dataesr/scientific-paragraphs-categorization huggingface dataset:
Motivations: De novo sequencing of genomes is followed by annotation analyses aiming at identifying functional genomic features such as genes, non-coding RNAs or regulatory sequences, taking advantage of diverse datasets. These steps sometimes fail at detecting non-coding functional sequences: for example, origins of replication, centromeres and rDNA positions have proven difficult to annotate with high confidence. Here, we demonstrate an unconventional application of Chromosome Conformation Capture (3C) technique, which typically aims at deciphering the average threedimensional organization of genomes, by showing how functional information about the sequence can be extracted solely from the chromosome contact map. Results: Specifically, we describe a combined experimental and bioinformatic procedure that determines the genomic positions of centromeres and ribosomal DNA clusters in yeasts, including species where classical computational approaches fail.
INTRODUCTION De novo sequencing of genomes is typically followed by analyses aiming to identifying functional genomic features such as genes, non-coding RNAs or regulatory sequences. This important so-called annotation step raises non-trivial questions, and led to the development of complex bioinformatics approaches taking advantage of multiple datasets. For instance, transcriptome analysis is conveniently used to annotate expressed coding sequences  (Grabherr et al. , 2011; Saha et al. , 2002)  and synteny conservation between related species can reveal or confirm the presence of regulatory elements  (Gordon et al. , 2011; Kellis et al. , 2004). Complementary to automated annotation through comparative approaches, experimental approaches such as ChIP-seq or MNase-seq have been conveniently used to map epigenetic marks, replication origins, or other functional elements of the genome  (Roy et al.
Generation of genome-wide chromosome contact frequency matrices 3C libraries of the yeast species S. cerevisiae (BY4741), N. castellii (CBS4309), D. hansenii (CBS767), and K. capsulata (CBS1993) were generated from log-phase cells growing in YPD medium and as previously described  (Dekker et al. , 2002; Oza et al. , 2009) , but using a frequently cutter enzyme (DpnII) as in  (Sexton et al. , 2012). Briefly, the cells were cross-linked with 1% formaldehyde, and resuspended in DpnII restriction buffer, which were subsequently processed into Illumina libraries. 3C libraries were sheared and resulting fragments with sizes between 400 and 800 bp were sequenced using 100 bp pair-end sequencing on an Illumina HiSeq2000. The raw data from all 3C-seq experiments was then processed as follow: first, short reads were mapped on the genomes of S.
To examine the effect of coverage (or sequencing depth), which determines the total number of reads involved in contacts in the matrix, we applied the bootstrapping method to a range of f smaller than 1 (i. e. to experimental matrices where contact events have been down-sampled), specifically: f = 0. 8, 0. 6, 0. 4, 0. 2, 0. 1, 0. 01, 0. 001. For each value of f, we computed the centromere position error from the Nbs samples (relative to the ground truth) and the mean over the 16 centromeres and the Nbs samples. Figure  4B  plots this mean error as function of the mean number of reads in the bootstrapped samples (  ). As expected, the localization accuracy generally improves with coverage, provided that the contact data are binned at adequate genomic resolution and that the qualities of the libraries are equivalent.
These unmapped sequences were blasted on a sequence dataset containing yeast ribosomal sequences retrieved from the NCBI server (parameters: blast2 -p blastn -e 2e-30) to keep only highly significant hits. The corresponding mates were then mapped along the genome divided into bins (Figure  5 ). The peak in the distribution was clearly apparent on chromosome 12 (~10,000 hits compare to an average of 20 along the rest of the genome). Zooming in the distribution along an unmodified genome (bin size = 10bp) clearly show that the peaks lie within chromosome 12 region comprised between 450,000 -490,000 bp, adjacent to the position of the ribosomal gene cluster (Figure  5B ; note the 5S variants lying between 470,000 and 490,000 positions of the cluster per se do not allow high quality mapping of the reads and appear also as a blank area along the genome).
hansenii was identified at 1,354,000bp (Figure  6H ). This region corresponds to an intergenic region containing a pseudogene and a gap, according to the published reference genome (Deha2G::1,353,661-1,356,925, www. genolevure. org). This region was blasted on the NCBI database, revealing two small (75bp) regions matching with ribosomal DNA at positions 1,354,446 and 1,355,863. We therefore inferred the position of a large, unique ribosomal DNA cluster within this window on chromosome G, ruling out the hypothesis regarding the existence of three intrachromosomal clusters in this genome. We then compared the chromosomal location of the rDNA between the genomes of D. hansenii and two other genomes for which rDNA location is known (Pichia stipitis and Yarrowia lipolytica;  Dujon et al. , 2004; Jeffries et al.
