dataesr/scientific-paragraphs-categorization huggingface dataset:
However, for large datasets, SegAnnDB can still be useful for creating a relatively small database of 5-10 visually annotated profiles that are representative of the bigger dataset (generated on the same platform and for the same tumor type). Then the protocols of  Hocking et al. (2013)  can be used for training and validating an algorithm for automatically detecting CNAs on the unannotated profiles. The main idea is that for each manually annotated region, one can check whether an algorithm predicts the indicated copy number or breakpoints in that region. If there is some disagreement, then the algorithm needs to be improved. So when there is not enough time to visually annotate every profile, an annotated region database is still useful for checking the validity of an automatic annotation algorithm.
For example, the center panel of Figure  5  shows a profile whose normal level appears at log ratio % 0:5. When it is visually obvious that normal copy number is at a non-zero log ratio value, then normal copy number regions can be used to indicate that. Finally, many algorithms have a parameter for the minimal number of probes required for identifying a CNA. For example, by default DNAcopy sets its min. width parameter to two probes. Instead of defining an arbitrary value that may not hold for all alterations in a dataset, breakpoint annotations can be used to define small alterations that are visually obvious, such as the loss shown in the right panel of Figure  5. Then, for example, the annotations can be used to check which values of the DNAcopy min. width parameter are appropriate for a given dataset.
Using annotations in later analyses The annotation database, segmentation and copy number calls created on SegAnnDB can be used as inputs to other analyses. SegAnnDB creates a list of breakpoints which is more accurate than other methods such as DNAcopy (Supplementary Table  S6 ), and this degree of accuracy can be critical for several applications. For example, to construct a genome alteration print,  Popova et al. (2009)  require a good segmentation algorithm: 'the absence of true breakpoints could significantly alter the GAP pattern'. Annotated regions can be used on SegAnnDB to ensure that the segmentation contains all visible breakpoints. As another example, the locations of detected alterations can be used to construct predictors when modeling survival outcome. SegAnnDB can be used to construct predictors that agree with an expert's visual interpretation of the data.
In other words, an expert with enough time can always find a consistent model by adding annotations, as our model always has zero training error with respect to the annotated regions. However, predicting accurate breakpoints for unannotated test data was difficult for the models we considered (Supplementary Fig. S5 ). Developing models that can more quickly achieve better breakpoint detection and copy number calling in unannotated test data remains an interesting research direction. Although different experts do not always provide consistent annotations (Supplementary Fig. S7 ), we are also investigating a multitask learning model that could potentially have better prediction accuracy for each of those experts. Fig. 5. Examples of noise and alterations that can be annotated using SegAnnDB.
Left: outliers and wave noise can be ignored using 0breakpoints annotations. Center: normal copy number at a non-zero log ratio value can be indicated using normal annotations. Right: small alterations can be annotated using 1breakpoints and copy number annotations 1545 SegAnnDB One current feature of SegAnnDB is the ability to plot a random unannotated chromosome. Instead of plotting a random profile, we could plot a profile that is likely to improve model predictions. We are interested in future research into sampling methods that improve the model faster than random sampling in DNA copy number profile annotation databases. Finally, we are interested in using visual annotations for modeling copy number changes in next-generation sequencing data. In particular,  Teo et al. (2012)  show figures with clear breakpoints in read depth and fragment count.
Background: The passion fruit (Passiflora edulis) is a tropical crop of economic importance both for juice production and consumption as fresh fruit. The juice is also used in concentrate blends that are consumed worldwide. However, very little is known about the genome of the species. Therefore, improving our understanding of passion fruit genomics is essential and to some degree a pre-requisite if its genetic resources are to be used more efficiently. In this study, we have constructed a large-insert BAC library and provided the first view on the structure and content of the passion fruit genome, using BAC-end sequence (BES) data as a major resource. Results: The library consisted of 82,944 clones and its levels of organellar DNA were very low. The library represents six haploid genome equivalents, and the average insert size was 108 kb.
