dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 4 and a maximum likelihood tree was constructed from the recombination-purged core gene alignment using RAxML v. 8. 2. 8 and 100 bootstrap repeats. Snp-dists version 0. 7. 0 (https://github. com/tseemann/snp-dists) was used to extract the number of SNPs between all isolates from the recombination-free core genome alignments. The resulting trees for both analysis were visualized using iTol v. 5. 5. 1 (http://itol. embl. de/itol. cgi). For both datasets a minimum-spanning tree (MST) based on cgMLST was also constructed and visualized using the plugin GrapeTree integrated in BIGSdb. 29 For human isolates, we focused on the 326 ST11, ST15 and ST307 genomes present among the 1717 human K. pneumoniae genomes investigated in the wide-scale EuSCAPE study, which included carbapenem non-susceptible and susceptible isolates.
These data most likely do not highlight direct transfers of ESBL/AmpC/ carbapenemase-producing K. pneumoniae clones among companion animals with unrelated epidemiological histories but may rather reflect indirect and unclarified transmissions. Animal K. pneumoniae genomes belonging to the three most prevalent STs in our study and K. pneumoniae genomes of humans and animals of varied geographical origins were analysed to Resistant K. pneumoniae in French animals explore animal/human transmission dynamics. Four groups of very closely related French human and animal isolates belonging to the three STs were detected: these isolates displayed ≤72 SNP differences within the ST15 cluster, ≤52 within the ST11 cluster, and ≤25 or ≤105 SNP differences in the two subclusters within ST307. David et al.
Funding This research was funded by institutional support from  ANSES  and  Institut Pasteur , and by the  MedVetKlebs  project, a component of  One Health European Joint Programme EJP , which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 773830. C. R. was also financed by a  Pasteur-Roux fellowship  from  Institut Pasteur. For the purpose of open access, the author has applied a CC-BY public copyright license to any Author Manuscript version arising from this submission.
Background: Accurate and comprehensive annotation of transcript sequences is essential for transcript quantification and differential gene and transcript expression analysis. Single-molecule long-read sequencing technologies provide improved integrity of transcript structures including alternative splicing, and transcription start and polyadenylation sites. However, accuracy is significantly affected by sequencing errors, mRNA degradation, or incomplete cDNA synthesis. Results: We present a new and comprehensive Arabidopsis thaliana Reference Transcript Dataset 3 (AtRTD3). AtRTD3 contains over 169,000 transcripts-twice that of the best current Arabidopsis transcriptome and including over 1500 novel genes. Seventy-eight percent of transcripts are from Iso-seq with accurately defined splice junctions and transcription start and end sites.
g. , Pitcher plants-Nepenthes spp. ), and medicinal plants (e. g. , Zanthoxylum, safflower, Salvia). The majority of the above applications of PacBio sequencing investigated transcriptome diversity and complexity and determined transcription start sites, AS events, and APA sites. However, significant issues surrounding the accuracy of SJs, TSS, and TES identification suggest that many of the above transcriptome studies would benefit from improved methods of transcript structure determination. Accurate and well-curated transcripts also play an important role in improving genome annotations and the identification of novel genes and, particularly, long non-coding RNAs. In this paper, we report the construction of a new, comprehensive Arabidopsis transcriptome, AtRTD3, based on a wide range of Arabidopsis tissues and treatments.
AtRTD3 contains over 169 k transcripts, 78% of which are derived from Iso-seq and have accurately defined SJs, TSS, and TES. It improves the precision of analysis of RNA-seq data for differential gene and transcript expression and differential alternative splicing and now allows analysis of differential TSS and TES usage. We used a new pipeline based on TAMA  [7]  to analyze the Iso-seq data and developed novel methods to address the impact of sequencing errors and incomplete transcripts. We developed (1) a splice junction-centric approach that allows the identification of high-confidence SJs and (2) a probabilistic 5′ and 3′ end determination method that effectively removes transcript fragments and identifies dominant transcript start and end sites. They allow accurate determination of SJs, TSS, and TES directly from the Iso-seq data and remove the requirement for hybrid error correction or parallel experimental approaches for detecting TSS and TES such as CAGE-seq or poly(A)-seq, respectively.
