dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational time comparison 16S-based metagenome classification using all the above tools was carried out on: i86linux32, 4. 0GB RAM × 8 cores (32. 8GB total). Computational time for each tool is summarised in Figure  8. All the tools except RiboTaxa were run on highquality metagenomic reads, trimmed and cleaned using BBTools while RiboTaxa was run on raw metagenomics reads as the latter performs its own quality control step using BBTools. Using the MOCK (Figure  8A ) and the synthetic human gut communities (Figure  8B ), RiboTaxa completed taxonomic assignment in 18 min 03 s and 26 min 08 s, respectively. Based on short k-mer approach, Kraken2 and Centrifuge were the fastest to infer taxa in both communities. On the other hand, alignment-based BLCA and DIAMOND-MEGAN took more than 1 hour to classify sequences of the synthetic community.
Evaluation of RiboTaxa on real datasets In addition to providing a fine taxonomic resolution down to the species level, RiboTaxa has proved to be a versatile tool with its ability to analyse metagenomics data from different environments including soil, ocean and human gut. In a study focused on corals' health involving 20 metagenomics samples (13 corals, 3 sediments and 4 seawater) (50), affiliation of prokaryotes was limited to the family level with Endozoicomonadaceae characterising healthy octocoral tissue. Using RiboTaxa, we identified that healthy octocoral harbors a total of 19 prokaryotic species, whereby E. gazella hosted 7, E. verrucosa 4 and L. sarmentosa 8 species. In comparison, 204, 67 and 22 species were detected in seawater, necrotic E. gazella tissue, and sediments respectively (Figure  9A ; Supplementary Figure  S2 ).
Furthermore, Ribo-Taxa highlighted two species of uncultured Endozoicomonas which were dominant in the healthy tissue of all three octocorals species (average = 67. 6%). The phylogenetic anal- ysis coupled with percent identity matrix revealed that both species belonged to new genus (<95% similarity) despite being relatively close to the genus Endozoicomonas (Supplementary Figure  S3 ; Table  S2 ). Other very close unclassified sequences have been retrieved by similarity search in Genbank database confirming the robustness of our approach, but no genome seems to be available for these new species which opens new research perspectives on corals' health. Applied to metagenomics data from a study on human gut and extreme longevity (51), RiboTaxa revealed a richer microbial diversity with 1135, 1495, 1756 and 2621 species in young adults, young elderly, centenarians and semi-supercentenarians, respectively (Figure  9B ; Supplementary Figure  S4 ).
While the authors compared the relative abundance of different species between the four groups, we focused on the presence of specific microbial signatures in the different groups. Interestingly, among the different archaeal species identified, RiboTaxa detected the presence of an uncultured archaeon in 4 semisupercentenarians. Phylogenetic analysis coupled with identity matrix revealed that the uncultured archaeon shared an identity <95% with Methanosphaera species highlighting a probable new archaeal genus not already described. By similarity search in Genbank database, we detected an uncultured clone, HM573433, initially isolated from faeces showing 99. 68% identity but no available genome has been detected reinforcing the interest in exploring this new archaeal genus (Supplementary Figure  S5 ; Table  S2 ).
25%. The two most abundant bac- terial species detected by RiboTaxa were unclassified Luteimonas and unclassified Sphingomonas with a relative abundance of 13. 3% and 8. 9%, respectively. Phylogenetic analysis coupled with identity matrix revealed that the two new species belong to the genus Luteimonas (Supplementary Figure  S9 ; Table  S2 ) and to the genus Tardibacter (identity = 96. 33%) instead of Sphingomonas (Supplementary Figure  S10 ; Table  S2 ), respectively. The initial misclassification into the genus Sphingomonas is due to the lack of Tardibacter sequences in SILVA 138. 1 database.