To check its utility for gene isolation, successful macroarray screening experiments were carried out with probes complementary to eight Passiflora gene sequences available in public databases. BACs harbouring those genes were used in fluorescent in situ hybridizations and unique signals were detected for four BACs in three chromosomes (n = 9). Then, we explored 10,000 BES and we identified reads likely to contain repetitive mobile elements (19. 6% of all BES), simple sequence repeats and putative proteins, and to estimate the GC content (~42%) of the reads. Around 9. 6% of all BES were found to have high levels of similarity to plant genes and ontological terms were assigned to more than half of the sequences analysed (940). The vast majority of the top-hits made by our sequences were to Populus trichocarpa (24.
Furthermore, a previous study has shown a wide genome size variation in a sample of eight taxa, ranging from 0. 915 pg (P. suberosa, n = 12) to 2. 680 pg (P. quadrangularis, n = 9)  [6]. Note that most of Passiflora nucleotide sequences available from public databases are microsatellite sequences used to develop genetic markers  [7, 8] , transcription factors and internal transcribed spacers, ribosomal RNA genes, or partial gene sequences used in phylogenetic studies  [9, 10]. In addition, information on genes involved in the flowering process of Passiflora spp. is accessible over the internet (passioma. ib. unicamp. br) [11]. As mentioned above, some Passiflora species are popular ornamentals, whereas others are grown for their edible fruits, including the giant granadilla (P. quadrangularis), the purple granadilla (P.
Such sequences, known as BAC-ends (BES), can be used for generating partial physical maps and performing comparative analyses  [24]. BES data analysis provides an overview of genome composition, as well as information on gene density and the occurrence and distribution of transposable elements (TEs) and Simple Sequence Repeat (SSRs), essential data for the development of molecular markers  [25] [26] [27]. In this study, we have constructed and characterized the first large-insert BAC library for a Passiflora species in order to facilitate genomic research. The long-term objective of our work is to study the structure, organization, and content of the yellow passion fruit genome, using BES data as a major resource. Specifically, our aims were (i) to construct a representative BAC library with six haploid genome equivalents using the estimation of 1,563 Mb genome size of P.
BAC library construction and characterization The Passiflora edulis BAC library consists of 82,944 individual BAC stored clones in 216 (384-wells) plates and is kept at the French Plant Genomic Resource Center (CNRGV). The library has the following designation: Ped-B-Flav [http://cnrgv. toulouse. inra. fr/fr/library/genomic_resource/ Ped-B-Flav]. To evaluate the medium insert size, 86 randomly chosen BACs were digested with NotI enzyme and analysed by pulsed field gel electrophoresis. The resulting restricted DNA typically showed the vector band and one or two insert bands per clone. The inserts ranged in size from 50 to 196 kb and the average insert size for the library was 108 kb. The insert size of most of the clones (87%) was larger than 90 kb (Figure  1 ). Our data confirmed that partial digestion of high molecular weight DNA with the restriction enzyme HindIII (1-2 U/plug as optimal concentration) was critical to produce fragments with a good size range, leading to a good quality of the BAC library containing fragments with acceptable insert sizes, important for future whole genome study.
To some extent, the above mentioned hybridization data confirmed the genome coverage of the library. Assuming that 41 clones contain organellar DNA, the number of clones harbouring yellow passion fruit nuclear DNA is 82,903. Since the haploid genome content of P. edulis is reported to be ~1. 6 pg or 1,563 Mb  [6] , a library composed of 82,903 clones with an average insert size of 108 kb affords coverage of ~6 genome equivalents of nuclear DNA [(82,903 × 0. 108 Mb) ÷ 1,563 Mb = 5. 73]. The probability of isolating any single-copy DNA sequence from the P. edulis library would be greater than 99. 7%, according to the Poisson distribution. To check the utility of our library in gene isolation, macroarray screening experiments were carried out with radiolabeled probes complementary to eight Passiflora gene sequences available in public databases (Table  1 ).
BAC-end sequence analyses In total, DNA from 5,974 BAC clones were purified for subsequent sequencing using an ABI 3500 × L automated platform. Of these BACs, 5,058 were sequenced at both ends (forward and reverse directions), and 916 (unpaired BACs) were sequenced in only one direction (512 forward and 404 reverse). Thus, 11,032 sequencing reactions were performed, yielding 9,698 good quality BAC-end sequences (or BESs), indicating efficiency of 87. 39%. The average length of the sequences was 587 bp, ranging from the minimum acceptable size (100 bp) to 1,255 bp. The redundancy in the sequenced set was 5. 4%. To summarize, our BAC-end sequencing effort generated approximately 6. 2 Mb (6,194,248 bp) of genomic information for P. edulis, covering around 0. 39% of its genome. First, we searched in plant databases for mitochondrial and chloroplast sequences.
