dataesr/scientific-paragraphs-categorization huggingface dataset:
The heavy reliance on visualization allowed us to rapidly detect key differences between datasets and identify ways to adapt the code for different datasets. Additionally, visualization provided built in quality control mechanisms. The ability to adapt analysis to different imaging conditions and consequently generate reliable annotated datasets can be useful in developing deep learning approaches. Further, improvements in camera technology both in terms of the speed of acquisition and the size of the chip has greatly The actual L(r) is shown in red, the median of the simulations is shown in orange and the 5% and 95% confidence intervals (CI) are shown as orange dashed lines. Actual L(r) above the 95% CI is considered clustered, below the 5% CI is considered dispersed and between the 5%-95% Cis is taken to be uniform, or random.
D. Collective data of VAMP2-sepHluorin exocytosis in developing cortical (n = 23). The normalized difference SEM between the actual L(r) and the median is shown in blue and the normalized difference SEM between the 95% CI and the median is shown in black. enhanced resolution, but comes at the cost of the ever increasing size of image files. To accommodate larger file sizes, we use a hybrid memory/disk based and "just-in-time" computing approach that easily handles datasets too large to be stored in memory alone. With this data handling architecture, our script does not preclude analysis of large files well over the 4. 29GB cutoff for so-called "big-tiff" files. Moreover, where data are stored, on disk or in memory, is actively managed to increase efficiency and calculations are heavily threaded to greatly improve overall processing sped.
One limitation to our reliance on ImageTank is that the graphics, user interface, and threading rely heavily on the Mac operating system. Therefore pHusion is unavailable for other operating systems such as Windows or Linux. In addition to altering the parameters of our processing script to accommodate diverse datasets, adjusting the script for dis-tinct imaging conditions is sometimes helpful and/or necessary as well. For example, though oligodendrocytes are sufficiently flat to enable epifluorescence imaging, the background was higher than in TIRF. To address this, we photobleached VAMP3-SepHluorin within the PM prior to imaging exocytosis; we found this increase in signal to noise to be beneficial in imaging mature hippocampal neurons as well. While useful, care must be taken to ensure cells are not damaged by phototoxicity as sensitivity to light varies greatly across cell types.
ACKNOWLEDGEMENTS We acknowledge funding from  NIH   R01NS112326  and  3R01NS112326-03S1  (S. L. G. ), S. E. R. was supported by the  Cellular Systems and Integrative Physiology Training Program   T32GM133364. K. L. was supported by  HHMI   Gilliam Fellowship   GT15781. C. L. would like to acknowledge funding by the  Agence Nationale pour la Recherche  ( ANR-20-CE13-0024  to C. L. ), and to thank the  Neuro-Cellular Imaging Service  and  Nikon Center for Neuro-NanoImaging at INP , funded by  CPER-FEDER  ( PlateForme NeuroTimone   PA0014842 ). J. B. Z. would like to acknowledge funding from the  McKnight Endowment Fund for Neuroscience  (J. B. Z), the  Stanford Bio-X   Interdisciplinary Initiatives Seed Grants Program  (IIP) [ R9-24 ] (J. B. Z ), the  National Multiple Sclerosis Society Harry Weaver Neuroscience Scholar Award  (J.
The phage kinetics were performed in flat bottom transparent 96 bacterial culture well-plates with lids (Thermo Scientific Optical-Bottom Plates, #265301) on a Synergy H1 multi-mode microplate reader (Agilent). Phage spotting was performed on square Petri dishes (Greiner Bio-One #688102). Microscopy was done with an Olympus IX81 inverted epifluorescence microscope mounted with a thermoplate (Tokai Hit). An imaging spacer (Grace Bio-Labs #654006) and microscopic slides (Fisher, #12-550-A3), and cover slides (Fisher #12542C) were used for bacterial lysis microscopic experiments. Purchased T7 genome (39. 9 kbp, GenBank V01146. 1, Fig. S4 ) was verified by NGS (Table  S1 ) observing two mutations compared to GenBank V01146. 1: (i) an insertion of an A base after position 1896 in the gene 0. 7; (ii) A to G mutation at position 22629 (N227S) in gene 9.
Acknowledgments The authors thank  David Garenne  and  Seth Thompson  for their help in the preparation of the TXTL system used in this work. This work and the materials are based on funding provided by the  National Science Foundation  ( CBET FMRG 2228971 ). A. L. and I. K. gratefully acknowledges financial support for this publication by the  Fulbright U. S. Student Program , which is sponsored by the  U. S. Department of State , the  Romanian-U. S. Fulbright Commission , and the  Franco-American Fulbright Commission. We thank the  University of Minnesota Genomic Center  for NGS and  Sorin Dinu  for his kind help with NGS data analysis. A. L and A. B. L thanks  Ecole Doctorale Frontières de l 'Innovation en Recherche et Education -Programme Bettencourt  and the  Bettencourt Schueller Foundation  for their generous support.
