ecosystem-level processes, the generation of quantitative indices for analyses of species, community diversity, and dynamics, and novel opportunities through the use of time-serial samples and unprecedented sensitivity for detecting rare or difficult-to-sample taxa. Although technical challenges remain, here we examine the current frontiers of eDNA, outline key aspects requiring improvement, and suggest future developments and innovations for research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Environmental DNA metabarcoding is a powerful tool for studying biodiversity. However, bioinformatic approaches need to adjust to the diversity of taxonomic compartments targeted as well as to each barcode gene specificities. We built and tested a pipeline based on read correction with DADA2 allowing analysing metabarcoding data from prokaryotic (16S) and eukaryotic (18S, COI) life compartments. We implemented the option to cluster Amplicon Sequence Variants (ASVs) into Operational Taxonomic Units (OTUs) with swarm, a network-based clustering algorithm, and the option to curate ASVs/OTUs using LULU. Finally, taxonomic assignment was implemented via the Ribosomal Database Project Bayesian classifier (RDP) and BLAST. We validate this pipeline with ribosomal and mitochondrial markers using metazoan mock communities and 42 deep-sea sediment samples. The results show that ASVs and OTUs describe different levels of biotic diversity, the choice of which depends on the research questions. They underline the advantages and complementarity of clustering and LULU-curation for producing metazoan biodiversity inventories at a level approaching the one obtained using morphological criteria. While clustering removes intraspecific variation, LULU effectively removes spurious clusters, originating from errors or intragenomic variability. Swarm clustering affected alpha and beta diversity differently depending on genetic marker. Specifically, d-values > 1 appeared to be less appropriate with 18S for metazoans. Similarly, increasing LULU's minimum ratio level proved essential to avoid losing species in sample-poor datasets. Comparing BLAST and RDP underlined that accurate assignments of deep-sea species can be obtained with RDP, but highlighted the need for a concerted effort to build comprehensive, ecosystem-specific databases.This article is protected by copyright. All rights reserved.

