dataesr/scientific-paragraphs-categorization huggingface dataset:
OMA is also available as a web browser  [81]  including a database of orthologues that, in 2015, included more than 2000 genomes and more than seven million proteins  [75]. SILIX is a software package  [82]  that aims at building families of homologous sequences by using a transitive linkage algorithm, and HOGENOM  [83]  is a database that contains families inferred by SILIX for seven million proteins. In addition to clustering genes into families, valuable information can be extracted from the connected components using network metrics. Highly conserved sequences tend to form CCs where most of the nodes are connected to each other by edges, while sequences from more divergent families will tend to form more sparsely interconnected CCs. This information can be easily assessed for each component using the clustering coefficient.
Exploiting SSNs for Ecological Studies Ecological studies increasingly involve the assembly, analysis, and comparison of large metagenome datasets. In addition to identification of functions and organisms associated with a particular environment, these studies enable the investigation of important hypotheses in microbial ecology at the level of organism or function, such as the often quoted hypothesis that "everything is everywhere, but the environment selects" from Bass Becking: the idea that microbial lineages are limitlessly dispersible in the environment, but the environmental conditions will select for certain lineages and control their distribution rather than any specific geographical separation  [21]. Networks are useful for these kinds of ecological studies because existing graph algorithms can be used to investigate the structure of the network.
SSNs in Remote Homologue Identification: Shedding Light on the Microbial Dark Matter Up to 99% of microbial species are not cultivable and thus have not been studied in isolated culture. Analysis of high-throughput sequencing and metagenomics datasets has shed light on these uncultivable organisms, often referred to as the "microbial dark matter"  [109] , and in some cases enabled the reconstruction of draft genomes  [110] [111] [112] [113] [114]. A considerable portion of most metagenome studies have predicted ORFs showing no detectable similarity to any known proteins, termed metaORFans  [115]. These can represent 25-85% of the total ORFs identified in metagenomes  [22]. Identifying distant homologues of ORFans may help to predict their functions and begin to unravel the microbial dark matter.
Recent work by Lopez et al. in 2015 probed the microbial diversity of metagenome datasets from a range of environments including the human gut microbiome, identifying homologues of genes from 86 ancient gene families that are distributed across archaea and bacteria. The majority of these gene families included environmental homologues that were highly divergent from any of their cultured homologues, and many branched deeply with the phylogenetic tree of life, highlighting our limited understanding of diverse elements of the microbial world and hinting at the existence of yet unknown major divisions of life  [24]  (Fig. 7 ).
Three criteria can be used to estimate intra-class genetic heterogeneity (Fig. 8a-c ). First, the average edge weights (measured as % of identity, PID) between pairs of sequences from genomes of the 60% Max % identity to homologues in databases ≥ 60% Max % identity to homologues in databases < 60% Fig. 7  Remote homologue detection to help characterize the microbial dark matter. (a) A hypothetical highly conserved cluster of genes from genomes present in sequence databases, where the average % of identity is high (!60%). (b) The same cluster after addition of divergent environmental sequences to the network. Environmental sequences in gray are more similar to those already identified from genome surveys (!60% max identity) so are connected directly to the conserved gene cluster in the network.
More divergent sequences in pink have <60% maximum identity to their homologues in the database. Many of these are only identified as linked to the sequences from the conserved database via intermediate gray nodes. This is the notion of "transitive homology" same class provide a trivial measure of intra-class genetic diversity. Second, the average proportion of Conserved Canonical Connections between sequences from the same connected component and from the same taxonomic class can be exploited (CCC, i. e. , in each connected component of the SSN, the total number of edges connecting sequences of a given class i (intra-group edges, denoted E ii ) divided by the theoretical maximal number of possible edges between sequences of that class in the connected component (CCC (i) ¼ 2*E ii /(N i Â (N i À 1)) where N i is the number of sequences of class i present in the connected component).