1 ). The numbers of reads, FLNCs and mapped FLNCs along with statistics are shown in Additional File 1: Table  S2. The 27 libraries generated 13. 7 million Iso-seq reads in total. The total number of CCS was 8. 7 M with an average of 322 k CCS per library. The total number of FLNCs generated using lima+refine (see "Materials and methods") was 7. 77 M with an average of 288 k per library. About 7. 36 M of the FLNCs mapped onto the Arabidopsis genome, generating 142. 9 k transcripts and 14. 3 k genes on average per library. We then merged the transcripts generated from the 27 libraries using TAMA merge, where unique transcripts including those with only a single-nucleotide difference at 5′ and 3′ UTR were kept (Fig. 1 ). The merged transcriptome assembly consisted of 33,154 genes and 2,239,270 transcripts.
2A ). We then extracted the mismatch profiles for the shared SJs and for those unique to Iso-seq transcripts and determined the number and percentage of mismatches in each Fig. 1  Workflow of analysis of PacBio Iso-sequencing. A Raw reads are analyzed using the PacBio Iso-seq 3 pipeline to generate FLNCs which are mapped to the genome (blue boxes). B Mapped FLNCs are collapsed and merged using TAMA to generate transcripts (pink boxes). C Transcripts are quality controlled using datasets of high-confidence (HC) splice junctions (SJs) and transcript start and end sites (TSS/TES). Transcripts with unsupported splice junctions where reads contain mismatches within ±10 nt of an SJ are removed. Transcripts with both high-confidence TSS and TES (determined by binomial probability for highly expressed genes and by end support with > 2 reads for low expressed genes) are retained as HC transcripts.
Validation of significant TSSs and TESs A transcription start site dataset for Arabidopsis genes at nucleotide resolution was generated previously using paired-end analysis of TSSs (PEAT)  [41]. In their study, using a pooled Col-0 root sample, 79,706 mapped and annotated PEAT tag clusters (groups of similar TSSs) were identified, and quality filtering generated 9326 strong tag clusters from protein-coding genes which had groups of TSS locations supported by at least 100 reads. The information for each tag cluster included the start, end, strand and the mode, which is the location within the cluster where the greatest number of 5′ ends was mapped  [41]. We compared the significant 61,014 RSGLs from the highly sequenced genes here with the PEAT tag clusters and found that 50. 8% were located within 8445 of the 9326 strong tag clusters (90.
Generation of high-level transcripts for AtIso To achieve accurate transcript isoforms from the Iso-seq data, we have adopted a strategy that requires evidential support for all the SJs, TSSs, and TESs. We generated datasets of high-confidence SJs, RSGLs, and REGLs which were then used to filter the 2,239,270 transcripts from all the libraries. Given the stochastic nature of TSSs and TESs, we applied a 100-nt window around each significant and supported RSGL and REGL (50 nt on each side) to define high-confidence TSS and TES regions (Additional File 2; Figure  S4 ). This generated 1,674,795 transcripts after sequentially removing transcripts containing poorly supported SJs (117,361 transcripts) or poorly supported TSS and TES (447,114 transcripts) (Fig. 1C ). The above filtering criteria also addressed the common issue of excessive numbers of single-exon gene models generated from Isoseq experiments and many other genome-wide annotation projects  [7, 51] , which could be the result of genomic DNA contamination.
The combined set was merged allowing 50 nt variations at the 5′ and 3′ ends, and the final AtIso dataset contained 24,344 genes with 132,190 high-level transcripts (Additional File 2: Fig. S4 ). To investigate the contribution to gene and transcript diversity from each of the different libraries to AtIso, we used the transcript merge information provided by TAMA (_trans_report. txt) to identify which transcripts and genes were merged from the individual libraries (Additional File 1: Table  S7 ; Additional File 2: Fig. S5 ). The nuclear RNA sample contributed the least number of genes and transcripts to AtIso despite high number of CCS reads generated from this library. The silique library contributed the second lowest number of genes and transcripts which is likely due to the flow cell having the lowest loading efficiency (21%) and generating the lowest number of CCS reads of all the libraries (Additional File 1: Table  S2 ).