Although DNA metabarcoding is an attractive approach for monitoring biodiversity, it is often difficult to detect all the species present in a bulk sample. In particular, sequence recovery for a given species depends on its biomass and mitome copy number as well as the primer set employed for PCR. To examine these variables, we constructed a mock community of terrestrial arthropods comprised of 374 species. We used this community to examine how species recovery was impacted when amplicon pools were constructed in four ways. The first two protocols involved the construction of bulk DNA extracts from different body segments (Bulk Abdomen, Bulk Leg). The other protocols involved the production of DNA extracts from single legs which were then merged prior to PCR (Composite Leg) or PCR-amplified separately (Single Leg) and then pooled. The amplicons generated by these four treatments were then sequenced on three platforms (Illumina MiSeq, Ion Torrent PGM and Ion Torrent S5). The choice of sequencing platform did not substantially influence species recovery, although the Miseq delivered the highest sequence quality. As expected, species recovery was most efficient from the Single Leg treatment because amplicon abundance varied little among taxa. Among the three treatments where PCR occurred after pooling, the Bulk Abdomen treatment produced a more uniform read abundance than the Bulk Leg or Composite Leg treatment. Primer choice also influenced species recovery and evenness. Our results reveal how variation in protocols can have substantial impacts on perceived diversity unless sequencing coverage is sufficient to reach an asymptote.© 2019 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Sequence alignment is a long standing problem in bioinformatics. The Basic Local Alignment Search Tool (BLAST) is one of the most popular and fundamental alignment tools. The explosive growth of biological sequences calls for speedup of sequence alignment tools such as BLAST. To this end, we develop high speed BLASTN (HS-BLASTN), a parallel and fast nucleotide database search tool that accelerates MegaBLAST--the default module of NCBI-BLASTN. HS-BLASTN builds a new lookup table using the FMD-index of the database and employs an accurate and effective seeding method to find short stretches of identities (called seeds) between the query and the database. HS-BLASTN produces the same alignment results as MegaBLAST and its computational speed is much faster than MegaBLAST. Specifically, our experiments conducted on a 12-core server show that HS-BLASTN can be 22 times faster than MegaBLAST and exhibits better parallel performance than MegaBLAST. HS-BLASTN is written in C++ and the related source code is available at https://github.com/chenying2016/queries under the GPLv3 license.© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Public DNA databases are composed of data from many different taxa, although the taxonomic annotation on sequences is not always complete, which impedes the utilization of mined data for species-level applications. There is much ongoing work on species identification and delineation based on the molecular data itself, although applying species clustering to whole databases requires consolidation of results from numerous undefined gene regions, and introduces significant obstacles in data organization and computational load. In the current paper, we demonstrate an approach for species delineation of a sequence database. All DNA sequences for the insects were obtained and processed. After filtration of duplicated data, delineation of the database into species or molecular operational taxonomic units (MOTUs) followed a three-step process in which (i) the genetic loci L are partitioned, (ii) the species S are delineated within each locus, then (iii) species units are matched across loci to form the matrix L × S, a set of global (multilocus) species units. Partitioning the database into a set of homologous gene fragments was achieved by Markov clustering using edge weights calculated from the amount of overlap between pairs of sequences, then delineation of species units and assignment of species names were performed for the set of genes necessary to capture most of the species diversity. The complexity of computing pairwise similarities for species clustering was substantial at the cytochrome oxidase subunit I locus in particular, but made feasible through the development of software that performs pairwise alignments within the taxonomic framework, while accounting for the different ranks at which sequences are labeled with taxonomic information. Over 24 different homologs, the unidentified sequences numbered approximately 194,000, containing 41,525 species IDs (98.7% of all found in the insect database), and were grouped into 59,173 single-locus MOTUs by hierarchical clustering under parameters optimized independently for each locus. Species units from different loci were matched using a multipartite matching algorithm to form multilocus species units with minimal incongruence between loci. After matching, the insect database as represented by these 24 loci was found to be composed of 78,091 species units in total. 38,574 of these units contained only species labeled data, 34,891 contained only unlabeled data, leaving 4,626 units composed both of labeled and unlabeled sequences. In addition to giving estimates of species diversity of sequence repositories, the protocol developed here will facilitate species-level applications of modern-day sequence data sets. In particular, the L × S matrix represents a post-taxonomic framework that can be used for species-level organization of metagenomic data, and incorporation of these methods into phylogenetic pipelines will yield matrices more representative of species diversity.© The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

DNA metabarcoding is a promising approach for rapidly surveying biodiversity and is likely to become an important tool for measuring ecosystem responses to environmental change. Metabarcoding markers need sufficient taxonomic coverage to detect groups of interest, sufficient sequence divergence to resolve species, and will ideally indicate relative abundance of taxa present. We characterized zooplankton assemblages with three different metabarcoding markers (nuclear 18S rDNA, mitochondrial COI, and mitochondrial 16S rDNA) to compare their performance in terms of taxonomic coverage, taxonomic resolution, and correspondence between morphology- and DNA-based identification. COI amplicons sequenced on separate runs showed that operational taxonomic units representing >0.1% of reads per sample were highly reproducible, although slightly more taxa were detected using a lower annealing temperature. Mitochondrial COI and nuclear 18S showed similar taxonomic coverage across zooplankton phyla. However, mitochondrial COI resolved up to threefold more taxa to species compared to 18S. All markers revealed similar patterns of beta-diversity, although different taxa were identified as the greatest contributors to these patterns for 18S. For calanoid copepod families, all markers displayed a positive relationship between biomass and sequence reads, although the relationship was typically strongest for 18S. The use of COI for metabarcoding has been questioned due to lack of conserved primer-binding sites. However, our results show the taxonomic coverage and resolution provided by degenerate COI primers, combined with a comparatively well-developed reference sequence database, make them valuable metabarcoding markers for biodiversity assessment.

