dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Hillis & Moritz, 1990; Soltis, Soltis & Doyle, 1992). The application of the polymerase chain reaction (PCR;  Mullis et al. , 1986) , cloning  (Sambrook, Fritsch & Maniatis, 1989 ) and Sanger sequencing (see below) produced data that allowed hypotheses previously based on morphological and other data sources to be tested, substantially increasing phylogenetic resolution and support. Molecular data have repeatedly provided evidence for major taxonomic rearrangements, thus greatly advancing our understanding of the Tree of Life. More recently, as NGS technologies came into broad use (see below; Table  1 ), a more general definition of phylogenomics now dominates: the use of genome-scale data to build phylogenetic trees. Here we distinguish between two commonly used meanings: the first refers to using trees to study e.
One recent example of such a study is the analysis of 360 complete plastid genomes (proteincoding regions) from public databases across the green plants  (Ruhfel et al. , 2014) , which at that time represented the largest plastid dataset yet analysed. The authors sampled comprehensively across the green plants, based on all publicly available complete plastid genome data, providing resolution and support for a great number of relationships, but also identifying areas of uncertainty. Another study  (Wickett et al. , 2014)  used transcriptome sequencing to generate a dataset of >1000 low/single copy nuclear loci across a sample of 92 representative green plant taxa and was able to improve resolution of some of the deepest but most recalcitrant nodes in the green plant tree of life with strong branch support.
In plants, this includes plastid genomes, mitochondrial genes or genomes, rDNA cistrons, transposable elements and other high-copy elements. Several samples may be tagged with unique barcodes, pooled, sequenced in multiplex and sorted bioinformatically, greatly increasing cost-effectiveness. Library preparation: Laboratory procedures necessary to prepare samples for NGS. This usually includes fragmentation of genomic DNA, followed by size-selection of fragments and ligation of sequencing adapters, primers and barcode indexes. Paired-end sequencing: In NGS, sequencing both ends of a genomic DNA fragment, as opposed to only one end (single end sequencing); e. g. 100 bp paired-end sequencing of ~500 bp fragments yields 100 bp of sequence data on each end of the fragment with 300 bp of unknown sequence between them.
Current nuclear genome sequencing efforts are targeting additional palms [e. g. Chamaedorea tepejilote Liebm. (subfamily Arecoideae), J. Tregear, unpubl. data; Geonoma undata Klotzsch (Arecoideae), C. Lexer, unpubl. data; Mauritia flexuosa L. f. (Calamoideae),  Tregear et al. , unpubl. data]. In addition to organellar and nuclear genomic sequencing, several transcriptome datasets generated via RNA-seq (defined in Table  1 ) are available, from representative species across three of the five palm subfamilies  (Bourgis et al. , 2011; Matasci et al. , 2014). These include: oil palm, coconut (both Arecoideae), and date palm; Nypa fruticans Wurmb (Nypoideae); Serenoa repens (W. Bartram) Small and Sabal bermudana L. H. Bailey (subfamily Coryphoideae); available at https://sites. google. com/a/ualberta.
, 2010)  or array-based capture (e. g. Hodges et al. , 2009). The enriched library is then sequenced using, for example, an Illumina machine (e. g. Lemmon & Lemmon, 2013). Similar to the development of probe sequences, wet laboratory and bioinformatics protocols for targeted sequence capture are generally similar and available online for consultation (e. g. https://github. com/AntonelliLab/palm_pipeline).
BIOINFORMATICS AND COMPUTATIONAL CONSIDERATIONS FOR PHYLOGENOMICS Although one can analyse NGS data on any operating system, many programs are written for a UNIX/ Linux environment. Therefore, a basic familiarity with the command line interface is required to process read data and for subsequent analyses (e. g. UNIX is excellent for basic text file manipulation, scripting and programming), although some proprietary graphical user interfaces such as Sequencher (GeneCodes Corporation, AnnArbor, MI, USA), Geneious (Biomatters, Ltd. , Auckland, New Zealand) and CLC Genomics (CLC bio, a QIAGEN Company, Venlo, Netherlands) are increasingly useful for NGS applications. NGS data are typically returned from the sequencer to most researchers in FASTQ format  (Cock et al. , 2010) , in which each read consists of four lines; the most important lines are the sequence itself and a quality score for each base.