We used SQANTI3, the latest version of SQANTI  [52]  to assess the quality of the long-read transcripts in AtIso and AtRTD3 in comparison with other reference transcriptomes (Araport11  [40]  and AtRTD2  [39] ). SQANTI catalogs long-read transcript as Full Splice Match (FSM) when the transcript matches a reference at all SJs, Incomplete Splice Matches (ISM), if the transcript misses SJs at either 5′ and 3′, Novel In Catalogue (NIC), when the long-read transcript includes a novel combination of existing donor or acceptor sites, and Novel Not In Catalogue (NNC), when the long-read transcripts contain at least one novel donor or acceptor site. Other categories are Genic, Intergenic, Fusion, and Antisense  [52]. When compared to the Araport reference, ca. 35% of AtRTD3 transcripts (ca. 59 k) were FSM and ca.
Genes and transcripts in AtRTD3 were characterized using TranSuite, a program which identifies mono-and multi-exonic genes and generates accurate translations of transcripts and transcript characteristics  [53]. The output includes translations of all transcripts in the RTD and multiple transcript features (Additional File 1: Table  S9 ). These results are summarized in Fig. 4  and Additional File 1: Table  S10A  and S10B. Fig. 4  Gene and transcript characteristics of AtRTD3. A Protein-coding and non-protein-coding genes. B Monoexonic and multi-exonic genes. C Mono-and multi-exonic genes with single/multiple transcript isoforms for all genes and D for protein-coding genes. E Distribution of transcripts from protein-coding genes (protein-coding and unproductive isoforms) and from non-protein-coding genes.
Disease resistance genes were the most common gene class and included 14 of the ca. 40 FPA-sensitive disease resistance genes with non-stop transcripts  [72]  as well as ten disease resistance genes with non-stop RNA transcripts not found in that study. Interestingly, two polyadenylation and cleavage factor homologs (PCFS1 and PCFS5) generated non-stop RNAs from premature polyadenylation and one of the FPA transcripts (AT2G43410. 8) was a non-stop RNA (Additional File 1: Table  S15A  and B ). The list of genes with non-stop RNAs is unlikely to be complete as only around one-third of the FPA-sensitive disease resistance genes were identified which may reflect the specific effect of the fpa mutant compared to the range of samples used here or differential coverage of genes in the Iso-seq and Oxford nanopore datasets.
We showed previously that redundant or missing transcripts, transcript fragments, and mis-annotation in the 5′ and 3′ ends of transcripts seriously impacted the accuracy of transcript and gene expression quantification with Salmon and Kallisto which require prior knowledge of transcripts  [39]. Initial analysis of the Iso-seq data identified issues with false splice junctions, degraded or fragmentary reads/transcripts and that error correction methods using short-read data often trim or split whole transcripts sequences in fragments or generated new errors (over-correction). In addition, the IsoSeq3 analysis pipeline from PacBio used polishing steps which removed splice site variation with small differences such as alternative splicing of a few nucleotides (e. g. , NAGNAG sites). These observations provided the motivation to improve methods of analysis of PacBio Iso-seq data.
To date, Iso-seq has been applied to a wide range of plant species (see "Background"); the novel methods here will improve analysis of transcripts in future studies and allow re-analysis of existing data. In addition, AtRTD3 can evolve further with the addition of new or existing Iso-seq datasets analyzed using the methods described here. The Iso-seq-derived transcripts in AtRTD3 (ca. 80% of transcripts) were full-length with accurate SJs and TES/TSS and correct combinations of TES/TSS and AS events but only covered ca. two-thirds of genes in Arabidopsis. This represents good coverage for Iso-seq in comparison to other studies. For example, a recent study of Iso-seq of nine tissues in rice covered only ca. one-third of rice genes  [29]. Coverage of the other genes and transcripts in AtRTD3 came from Araport11 and, primarily, from AtRTD2 due to its far greater transcript diversity  [39].
20 k protein-coding transcript isoforms with AS only in the 5′ and/or 3′ UTR such that isoforms coded for the same protein. AS in UTRs can be involved in regulation of expression by introducing short or overlapping uORFs to trigger NMD or affecting translation  [54]  or nuclear retention of mRNAs determining export of mRNAs  [75]. The detailed characterization of such transcripts here provides a basis for future investigation into the regulatory roles of AS in UTRs. The power of exploiting comprehensive RTDs in analyzing differential expression and differential alternative splicing was demonstrated in Arabidopsis using a cold timeseries dataset and AtRTD2  [63, 64]. Thousands of genes with rapid cold-induced significant changes in expression and AS were identified due to the transcript level resolution of expression  [63, 64].