CCC ranges between 0 and 1. Within a connected component, if all pairs of sequences from the same class are directly connected, CCC equals 1, since all these sequences are more conserved than a given %ID threshold. By contrast, low CCC are observed when sequences from genomes from the same class lack cohesive evolution, for example, when some related sequences evolved so fast that they show less than the minimal similarity required to be directly connected to their homologues in the graph. Third, the genetic consistency of a class can be estimated by  (1)  identifying what cluster of sequences was present in the largest number of genomes of the class and then (2) by quantifying the proportion (in %) of the class members harboring that most ubiquitous cluster (maxCore%). When max-Core% of a class is <100%, it means that, for this dataset, there is no gene family shared by all members of that class (i.
Gene-sharing networks are useful tools for exploring overall patterns of gene sharing between genomes. Recently, Lord et al. developed BRIDES, a software package that specifically identifies different kinds of patterns in evolving gene-sharing networks after the addition of new genome nodes  [144]. However, in genesharing networks the kind of gene families that are being shared is often overlooked. To explore how functions are shared between different genomes, gene-sharing networks can be built from genes using different subsets of functions (Fig. 10 )  [29]. An alternative form of the gene-sharing network is the multiplex network. In this network nodes can be linked by edges of different types, for example, each edge representing a different gene family or different functional groups of gene families, thus retaining additional information compared to a simpler gene-sharing network (Fig.
Exploring Routes of Gene Sharing in Gene-Sharing Networks Two network metrics, also useful in the analysis of gene networks, can be used to attempt to identify "hubs" of gene sharing in the context of gene-sharing networks: node "degree" and "betweenness. " Both metrics aim to determine the centrality of a node in a network. The degree of a node is simply the number of edges that it is connected to. The betweenness of a node is the frequency at Fig. 10  Functional gene-sharing network reflecting the chimeric nature of eukaryotes. These gene-sharing networks describing how genes in different functional categories are shared between bacteria (green), archaea (yellow), eukaryotes (gray), plasmids (purple), and viruses (red) from a published dataset  [29]. In both cases, a giant connected component is shown alongside examples of smaller connected components (a) Gene-sharing network for COG category D: cell division control.
Bipartite Graphs Bipartite graphs are excellent at summarizing what genes are shared between sets of genomes, and as such are ideal for comparative genomics, including for the comparison of genomes reconstructed in metagenomic analyses. The potential to extend this approach to multilevel graphs, adding additional layers of information such as the environment in ecological studies, could provide a powerful summary of gene sharing in relatively complex datasets. A multilevel network is a network in which edges exclusively connect nodes of different types, i. e. , representing different levels of biological organization. Thus, a bipartite graph is a graph with two types of nodes (top and bottom nodes), where edges exclusively connect nodes of different types (Fig. 11 )  [146]. The types of nodes used can vary widely depending on the biological question, from linking diseases (top nodes) to their associated genes (bottom nodes) in order to explore the association between related disease phenotypes and their genetic causes  [147, 148] , to exploring the concept of flavor pairings in food based on a graph of ingredients (top nodes) and the flavor compounds they contain (bottom nodes)  [149].
Two recently developed tools, AcCNET  [150]  and MultiTwin (forthcoming), have simplified the process of constructing and analyzing multilevel graphs without the need for custom programming (Boxes 3 and 4). Box 3: Generating Gene-Sharing Networks and Bipartite Graphs 1. Dataset assembly: The same rules for dataset assembly as described in SSN generation apply to assembling the dataset for bipartite and gene-sharing graphs. It is especially important to maintain an annotation file that maps gene IDs to their genome of origin. 2. Definition of gene families: Gene family identification can be carried out following the construction of sequence similarity networks, as described in Subheading 2. There are a broad range of alternative approaches for construction of gene families that are beyond the scope of discussion in this chapter; however, all of these can also be applied to the generation of gene-sharing and bipartite graphs.
Network construction: From the definition of gene families, it is possible to construct both gene-sharing networks and bipartite graphs. (a) In a gene-sharing network, two genomes are connected by an edge when they encode genes belonging to the same gene family. Generating this kind of network can be automated from BLAST or fasta sequence data using EGN  [52]. (b) In a bipartite graph, there are two types of node, genome nodes and gene family nodes. An edge is drawn between a genome node and a gene family node if that genome encodes a member of the gene family. AcCNET  [150]  and MultiTwin (forthcoming) tools both include pipelines for generating bipartite graphs from sequence data. MultiTwin can also generate a bipartite graph from two files: a tab-delimited file mapping gene identifiers to their corresponding genome identifier and a tab-delimited file mapping gene identifiers to their corresponding gene family.