Base calls towards the 3 0 ends of reads tend to decrease in quality. Quality is typically described using the PHRED scale  (Ewing & Green, 1998) , which is a logarithmic expression of sequencing error probability. For example, a PHRED score of 10 = 10 À1 or a probability of a sequencing error = 0. 1; whereas PHRED 20 = 10 À2 or 0. 01 probability of an error; and so on, with a limit at PHRED = 40. There are numerous freely available programs and scripts [e. g. NGS QC Toolkit,  (Patel & Jain, 2012); Trimmomatic (Bolger, Lohse & Usadel, 2014) ] that allow filtering and/or trimming of poor quality reads or bases, according to user-defined PHRED thresholds, removal of any remaining adaptor contaminants, merging of overlapping paired-end reads, removal of non-unique reads etc. These are important steps that contribute to the quality and fidelity of assembled genes and genomes.
Methods of annotation can be complicated (in line with the complexity of eukaryotic genomes), although many tools exist to help researchers: e. g. DOGMA  (Wyman, Jansen & Boore, 2004) , ACRE  (Wysocki et al. , 2014), and Verdant (McKain M & Hartsock R, unpubl. data)  for plastomes; Mitofy for mitochondrial genomes  (Alverson et al. , 2010) , and a large suite of software available for nuclear genomes [e. g. The NCBI Eukaryotic Genome Annotation Pipeline, http://www. ncbi. nlm. nih. gov/genome/annota-tion_euk/process/; Galaxy  (Giardine et al. , 2005) , available via web interface at usegalaxy. org; Genome Tools  (Gremme, Steinbiss & Kurtz, 2013) ]. Particular challenges emerge when dealing with polyploidy, which is common in many plant taxa, due to the difficulties in phasing allelic variation.
, 2012) , which help to divide the workload and speed up analyses. Some freely available online clusters can often handle modest to medium-sized analyses [e. g. CIPRES;  Miller, Pfeiffer & Schwartz (2010) , https://www. phylo. org] and many research-oriented institutions offer their own computational resources. Despite these computational advances in parallelizing phylogenetic analyses, many challenges remain when facing large NGS datasets, which as of late routinely exceed 100 loci. For example, Bayesian methods (e. g. BEAST), which are a popular choice for phylogenetic inference, are pushed to their boundaries when analysing large, multi-locus datasets, as the underlying Markov chain Monte Carlo algorithms may take tens of millions of iterations (and weeks to months) to converge among replicate runs, if they converge at all.
Methods that estimate gene trees and species trees jointly can provide a dynamic exploration of parameter space (e. g. Liu & Pearl, 2007; Liu, 2008). When adding a large number of loci to the analysis, the corresponding, introduced operators may lead to an overwhelming number of variables, for which it may not be possible to explore parameter space efficiently and thus may cause issues with parameter identifiability (see  Ponciano et al. , 2012). Commonly chosen alternatives to Bayesian tree estimation are fast maximum likelihood (ML) methods such as RAxML, which are computationally more tractable when facing massive phylogenomic datasets. When using ML methods, gene trees and species trees have to be estimated sequentially. The common practice is to estimate a separate gene tree for each individual locus and use all gene trees to estimate a most likely species tree (e.
This is currently a topic of intense debate (e. g. Song et al. , 2012; Wu et al. , 2013; Gatesy & Springer, 2014; Xi et al. , 2014; Simmons & Gatesy, 2015; Springer & Gatesy, 2016) , which remains to be settled. Furthermore, despite the obvious benefits of obtaining data from hundreds to thousands of variable loci from across the genome, it may not yet be possible to use the full power of these data effectively due to the immense computational burden required to model robustly the complex evolutionary processes that have shaped these loci (e. g. Roch & Warnow, 2015). This is especially true for the inference of divergence times and species trees (among other data-intensive analyses), due to the sheer dimensions of these datasets in terms of taxa, loci, partitions and corresponding model parameter space.