, 2007)  using SynChro  (Drillon et al. ). No synteny conservation could be found between these 3 genomes. In addition, we checked if any rDNA annotation could be retrieved from the genomes of 11 species belonging to the CTG clade at the locus corresponding to D. hansenii rDNA using the CGOB database  (Fitzpatrick et al. , 2010). No such information was present in the database. In conclusion, no indication of synteny conservation of the rDNA locus between D. hansenii and other yeast species could be identified, consistent with the hypothesis that rDNA is mobile in the Candida clade  (Proux-Wera et al. , 2013). Overall, we showed that genome-wide chromosome conformation capture can be used to unveil important functional elements that sometimes escape standard genomic analyses. After validating the procedure on the well-known yeast S.
Funding: This research was supported by funding to R. K. from the  European Research Council  under the  7th Framework Program  ( FP7/2007-2013)  /  ERC  grant agreement [260822] and by  Agence Nationale de la Recherche  [ ANR-09-PIRI-0024 ] to C. Z. and R. K. H. M-N. is supported by a fellowship from  Fondation pour la Recherche Médicale (FRM). MM is the recipient of an  Association pour la Recherche sur le Cancer  fellowship [20100600373].
Analyses of divergence times are fossil calibrated at the divergence of Mirandornithes (flamingos + grebes) and the divergence of crown grebes. Results: mtDNA sequences confirmed the presence of a single metapopulation represented by two minimally varying mtDNA barcodes in Chilean flamingos. Likelihood and Bayesian methods recovered identical phylogenies with flamingos falling into shallow-keeled (comprising the Greater, American and Chilean Flamingos) and deep-keeled (comprising the Lesser, Andean and James's Flamingos) sub-clades. The initial divergence among flamingos occurred at or shortly after the Mio-Pliocene boundary (6-3 Ma) followed by quick consecutive divergences throughout the Plio-Pleistocene. There is significant incongruence between the ages recovered by the mtDNA and nDNA datasets, likely due to mutational saturation occurring in the mtDNA loci.
Population genetics of the Chilean Flamingo Sequence data for 3 mtDNA loci were obtained and analyzed from 17-26 Chilean Flamingos representing the entire breeding range of the species except Ecuador (Additional file 1). Only two out of 18 individuals showed variation in the COI gene (one out of 142 bp, or 0. 7%), exposing the same substitution recovered previously by a DNA barcoding study of neotropical birds  [24]. That study indicates the presence of only one substitution (0. 15%) in the entire barcode portion of the COI. From the current study, the common haplotype was distributed throughout the range, while the minor variant was found in an individual from Peru (this study) and in three individuals from the Argentinean Andes (this study and  [24] ). Both haplotypes coexist in these localities.
The Bayes factor analysis supported partitioning of the total dataset by locus (Bayes factor = 231), though both partitioned and non-partitioned BI analyses recovered the same topology with complete support (posterior probability = 1. 0); the ML analysis had >85% bootstrap support for all nodes. A *BEAST analysis including multiple individuals for each flamingo species again recovered the same topology with complete support (posterior probability = 1. 0). All jackknife analyses excluding a single locus at a time from the total (nDNA + mtDNA) dataset recovered the complete topology with high (>0. 9) posterior probability, suggesting that no single locus was driving the results. The nDNA dataset recovered the same topology, while the mtDNA dataset differed only by recovering a (Andean + (Lesser + James's)) clade, albeit without significant support.
Divergence time estimation The dates recovered by the mtDNA dataset are consistently ≥2 times the ages recovered by the nDNA dataset, while those recovered by the total dataset fall in between (Table  1 ). Crown-clade Phoenicopteridae likely arose at or shortly after the Mio-Pliocene boundary (3. 0-6. 5 mya). The deep-and shallow-keeled clades diverged in either the Pliocene or earliest Pleistocene (1. 7-3. 9 mya) followed by the American-Greater split (0. 9-1. 5 mya) and the Andean-James's split (0. 5-2. 5 mya). Age ranges represent the total (upper bound) and the nDNA only (lower bound) datasets. See Table  1  for divergence dating results of the all datasets (ages in bold, 95% confidence intervals in parentheses).
