dataesr/scientific-paragraphs-categorization huggingface dataset:
As sequencing depth depends on GC-content, we used the experimentally observed GC-content dependency function from the ENCODE dataset for the MCF-7 cell line (input and H3K9me3). The code for read generation together with parameters used and necessary files can be found at the HMCan webpage (package 'GenerateReadsChIP-seq'). Generated reads were aligned to the reference genome with BWA  (Li and Durbin, 2009)  using default parameters. To quantify the quality of predictions of HMCan and other tools, we calculated overlap between the predicted regions and the simulated regions at the base pair level. If a base pair within a predicted region overlapped with a simulated one, this base was counted as true positive. If it lay outside of the simulated region, it was counted as false positive. Finally, if a base pair within a simulated region was not covered by any prediction it was counted as false negative.
, 2010) , MACS  (Zhang et al. , 2008)  and SICER  (Zang et al. , 2009)  (Fig. 2 ). CCAT applies an iterative method to estimate the noise-to-signal ratio in ChIP-seq and control data based on a linear model. MACS shifts the reads toward the fragment centers and uses a dynamic Poisson model that is able capture the mean and standard deviation of the data. SICER applies a read clustering approach to detect regions enriched with histone marks. For each tool, we ranked the predicted regions according to the in-built score or P-value and grouped them in sets of regions having similar scores. By using a threshold on this score or P-value, we obtained 'precision vs recall' curves. The accuracy of predictions was qualified on the basis of the closest (Euclidian) distance from the ideal predictor performance as introduced in  Bajic´(2000) , which in our case is the distance from the (1,1)corner of the 'precision vs recall' graph (Fig.
Evaluation on H3K27me3 data To assess the performance of HMCan on real data, we generated ChIP-seq dataset for trimethylation of lysine 27 on histone H3 (H3K27me3) for the CL1207 human bladder transitional cell carcinoma cell line (see Materials and Methods). We compared HMCan and CCAT on this dataset. As the MACS and SICER tools demonstrated a bias toward high copy number regions (Figs  3  and 4 ), the comparison of HMCan with MACS and SICER is given in Supplementary Materials (Supplementary Figs S4 and S5 and Supplementary Table  S3 ). To detect the H3K27me3 mark, we ran HMCan and CCAT with the parameters learned from the simulated data study. We considered all regions detected by HMCan or CCAT regardless of the score (see justification in Supplementary Methods). Overall, 32. 8 and 28% of the genome were covered by regions predicted by HMCan and CCAT, respectively.
g. MACS, CCAT or SICER) and may require up to 1 h on a standard PC (Supplementary Table  S4 ). Running iterative HMMs is the most time-consuming step. However, we believe that the greater accuracy of HMCan demonstrated in this study compensates for a longer run time. In some cases, it might be interesting to detect differential histone modifications using two ChIP-seq datasets generated for two different conditions. HMCan does not as yet contain such a function, and we advise users to apply standard tools such as DESeq  (Anders and Huber, 2010)  to the output from HMCan.
CONCLUSION HMCan was specifically developed to analyze histone modification data obtained for cancer genomes. Cancer genomes are characterized by frequent copy number alterations: gains and losses of large regions of chromosomal material. The designed algorithm explicitly corrects for copy number alterations, and thus does not demonstrate bias within the number of predicted sites in the regions of gain or loss. In addition, HMCan corrects for possible GC-content bias independently in the ChIP and control sample. This guarantees GC-content-unbiased results even in the case when the two experiments are performed in different laboratories and even using different sequencing techniques. Also, the iterative HMM method formulated in HMCan allows for getting the best distinction between signal and noise on sequential data.
HMCan accepts the most common alignment formats: SAM/BAM and BED, and outputs predicted regions in BED and WIG formats. We successfully applied HMCan on both simulated and experimental data. On simulated data, we demonstrated HMCan's higher accuracy in signal prediction compared with the tools designed for normal genomes (MACS, SICER and CCAT). Unlike MACS and SICER, HMCan did not show bias in number of identified peaks toward gained regions. In our simulations, we modeled signal in regions present in 1, 2, 3 or 4 copies. HMCan detected the signal in all of them, including cases where the signal was initially present in only one out of four alleles. On the experimental ChIP-seq dataset generated for the repressive mark H3K27me3 in the CL1207 human bladder transitional cell carcinoma cell line, peaks in proximity of gene TSSs that were detected only by HMCan corresponded to lower gene expression compared with the peaks detected only by CCAT.