SOME RECENT PHYLOGENOMIC STUDIES IN PALMS Phylogenomics applied to palm systematics and evolution is a relatively new endeavour and has benefitted immensely from ongoing genome and transcriptome sequencing projects in date and oil palms and from widely available NGS technologies. These advances are having a profound effect on palm systematics. Up to this point, palm molecular systematics has relied heavily on a few loci from the plastid and nuclear genomes and the focus has been on increasing taxon sampling. Asmussen et al. (2006) ,  Baker et al. (2009 Baker et al. ( , 2011)) ,  Roncal et al. (2010 Roncal et al. ( , 2012) )  and  Bacon et al. (2012)  have provided recent examples of dense taxon sampling using data generated by Sanger sequencing across the palms at various levels (among and within subfamilies, tribes etc.
Using this approach, they were able to resolve many of the deep relationships among tribes of Arecoideae with strong branch support, although some relationships among the 'core arecoid' clade remain unresolved based on protein-coding regions of the plastome. This study has important implications for the biogeographic history of Arecoideae, which contains over half of all palm species and has a pantropical distribution, and further demonstrates the effectiveness of capture-based approaches at higher taxonomic levels. Current efforts are focused on using sequence capture to generate a dataset of several hundred single copy nuclear loci  (Comer et al. , in review)  to help resolve deep-level relationships of this subfamily. Sequence capture is being used across populations of the African genera Podococcus G.
GENERAL RECOMMENDATIONS FOR PALM BIOLOGISTS INTERESTED IN PHYLOGENOMICS SEQUENCING TECHNOLOGY AND EXPERIMENTAL APPROACH The most important piece of advice to palm researchers would be to let the questions decide the technology and not the other way around. In other words, NGS data files are massive, often in the order of gigabytes per file. Data from just a few projects can easily take up terabytes of storage space and, when coupled with the need for backing up, this means a simple internal hard drive will not suffice. Data storage on servers, the cloud and external hard drives (as backups) is strongly recommended and should follow well-developed protocols and guidelines (e. g. Osborne et al. , 2014). Assembling reads into contigs and eventually genomes often requires a massive amount of RAM.
ORGANIZATION In order to carry out phylogenomic research, one needs to be highly organized not only in the laboratory, but in terms of data management  (Noble, 2009). NGS data from just a few projects will quickly fill hard drives and the numerous files resulting from the various steps of bioinformatics analysis will quickly become overwhelming. It is strongly recommended to keep a hierarchically structured, clean file (directory) system, with directory and file names as detailed as possible. A systematic naming system of files is also recommended, to allow one to find specific files several months or even years after they are created. When not in use, files should be zipped (compressed) to save storage space and all data/results should be backed up regularly and systematically, ideally in more than one location.
ACKNOWLEDGEMENTS C. F. B. was supported by  National Science Foundation  Award  DEB-0830020  ( Monocot Tree of Life Project ), the  California State University Program for Education and Research in Biotechnology , and the  Alexander von Humboldt Institute. C. D. B. , T. H. and A. A. were supported by the  Swedish Research Council  ( B0569601 ), the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013,  ERC  Grant Agreement n. 331024), and a  Wallenberg Academy Fellowship. We thank  Karolina Heyduk ,  Jason Comer  and  Jim Leebens-Mack  for expert technical and bioinformatics advice/assistance. We especially thank  Julissa Roncal  and  Wolf Eiserhardt  for their valuable insights into the manuscript and  Rodrigo Bernal ,  Gloria Galeano ,  Jean-Cristophe Pintaud ,  James Tregear  and  Henrik Balslev  for organizing the  World Palm Symposium 2015 in Quind ıo, Colombia.
Results and conclusions: Here, the python library bioalerts is presented, which comprises functionalities for the automatic derivation of structural alerts from categorical (dichotomous), e. g. toxic/non-toxic, and continuous bioactivity data sets, e. g. K i or pIC 50 values. The library bioalerts relies on the RDKit implementation of the circular Morgan fingerprint algorithm to compute chemical substructures, which are derived by considering radial atom neighbourhoods of increasing bond radius. In addition to the derivation of structural alerts, bioalerts provides functionalities for the calculation of unhashed (keyed) Morgan fingerprints, which can be used in predictive bioactivity modelling with the advantage of allowing for a chemically meaningful deconvolution of the chemical space. Finally, bioalerts provides functionalities for the easy visualization of the derived structural alerts.