Phylogenetics Our study provides the first sequence-based molecular support for the relationships among flamingos suggested previously  [15, 16]. By contrasting the lack of full resolution for any of the individual loci with the complete resolution found for the nDNA and total datasets, the need for multiple independent loci to resolve temporally young divergences is underscored. Conflicting phylogenetic signal exists between the mtDNA loci as COI was the only single locus to fully resolve the flamingo relationships while cyt b recovered the Andean Flamingo at the base of the deep-keeled clade with high support. The relationships recovered by the total dataset agreed with those recovered by COI, as well as by the nDNA dataset and those recovered previously  [15] , indicating that the nDNA data sufficiently overpowered the conflicting cyt b signal.
Divergence time estimation Even though the various subsets of data recovered incongruent divergence times (Table  1 ), all agreed on a recent divergence for crown Phoenicopteridae, indicating that living flamingos are among the youngest lineages within Neoaves. This young age is counter to the general perception of the Phoenicopteridae as among the oldest lineages of living birds based on fossil data (e. g. [1] ) but is congruent with the DNA-DNA hybridization data of Sibley and Ahlquist  [15] , which identified the close genetic relatedness of the flamingos and predicted an age of 5-6 Ma for the basal divergence among the extant species. The nDNA dataset recovered ages significantly (≥2×) younger than those of the mtDNA dataset. This pattern is likely due in part to artificial signal caused by saturation of the more quickly mutating mtDNA genes  [30].
Though saturation is unlikely to be an issue with divergences as shallow as those of crown Phoenicopteridae, the use of temporally more distant calibrations external to the clade of interest (e. g. within grebes) may have introduced saturation artifact. It is likely that Ages of divergences within flamingos are in bold followed by the 95% confidence intervals in parentheses. Extant flamingos likely originated at or shortly after the Miocene-Pliocene boundary, making them one of the youngest lineages among living birds. The ages recovered by the nuclear data are consistently much younger than the ages recovered by the mitochondrial data, likely the result of mutational saturation in the mitochondrial sequences. homoplasy within the relatively longer and more quicklyevolving mtDNA sequences, with more mutational sites, conflicted with the signal of the shorter and more slowly evolving nDNA sequences, resulting in artificially old age estimates.
Population genetics methods Field sampling of all flamingo populations remains impractical for logistic reasons, including geographic inaccessibility and conservation protection. Sampling from captive birds alone is not guaranteed to capture species-wide variation due to a persistent lack of information regarding the geographic origins of the ancestors of captive populations. To assess phylogeographic structure, we thus set out to sample museum specimens of Chilean Flamingos with known locality. Available DNA barcodes from Chilean Flamingos suggest the presence of at least two haplotypes in Argentina  [19]  but no data is available from other portions of their range. Thus, we examined portions of three mitochondrial genes through amplification from historic toe pads from Chilean Flamingos (n = 27) with known localities (see Additional file 1 for specimen information).
The samples used spanned nearly the entire range known of Chilean Flamingos (excluding Ecuador) and were obtained through several museum loans. The three loci were chosen based on known intraspecific variation in flamingos (COI in Chilean and Greater Flamingos; NADH 2 in Lesser Flamingos) or known high rates of mutation (control region). Primers were manually generated using sequences available in GenBank (Additional file 4). In order to prevent contamination, ancient DNA procedures for extraction and amplification were followed  [44]  (see Additional file 5 for methods).
Genomic sampling Genomic DNA was extracted from tissue samples using the DNeasy Tissue Kit (Qiagen) and was amplified for 10 nuclear and two mitochondrial loci from one individual per species, which comprised the primary dataset (Table  2 ). Two additional nuclear loci were also amplified to test intraspecific variation among flamingos only. See Additional file 4 for primer sequences. The nuclear loci consisted primarily of intronic regions with primers designed using an exon-primed intron spanning approach and sequences from the chicken and zebra finch genome (following  [46] ). Polymerase chain-reaction (PCR) amplifications were carried out in 50 μl reactions comprising: 31 μl dH 2 O, 5 μl 10× detergent-and Mg + -free reaction buffer, 5 μl MgCl 2 (25 mM), 1 μl dNTP (10 mM each), 2. 6 μl each primer, 0.