Introduction Of all genetic entities in the biosphere, extrachromosomal elements of archaea may be the least well understood. Existing databases of archaeal viruses, plasmids and mini-chromosomes are limited (NCBI Virus, 556 viruses, Jun-02-2023 and NCBI Plasmids, 481 plasmids; www. ncbi. nlm. nih. gov/genome/browse#!/overview/) and generally taxonomically restricted to better known archaeal groups, such as the orders Sulfolobales and Halobacteriales. For many major groups of archaea not even a single ECE has been described and those that affiliate with uncultivated archaea are only inferred from metagenomic sequence information. We recently described a seemingly new type of archaeal ECE that is not classifiable as virus or plasmid  1. They are unusually large, up to 1. 1 Mbp in length, and have linear genomes terminated by kilobase-scale long inverted terminal repeats (ITR).
The genes are encoded on two replichores of very uneven length, with essentially all genes on each replichore carried on a single strand. These ECEs are predicted to replicate in Methanoperedens archaea based on sequence similarity, phylogeny, shared abundance-based co-occurrence patterns and CRISPR targeting  1. Because of their propensity to assimilate large numbers of genes from organisms, especially but not limited to their host Methanoperedens, they were named "Borgs" 1. To date, their sequences have only been recovered from environmental metagenomic datasets from terrestrial subsurface ecosystems and interestingly often encode components of key metabolic processes such as anaerobic methane oxidation and nitrogen fixation  1. When new and unusual ECEs are recovered from metagenomic datasets, they may be questioned because methods for curation of short read assemblies into complete genomes are poorly understood and rarely undertaken.
One of these matches the DarG toxin (subfam2258, PDB match 5M3E) of the toxin-antitoxin system DarTG. DarG expression affects DNA replication by catalyzing the reversible ADP-ribosylation of thymidines in ssDNA  8. We speculate that this protein could be involved in regulating Borg or host replication. The other four have credible matches to viral capsid proteins (see below). Some core proteins are predicted to be involved in replicating Borg genomes. The 18 (including Blue) Borg genomes have a marker protein encoded relatively close to the start of the large replichore that has functional domains that match those of the Herpesvirus OBP (PF02399, Herpes_ori_bp). OBP binds to the origin of replication and initiates the formation of a prereplication complex  9. Emerald is the first reported Borg to encode a typical replication-initiation protein, cdc6, situated just five genes downstream from the OBP.
5 to 8 x; 1 ). Given abundance ratio variation and some very large predicted Borg copies per most abundant potential host genome, as well as tentative indications of viral proteins and genes involved in encapsulation, we hypothesized that Borgs and Methanoperedens have distinct methylation patterns to distinguish between self and non-self DNA. Thus, we searched for DNA methylation motifs in the nanopore datasets by comparing the mean current of native DNA vs. MDA amplified DNA using Nanodisco. This brought to light nine methylation motifs in the Methanoperedens genome, six of which were substantiated by read-level methylation calling with Megalodon using its models for 6mA and 5mC detection (Figure  2a , Extended Data Table  3 ). Performing the same analysis on the genomes of Orange, Ochre, Black, and Purple Borg revealed that they have a modification motif composed of YC, where Y stands for either pyrimidine T or C.
PEGA-domain proteins are additional surface proteins that are exclusively found in Borgs, with four to 21 genes largely colocalized in an individual Borg genome (Supplementary Table  2 , Supplementary Table  12 ). For an explanation of the abbreviations and locus tags please refer to Supplementary Table  12. We then performed full-length cDNA and gDNA nanopore sequencing on newly collected wetland soil samples (two samples from 50 cm, one sample from 90 cm, 100 cm, 115 cm) in search of transcripts indicating that Borgs and Methanoperedens were active at the time of sample collection. Analysis of the metagenomic dataset revealed that these samples consistently contained Methanoperedens bMp and Black Borg but no other Borgs. Mapping the cDNA reads revealed that only 8-16% of bMp genes and ≤1% of cMp genes had transcripts detected (Supplementary Table  13 ).