We present genesis, a library for working with phylogenetic data, and gappa, an accompanying command-line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies and other relevant data types, offer high-level simplicity as well as low-level customizability, and are computationally efficient, well-tested and field-proven.Both genesis and gappa are written in modern C++11, and are freely available under GPLv3 at http://github.com/lczech/genesis and http://github.com/lczech/gappa.Supplementary data are available at Bioinformatics online.© The Author(s) 2020. Published by Oxford University Press.

Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification.UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.

In this paper, the soil biodiversity and its functioning in ecosystem were briefly summarized, and the history and development of the discipline of soil biology were also reviewed. Meanwhile, I pointed out some issues on soil biology to be addressed for a few years to come. Due to the importance of soil biodiversity to the maintenance of ecosystem functions but inadequate study on soil biota in China, a series of articles on soil biota were collectively published in this issue of <em>Biodiversity Science</em>. The objectives of this issue were to enable the Chinese scientists to better understand the functions of soil biodiversity and to stimulate the interest of young scholars in the discipline of soil biology. The ultimate goal was to push forward the research and development of soil biology in China and to apply the knowledge and techniques of soil biology in the development of national economy.

本文概括性地介绍了土壤生物类群的多样性及其在生态系统中的作用; 同时简要地回顾和比较了国内外在土壤生物学方面的研究动态, 分析了土壤生物学今后的发展趋势。鉴于土壤生物在生态系统中的重要性以及我国在土壤生物学研究方面的不足, 《生物多样性》本期刊登了一系列有关土壤生物的文章, 目的是为了使国内科学家对土壤生物多样性在生态系统中的作用有更好的认识, 并希望能够唤起更多的年轻学者加入到土壤生物学研究的行列, 以推动土壤生物学在我国的迅速发展并将土壤生物学的研究成果应用于国民经济的发展中。

Implementing cost-effective monitoring programs for wild bees remains challenging due to the high costs of sampling and specimen identification. To reduce costs, next-generation sequencing (NGS)-based methods have lately been suggested as alternatives to morphology-based identifications. To provide a comprehensive presentation of the advantages and weaknesses of different NGS-based identification methods, we assessed three of the most promising ones, namely metabarcoding, mitogenomics and NGS barcoding. Using a regular monitoring data set (723 specimens identified using morphology), we found that NGS barcoding performed best for both species presence/absence and abundance data, producing only few false positives (3.4%) and no false negatives. In contrast, the proportion of false positives and false negatives was higher using metabarcoding and mitogenomics. Although strong correlations were found between biomass and read numbers, abundance estimates significantly skewed the communities' composition in these two techniques. NGS barcoding recovered the same ecological patterns as morphology. Ecological conclusions based on metabarcoding and mitogenomics were similar to those based on morphology when using presence/absence data, but different when using abundance data. In terms of workload and cost, we show that metabarcoding and NGS barcoding can compete with morphology, but not mitogenomics which was consistently more expensive. Based on these results, we advocate that NGS barcoding is currently the seemliest NGS method for monitoring of wild bees. Furthermore, this method has the advantage of potentially linking DNA sequences with preserved voucher specimens, which enable morphological re-examination and will thus produce verifiable records which can be fed into faunistic databases.© 2019 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Both the species diversity and distribution pattern of the superfamily Acridoidea of the suborder Caelifera have important contributions to understanding the local biodiversity of the Baiyangdian Wetland. This research tries to study the species diversity and distribution pattern of the superfamily Acridoidea within the Baiyangdian Wetland and test the feasibility of DNA barcoding in species identification for this superfamily. Sequences of the cox1 gene were obtained from 97 individuals of 21 species of the superfamily Acridoidea. Phylogenetic, genetic distance and sequence difference threshold analyses using the Neighbor Joining (NJ), Automatic Barcode Gap Discovery (ABGD) and Molecular Defined Operational Taxonomic Units (MOTU) methods, respectively, were performed for these and the 25 additional sequences of 10 species downloaded from GenBank. The results indicate that there are 34 species, 23 genera, and 6 families of the superfamily Acridoidea insects around the farmland, dam, and grassland of the Baiyangdian Wetland, including a new-record genus, Euchorthippus, and three new-record species, Euchorthippus unicolor, Atractomorpha psittacina and Oxya japonica. The DNA barcoding technology therefore is very efficient and helpful for identifying the species of the superfamily Acridoidea, although the morphological approach is still playing a key role in the species identifications.