2 μl Hot MultiTaq (US DNA) polymerase (5 u/μl) and 2. 5 μl DNA template (20 mM). Thermocycling comprised an initial denaturation at 95°C for 5 min followed by 35-40 cycles of annealing at 95°C for 30 sec, extension at 50°C for 30 sec and denaturation at 95°C for 40 sec, followed by a final annealing/extension at 72°C for 5 min. Excess dNTPs and primers were removed from PCR product by addition of 1. 45 μl Exonuclease 1 (Fermentas; 5 u/μl), 2. 85 μl FastAP (Thermo Scientific) shrimp alkaline phosphatase (1 u/μl) and 2. 85 μl dH20 and held at 37°C for 30 min and then 80°C for 15 min. Product was sequenced offsite at MacroGen USA  (Maryland). Sequence data for one to two additional flamingo individuals per species for five loci (Additional file 8) were collected in the Dijon, France lab. For these samples, genomic DNA was extracted from blood samples for the Greater, American and Chilean Flamingos and feather samples for the remaining three species using a standard phenolchloroform method.
Sequence treatment and phylogenetic analysis Sequences were aligned within Sequencher 4. 8 and alignments were confirmed within MEGA 5. 2  [47]  using ClustalW with default settings. Pairwise genetic distances were calculated in MEGA 5. 2 using the default Maximum Composite Likelihood substitution model except for COI, which was calculated using Kimura 2-Parameter. Maximum likelihood phylogenetic analyses were carried out within MEGA 5. 2 and the loci were concatenated. Unless the dataset comprised only coding genes (ZENK, COI and cyt b), each dataset was treated as noncoding; optimal substitution models were found within the hierarchical BI and ML framework in MEGA 5. 2 (Table  2 ) and the analysis used a nearest-neighbor-interchange heuristic search. Bootstrap support was estimated from 500 replicates using the same settings.
Primary phylogenetic analyses using Bayesian inference were carried out in MrBayes 3. 2. 1  [48]. Datasets were fully partitioned based on the optimal substitution models estimated within MEGA 5. 2. Each search comprised two concurrent runs of four chains each for 10,000,000 generations sampled every 1,000 generations with the first 1,000,000 generations discarded as burn-in. The results of each analysis were tested for convergence of phylogenetic signal in Tracer 1. 5  [49] , where estimated sample size (ESS) values >200 were treated as reliable signal. The primary analysis employed all 10 nDNA and two mtDNA loci for one individual per species. All secondary analyses employed the same parameters as the primary analysis except where noted. To test for the presence of intraspecific variation, a series of singlelocus analyses were carried out using two to three sequences per flamingo species for five loci (Additional file 2).
To recover individual gene trees, each locus was analyzed independently. To test the relative weight of each locus to the combined dataset, a series of analyses were run excluding a single gene at a time. To test for conflicting signal between the nDNA and mtDNA datasets, each was analyzed independently. To test for the effects of over-partitioning of the Bayesian inference analysis, a Bayesian analysis was carried out treating the total dataset as non-partitioned (as in the ML analysis). A Bayes factor analysis was carried out in Tracer 1. 5 to compare the strength in phylogenetic signal of the fully partitioned and non-partitioned BI treatments. *BEAST 1. 8. 0  [50]  was used to test for discordance between individual gene trees and the species tree. The dataset was fully phased by individual and gene.
Acknowledgements We would like to thank the many people and institutes for sample collection and/or approving sample loans:  Sharon Birks  ( Burke Museum, Seattle WA ),  Brooks Childress  and  Rebecca Lee  ( Wildfowl & Wetlands Trust, Slimbridge, UK ),  James Dean  ( National Museum of Natural History, Washington DC ),  Jack Demboski (Denver Museum of Nature and Science, Denver CO) ,  Becky Desjardins  and  John Gerwin  ( NC Science Museum, Raleigh NC ),  Steve Emslie  ( UNCW bird collection, Wilmington NC ),  Maureen Flannery  and  Jack Dumbacher  ( California Academy of Sciences, San Francisco CA ),  Blair Hedges  ( Penn State University, University Park PA ),  Janet Hinshaw  ( University of Michigan Museum of Zoology, Ann Arbor MI ),  Peter Houde  ( New Mexico State University, Las Cruces NM ),  Diana Humple  ( Point Reyes Bird Observatory, Petaluma CA ),  Irby Lovette  ( Cornell University Museum of Vertebrates, Ithaca NY ),  Steve Cardiff ,  Donna Dittman  and  Van Remsen  ( Louisiana State University Museum of Natural Science, Baton Rouge LA ),  Mark Robbins  ( Kansas University Biodiversity Institute, Lawrence KS ),  Paul Sweet  and  Joel Cracraft  ( American Museum of Natural History, New York NY ),  Krystof Ziskowski  ( Yale Peabody Museum of Natural History, New Haven CT ), Sylvan Heights Waterfowl Centre  ( Scotland Neck NC ),  Parc des Oiseaux (Villars-les-Dombes, France) ,  Parc Zoologique d'Amnéville (Amnéville-les-Therme, France) ,  Salins Group and Tour du Valat (Camargue, France).