For example, in any given dataset, any "core" set of gene families encoded by all species in the analysis will be represented by a single twin node. The gene families combined in twin supernodes can be viewed as gene families that are likely to be transmitted together  [23]. An articulation point is a node that, when removed, will split the graph into two or more connected components. Within a gene family-genome bipartite graph, articulation points are expected to help to identify "public genetic goods," gene families that are shared by distantly related entities that may confer an advantage independent of genealogy  [23, 154] , as well as selfish genetic elements such as transposases that also spread across multiple genomes.
Conclusions This chapter has offered a brief introduction to the generation of commonly used sequence similarity networks in molecular biology and a guide to how they can be generated and applied to a broad range of studies (Fig. 12 ). Networks provide a highly scalable framework for the study of an increasingly broad range of applications in molecular biology and evolution and have already contributed to a number of important discoveries in the field. These include exploring patterns of introgression and horizontal transfer across all domains of life and mobile elements, the origin of eukaryotes, the contribution of new genes including novel fusion genes to major evolutionary transitions, shedding light on the "microbial dark matter" in metagenome sequencing datasets and in testing ecological hypotheses about organism and gene distribution and environmental selection.
Spooner et al. 12  provided the last conspectus of the section and recognized 107 wild species and four cultivated species partitioned into three main nuclear clades but not recognized as series, basing their decisions on a variety of morphological and molecular datasets including DNA markers (e. g. , AFLPs, microsatellites), DNA sequence data of single and multiple nuclear orthologs that documented many polyploids to be allopolyploids among the three clades, and plastid DNA restriction site data  [13] [14] [15] [16]. The present study reports the first whole-genome plastid DNA sequence phylogeny of section Petota, using 202 diploid accessions from all major clades of the section except a small clade of two diploid species that was discovered after this study was initiated, namely the "Neocardenasii clade" containing S.
Materials and Methods Sample preparation and sequencing. Total DNA of 201 wild and cultivated potatoes and outgroups (Supplemental Table  5 ) was sequenced using the same short reads of Illumina HiSeq. 4000 (2 × 150 bp) as in Li et al. 19  and the reads were pre-processed as described in that paper. The nuclear data of Li et al. 19  had the same accessions except for S. lycopersicum (https://www. ncbi. nlm. nih. gov/; DQ347959. 1) that we added here, bringing our total database to 202 accessions. Briefly, a total of 1. 5 ug DNA of each accession of 202 wild and cultivated potatoes and outgroups (Supplemental Table  5 ) were used to make DNA sequencing libraries, and each library was labeled by index. During the sequencing process, one HiSeq run includes 8 lanes and each lane can produce 100 G data, and therefore a few samples were mixed for sequencing in each lane and were separated by index barcodes.
These libraries were sequenced to an average of >12 X genome coverage depth, which produced 1. 5 T data in total. Reference assisted de novo assembly. High quality reads were mapped to the plastid reference sequence (JF772170, Solanum tuberosum isolate DM1-3-516-R44 as downloaded from NCBI (https://www. ncbi. nlm. nih. gov/) with BWA-MEM version 0. 7. 15  29  ). The resulting SAM file was converted to BAM format (view), sorted (sort), and filtered for both reads in pair mapping (view -F 8) using SAMtools version 0. 1. 19  30. Reads that mapped to the plastid were pulled with Picard SamToFastq version 1. 127 (https://broadinstitute. github. io/picard/), and de novo assembled with ABySS (k = 64)  31. Contigs with lengths greater than 1000 bp were placed in order and oriented (as compared to the reference) using the MUMmer package, NUCmer version 3.