Conserved Borg proteins enable the discovery of new Borgs The existence of marker proteins distributed over most of the Borg genomes opened a way for improved identification of new Borgs. Previously we used ribosomal protein L11A (rpL11), a near marker Borg protein, to locate and distinguish Borgs. A SingleM search identified one publicly available dataset from 1. 5 m depth in a peatland ecosystem warming experiment in Northern Minnesota, USA  35  with reads encoding parts of rpL11 from three novel Borgs. Thus, we searched a new assembly of this metagenome with the 40 marker protein sequences and identified 37 to 40 homologs for three Borgs named Maroon (~65x coverage), Mauve (~65x coverage) and Liserian (~17x coverage). The Maroon contigs could be distinguished from those of Mauve as the marker proteins are similar to those of Sky and Rose Borgs whereas Mauve and Liserian proteins are related to those of other Borgs, including Amethyst and Ruby Borgs.
Tandem repeat identification, replication prediction and genome topology verification Tandem repeats were identified using a custom Python script (https://github. com/rohansachdeva/tandem_repeats) published previously  5. Nucleotide TRs were searched using a stringent threshold of ≥50 nt regions and ≥3 TR units and allowing no mismatch (-min_region_len 50-min_repeat_count 3). aaTRs were identified in the concatenated proteins file using a threshold of ≥16 amino acids and ≥3 TRs (-l 3-min_repeat_count 3-min_uniq_nt 1-min_region_len 16). GC skew (G-C/G+C) and cumulative GC skew were calculated to identify the replication origin and termini, and verify genome topology using the iRep package (gc_skew. py)  43.
Structural modeling and structure-based homology search Structural modeling of all Orange Borg proteins was performed using AlphaFold2 7 via a LocalColabFold (-use_ptm-use_turbo-num_-relax Top5-max_recycle 3)  44. The AF2 rank1 models were profiled against the Protein Data Bank (http://www. rcsb. org/pdb/) (v5e639, date: 2023-04-20) using foldseek  45  (v53465) and the easy-search module, or manually queried in PDBeFold  46. Protein structures were visualized and superimposed in PyMOL  47 (v2. 3. 4).
Protein family clustering and functional annotation of protein subfamilies A dataset of 20,280 Borg proteins was constructed using all 8,322 protein-encoding ORFs of the 7 new Borg genomes, and the 11,958 Borg proteins from the previously published ten complete Borg genomes  5. All proteins were clustered into protein subfamilies using MMseqs2  48 (v7e284)  in an all-vs. -all search (e-value: 0. 001, sensitivity: 7. 5, and cover: 0. 5). A sequence similarity network was built based on pairwise similarities, and protein subfamilies were defined using the greedy set-cover algorithm. HHblits 49 (v3. 0. 3) was used to construct Hidden-Markov models (HMMs) for these protein subfamilies based on the results2msa of MMseqs2. These were then profiled against the PFAM database using HHsearch 50 (v3.
Functional annotation of individual proteins Proteins were profiled using InterProScan 58 (v5. 51-85. 0) and HMMER (hmmer. org) (v3. 3, hmmsearch) against the PFAM (--cut_nc) and KOFAM (--cut_nc) HMM databases  59, 60. TMHs were predicted with TMHMM 61 (v2. 0) and cellular localization using PSORT  62  (v2. 0, archaeal mode). tRNAs were searched with tRNAscan 57 (v. 2. 0. 9) and rRNAs with SSU-ALIGN  63 (v0. 1. 1). Protein subfamilies were functionally annotated using HMMER (hmmer. org) (v3. 3, hmmsearch) and the PFAM (--cut_nc) HMM database  51. Homology search was performed with blastp  52. Metabolic reconstruction was based on the Distilled and Refined Annotation of Metabolism (DRAM) framework  53  (v1. 2. 2) and on custom annotations.
Correlation analysis In order to measure correlations between abundance patterns of Borgs and potential Methanoperedens hosts, a dataset was compiled consisting of genomic sequences from both Borg and Methanoperedens genomes in addition to phylogenetically distinct unbinned contigs containing rpL11 genes profiled as Methanoperedens as per Al-Shayeb et al. 2022. These sequences were aligned to 83 metagenomic samples obtained from the wetland soil site. Alignments were performed using bbmap. sh  36  using the following parameters: ambiguous=random minid=0. 96 idfilter=0. 97. For samples sequenced with 150bp reads, the editfilter=5 parameter was used; for those samples sequenced with 250bp reads, editfilter=8 was used. Reads aligning to each genome or rpL11-bearing contig were counted and used to calculate average coverage for each.
