dataesr/scientific-paragraphs-categorization huggingface dataset:
The expression vector with gRNA (pML104_Cas9_HygR_Nup60) was rescued from cell material, propagated in bacteria and confirmed by digest analysis and sequencing. Genomic DNA was extracted from single yeast clones and the presence of the point mutation was confirmed by sequencing. Positive clones were propagated on YPD to get rid of the expression vector. -Microscopy For fluorescent microscopy, yeast cells were precultured for minimally 24h in synthetic drop-out medium. 1 ml of cells from exponential growing cultures with OD<1 were concentrated by centrifugation at 1. 000xG, resuspended in ~5 ul of low fluorescent SD-medium, spotted on a round coverslip and immobilized with a SD/agar patch. The cells were imaged in z-stacks of 6 slices with 0. 5 μm spacing, with a 100×/1. 4 NA objective on a DeltaVision microscope (Applied Precision) equipped with a CCD HQ2 camera (Roper), 250W Xenon lamps, Softworx software (Applied Precision) and a temperature chamber set to 30°C.
For imaging we used an epi-fluorescent microscope (TiE, Nikon Instruments, Tokyo, Japan) controlled by Micro-Manager 1. 4. 23 software (μManager, PMID 25606571), with a Plan Apo 60x 1. 4 NA objective. For fluorescence illumination of the GFP and mCherry labeled proteins, a Lumencor Spectra-X LED Light Engine was used. Stacks of 7 slices with 0. 3 μm spacing were recorded during. The age of the cell was defined by the number of daughter cells that emerged during the budding cycles. For the nucleoporin correlation analysis, a cell of interest was manually selected if it stays in the focal plane in the bright field channel. Its age was determined and a segmented line was drawn through the nuclear envelope in an image in the focal plane, using Fiji/ImageJ 1. 51n  (Schindelin et al. , 2012) , and the intensity profiles were recorded for both fluorescence channels.
Acknowledgements We thank  Joachim Hehl ,  Tobias Schwartz  and the light microscopy center of  ETH Zürich (ScopeM)  for microscopy support;  Rodrigo Merayo Martínez ,  Irina Remple  and  Liesbeth Veenhoff  for their help with running the microfluidic chips. We acknowledge financial support by  Swiss National Science Foundation  (grant  31003A-105904  to Y. B. ).
Phylogeny Reconstruction and Evolutionary Analyses We selected 58 Legionella species for which all the coding sequences deduced from their genomes are freely available in the NCBI database (accession numbers in Supp. T1). The completeness of these genomes was checked using Busco 4. 0. 5 with legionellales_odb10 database (April 24, 2019)  (Seppey et al. 2019). Each genome was over 95% complete except for L. oakridgensis (92. 0%), L. norrlandica (91. 9%), and L. tunisiensis (68. 7%). The coding sequences of HdeD, LqsA, LqsR, LqsS, and LqsT were searched by TBlastN, using the already annotated sequences of L. pneumophila as queries (Evalue < 10 À6 and qcov_hsp_perc > 65%). For LqsA, we also fixed a minimum of 50% of identity (pident). The ID of each coding sequence of the synthase and receptors are listed in supplementary table  S1 , Supplementary Material online.
Results The lqs Cluster Is Present in Numerous Legionella Species This study was performed on genomes from Legionella species published in the NCBI database with fully sequenced and annotated genomes (supplementary table T1, Supplementary Material online). To investigate the unexpected presence of a LAI-1-based QS system or that of a homologous receptor in other species, we performed a TBlastN search using all the genes composing the lqs cluster present in L. pneumophila. The protein sequences of L. pneumophila Paris were used: LqsA (Lpp2787), LqsR (Lpp2788), HdeD (Lpp2789), LqsS (Lpp2790), and LqsT (Lpp2574). All protein candidates In order to map the distribution of the elements of the L. pneumophila QS system in the Legionella genus, the homologue genes were plotted on a tree reflecting the phylogeny of the Legionella species, constructed de novo according to Burstein et al.