1  32. From this information, the contigs were manually concatenated and gaps between large contigs were manually filled in by using overlapping sequences of the smaller contigs (<1000 bp) of the highest coverage. Plastid correction for assembly errors. We mapped the reads to the de novo assembled plastid (one inverted repeat only due to reads being filtered out if they map to more than one location) using BWA-MEM version 0. 7. 15  29. We converted the SAM file to BAM, sorted, and filtered with SAMtools version 0. 1. 19  30. PCR duplicates were marked with Picard MarkDuplicates version 1. 127 (https://broadinstitute. github. io/picard/). We identified SNPs and small insertions and deletions (indels) with GATK version 3. 7-0 33 using the following pipeline: RealignerTargetCreator (standard protocol), IndelRealigner (standard protocol), and UnifiedGenotyper (-ploidy 2 -glm BOTH -baq CALCULATE_AS_NECESSARY -dt NONE)  34, 35.
In this particular case, we used UnifiedGenotyper instead of HaplotypeCaller because of the plastid's high coverage; HaplotypeCaller downsamples reads, leading to a problem where SNPs that are from similar sequence regions in the mitochondria are oversampled and can lead to errors in SNP calls. We corrected variants in the sequence using a custom script. Soft clips were detected with bb. softclip (github. com/dsenalik/bb/blob/master/bb. softclip), manually reviewed, and corrected by identifying sequence of the reads in this region and filling in the gap with the correct contig (from the de novo assembly step). We repeated the process to check for errors until no more variants or soft clips occurred. The final coverage of the plastid regions was determined using GATK DepthofCoverage (standard protocol)  33.
Phylogenetic analyses. We analyzed the plastid sequence data of all 202 diploid accessions with two phylogenetic analyses, rooting all trees with Solanum etuberosum and S. palustre, and deleting inverted repeat (IR) B from analyses as it provided redundant information to IR A: (1) MP using the program PAUP* version 4. 0a147  37  , with all characters treated as unordered and weighted equally  40  , and (2) ML using the program RAxML version 8. 0. 0 41 , using the GTR + G model and estimating individual alpha-shape parameters, GTR rates, and empirical base frequencies for each individual gene, and running 1000 nonparametric bootstrap inferences. We found the most parsimonious trees using a heuristic search 42 by generating 100,000 random-addition sequence replicates and one tree held for each replicate.
Branch swapping used tree-bisection-reconnection (TBR) retaining all most parsimonious trees. Then we ran a final heuristic search of the shortest trees from this analysis using TBR and MULPARS. We estimated bootstrap values  43  using 1000 replicates setting maxtrees at 1000 and using TBR and MULPARS, and viewed the resulting trees in FigTree version 1. 4. 0 (http://tree. bio. ed. ac. uk/software/ figtree/). In order to compare old and new taxonomies, the taxon labels include, when appropriate, the older names accepted by Hawkes  28  and in parentheses the new names  12. Using this system, we examined 53 species using the prior taxonomy of Hawkes  28  and others and 36 species using new taxonomy of Spooner and collaborators  12.
Acknowledgements We thank  Jesse Schartner  and  Timothy Kazmierczak  at the  USDA ARS  for preparing and sending seeds from the  U. S. Germplasm System  and  David Gagneul ,  Eva Colleoni ,  Malika Chabi ,  Camille Carpentier  and  Marion Evronsart  at  UGSF-CNRS  for helping to harvest and freeze-dry the leaves, and two anonymous reviewers, for review. This research was supported in part by  NSF   Planetary Biodiversity Inventory Program  grant  DEB-0316614  (DS) entitled  PBI Solanum: A Worldwide Treatment. No conflicts of interest are declared.
NOEMA We observed the high-mass star-forming region IRAS 23033+5951 during five epochs between June 2014 and March 2016, using NOEMA at Plateau de Bure (France) in the A, B, and D array-configurations, with a phase center at RA 23 h 05 m 25 s. 00 and Dec. +60 • 08 15. 49 (J2000. 0). These observations are part of the NOEMA large program CORE  (Beuther et al. 2018). The projected baselines range from 20 m up to 750 m and an example uv-coverage is presented in the survey overview  (Beuther et al. 2018). The receivers were tuned to the 1. 3 mm (220 GHz) band. The key molecular transition lines in this spectral window are summarized in Table  1. The spectral resolution of the broadband correlation units is 1. 95 MHz or 2. 7 km s - 1  at 1. 37 mm and we have eight narrow band units achieving 0.