Detection of Borg and Methanoperedens DNA modification patterns Methylation motifs and type predictions were found using Nanodisco, which identifies loci with high contrast between the ionic current levels of the native and multiple displacement amplification datasets. Although the Borg motifs were too short to be identified directly, Nanodisco identified several that contained CC, TC, or GAA. The true motifs were narrowed down to YC and GAA by manual inspection of Nanodisco refinement plots, which indicated that additional bases were extraneous. Independently, methylation was called on individual reads with Megalodon, using Rerio models for all context 6mA and 5mC modifications. However, the YC motif was not found with this method, supporting Nanodisco's predicted modification type of 4mC.
Taxonomic classification 16S rRNA gene sequences were used for taxonomic classification of archaeal genomes (Methanoperedens, Methanomicrobiales). The 16S rRNA genes were identified using a custom HMM  56  (16SfromHMM. py, available at GitHub https://github. com/christophertbrown/bioscripts) and taxonomically classified using the SILVA database  57. Taxonomic classification was also routinely performed for all genes as part of an integrated pipeline in ggkbase. The gene annotations are compared to the uniprot and uniref databases and taxonomically classified by the best match in these databases.
Marker-based identification of a Borg-containing peat metagenome SingleM (Woodcroft et. al. unpublished, https://github. com/wwood/singlem) was used to identify rpL11 sequences from an in-house database of public metagenomes (published June 2019 or earlier) that was scanned using the SingleM "query" subcommand using --max-divergence 3, using rpL11 OTU sequences derived from Borg reference genomes. This identified the run SRR7028199 as potentially containing Borg genomes. Draft Borg genome bins were identified based on shared low GC, coverage and dominance by novel proteins without phylogenetic affiliations. The bin for the Borg genome that was curated to completion was refined by alignment to the Sky Borg genome and for contigs carrying the Borg marker genes.
Phylogenetic tree construction Individual phylogenetic trees were constructed using the protein sequences for the 40 marker proteins to confirm concordant topologies, consistent with vertical inheritance. Blastp searches used the 40 marker proteins to identify homologs in additional Borgs (for which complete and near complete genomes were not available) in the wetland soil and SPRUCE peat data. The singlemarker datasets were aligned with MAFFT 58 (v7. 481) using the option L-INS-I, and alignments were trimmed using BMGE-1. 12  59  with the BLOSUM30 substitution matrix. Trimmed alignments were then concatenated into a character supermatrix totalling 9,185 amino acid positions and 28 Borgs. A maximum likelihood tree was then built from this supermatrix with IQ-TREE 60 (v1. 6. 12) using the mixture model LG+C60+F+R4 with ultrafast bootstrap support 61 calculated from 1,000 replicates.
Bioinformatic analysis of Borgs OmcZ homologs Protein phylogeny tree of nanowire-forming OmcZ was constructed by maximum likelihood method with MEGA X 62 and presented by iTOL  63. Nanowire-forming OmcZ sequences were extracted from full length OmcZ homologs by removing signal peptide and self-inhibitory part after the subtilisin cleavage site. Signal peptide cleavage sites were predicted by SignalP 5. 0  64. The ozpA cleavage sites were chosen at the corresponding positions to the OzpA cleavage site in G. sulfurreducens OmcZ (after FGNSS) in the multisequence alignment. OmcZ homologs without signal peptides were used for AlphaFold 7 modeling. The conservation of OmcZ homologs, which were aligned by Clustal Omega  65  , was mapped to the structure of nanowire-forming OmcZ (PDB: 7lq5) by Consurf 66 and presented by PyMOL  47.
Acknowledgements This publication is based on research in part funded by the  Bill & Melinda Gates Foundation  (Grant Number:  INV-037174  to JFB). The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the  Bill & Melinda Gates Foundation. Funding was also provided by the  Innovative Genomics Institute at UC Berkeley (IGI Climate  fund, philanthropic donation, to JFB), a  DFG fellowship  (Project Number:  447383558  to MCS), the  NSF-ANR  award no. 2210473  (to NSM) as well as the  Climate Impact Innovation Fund  and the  Natural Carbon Solutions Fund  (to NSM). SG received support from the  Miller Foundation  for a visiting professorship at  UC Berkeley. BJW is supported by an  Australian Research Council   Future Fellowship  (# FT210100521 ).
CWS is supported by the  US Department of Energy (DOE) ,  Office of Biological and Environmental Research, Terrestrial Ecosystem Science Program , under contract  DE-AC05-00OR22725  to  Oak Ridge National Laboratory/UT-Battelle. We thank  Luis Valentin Alvarado  and  Alex Crits-Christoph  for their contribution to field work and generation of sequence datasets, and  Shufei Lei  and  Jordan Hoff  for bioinformatics support.