Our results show that the increasing use of wocDNA COI metabarcoding for metazoan diversity is characterised by a clear absence of bioinformatic harmonisation, and the temporal trends show little change in this situation. The reviewed literature showed (i) high heterogeneity across pipelines, tasks and tools used, (ii) limited or no adaptation of bioinformatic procedures to the nature of the COI fragment, and (iii) a worrying underreporting of tasks, software and parameters. Based upon these findings we propose a set of recommendations that we think the metabarcoding community should consider to ensure that bioinformatic methods are appropriate, comprehensive and comparable. We believe that adhering to these recommendations will improve the long-term integrative potential of wocDNA COI metabarcoding for biodiversity science.
g. , OBItools  Boyer et al. , 2016; QIIME Caporaso et al. , 2010 ; USEARCH/UPARSE  Edgar, 2013 ; and its open-source derivative  VSEARCH Rognes et al. , 2016). These tools have largely been developed for metabarcode loci other than the COI region, with very few tools explicitly developed for protein coding metabarcodes (although see  Andújar et al. , 2021; Nugent et al. , 2020; Ramirez-Gonzalez et al. , 2013). To fully capitalise on the COI barcode for metabarcoding, bioinformatics should be specifically tailored to its evolutionary properties, such as the ability to interrogate the amino acid translation, and accounting for established patterns of sequence variation in protein coding genes for strict filtering. Additionally, metabarcoding employs a number of key bioinformatic tasks for which multiple alternative algorithms have been developed (e.
g. , denoising algorithms), with considerable variation in outcomes depending on parameters and thresholds applied. The structure of a bioinformatic metabarcoding pipeline will depend strongly on the research aim, amplification and sequencing protocols, target locus, and target biodiversity fraction. The diversity of bioinformatic tasks and the software approaches to implement them is of course beneficial for designing appropriate pipelines, but such heterogeneity may also restrict integrated, standardised and synergistic growth in the field. As metazoan wocDNA metabarcoding becomes more accessible to researchers from a range of fields and backgrounds, harmonisation of bioinformatic approaches is important to ensure (i) high-quality, reproducible data amenable to qualitative or quantitative reviews and meta-analysis across studies, and (ii) a reliable, consistent methodology for wider implementation, development and expansion of wocDNA metabarcoding.
We consider harmonisation not to mean strict prescription of the tasks and software to use, nor their order. Instead a harmonised field would recognise the diversity of approaches available, while recording key steps and establishing the effects of parameter choice on the outcome of metabarcoding studies. This approach could be enabled by the adoption of universal aligned standards for data generation and processing, while allowing for flexibility in implementation to adapt to varying research goals and take advantage of novel methodological development. Harmonisation requires comprehensive examination of current practice to understand the aims and approaches of prior work, and a synthesis of the successes and failures in past implementations for the purposes of elaborating a framework to guide future research.
, 2013; Aylagas et al. , 2016). This allows for more objective stringency thresholds in bioinformatic filtering and delimitation of operational taxonomic units (OTU). We conducted a systematic search of peer-reviewed studies in the Web of Science (WOS) Core Collection (Science Citation Index Expanded, 1900-present) on 3 November 2020, using the search "TS = (metabarcoding) NOT TS = (*micro* OR *bacteria* OR *myco* OR *archaea* OR fungi OR plant OR eDNA OR environmental DNA)". These search parameters were selected in order to obtain a comprehensive set of wocDNA metabarcoding studies limited to Metazoa. The systematic search resulted in 692 records, which were screened to to select only those studies that: (i) amplified some portion of the standard COI barcode "Folmer" region  (Folmer et al.