Data reduction The data were calibrated using the gildas/clic 1 software. The respective sources for the calibration process are listed in Table  2. The imaging and deconvolution processes were conducted using gildas/mapping. The continuum data were imaged with uniform weighting to obtain a high spatial resolution, where the low number of channels containing spectral line emission were excluded by hand. We applied self-calibration on the continuum data using casa  (version 4. 7. 2, McMullin et al. 2007) , to reduce the rms noise from 0. 46 to 0. 28 mJy beam -1 (cf. Beuther et al. 2018). For the spectral line cubes, we subtracted the continuum emission from the high-resolution narrow band data in the uvdomain and resampled to a spectral resolution of 0. 5 km s -1. The resulting tables were imaged applying uniform weighting (robust weighting parameter of 0.
2009) : N H 2 = I peak • R B ν (T dust ) • κ ν • µm H • Ω beam , (2) where µm H is the product of the mean molecular weight, assumed to be 2. 8 (see Appendix of  Kauffmann et al. 2008) , and the mass of atomic hydrogen m H , Ω beam is the beam solid angle. We furthermore compute H 2 column density maps, presented in Fig. 8 , using the map of mm continuum intensity I ν (Fig. 1 ) and Notes. ( a) The rest frequencies were extracted from the Cologne Database for Molecular Spectroscopy (CDMS). ( b) The critical density was estimated from the approximation n crit ≈ A/Γ (Shirley 2015), for collision rates at T = 100 K. Both, the Einstein A and the Γ coefficients were taken from the Leiden Atomic and Molecular Database (LAMDA). DCN is approximated by the corresponding values for HCN in the database.
( c) The rms noise of the images was estimated from line-free channels. The beam size is 0. 43 × 0. 35. ( d) Only for 13 CO we also imaged the interferometric data merged with the single-dish data. The resulting cube has an rms noise of 2. 15 mJy beam -1 , with a synthesized beam of 0. 8 × 0. 67. Notes. ( a) We derived the signal-to-noise ratio S /N from the peak intensity I peak in units of σ = 0. 28 mJy beam -1. ( b) The flux densities were integrated inside the 5 σ contour levels of the core. ( c) The core mass was estimated from the flux density S ν using Eq. (  1 ) for the clump-averaged temperature of T gas = 55 K, see text. ( d) The second estimates for MMS1a & b were calculated from the core-averaged rotational temperature estimate from CH 3 CN (see Fig. 8 ). ( e) The column density was estimated from the peak intensity I peak using Eq.
Spectral line emission: spectra and moment maps The spectra of the three dominant cores MMS1a,b and 2a in Fig. 2  were averaged over the central 0. 5 of each core to estimate the respective chemical composition. The parameters in Table  1  are given only for the transitions, which were analyzed in the context of kinematics, and were extracted from the CDMS 2   (Müller et al. 2001 (Müller et al. , 2005;; Endres et al. 2016 ) and LAMDA 3   (Schöier et al. 2005)  databases. The spectrum of MMS2a only shows spectral lines from comparably simple molecules like 13 CO, H 2 CO and CH 3 OH, all having upper energy levels 70 K. Compared to the emission from the other two cores, MMS2a emits only weakly < 0. 03 Jy beam -1. In contrast to this, the northern cores show a variety of species with stronger emission ( 0.
2017 ) for fitting the CH 3 CN spectra. This package solves the radiative transfer equations for a medium in LTE, utilizing the VAMDC 4  and CDMS 5  databases  (Müller et al. 2001 (Müller et al. , 2005;; Endres et al. 2016). From the xclass package, we utilize the xclassmapfit function, which fits the spectra from a data cube pixel-by-pixel, yielding best-fit parameter maps for rotational temperature, column density, peak velocity and velocity dispersion. Since significant emission, > 6σ, is detected only towards small regions around the continuum emission peaks of the two major cores MMS1a & b, we consider only a 2. 2 × 2. 2 area around each core and extract the corresponding regions from the CH 3 CN (12 K -11 K , K = 0 -6) data cube. Tests revealed that good fitting is achieved by a sequence of 300 iterations of the genetic algorithm and another 50 iterations of the Levenberg-Marquardt algorithm (see  Möller et al.