These public data were obtained from the NCBI website (www. ncbi. nlm. nih. gov) using the same software and parameters used in the exploitation of microsatellites in BAC-end sequences of P. edulis. The results of these comparisons are shown in Figure  4. Among the mononucleotides, A/T exceeded by far the proportion of G/C repeats (62% compared to 38%). In fact, analyses carried out on Arabidopsis thaliana, Vitis vinifera and Populus trichocarpa have shown that, in general, dicots have a high frequency of A/T-rich microsatellites. The most frequent dinucleotides were AT, followed by AG repeats. None of the classes contained significant frequencies of GC-rich motifs. This is consistent with studies on Arabidopsis thaliana  [56] , Carica papaya  [38] , and Malus × domestica  [25]  showing that AT-rich sequences are much more prevalent in the genomes of higher plants.
Protein coding regions With the aim of identifying protein-coding regions, the BAC-end sequences were compared to the nonredundant protein data bank using the BlastX search engine (www. ncbi. nlm. nih. gov/BLAST). Around 9. 6% of all BES (940 sequences) were found to have high levels of similarity (e-values smaller than 1 × 10 -6 ) to genes of several plant species. The vast majority of the top-hits made by our sequences were to Populus trichocarpa (24. 8% of the total occurrences), Theobroma cacao (21. 6%), Ricinus communis (14. 3%), Vitis vinifera (6. 5%) and Prunus persica (3. 8%). This scenario was expected since these are species related to P. edulis on which there is an abundance of genomic data. The list of species hits in descending order is shown in Figure  5. Assuming that 9. 6% of the BES analysed had matching coding regions, we can estimate that ~151 Mbp of the passion fruit genome consist of coding sequences.
trichocarpa genome, compared to P. edulis. It is interesting to note that paired sequences Pe75Q4B6 and Pe164Q1F9 were both assigned to chromosome 14 in P. trichocarpa, making these BACs good candidates for a BAC-by-BAC sequencing project based on a BES database. The pair consisting of Pe173Q4A8 (F/R, 112 kb) sequences was classified as 'rearranged' because of their incorrect orientation with respect to each other, and the pair consisting of Pe164Q1G7 (F/R, 97 kb) sequences was classified as 'gapped' , since the distance between the sequences is around 6 Mb, much larger than the maximum acceptable span. This huge distance could be the result of a major chromosome event in an ancestral genome, for instance, involving massive insertion as the predominant event. It is interesting that this pair was also mapped in the genomes of A.
Conclusions We explored approximately 10,000 BAC-end sequences (BES) of the yellow passion fruit (Passiflora edulis) genome after generating a BAC library for the species. This is the first large-insert library for a member of Passifloraceae and a valuable tool for future sequencing and genome studies. We were able to verify the quality of the library (approximately 83,000 clones with very low mtDNA and cpDNA contamination), its genome coverage (~6 ×) and utility for gene isolation. This was done using exclusively the few Passiflora gene sequences deposited in public databases as probes in macroarray hybridizations, and the corresponding BACs used in FISH experiments allowed us to map four genes to three chromosomes of the species. We were able to identify reads likely to contain repetitive mobile elements (19.
6% of all BES), simple sequence repeats and putative proteins. We estimate a ~42% GC content (~42%) from the analysis of the reads. Approximately 900 BAC end-sequences contained protein sequences, and ontological terms were assigned to 507 of them. Comparison analyses of the sequences generated were performed against sequence data from two phylogenetically close species P. trichocarpa and V. vinifera, as well as from A. thaliana. Around 680 positive matches or sequence similarities were detected. Finally, a number of BAC-end pair sequences could be mapped to intervals of A. thaliana, V. vinifera and P. trichocarpa chromosomes, representing regions of potential microsynteny.
PCR products were purified with the NucleoSpin Extract II kit (Macherey-Nagel). The probes were labeled with [ 32 P]dCTP the using Ready-To-Go DNA Labelling Beads (-dCTP) kit (GE Healthcare), and unincorporated nucleotides were then removed using ProbeQuant TM G-50 Micro Columns G-50 (GE Healthcare). A set of probes was also used to facilitate the detection of hybridization to eight Passiflora gene target sequences deposited in Genbank. These genes were the only ones whose sequences were publicly available; details about gene codes and products, and primer sequences used for screening the library are shown in Table  1.