为了解白洋淀湿地蝗虫的物种多样性及其分布情况, 探究DNA条形码技术在物种鉴定方面的可行性, 本研究对白洋淀湿地的蝗虫进行了采集调查。采用自行设计的cox1基因引物, 本研究共扩增得到21种蝗虫的cox1序列97条, 并利用构建NJ系统发育树法、计算遗传距离的ABGD法以及序列差异阈值分类的MOTU法对已得到的序列连同GenBank下载的10种25条cox1序列进行了比较分析。结果表明: 白洋淀湿地蝗总科昆虫共有6科23属34种, 包括1个新记录属, 即异爪蝗属(Euchorthippus), 和3个新记录种, 即素色异爪蝗(E. unicolor)、柳枝负蝗(Atractomorpha psittacina)和日本稻蝗(Oxya japonica); 蝗虫在白洋淀湿地主要分布在农田及其周围、堤坝及其周围以及淀边草丛; DNA条形码技术能够在种级阶元对蝗总科物种进行良好地解析, 但物种鉴定时, 不能完全脱离形态学方法。

To manage and conserve biodiversity, one must know what is being lost, where, and why, as well as which remedies are likely to be most effective. Metabarcoding technology can characterise the species compositions of mass samples of eukaryotes or of environmental DNA. Here, we validate metabarcoding by testing it against three high-quality standard data sets that were collected in Malaysia (tropical), China (subtropical) and the United Kingdom (temperate) and that comprised 55,813 arthropod and bird specimens identified to species level with the expenditure of 2,505 person-hours of taxonomic expertise. The metabarcode and standard data sets exhibit statistically correlated alpha- and beta-diversities, and the two data sets produce similar policy conclusions for two conservation applications: restoration ecology and systematic conservation planning. Compared with standard biodiversity data sets, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, less reliant on taxonomic expertise and auditable by third parties, which is essential for dispute resolution. © 2013 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing.We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics.Program and sources freely available for download at https://github.com/blinard-BIOINFO/RAPPAS.Supplementary data are available at Bioinformatics online.© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Background: Microbiome studies often involve sequencing a marker gene to identify the microorganisms in samples of interest. Sequence classification is a critical component of this process, whereby sequences are assigned to a reference taxonomy containing known sequence representatives of many microbial groups. Previous studies have shown that existing classification programs often assign sequences to reference groups even if they belong to novel taxonomic groups that are absent from the reference taxonomy. This high rate of "over classification" is particularly detrimental in microbiome studies because reference taxonomies are far from comprehensive.Results: Here, we introduce IDTAXA, a novel approach to taxonomic classification that employs principles from machine learning to reduce over classification errors. Using multiple reference taxonomies, we demonstrate that IDTAXA has higher accuracy than popular classifiers such as BLAST, MAPSeq, QIIME, SINTAX, SPINGO, and the RDP Classifier. Similarly, IDTAXA yields far fewer over classifications on Illumina mock microbial community data when the expected taxa are absent from the training set. Furthermore, IDTAXA offers many practical advantages over other classifiers, such as maintaining low error rates across varying input sequence lengths and withholding classifications from input sequences composed of random nucleotides or repeats.Conclusions: IDTAXA's classifications may lead to different conclusions in microbiome studies because of the substantially reduced number of taxa that are incorrectly identified through over classification. Although misclassification error is relatively minor, we believe that many remaining misclassifications are likely caused by errors in the reference taxonomy. We describe how IDTAXA is able to identify many putative mislabeling errors in reference taxonomies, enabling training sets to be automatically corrected by eliminating spurious sequences. IDTAXA is part of the DECIPHER package for the R programming language, available through the Bioconductor repository or accessible online (http://DECIPHER.codes).