SSU rRNA gene reconstruction versus metagenome-based approaches Microbial profiling performed by Kraken2 using the GTDB database (47) revealed a high alpha diversity in each sample at all taxonomic levels. In healthy corals, an average of 12 949 species were detected compared to 20 321 species in necrotic corals. Due to this high microbial diversity, Bracken identified the most abundance family, Endozoicomonadaceae, at only 1. 2%. Within this family, Kraken2 detected the genera Endozoicomonas (33 assigned reads), Parendozoicomonas (2 assigned reads) and Kistimonas (1 assigned read). However, unlike RiboTaxa, Kraken2 could not detect new genera within the Endozoicomonadaceae family consisting of two new species revealed by Ribo-Taxa. This is because Kraken only classifies the reads from genomes present in the reference database.
Thus, lack of reference genomes in the GTDB database, particularly, for less explored microbial communities, hinders the identification of novel species  (53). Similarly, in the semi-supercentenarian's samples, only bacterial diversity was detected with an average of 18 266 species compared to Rib-oTaxa, which identified the presence of an uncultured archaeon among other archaea detected. Finally, RiboTaxa revealed a microbial richness of 97 species in N160 permafrost soil samples which Kraken2 detected an average of 23 191 species in the same samples. This high alpha diversity provided by Kraken2 could be the result of short-reads misclassification conducting to diversity overestimation as demonstrated previously.
DISCUSSION Shotgun metagenomics sequencing is a powerful method to characterise microbiota. Metagenomic classifiers have been developed to taxonomically classify metagenomics data and estimate taxa abundance profiles  (54). Algorithmic approaches also ensure that classification speeds are fast enough to exploit very large numbers of sequencing reads. In the present study, we compared bioinformatics tools to exploit SSU reads for microbial community profiling from metagenomics data. A major advantage of rRNA gene analysis is that databases contain genes from hundreds of thousands species, making them far more comprehensive than current genome databases even if we observe continuous exponential genome accumulation. Furthermore, unknown microorganisms could be identified even for rare taxa using nearly full-length phylogenetic marker and positioned in phylogenic tree  (33).
Thus, phylogenetic approaches are designed to be able to detect distant homology, enabling the characterization of previously unidenti-NAR Genomics and Bioinformatics, 2022, Vol. 4, No. 3 17 fied organisms. However, high conservation of 16S rRNA sequence cannot discriminate all species as it is the case for certain genera, or even in some cases, 16S rRNA gene may not be a reliable predictor of genus-level taxonomy  (49). We observed this latter situation for a few microorganisms from Enterobacteriaceae and Lachnospiraceae families. To overcome these issues and reach strain-level identification, complete rDNA operon (which could still miss some proportion of bacterial species on account of unlinked rRNA genes)  (55)  or other marker databases for prokaryotic  (56)  and/or eukaryotes profiling (57) should be used as references.
Low biomass microbiota, rare and unknown taxa characterizations are other challenges in metagenomics sample exploration. Hybridization capture targeting SSU rRNA genes overcomes such difficulties  (33, 58)  and RiboTaxa has been designed and optimized for 16S/18S sequencing data analysis as well as other universal markers targeted by hybridization for microbial structure characterization  (59). Another difficulty of using shotgun DNA for rRNA gene analyses is the identification of SSU rRNA fragments in large sequence datasets  (60). In this study, we extracted rRNA reads using SortMeRNA, an RNA-specialized tool and BBMap, a general-purpose read mapper. Sort-MeRNA performed better than BBMap in extracting reads that could be assembled using different tools such as EMIRGE (Emirge. py and Emirge amplicon.
However, it is possible that chimeric database sequences could carry over into SSU reconstructions, if reads map across the full length of the chimera as indicated by EMIRGE developers  (27). This study emphasised on targeted-sequence reconstruction to maximise taxonomic assignment at species level. Comparing taxonomic profiling using short-length sequences and reconstructed SSU sequences proved that inferring taxa directly to short-length Illumina reads resulted in a high rate of false positives while reconstructed sequences were correctly assigned, though it required an assembly step prior to classification. As closely related species share very similar or identical genome segments, short reads often map to multiple species in the reference dataset restraining classification efficiency  (18).