Hybridization mixes consisted of 50% (v/v) formamide, 10% (w/v) dextran sulfate, 2× SSC, and 2-5 ng/μl probe. The P. edulis C 0 t-100 fraction, isolated according to Zwick and co-authors  [63] , was added in 50-or 100-fold excess to the hybridization mix to block repetitive sequences when necessary. The slides were denatured for 5 min at 75°C in the hybridization mix and hybridized for up to 2 days at 37°C. The final stringency was 76%. Preparations were counterstained and mounted with 2 μg/ml DAPI in Vectashield (Vector). Re-probing of slides for localization of different DNA sequences on the same cell was performed according to Heslop-Harrison and co-authors  [64]. The best slides were analysed using a Leica DMLB microscope and images were captured with a Cohu digital camera using the QFISH software (Leica).
5 M), 2. 5 μl of EDTA (250 mM) and 35 μl of cold absolute ethanol. Each pellet was resuspended in 10 μl of formamide. Sample injection and capillary electrophoresis were performed on an ABI 3500 xL DNA analyzer (Applied Biosystems). PHRED software  [65]  was used for base calling and assignment of base quality values, and vector and low quality-sequences were removed from the reads on the Egene platform  [66].
Identification of transposable elements and microsatellite sequences The repetitive elements present in BES were identified by searches for similarity to sequences in the Viridiplantae section of the RepBase repeat database (released April 18, 2012), using the RepeatMasker ver 3. 3. 0 and Cross Match ver 0. 990329 search engines and a cutoff value of 1 × 10 -20. The density of repetitive elements was calculated as the percentage of nucleotides in the BES with at least one hit matching the repeat database. Repetitive element families were classified based on the annotation in the RepBase database, following the guidelines for classification and identification in the TIGR Codes for Repetitive Plant Sequences  [67]. Once identified, the repetitive element regions were masked to avoid bias in further analysis.
Gene content and functional annotation After masking for the occurrence of any kind of repetitive elements, sequences were compared to all records of the TIGR Plant Gene Indices  [69]  and NR database from NCBI, containing records of non-identical proteins from various databases, such as GenBank, RefSeq and UniProt. This was done using the BLASTX search tool  [70]  with a cutoff value of 1 × 10 -5. The functional annotation of the sequences was based on the BLAST2GO tools  [71]  for assigning ontological terms in accordance with BLASTX results, using 1×10 -6   as the default annotation cutoff value. Gene Ontology (GO) is a controlled vocabulary that allows gene products from different species to be compared based on their annotations and the three independently organized gene ontologies, molecular function, biological process, and cellular component  [72].
Vocabularies were reduced using the term plant, with the aid of a simplified version of the full ontologies (GOslim terms). The count of GC bases and the estimate of GC content of the yellow passion fruit genome was performed by a PERL script (Practical Extraction and Report Language), developed in our laboratory. Percentage GC content was calculated according to the formula: 100 x G þ C ð Þ= A þ T þ C þ G ð Þ.
Microsynteny of genome regions Areas of potential microsynteny were identified using the algorithm BWA-SW (Burrows-Wheeler Aligner's Smith-Waterman Alignment)  [73]. BWA is a software package for mapping low-divergent sequences against a large reference genome, and BWA-SW was designed for sequence reads ranging from 70 bp to 1,000 bp. Here, the BES sequences were compared to the complete genomic sequences of Arabidopsis thaliana, Vitis vinifera and Populus trichocarpa, downloaded from the Phytozome database  [74]. The last two species are the closest Passiflora edulis relatives whose genomes have been annotated. Each comparison, containing both the BAC-ends sequenced (forward and reverse sequences of the same BAC) was classified according to the position and orientation of the sequences. A particular region was considered to be potentially microsyntenic if it had been mapped to the same chromosome with a minimal distance between the paired matches of 15 kb and a maximum of 350 kb.