The important roles of soil fauna diversity and associated indicative functions of environment changes have received increasing attention from both academic circles and government decision makers. This paper summarizes the current situation of soil fauna monitoring in developed countries and related work in China. We introduce the objectives and structure of the thematic monitoring network of soil fauna diversity (TMNSFD), and highlighted some aspects that need attention. The TMNSFD proposed to establish permanent monitoring plots within forest plots established by Chinese Forest Biodiversity Monitoring Network for monitoring soil fauna including earthworms, mites, springtails, nematodes and protists. During the years 2016-2020, TMNSFD may choose typical forest ecosystems as priority ecosystems for soil fauna monitoring, which cover temperate forest ecosystems (including broadleaved Korean pine mixed forests in Changbaishan, Jilin Province and warm temperate deciduous broadleaved forests in Donglingshan, Beijing), subtropical forest ecosystems (including typical subtropical evergreen broadleaved forests in Gutianshan, Zhejiang Province, lower subtropical evergreen broadleaved forests in Dinghushan, Guangdong Province, and north subtropical evergreen broad-leaved forests in Dujiangyan, Sichuan Province), tropical forest ecosystems (tropical rainforests in Xishuangbanna, Yunnan Province and Jianfengling, Hainan Province), as well as mountainous dark coniferous forests in Liziping, Sichuan Province. By 2030, TMNSFD soil fauna monitoring plots may cover various ecosystems including forests, grasslands, wetlands, deserts, farmland, urban areas and other typical ecosystems in different regions of China. TMNSFD emphasizes the value of applied molecular biology technology, unified monitoring methods, and manipulation experiments to simulate the effects of global change on soil fauna during the processes of monitoring. We propose monitoring soil fauna diversity once every 5 years in established monitoring plots. The objective of TMNSFD is to provide reliable and integrated data of soil fauna diversity via the establishment of standard monitoring methods and a data-sharing network at the national level, which could support the development of ecological civilization in China.

土壤动物多样性变化及其对环境的指示作用已被学术界和政府决策部门高度关注。本文从土壤动物多样性监测的重要性及面临的挑战、国内外土壤动物多样性监测概况等方面进行了评述, 提出了未来、尤其是2016-2020年我国土壤动物多样性监测的目标、站点布局、样地设置、监测类群和指标等, 并讨论了在制定土壤动物多样性监测方案时需考虑的问题, 有助于在全国开展多点化土壤动物多样性及分布状况的监测工作, 建立标准统一、数据共享的土壤动物监测网, 提供完整的、可信的监测数据, 为国家生态文明建设提供科技支撑。

The National Center for Biotechnology Information (NCBI) Taxonomy is widely applied in biomedical and ecological studies. Typical demands include querying taxonomy identifier (TaxIds) by taxonomy names, querying complete taxonomic lineages by TaxIds, listing descendants of given TaxIds, and others. However, existed tools are either limited in functionalities or inefficient in terms of runtime. In this work, we present TaxonKit, a command-line toolkit for comprehensive and efficient manipulation of NCBI Taxonomy data. TaxonKit comprises seven core subcommands providing functions, including TaxIds querying, listing, filtering, lineage retrieving and reformatting, lowest common ancestor computation, and TaxIds change tracking. The practical functions, competitive processing performance, scalability with different scales of datasets and good accessibility could facilitate taxonomy data manipulations. TaxonKit provides free access under the permissive MIT license on GitHub, Brewsci, and Bioconda. The documents are also available at https://bioinf.shenwei.me/taxonkit/.Copyright © 2021 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.