This limitation was depicted by BLCA and DIAMOND-MEGAN6, which rely on sequence alignment based on matched database sequences. Despite classifying sequences at species level, they failed to consider the differing degrees of similarity between certain query and the database hit sequences  (12)  and either stopped at genus level or produced false-positives. Kraken2 and SPINGO, which used the exact k-mer approach and calculated the lowest common ancestor (LCA), were very rapid at classifying short reads. However, according to Tovo et al  (63) , the choice of the length k highly influences the classification and default value (k = 35) is frequently used because it is difficult or impossible to determine the optimal value for unknown environments. METAXA2 achieved the highest recall of the microbial community at genus level but also produced false sequence classifications, jeopardising the precision of the taxonomic profiling.
Another study demonstrated that Centrifuge, Kraken2 and KrakenUniq yielded many more taxa than the number included in the test datasets  (64). Thus, where reads are classified individually, multiple reference sequences can have identical levels of similarity, leading to a high number of false positives. On the contrary, using nearly full-length SSU sequences produced by phyloFlash and RiboTaxa resulted in correct taxa assignment of the mock community and yielded no falsepositive results. We demonstrated that RiboTaxa has assigned the highest number of species-level taxa compared to other tools and identified even low abundant taxa. The application of RiboTaxa to both the MOCK and synthetic human gut communities allowed the identification of all the microorganisms in each community, providing evidence that longer sequences contain stronger phylogenetic signals and yield higher precision for taxonomic profiling than short-read analysis  (46).
Moreover, nearly full-length 16S sequences contain a wealth of information that allows their accurate classification at appropriate taxonomic ranks, i. e. at a high rank when the sequences are divergent or highly novel and at a low rank when closely related organisms are present in the database  (65). However, very closely related species evolve slowly and differ very little between their 16S rRNA genes  (66). Consequently, due to their nearly identical 16S rRNA gene sequences, RiboTaxa could not assign certain sequences at species level and stopped at genus level classification. Although, 16S rRNA sequences provide real and significant advantages compared to targeted variable regions, it will never provide a perfect representation of bacterial species diversity  (46). To improve this, the 16S ribosomal RNA gene was coupled with the 16S-23S rRNA internal transcribed spacer region sequences to characterize Escherichia species and to identify new strains  (67).
Likewise, combined with the 16S rRNA gene, the 16S-23S rDNA ITS region can improve the description of microbial diversity within and across group. We used the SILVA SSU 138. 1 NR99 database for taxonomic classification as it contains 510 508 SSU rRNA gene sequences from Bacteria, Archaea and Eukaryota domains (https://www. arb-silva. de/). However, RiboTaxa can also use other existing rRNA sequence databases such as the Ribosomal Database Project (RDP)  (37)  or Greengenes  (68)  or genome-based databases such as the GTDB  (47). RiboTaxa could also classify eukaryotic organisms using 18S rRNA gene as phylogenetic marker. CCMetagen  (64) , which has recently been developed, uses the read mapping ConClave sorting scheme, implemented in the KMA software (69) to include microbial eukaryotes in metagenomics data exploration.
In the same way, MetaPhlAn2 indexes several different gene families in its database to identify taxa from other microbial kingdoms  (70). Abundance estimation performance of different profilers varies considerably even on the same benchmark datasets  (54). This apparently high-performance variation largely arises because the profilers report either one of two fundamentally different types of relative abundances: sequence abundance or taxonomic abundance  (71). Furthermore, false positives and false negatives impact microbial structure characterization and by consequence true abundancies. As RiboTaxa shows high precision and recall, abundance evaluation appears close to true abundancies even if it is impossible to obtain exact profile. The presence of multiple copies of rRNA gene makes the community abundance data distorted and gene copy normalization should be necessary for correction  (72).
Expanded isolate genome availability is essential to improve detection capabilities as demonstrated recently by MetaPhlAn3  (79). However, it is still challenging to precisely analyze environmental samples using approaches based on genome databases, as most reference databases are mostly based on human-associated microorganisms  (80). The uniqueness of RiboTaxa is the benchmarking of tools and metrics optimization at each step, from quality control to taxonomic classification. The mock communities also proved to be integral for parameter optimization, and the accuracy of most taxonomic assignment was notably controlled for sequence artefacts. Thus, users can apply de-fault optimized parameters on their datasets coming from any environment except for the sequence reconstruction parameters, -max read length, -insert mean, -insert stddev, which exclusively depend on the sequencing length of the input datasets.