Analysis of PacBio Iso-seq reads The workflow of the analysis is shown in Fig. 1. The PacBio sequencing data was analyzed using the PacBio IsoSeq3 pipeline to generate and map full-length non-chimeric (FLNC) reads. Further analysis was performed using TAMA  [7]  (https://github. com/ GenomeRIK/tama) to collapse and merge reads/transcripts and apply novel methods to define splice junctions (SJs) and transcript start and end sites (see below).
Processing of raw PacBio IsoSeq reads to FLNCs The raw PacBio sequencing data (. subreads. bam) from each library was processed individually using the following procedures: (1) CCS calling was carried out using ccs 4. 0. 0 using the following parameters: --min-rq 0. 9 -j 28. (2) Primer removal and demultiplexing was carried out using lima (version v1. 10. 0) with the parameters: --isoseq --peekguess. (3) IsoSeq3 (v3. 2. 2) refine was used to trim poly(A) tails and for rapid concatemer identification and removal to produce the FLNC transcripts (Fig. 1A ). For the Clontech libraries, --require-polya is used while for Teloprime 5′ captured reads, lima is run with this parameter turned off. We have deliberately avoided the clustering steps in the IsoSeq3 pipeline in order that small variances around the splice junctions, such as NAGNAG splice junctions can be preserved.
Splice junction-centric approach for accurate splice junctions From this point, we adopted the TAMA analysis pipeline for the next steps of transcript isoform analysis (Fig. 1B ). To overcome the generation of false splice junctions due to mis-mapping of FLNCs to the genome, we developed a splice junction-centric approach to provide highly accurate alignment around splice junctions. An improvement of TAMA was developed that allowed us to examine the mapping mismatches (replacement and indels) between the FLNCs and genome reference. Using this new parameter (-sjt and -lde), we were able to extract the mapping details of any defined regions around the SJs. For each library, we ran the TAMA collapse using the following parameters "-d merge_dup -x no_cap -m 0 -a 0 -z 0 -sj sj_priority -lde 30 -sjt 30" so that (1) small variations of up to 1 nt at SJs, as well as transcription start and end sites, are preserved in the FLNC reads and (2) mapping details of 30 nt around each SJ were extracted.
Then TAMA merge (merged -m 0 -a 0 -z 0 -d merge_dup) was used to merge all the transcripts from the libraries and all the redundant FLNCs were removed, while the small variations up to 1 nt at SJs, as well as transcription start and end sites, were preserved in the merged FLNC reads. To accurately determine splice junctions, we examined the high-resolution alignment information around the SJs and found that high-confidence SJs are always supported by at least one alignment with a perfect match between the FLNCs and the genome reference around the SJ. SJs were also compared to those of AtRTD2 and their sequences assessed using position weight matrix-PWM  [78]. To derive a list of high-confidence SJs (Fig. 1C ) (and thereby identify falsely aligned SJs), our SJ-centric approach employed the following criteria: (1) the presence of canonical splice junction motifs and (2) no mapping mismatches including substitution, deletions, and insertions, with 10 nt around the SJs with support of at least one read.
Thus, to achieve accurate transcript isoforms from the PacBio data and generate AtIso (Fig. 1C ), we have adopted a strategy that seeks evidence to support all SJs, TSSs, and TESs. Finally, to increase the gene coverage using existing annotations and make the maximum use of the Iso-seq long reads, we retained genes that overlapped with Araport annotation on the same strand (> 50%). These were combined with the genes with TSS/TES support to generate the final set of genes and transcript in AtIso.
TSS and TES motif enrichment analysis To search for known TSS/TES-related motifs around significant RSGL and REGLs as well as the identified loci of interest in other datasets (potential TSS and TES sites), the following approach was taken. A number of motifs associated with TSS and TES sites were identified (Additional File 1: Table  S6 ). For each identified TSS and TES, the sequence within ±500 nucleotides on each side was extracted from the genome. A regular expression search was carried out in the extracted sequences searching for the known enriched motifs related to TSS and TES. All matching motifs and their positions relative to the site of interest were extracted. From this, the number of instances of the motif was calculated for every position ±500 nucleotides relative to the TSS/TES.