However, the notion of identifying chemical fragments that enrich for a biological endpoint, be it toxicity or in vitro enzyme inhibition, can be extended to data sets reporting continuous compound activity values. Here, the python library bioalerts is presented, which comprises functionalities for the automatic derivation of structural alerts from categorical (dichotomous), e. g. toxic/non-toxic, and continuous bioactivity data sets, e. g. K i or pIC 50 values. The workflow proposed by Ahl- berg et al. [13]  is followed for the derivation of structural alerts from categorical data-with the exception that to generate compound substructures circular Morgan fingerprints are used instead of a signature descriptor  [14, 15] -whereas the pipeline published by Cortes-Ciriano et al. [16]  is followed when using continuous bioactivity data.
Implementation The library bioalerts is implemented in python. It is mainly based on the RDKit implementation of circular Morgan fingerprints, licensed under a 3-clause BSD license  [20] , and depends on the following python modules: numpy, operator, os, pandas, scipy, and sys  [21] [22] [23]. The library is divided in three modules, namely: (1) Load-Molecules, (2) Alerts, and (3) FPCalculator. These modules allow to read molecule files, compute hashed and unhashed Morgan fingerprints in binary and count format, and to derive structural alerts from continuous and categorical bioactivity data sets. The implementation of the three modules is serial. Their functionalities are presented in the following subsections.
Reading molecule files Molecule files can be read with the method ReadMolecules() from the class bioalerts. LoadMolecules. LoadMolecules(). The available input file formats are: (1) SMILES, (2) SDF, and (3) mol2. Once the molecules are loaded into a python list, the method extract_substructure_ information() from the class bioalerts. LoadMolecules. GetDataSetInfo() permits the generation of a dictionary of substructures, whose keys correspond to substructure unambiguous integer identifiers, and the values to the molecule indices within the molecule list. Only the substructures with a bond radius allowed by the user through the argument radii are considered to build the substructure dictionary. This dictionary, which defines in terms of substructure composition the input set of molecules (e.
Derivation of structural alerts from continuous bioactivity data sets The module calculate_p_values() from the class bioalerts. Alerts. CalculatePvaluesContinuous() permits the derivation of structural alerts from data sets reporting continuous compound activity values, e. g. pIC 50. To identify which substructures from a test molecule significantly contribute to bioactivity on the biomolecular system under study, two bioactivity distributions are defined (Fig. 1b ), namely: (1) distribution A, comprising the bioactivity values for those compounds from the training set presenting a given substructure, and (2) distribution B, comprising the bioactivity values for those compounds not presenting that substructure in the same training set. The normality of these distributions is assessed with the Shapiro-Wilk test (α = 0.
In some cases, depending on the sample sizes, a highly significant P value might be obtained for a small size effect, e. g. a tenth of a pIC 50 unit. From a medicinal chemistry standpoint that difference might be negligible  [30]. Therefore, it is paramount to pay especial attention to the effect size in addition to the P values. Overall, bioalerts constitutes an open source python library for the derivation of structural alerts from categorical and continuous data sets using two methodologies that have been previously validated. In addition, bioalerts functionalities include the computation of unhashed Morgan fingerprints, which can be further used in e. g. predictive bioactivity modelling, clustering or similarity searching. encouraged to visit this site for future versions and improvements.
Lambda Ladder PFG Marker (BioLabs) 50 μg/ml was used as molecular weight marker. Agarose gels were stained with ethidium bromide and visualized by transillumination under UV (Pharmacia Biotech, Thermal Imaging System FTI-500). BioNumerics 7. 5 software (Applied Maths, Kortrijk, Belgium) was used to register macrorestriction patterns and clustering analysis was performed using DICE similarity coefficient and the unweighted-pair group method with arithmetic mean (UPGMA).
S. aureus multilocus sequence typing (MLST) Amplification of seven housekeeping genes, including carbamate kinase arcC, shikimate dehydrogenase aroE, glycerol kinase glpF, guanylate kinase gmk, phosphate acetyltransferase pta, triosephosphate isomerase tpi, and acetyl coenzyme A acetyltransferase yqiL, was done according to the already published protocols  [30]. DNA sequencing was performed by Stabvida (Portugal). MLST sequences were analysed using Bionumerics 7. 5 software (Applied Maths, Kortrijk, Belgium) and sequence types (ST) assigned according to the S. aureus MLST database (http://saureus. mlst. net) The eBURST algorithm, available at (http://eburst. mlst. net), was used to classify different ST into clusters or clonal complexes (CC). A minimum spanning tree (MST) constructed with BioNumerics 7.