Acknowledgements This work was mostly supported by the  Fundação de Amparo à Pesquisa do Estado de São Paulo, Brazil (FAPESP , Grant no. 2011/51823-1 ), and in part by  Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil  ( CNPq, Projeto Universal ) and the  French Plant Genomic Resource Center (CNRGV). AAS and HAP received scholarships from  FAPESP  and  CNPq , respectively. APH and MLCV were supported by  CNPq. Anete Pereira de Souza  ( Universidade Estadual de Campinas, Unicamp, Brazil ) and  Maria Helena Pelegrinelli Fungaro  ( Universidade Estadual de Londrina, UEL, Brazil ) are acknowledged for providing the facilities to sequence the BAC-ends. We thank  Mr. Steve Simmons  for proofreading the manuscript.
SED fits We use an updated version of the Hyperz photometric redshift code of  Bolzonella et al. (2000) , modified to include the effects of nebular emission in its fitting procedure, as described in  Schaerer & de Barros (2009 , 2010). Designed to derive redshifts from broad-band SED fits of UV-NIR photometry and physical parameters of the galaxies, our version was also adapted to use data up to the sub-millimeter range. The redshift of our sources is fixed to the spectroscopic value and is not considered as a free parameter in the present work. Using (semi-)empirical and theoretical templates described below, we perform fits of three sets of photometries per object: the full photometry (i. e. from the rest-frame UV to the FIR) the dust processed FIR emission (from the MIPS 24 µm band longwards) the stellar SED photometry up to the IRAC bands.
IR properties of the sample The main "observed" quantities of our sample, derived from simple SED fits, are summarized in Table  3. The SPIRE and PACS data provide good constraints on the dust emission peak, allowing us to evaluate precisely the FIR luminosity, L IR , determined from integration of the best fit SED in the wavelength interval  [8, 1000]  µm. Fits with the different libraries we used agree within typically 0. 05 dex when they accurately reproduce the photometry. A comparison with the code Cmcirsed of Casey (2012) yields a mean < log(L IR (CMCIRSED))log(L IR (Hyperz)) > = 0. 016 ± 0. 079, showing no systematic offset. Best-fit SEDs are shown and discussed below. Based on these values and correcting for the lensing magnification factor ID z µ β L UV × µ Library of L IR × µ SFR IR T dust [10 12 L ] best FIR fit [10 12 L ] [M yr -1 ] [K] A68/C0 1.
SMC models tend to mostly favor long timescale scenarios, and the rising SFH models converge regardless of timescale as they must be at peak starformation at age t, and thus will tend to have the same extinction to reproduce the SED's colors. After a case-by-case discussion we will come back to this in Sect. 6. 1. In order to better accommodate the reader, the following subsections are organized in a standard pattern, with two main paragraphs each, a first one discussing the stellar models, and the other the FIR fits. In particular we discuss how the physical parameters depend on the model assumptions (mostly SFH and extinction law) and we examine how the FIR data allow one to rule out some of the assumptions.
Right: Same as left panel for the Cosmic Eye adopting the Calzetti attenuation law. Sources of the observational data are described in the text. Blue points show the predictions for models with exponentially declining SFHs and applying a higher extinction to nebular emission (i. e. E(B-V) = 0. 44×E(B-V) neb ). Red and black dashed symbols show the same for the rising and constant SFR models respectively. The physical properties derived from a subset of models are summarized in Table  4. Typically, the median mass is M ≈ 6. 5 × 10 9 M , the instantaneous SFR ≈ 13-66 M yr -1 , the extinction A V ≈ 0. 3-0. 9, with uncertainties of a factor ∼ 2 approximately. Ages (defined as time since the onset of star formation) are 30-60 Myr for declining SFHs, 50-90 Myr for delayed SFH, and older (100-250 Myr) for constant SFR or rising SFHs.
g. on the star formation histories and attenuation laws of high-z galaxies, it is, however, not possible to demonstrate that our conclusions may not be altered if a different, more complex model was adopted. Also, in a next step our model could include the energy balance as a constraint in the fitting procedure, in a similar fashion to CIGALE, GRASIL or the MAGPHYS code da  Cunha et al. (2008) , or it could also include available emission line measurements as constraints. Obviously such an approach would reduce the uncertainties on the derived physical parameters. However, some methodological issues remain, e. g. whether to include observed fluxes in all the IR bands or more fundamentally the derived IR luminosity and others. Such improved models will be applied in the future.