Effective marine management requires comprehensive data on the status of marine biodiversity. However, efficient methods that can document biodiversity in our oceans are currently lacking. Environmental DNA (eDNA) sourced from seawater offers a new avenue for investigating the biota in marine ecosystems. Here, we investigated the potential of eDNA to inform on the breadth of biodiversity present in a tropical marine environment. Directly sequencing eDNA from seawater using a shotgun approach resulted in only 0.34% of 22.3 million reads assigning to eukaryotes, highlighting the inefficiency of this method for assessing eukaryotic diversity. In contrast, using 'tree of life' (ToL) metabarcoding and 20-fold fewer sequencing reads, we could detect 287 families across the major divisions of eukaryotes. Our data also show that the best performing 'universal' PCR assay recovered only 44% of the eukaryotes identified across all assays, highlighting the need for multiple metabarcoding assays to catalogue biodiversity. Lastly, focusing on the fish genus Lethrinus, we recovered intra- and inter-specific haplotypes from seawater samples, illustrating that eDNA can be used to explore diversity beyond taxon identifications. Given the sensitivity and low cost of eDNA metabarcoding we advocate this approach be rapidly integrated into biomonitoring programs.

Virtually all empirical ecological studies require species identification during data collection. DNA metabarcoding refers to the automated identification of multiple species from a single bulk sample containing entire organisms or from a single environmental sample containing degraded DNA (soil, water, faeces, etc.). It can be implemented for both modern and ancient environmental samples. The availability of next-generation sequencing platforms and the ecologists' need for high-throughput taxon identification have facilitated the emergence of DNA metabarcoding. The potential power of DNA metabarcoding as it is implemented today is limited mainly by its dependency on PCR and by the considerable investment needed to build comprehensive taxonomic reference libraries. Further developments associated with the impressive progress in DNA sequencing will eliminate the currently required DNA amplification step, and comprehensive taxonomic reference libraries composed of whole organellar genomes and repetitive ribosomal nuclear DNA can be built based on the well-curated DNA extract collections maintained by standardized barcoding initiatives. The near-term future of DNA metabarcoding has an enormous potential to boost data acquisition in biodiversity research.© 2012 Blackwell Publishing Ltd.

Soil is one of the most biodiverse terrestrial habitats. Yet, we lack an integrative conceptual framework for understanding the patterns and mechanisms driving soil biodiversity. One of the underlying reasons for our poor understanding of soil biodiversity patterns relates to whether key biodiversity theories (historically developed for aboveground and aquatic organisms) are applicable to patterns of soil biodiversity. Here, we present a systematic literature review to investigate whether and how key biodiversity theories (species-energy relationship, theory of island biogeography, metacommunity theory, niche theory and neutral theory) can explain observed patterns of soil biodiversity. We then discuss two spatial compartments nested within soil at which biodiversity theories can be applied to acknowledge the scale-dependent nature of soil biodiversity.

Biologists frequently sort specimen-rich samples to species. This process is daunting when based on morphology, and disadvantageous if performed using molecular methods that destroy vouchers (e.g., metabarcoding). An alternative is barcoding every specimen in a bulk sample and then presorting the specimens using DNA barcodes, thus mitigating downstream morphological work on presorted units. Such a "reverse workflow" is too expensive using Sanger sequencing, but we here demonstrate that is feasible with an next-generation sequencing (NGS) barcoding pipeline that allows for cost-effective high-throughput generation of short specimen-specific barcodes (313 bp of COI; laboratory cost <$0.50 per specimen) through next-generation sequencing of tagged amplicons. We applied our approach to a large sample of tropical ants, obtaining barcodes for 3,290 of 4,032 specimens (82%). NGS barcodes and their corresponding specimens were then sorted into molecular operational taxonomic units (mOTUs) based on objective clustering and Automated Barcode Gap Discovery (ABGD). High diversity of 88-90 mOTUs (4% clustering) was found and morphologically validated based on preserved vouchers. The mOTUs were overwhelmingly in agreement with morphospecies (match ratio 0.95 at 4% clustering). Because of lack of coverage in existing barcode databases, only 18 could be accurately identified to named species, but our study yielded new barcodes for 48 species, including 28 that are potentially new to science. With its low cost and technical simplicity, the NGS barcoding pipeline can be implemented by a large range of laboratories. It accelerates invertebrate species discovery, facilitates downstream taxonomic work, helps with building comprehensive barcode databases and yields precise abundance information.© 2018 John Wiley & Sons Ltd.