Funding This work was jointly supported by funding from the  Biotechnology and Biological Sciences Research Council (BBSRC)   BB/P009751/1  to JB;  BB/R014582/1  to RW and RZ;  BB/S020160/1  to RZ;  BB/S004610/1  ( 16 ERA-CAPS BARN ) to RW; the  Scottish Government Rural and Environment Science and Analytical Services division (RESAS)  [to RZ, RW, and JB]; the  National Science Foundation  ( MCB-2014408 ) and the  National Institute of Health (NIH)  ( GM-114297 ) to E. H. ; S. H. was supported by funding to K. D. from the  University of York ; the  Austrian Science Fund (FWF)   SFB F43  to AB and MJ and [P26333] to MK; The  French Agence Nationale de la Recherche  grant  ANR-16-CE12-0032  to MC; the  Japan Science and Technology Agency (JST) , the  Core Research for Evolutionary Science and Technology (CREST ; Grant Number  JPMJCR13B4 ) to M.
Availability of data and materials All the sequencing data in this study have been deposited in the Short Read Archive (SRA) (https://www. ncbi. nlm. nih. gov/sra) with BioProject ID: PRJNA755474  [79]. The AtRTD3 annotations are available in fasta, bed, and gtf format at https://ics. hutton. ac. uk/atRTD/RTD3/. The script that used to carry out the analysis in this manuscript can be found at https://github. com/ZhangTranscriptomislab/atRTD3 [80]. The source code is also published on Zenodo with DOI: https://doi. org/10. 5281/zenodo. 6616514 [81].
High-resolution whole-brain microscopy provides a means for post hoc determination of the location of implanted devices and labelled cell populations that are necessary to interpret in vivo experiments designed to understand brain function. Here we have developed two plugins (brainreg and brainregsegment) for the Python-based image viewer napari, to accurately map any object in a common coordinate space. We analysed the position of dye-labelled electrode tracks and two-photon imaged cell populations expressing fluorescent proteins. The precise location of probes and cells were physiologically interrogated and revealed accurate segmentation with near-cellular resolution. Systems neuroscience often relies on the use of implanted devices and viral injections for the stimulation and recording of anatomically-or genetically-defined neuronal populations.
To correctly interpret the resultant data, it is critical to map the location of the implanted devices or injections, and to pool results from multiple animals in a common anatomical coordinate system. Recent developments in microscopy and tissue clearing now allow for fully automated, high-resolution imaging of intact rodent brains 1. There exist many methods for registering these 3D whole-brain microscopy datasets to an atlas, but these are often inflexible, time-consuming and require considerable computational skills 2. Also, once registered there exist no open-source, user-friendly tools for the segmentation and analysis of any type of structure in these images. Here, we have therefore developed brainreg and brainreg-segment, two user-friendly tools that may be used to register and segment whole-brain microscopy datasets within minutes.
Results Brainreg and brainreg-segment were developed as plugins for the napari  3  multidimensional image viewer to leverage the existing scientific Python ecosystem for brain mapping, without requiring any computational knowledge. Using napari also allows other plugins to be used in combination with ours, such as those for reading of most microscopy image file formats. Brainreg allows drag-and-drop loading of raw data, and one-click registration to an array of brain atlases, provided by the BrainGlobe Atlas API  4  and the algorithms underlying it have already been validated  5. Brainreg loads the atlas (Fig. 1a ) and the raw data (Fig. 1b ), which are resampled and filtered prior to registration. The two images are aligned using affine, and then non-linear free-form deformation  6. This process is entirely automated and requires no user input (for more details, see Ref.
3 ) or miniscopes  14. Since they are built upon open-source Python data analysis and visualisation tools including the BrainGlobe Atlas API  4  they are compatible with multiple existing, as well as future brain atlases. Because the tools in the BrainGlobe suite are compatible with these atlases, the results of brainreg-segment can be directly compared with results from other software, for example cellfinder, to compare the distribution of individually labelled cells to a bulk injection site in physiological conditions  15  or after experimentally-induced trauma  16. Also, these tools were developed as plugins for napari to provide interoperability with other analysis software, and importantly to streamline installation and use. The bundled napari application allows use without installing Python and the plugins can be installed directly from the graphical user interface, making these tools accessible to the wider neuroscience community.