Run time is another recurring challenge for taxonomic classifiers. RiboTaxa is not as fast as shortread classifiers (i. e. Kraken2, Centrifuge). Indeed, the combined approach (EMIRGE-METARIB) during rRNA sequence reconstruction is responsible for this moderately high but reasonable computational time. However, it also allows to reach a higher precision of taxonomic assignments compared to fast short-read analysis which may assign sequences to wrong taxa (false positives). Thus, the user may need to choose between taxonomic accuracy and speed. The efficiency of RiboTaxa was finally demonstrated using real datasets obtained from previous studies. In addition to providing a fine taxonomic resolution down to the species level, RiboTaxa has proved to be a versatile tool with its ability to analyse data from different environments including ocean, soil and human gut revealing novelties not detected by current approaches.
Metagenome-based approaches were also surpassed by the dynamic approach of RiboTaxa. In fact, lack of reference genomes in the GTBD database, particularly, for less explored microbial communities, hinders the identification of novel species (53) while 16S rRNA genes have the advantage of more accurately identifying distinct microorganisms while reflecting real community richness  (81). In octocoral samples, phylogenetic analysis of RiboTaxa derived sequences allowed the identification of a new genera within the Endozoicomonadaceae family consisting of two new species whose abundances are positively correlated with coral health, opening new perspectives to a better understanding of beneficial microbial interactions and coral preservation  (82). It will be interesting to access genome information for these bacteria that are not currently available to decipher positive cross talks in coral holobiont.
Throughout this study, we supported the identification of a potentially new genus or species with an alignment of > 97% with sequences from GenBank annotated as 'uncultured' bacteria from corresponding environment. We cannot exclude that a part of this diversity originates from artificial diversity created in silico during full-length sequence reconstruction even though intragenomic 16S gene sequence variation can be a valuable method to provide accurate representation of bacterial species  (61). As in-dicated by EMIRGE developers, occasional presence of small indel errors in the reconstructed sequence could occur but in practice, these rare indels have little effect on taxonomic classification. Accuracy of 16S sequence reconstruction can be controlled as recently demonstrated by Marre et al  (85).
The authors designed specific primers targeting nearly full-length reconstructed 16S rRNA genes using the KASpOD algorithm  (86)  and tested them on biological samples through PCR experiments coupled with Sanger sequencing. Using RiboTaxa, we detected rare microorganisms (<0. 1%) in real datasets from different environments (soil, ocean, human gut) and different techniques (metagenomics, gene capture by hybridization), confirming the sensitivity of this approach. Most microbial studies focus on dominant taxa, bypassing the inexhaustible source of metabolic functions of rare microbial taxa, also called the 'rare biosphere'  (87). Unfortunately, rare taxa detection is highly influenced by sequencing depth and deep sequencing renders downstream processes computationally exhaustive. In this study, we also demonstrated the ability of RiboTaxa to process huge datasets using hybridization capture, whereby SSU rRNA genes accounted for 55.
12% of the reads in the soil compared with shotgun sequencing, in which usually <1% of sequences carry the biomarkers. We also revealed microbial novelties in such datasets. In conclusion, RiboTaxa is a user-friendly metagenomic classifier which supports database preparation, read trimming and SSU read extraction, SSU rRNA gene targeted assembly and taxonomic profiling. This complete workflow demonstrates high specificity and sensitivity without false positive detection and output species relative abundance close to reality, enabling to manage large amount of metagenomics and gene capture by hybridization datasets from any environment. RiboTaxa could efficiently reveal microbial novelties not detected by current approaches as well as, nearly full-length 16S sequences could potentially be connected to metagenome-assembled genomes (MAGs) to improve linking between taxonomy and metabolic functions  (88).