The research leading to these results has received funding from the  National Science Foundation of Hungary  ( OTKA K83710 ,  K100722 ), the  National Development Agency  ( BONUS HU 08/2-2011-0006 ) and the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  nu245009. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Results To bypass the early embryonic lethality of dicer-null mice  [2] , we developed a new Cre-loxP-mediated conditional deletion of dicer in tie2-expressing cells in order to investigate its role in vascular development. To this end, we crossed dicer-floxed mice (dicer fl/fl )  [4]  with tie2-Cre transgenic mice  [4, 34]. The resulting heterozygous double transgenic mice (dicer ΔEC/+ ) were viable. Intercrosses of dicer ΔEC/+ male with dicer fl/fl females yielded no dicer ΔEC/ΔEC pups out of 293 viable offspring at birth (see Table  1 ). These data suggested that mice bearing dicer gene deficiency in tie2-expressing cells do not survive embryogenesis. To determine when the dicer ΔEC/ΔEC mice died, embryos were examined from E10. 5 to birth. Embryos were removed and embryonic DNA was analyzed for homo-or heterogeneity of the floxed allele.
Quantitative RT-PCR analysis RNAs were extracted from at least 30 ovary pairs of 3-4-days old flies with Trizol followed by DNase treatment, and quality of the RNA samples was assessed by Agilent 2100 Bioanalyser (Agilent Technologies Inc, Palo Alto CA). cDNA were prepared using random priming of 0. 5 to 1 mg of total RNA and the PrimeScript (TAKARA BIO Inc. ) or the SuperScript III RT (Invitrogen) enzymes. Quantitative PCRs were performed using Power SYBR Green Master Mix (Applied Biosystems), on a SDS 7900 HT instrument (Applied Biosystems). Each experiment was performed in biological triplicates and technical triplicates. Relative RNA levels were calculated using the GeNorm method  [32]  and normalized to the four control genes tubulin a, rp49, Ef1g and Gapdh1. All real-time PCRs were performed at the Genomics Platform, NCCR ''Frontiers in Genetics'' (http:// www.
Small RNA sequencing and analysis Small RNAs were extracted from heterozygous and homozygous Su(var)3-7 R2a8 mutants ovaries using RNeasy Mini Kit (Qiagen) followed by DNase treatment. RNA fractions ranging from 15-30 nucleotides were isolated and purified from 15% polyacrylamide gels, modified by sequential ligation of 39 and 59 adapters. The constructs were purified again on an acrylamide gel to remove emtpy adapters and then reverse-transcribed and PCRamplified. High-throughput sequencing was performed on a Illumina HiSeq 2000 (1650 cycles) (FASTERIS SA, Switzerland). After demultiplexing and adapter removal, 8. 79 M and 15. 89 M reads were obtained for these libraries. The libraries are deposited at GEO (Gene Expression Omnibus) under the accession number GSE53015 (http://www. ncbi. nlm.
nih. gov/geo/query/acc. cgi?acc = GSE53015). The resulting libraries were analyzed as previously described  [12]. Only piRNA populations ranging from 23-29 nt matching the Drosophila melanogaster genome release 5. 47 were considered for downstream analysis. Inserts mapping to piRNA clusters (perfect matches, unique position) and transposon sequences (allowing 1 mismatch) were identified using Samtools according to  [12]. Quantification of inserts overlapping the piRNA clusters and transposon sequences was done using SeqMonk V0. 19 software (Babraham Bioinformatics), and libraries were normalized for comparison to miRNAs (see Figure  S4 ). Post-processing of the raw counts was performed using R 2. 7. 1 software (R Foundation for Statistical Computing).
Acknowledgments We are thankful to  Mylene Docquier  from the  Genomics Platform  of the  NCCR program  '' Frontiers in Genetics '' for help in performing and analyzing the real-time PCR experiments and  Patricia Otten  ( FASTERIS SA ) for bioinformatic analysis of the small RNA librairies. We thank  H. Lin  for fly stocks and  K. Saito  for Piwi antibody. Special thanks to  Jean Michel Gibert  and  Francois Karch  for support and to  Benjamin Barandun  for taking care of the fly stocks.