| The core studies All papers were systematically processed to record (i) the research aim and type of samples analysed, (ii) the bioinformatic tasks and pipelines implemented, and (iii) the software tools used and the reproducibility of the bioinformatic procedures employed. We define these terms as follows: • Task: a specific, self-contained action in a pipeline, generally with a clearly-defined purpose and performed by a single tool. e. g. , demultiplexing. • (Software) tool: a specific piece of software, or a specific identifiable function within a software package. e. g. , Cutadapt, or USEARCH cluster_otus. • Pipeline: a sequence of steps in a specific order, each step performing a particular task and using a specific tool. The research aim was categorised according to whether the focus was (i) the comparison of molecular and/or bioinformatic procedures for metabarcoding, (ii) a proof-of-concept or feasibility study into the success of metabarcoding for uncovering accurate community data in the taxon/community/biome studied, or (iii) principally the study of ecological patterns and processes.
Similarly, we recorded tasks that performed filtering of community data for the purposes of removing OTUs or OTU records arising from erroneous sequences or from cross-talk/contamination  (Edgar, 2018 ), but we did not record tasks that filtered community data for the purposes of statistical correction, such as normalisation or rarefaction. Once the different tasks implemented by each article were identified, the pipeline used was also recorded based on the order in which the different tasks were mentioned in the text, figures, Appendix S1 and/or cited papers. Where multiple mutually exclusive TA B L E 1.
Reference taxonomic assignment The assignment (identification or classification) of taxonomy to OTUs using a purpose-built and/or specially curated reference set of sequences 60 9 18 23 1 Note: Tasks are grouped into four groups by broad purpose, and a detailed definition of each task is given along with summary statistics of the implementation of each task across the 111 papers. For a list of the software used for each task, Table  S1  is an expanded version of this table. tasks were employed for the purposes of comparison of pipelines, we recorded that pipeline that the authors concluded to be empirically superior, or from which the authors used the output data for subsequent analysis. A detailed description of the systematic processing of methods is described in the Appendix S1. For each of the bioinformatic tasks identified across the papers, we calculated (i) the number of papers implementing the task, (ii) the task's relative position within the pipelines, (iii) the information reported on the software, version and parameters used, and (iv) the homogeneity in the software tools used to implement the task.
| Infrequent adaptation of pipelines to COI The COI locus differs from many other metabarcoding loci (e. g. , 18S, 16S, 12S, ITS) in that it is a protein coding gene, imparting strict expectations of amplicon sequence read properties that can be exploited in metabarcoding bioinformatics  (Andújar, Arribas, Yu, et al. , 2018). However, the adaptation of pipelines to this fragment are in general rarely implemented in the papers of the core set. For example, only 22 papers (20%) used amino acid translations to identify erroneous sequences ("translation filtering"), using 11 different software tools for the task. The reason for low implementation of translation filtering is probably that none of the major metabarcoding software packages include functions for translation filtering and that the available methods are limited.
Those papers that carry out translation filtering do so by using one of three main approaches: (i) sequences are viewed and translated in a GUI application such as Geneious (https://www. genei ous. com) or MEGA  (Kumar et al. , 2018) , and those with stop codons manually removed, (ii) sequences are processed through a custom script, some of which are available on github but none of which are used by research groups separate from the author, and (iii) sequences are aligned against references using MACSE  (Ranwez et al. , 2011)  and those containing indels or stop codons are removed. The first option is time consuming and prone to human error, and custom scripts are challenging to document and maintain for a wider number of users. While MACSE is the most frequent single approach, it is computationally efficient only for small data sets.
There may be some potential in the recent coil R package  (Nugent et al. , 2020)  that uses Hidden Markov Models to identify and filter translationbased errors and appears to scale well to large data sets, although the R implementation presents a slight barrier to efficient inclusion in pipelines. Furthermore, the majority of translation filtering approaches are based solely on removing stop codons, while there may be other potential avenues for filtering based on amino acid translation. The extent to which expectations for protein structural properties can be applied to metabarcoding sequences for filtering F I G U R E 3 Violin plot of standardised task position within pipelines. Increasing x-axis position denotes later placement of task within pipelines, vertical dashed lines denote 25%, 50% and 75% of the way through the pipeline, respectively.