FUNDING Agence Nationale de la Recherche  (project  MICRO-PRONY ) [ ANR-19-CE02-0020 ] and ECOS-sud [ECOS n • C20B02]; O. C. and S. C. -M. were supported respectively by  ANR Intelligence Artificielle  ( MIA: Artificial Intelligence  for clerMont) co-financed by  FEDER  funds and MI-CROPRONY projects.
DATA AVAILABILITY RiboTaxa is open source and free for all use. It is licensed under a GNU Affero General Public License 3. 0. Source code is available at https://github. com/oschakoory/ RiboTaxa and can be easily installed using miniconda3 (https://docs. conda. io/en/latest/miniconda. html). All data generated or analysed during this study are included in this published article and its supplementary information files. All scripts used to generate graphs and figures are available at https://github. com/oschakoory/ RiboTaxa Supp.
Plans for assessment and collection of outcomes {18a} Quantitative data such as malaria prevalence, clinical malaria cases, mosquito density, feasibility, cost, adverse effects, attrition, integrity and use practice of the intervention will be entered using Open Data Kit (ODK)  [15]  installed on android tablet computers. Paper data forms will be available to the field team for use in the eventuality of tablet computer failure. Data collected will be uploaded to the IHI secure data repository for data analysis. Qualitative data will be audio recorded and transcribed  [16]. Interviews will be saved in an external hard-drive and locked in a secure file cabinet. Mosquitoes will be collected using CDC light traps to measure the effect of the intervention on the indoor density of malaria vectors and nuisance mosquitoes  [9].
Blood samples will be collected using New MiniCollect ® K2EDTA Capillary Tube, manufactured by Greiner Bio-One, Austria. The blood samples will be kept in cool box before they are transferred to the lab for qPCR analysis  [8]. To assess clinical malaria cases, calibrated ear thermometer will be used to record body temperature of those with positive mRDT. The study personnel, field workers/data collectors are trained on correct data input methods to ensure data integrity.
Plans to promote participant retention and complete follow-up {18b} Field interviewers recruited to support data collection will be from the study villages. Phases of data collection will involve door-to-door surveys, with respective subvillage heads guiding field interviewers to households, as participants are more likely to stay in the trial when familiar faces are involved. Achieving complete followup in village settings may also be influenced by time of data collection, as most of the study village residents are farmers and are more likely to be in their farms throughout the daytime during cultivation and harvesting season; therefore, follow-up visits for data collection will occur at a time when participants are available, e. g. in the early morning or evening. Data collectors will revisit absent households as much as possible to ensure completeness of data collection.
Data management {19} Data collected using the ODK application installed on android tablets will be uploaded onto the IHI secured server and downloaded for analysis. Data collected using paper forms will be double entered into a computer using double-key entry methods to facilitate cross-referencing and validation. The two sets of entries will be compared, and any discrepancies found between the two databases will be resolved by checking the data forms as per analysis plan. Audio recordings and transcriptions, as well as notes taken during the focus group discussions will be stored securely. The data, both in hard copy and digital format, will be kept for the purpose of analyses until consideration and clearance of the final report. Research records for all study participants including history, physical findings, and laboratory data are to be maintained in a secure storage facility for 10 years or until notified by grantee as per Tanzanian ethical guidelines.
Confidentiality {27} All study-related information will be stored securely at IHI. Data collectors are trained on maintaining confidentiality of study participants' information. Households and participants will be given unique identification numbers; thus, participant's study personal information will be anonymized before analysis or if data will be shared based on data sharing agreement. Personal information will only be shared based on written permission of the participant, for purposes of independent monitoring among representatives of either government and regulatory authorities, or site Institutional Review Board committees/Ethics Committees.
Dissemination plans {31a} Data will be disseminated by report to the sponsor, followed by peer-reviewed publication targeted towards scientists and policy-makers. We will publish the results and data sets in open-access, indexed, peer-reviewed journals, making the findings and the anonymised data available to all stakeholders at reasonable requests. Data obtained from the study will be presented at international conferences and stakeholder meetings including the National Malaria Control Program. At the end of the project a meeting will be held to update the local community and the District Medical Officer (DMO), to present findings and answer any questions arising.