These less frequent ST have already been described in Portugal  [20, 61, 62] , with the exception of the ST1507 and ST2599, but little information is available regarding these ST. In fact, the only description found in the S. aureus MLST database (http://saureus. mlst. net), refers to a MRSA ST1507 isolated in 2006 in South Korea from a foodborne source and a MRSA ST2599 isolated from urine in 2013 in the USA. In our study the patient from which ST2599 (CC5) was recovered, also presented another S. aureus belonging to ST105 (CC5), being the only case where it was possible to identify two different ST in the same patient. In the other six cases in which the same patient showed two similar, but not identical pulsotypes, MLST revealed that they belonged to the same ST. Interestingly, some clones belonging to different CC presented a higher PFGE similarity than clones included in the same CC, as already observed  [18].
Cluster II included only one isolate, MSSA agrI ST944 (CC182). MSSA ST944 was described in Switzerland being isolated from nasal swabs of healthy risk-free adult carriers  [63]  and in China, where it was present with high frequency in nasal carriage of healthy children in a kindergarten  [64]. In the S. aureus MLST database, a MSSA ST944 has also been described in Norway, related with nasal swab carriage (http://saureus. mlst. net). Cluster III was the most heterogeneous cluster, including mainly MSSA agrI isolates, belonging to the following ST: ST7 (CC7), ST8 (CC8), ST34 (CC30) and ST45 (CC45). In fact, a previous study concerning the population structure of MSSA in Portugal showed that these CC were, among others, the most predominant clonal types found between 1992 and 2011, both in the community and hospitals settings  [20].
Patients with DFI constantly attend clinical centres for wounds healthcare, which may explain the high diversity of pulsotypes and ST found, including the main hospitalacquired clones present in Portugal (CC5 and CC22). It is important to refer that several less frequent clones, seldom described in literature and MLST database, were also found in this study. Therefore, diabetic patients can be important vehicles for clonal dissemination from the hospitals into the community and contrariwise, including less common clones.
Acknowledgments This study was conducted with the financial support of  "Centro de Investigação Interdisciplinar em Sanidade Animal, Faculdade de Medicina Veterinária da Universidade de Lisboa " -Project  UID/CVT/00276/2013  and  FCT  Project  PTDC/SAU-MIC/122816/2010-Biofilms  in diabetic foot: microbial virulence characterization and cross-talk of major isolates (both funded by  Foundation for Science and Technology -FCT). Carla Mottola  holds a PhD fellowship ( SFRH/BD/72872/2010 ) from  FCT , Portugal.
Microarray data normalization and analysis Raw microarray data files (. cel format) were normalized using either R or Affymetrix Tiling Analysis Software (TAS). TAS was used to generate log2 ratios of Fft2-myc or Fft3-myc to a control no-epitope myc-ChIP (Hu303) using two-sample analysis quantile normalization together and a bandwidth of 100. Probe signals were assigned to S. pombe genome coordinates (Sanger 2007). TAS was also used to generate log2 ratios of mutant to WT expression arrays using the same normalization. Data analysis was performed in R (http://www. r-project. org) using the Bioconductor (http://www. bioconductor. org) packages "affy", "affxparser", and "preprocessCore" with standard parameters. CEL-files were imported and quantile normalized as described in  [73]. Data was visualized using Podbat  [33] , R, and the Integrated Genome Browser (IGB, Affymetrix).
Micrococcal nuclease (MNase) digestion Mononucleosomal DNA fragments were prepared and purified as described in  [74]. Fragments were amplified and labeled with the NEBNext â ChIP-Seq Library Prep Master Mix Set for Illumina â (NEB #E6240) and sequenced on an Illumina Miseq v. 3. Pairedend reads were mapped to the S. pombe genome using Bowtie2  [75]  with standard parameters. DANPOS  [76]  was then used to remove clonal reads, normalize by quantile normalization, calculate nucleosome occupancy, and compute differential signals. Averaging of occupancy data and visualization was carried out using R. Primers 21-24 were used for quantification by qPCR. We normalized LTR nucleosome occupancy against the Tf2 ORF +1 nucleosome because occupancy of this nucleosome appeared very stable in our MNase-seq data.