However, only half (n = 54) of papers reported using length filtering, despite this being a relatively trivial procedure and with functions available in all metabarcoding software packages and as options in many more software tools. There may be some underreporting here; given the implementation of a length filtering parameter in many software tools that have a different primary purpose, authors may not have explicitly reported that length thresholds had been applied as part of a different procedure (note that we recorded when a single tool was reported to have fulfilled multiple tasks). Despite length filtering being widely available, and the relative algorithmic simplicity of implementation, there are no length filtering tools that allow for specification of thresholds outside of a simple minimum-maximum range, despite the internal barcode region of protein coding genes generally being expected to vary in length only by multiples of three bases.
Nonetheless, there remains relatively widespread underreporting, and this has remained unchanged over time (Figure  5b ). Within the reported software, we identified 93 independent pieces of software used in metabarcoding bioinformatic pipelines (Table  S3 ), of which 27% (25) were software packages. When taking into account distinct functions within packages, a total of 169 unique software tools were recorded; however, this is probably an inaccurate picture given low reporting rates of functions used within software packages. There is a clear increase in the number of different software and software functions employed across all papers over time (Figure  5a ). Examining the diversity of software used within tasks over time, controlling for the number of papers published, there is limited improvement in homogeneity and a decrease F I G U R E 5 Consistency in software reporting and use over time.
(a) The total number of unique software functions reported across all papers for each year of publication. (b) For each paper, the proportion of the total number of bioinformatic tasks for which the software used for a task was not reported. (c) The software homogeneity rate, calculated only when more than one paper reported a task in a given year. A value of 1 means all papers used the same tool for a given task in a given year. (d) The software dominance rate, calculated only when more than one paper reported a task in a given year. A value of 1 means all papers used the same tool for a given task in a given year. Given that the number of metabarcoding publications is increasing year-on-year, there is thus a concomitant increase in the diversity of software used for a given task, and previously commonly used software are being used less (Figure  5c, d ).
| Toward a bioinformatic harmonisation of COI metabarcoding for metazoan wocDNA samples Our results show that the increasing use of wocDNA COI metabarcoding for metazoan diversity is characterised by a clear absence of bioinformatic harmonisation, and the temporal trends show little change in this situation. The reviewed literature showed (i) high heterogeneity across pipelines, tasks and tools used, (ii) limited or no adaptation of bioinformatic procedures to the nature of the COI fragment, and (iii) a worrying underreporting of tasks, software and parameters. The development of metabarcoding as a method for community ecology began with microbial studies over a decade ago, which have revealed the extensive diversity of bacteria and archaea on our planet and demonstrated the potential of metabarcoding for global biodiversity syntheses  (Bates et al.
, 2021; Arribas, Andújar, Salces-Castellano, et al. , 2021). In contrast, there has been little advance in the development and validation of best practices associated with the bioinformatic processing of wocDNA COI metabarcoding data (but see  Yang et al. , 2021  for error reduction). Outside of taxonomic assignment tasks for which adaptations and parameterization for using the COI barcode fragment have been further discussed (see e. g. ,  Hleap et al. , 2021) , the discussion of customising or parameterising tools for the purposes of working with wocDNA COI metabarcoding is very rare (but see e. g. ,  Andújar, Arribas, Gray, et al. , 2018; Andújar et al. , 2021; Antich et al. , 2021) , with most papers simply reporting using tools with default settings. Our review has revealed heterogeneity in the number of tasks, the order of these within pipelines, and the tools used to implement them, along with a lack of even basic adaptations to the COI metabarcode for most of the papers.