Data availability Newly released Borg and Methanoperedens genomes used in this manuscript are available via: https://ggkbase. berkeley. edu/borgs_mp_nanopore/organisms. The publicly available datasets used in this study are available on NCBI under PRJNA866293. The SPRUCE dataset is accessible under GOLD Project ID# Gp0213362 (https://gold. jgi. doe. gov/search). Protein sequences, structural models and the phylogenetic tree of the 28 Borgs from this study are available through Zenodo (10. 5281/zenodo. 8162866). Detailed annotations and larger datasets are available in Supplementary Tables 1-15.
Sampling and nucleic acid extraction We recovered deep soil samples from 75 -120 cm below the soil surface from a wetland in Lake County, California, USA, in September 2021 to construct new metagenomic datasets. DNA was extracted from 5-10 g of soil per sample with the DNeasy PowerMax Soil Kit (Qiagen) and used for short read and long read sequencing. The same site was sampled again at four different depths (twice at 50 cm, once at 90cm, 100cm and 115cm) in November 2022 to construct metatranscriptomic and matching metagenomic datasets. RNA and DNA was co-extracted using a combination of the RNeasy PowerSoil Total RNA Kit and the RNeasy PowerSoil DNA Elution Kit (Qiagen), and then further processed for long read nanopore sequencing.
5 was added to all windows prior to scaling and input normalisation to prevent windows with zero reads in the input sample generating a normalised count of infinity. Regions where coverage was 0 in all samples were removed from the analysis. We also excluded windows overlapping poorly mapped regions of the human genome (https://github. com/Boyle-Lab/Blacklist)  (Amemiya et al. , 2019)  using BEDtools intersect. Gaps and centromeres identified using annotations from the UCSC browser (hg38 gap and centromere tracks) were similarly removed. First, annotations were downloaded from the UCSC table browser and regions annotated as heterochromatin, short arm and telomeres from the gaps track were merged with the centromeres track using BEDtools merge withd set to 10 Mb. Windows overlapping this merged file were then excluded from the analysis using BEDtools intersect.
Definition and comparison of heterochromatic domains H3K9me3 and H3K27me3 domains were called using a hidden Markov model as previously described  (Spracklin and Pradhan, 2020). Normalised mean H3K9me3 and H3K27me3 ChIP-seq coverage in 25 kb genomic windows was analysed using the bigwig_hmm. py script (https://github. com/gspracklin/hmm_bigwigs) to define two states (-n 2). We excluded poorly mapped regions of the genome from these domains using annotations of gaps and centromeres from the UCSC browser (hg38 gap and centromere tracks). Annotations were downloaded from the UCSC table browser. Regions annotated as heterochromatin, short arm and telomeres from the gaps track were merged with the centromeres track using BEDtools merge with -d set to 10 Mb. This merged file was then excluded from the domains BED files using BEDtools subtract.
H3K27me3 only domains were then defined as those which did not overlap a H3K9me3 domain using BEDtools intersect. Comparisons between heterochromatic domains were conducted using BEDtools jaccard and fishers modules. PMDs were defined in HCT116 cells using methpipe (v5. 0. 0)  (Decato et al. , 2020). WGBS BAM files generated by Bismark before being processed and de-duplicated using methpipe. PMDs were then called using the pmd module from methpipe and the recommended settings (bin size was determined as 8 kb by methpipe using the setting -i 1000). We excluded poorly mapped regions of the genome from PMDs as for the H3K9me3 and H3K27me3 domains. We also excluded PMDs shorter than 200 kb. We downloaded a BED file of regions resistant to nuclease digestion in HCT116 cells from the 2-state definitions in Gene Expression Omnibus accession GSE135580  (Spracklin and Pradhan, 2020).
In silico analysis of PWWP domain and DNA binding residues Building on previous studies reporting DNMT3B binding to DNA  (Zhou et al. , 2018) , the structures of DNMT3B's PWWP domains (mouse Protein Data Bank code 1KHC,  (Zhou et al. , 2018) , human 5CIU  (Rondelet et al. , 2016) ) were compared to homologous PWWP structures found to bind to DNA either in isolation (Protein Data Bank code 6IIT,  (Tian et al. , 2019) , 7LH9  (Zhang et al. , 2021) ) or in a modified nucleosome core particle (LEDGF PDB 6S01,  (Wang et al. , 2020) ). Rigid body modelling revealed conserved lysine and arginine residues as potential DNA binding residues. We identified two separable patches distal to the main H3 binding pocket, patch 1 comprising Lys-251 Arg-252 and patch 2 comprising Lys-276 Lys-232. We also observed a further two positively charged residues, Lys-234 and Lys-268.