The majority of available software and resources for metabarcoding bioinformatics are still those that have been developed around the 16S rRNA gene (the primary target for microbiome metabarcoding), including the most popular software packages (e. g. , USEARCH) and sets of wrapper scripts (e. g. , QIIME, OBItools). While in many cases these methods may carry over to COI without issue, we observe very few studies that report consideration or analysis that assesses or validates the suitability of software choices for COI. These issues suggest that the expansion of wocDNA COI metabarcoding is proceeding at a pace and manner that could lose sight of or simply ignore the challenges inherent in producing high-quality data and reproducible methods  (Baker et al. , 2016; Zinger et al. , 2019) , and lose out on the potential for exploiting the benefits of the COI marker for wocDNA metabarcoding of Metazoa.
DNA metabarcoding has broad multidisciplinary potential, as demonstrated by the expansion in use of metazoan wocDNA COI metabarcoding among users from very diverse backgrounds. The diversity of applications of metabarcoding requires the concomitant bioinformatic techniques to be flexible and adaptable, and the field remains under active development. Thus it would not be productive to attempt to prescribe pipelines, tasks or even software tools in the name of standardisation, as there is no one-size-fits-all approach in metabarcoding. However, some degree of harmonisation is required to ensure quality, reproducibility and potential integration in metastudies  (Tedersoo et al. , 2015). Additionally, the absence of a harmonised framework of bioinformatic processing can act as a barrier for potential new users  (Liu et al.
, 2020) , hampering the growth of the field. To these ends, we thus propose a set of recommendations that we believe all researchers in the field should consider when designing and reporting their wocDNA COI metabarcoding bioinformatics pipeline, with the hope that they will catalyse harmonised implementation. Fully report all tasks, software, software versions and parameters used, even if just the defaults. Our results show that underreporting is a recurrent problem. Comprehensive reporting of the tasks, pipelines and software used is essential for further integrating results in future reviews or meta-analyses  (Tedersoo et al. , 2015). Furthermore, care should be taken to report not just the name of the software package, but also the exact function, and if wrapper scripts are used then the underlying functions should be reported.
Considering the trade-off with current constrictions for manuscript length, this could be achieved by the inclusion of a supporting table following the STAR-METHODs philosophy  (Marcus, 2016) , where task reference, order within the pipeline and software used are included. Note that the task lexicon and software lists compiled in this review (see Table  1 ) are a very useful resource for this purpose. This reporting effort for all the wocDNA COI metabarcoding will promote rigour and robustness with an intuitive, consistent framework that makes reporting easier for the author and replication easier for the reader. Implement filtering tasks such that spurious sequences are sufficiently removed to meet the assumptions of the research ques-   (Creedy et al. , 2020; Elbrecht et al. , 2018). This can be exacerbated for mitochondrial markers like COI, due to the coamplification of NUMTs and other nonauthentic ASVs that are missed by denoising and require stringent, optimised filtering based on read abundances such as that implemented by the metaMATE software  (Andújar et al.
Some recent advances have been made in filtering tasks (metaMATE,  Andújar et al. , 2021; coil, Nugent et al. , 2020; entropy-based denoising, Turon et al. , 2020; Antich et al. , 2021)  but further development in these promising areas is essential to fully exploit the potential of the COI gene for metabarcoding. As mentioned previously, there are no tools that enable simple length filtering variation that accounts for codon-level insertion or deletion. To our knowledge there is limited work exploring the extent to which protein structure inference might allow identification of erroneous sequences: for example the SOAPbarcode pipeline  (Liu et al. , 2013)  includes a script that filters sequences based on translation hydrophilicity, but this is not comprehensively documented or discussed in the associated publications.
Our review, which is the first evaluating the state of the art on this topic, highlights that this danger is clearly present in the field of metazoan wocDNA COI metabarcoding. The results of our assessment and the recommendations derived from it may help to improve bioinformatic harmonisation and thus the long-term integrative potential of wocDNA COI metabarcoding for biodiversity science. ACK N OWLED G EM ENTSThis research was supported by the iBioGen project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 810729. CO N FLI C T O F I NTE R E S T Alfried P. Vogler is a cofounder and scientific advisor of NatureMetrics, a private company providing commercial services in DNA-based monitoring. The authors declare that they have no other conflicts of interest.