ACKNOWLEDGEMENTS The authors thank  A. Zinovyev  for helpful suggestions and critical comments and  K. Bleakley  for english proofreading. Funding: Project  ABS4NGS , funded by the ' Bioinformatique 2011' call, action 'Sante´Biotechnologie' of Investissement d'avenir  and  Agence Nationale de la Recherche , under contract [ ANR-11-BINF-0001 ]; grant  INCA LABEL Cance´ropole Ilede-France 2011-1-LABEL-1  and the grant INVADE from  ITMO Cancer  ( Call Systems Biology 2012 ). E. B. and V. B. are members of the team ' Computational Systems Biology of Cancer ',  Equipe labellise´e Ligue Nationale Contre le Cancer. KAUST Base Research Fund of V. B. B  (to H. A. and V. B. B. ).
(2010b)  and  Boselli et al. (2012) , while the dust properties of the early-type systems in  Smith et al. (2012a). Some of these works are already taking advantage of the spectroscopic data presented here. The spectroscopic data presented in this work, as well as those collected at other frequencies, will be made available to the community through the dedicated HeDaM database (http://hedam. oamp. fr/; Roehlly et al. 2012).
cluster, to loose groups and fairly isolated systems. As defined, the present sample is ideal for any statistical study of the mean galaxy population of the nearby universe. This paper is focused on the late-type galaxies of the sample. We present new spectroscopic observations of 134 galaxies 2  , and we combine this new dataset with the one already published by our team for Virgo cluster objects  (Gavazzi et al. 2004)  or available in the literature  (Kennicutt 1992a (Kennicutt , 1992b;; Jansen et al. 2000; Moustakas & Kennicutt 2006; Moustakas et al. 2010)  with the purpose of providing the most complete and homogeneous list of spectroscopic parameters of these late-type systems. The resulting spectroscopic sample is fairly complete since it includes 238 out of the 260 late-type systems (see Figure  1 ).
For those few objects with angular size larger than the size of the slit, the drift was done in the direction perpendicular to the major axis. Drifting was obtained by slewing manually several times the telescope between two extreme positions checked on one offset star or on the galaxy itself. Not unexpectedly spectra obtained in this way have lower S/N ratio than traditional long-slit spectra of similar integration time, because a large fraction of the time is spent on low surface brightness regions. Bright stars overposed on the disc of the galaxies were avoided, whenever possible, during the observations, or identified as high-surface brightness regions in the spectra and removed during the data reduction. Our spectra cover the wavelength range 3600-6900 (from  [Oii]  to  [Sii] ) with a resolution of R ≃ 1000.
Data reduction In order to form a homogeneous dataset with the existing observations of  Gavazzi et al. (2004) , we apply the same data reduction method to the new observations presented in this work. The reduction of the observations from the raw, two-dimensional images into calibrated, one-dimensional spectra is performed using standard tasks within the IRAF 5  package. Bias subtraction is applied using the median of several bias frames and the median of exposures of quartz lamps is used for correcting the flatfield. Cosmic rays are removed by using cosmicray. Under visual inspection, remaining extended features are manually removed and any bad pixel is masked. The wavelength calibration is carried out using identify, reidentify and fitcoord on combined exposures of helium and argon lamps, typically using 20-30 arc lines.
1%, and this increases to 3. 6% at the blue end (λ < 5500Å). The largest discrepancy occurs at λ < 4000 Å, where the difference in one object reaches ∼ 20 %. In the red (λ > 6800 Å) the differences are 15%. Although we are limited by the low number of objects for comparison, it appears that the largest discrepancies occurs in a galaxy observed in cirrus conditions (HRS 54). Ten galaxies observed in this survey and six in  Gavazzi et al. (2004)  have integrated spectroscopy data available in the literature from  Jansen et al. (2000) ,  Moustakas & Kennicutt (2006)  and  Moustakas et al. (2010). We compare our results in Fig. 3. Despite various differences in how these sets of spectra were obtained, we find an average relative difference of 7. 0% with some wavelength dependence present for some objects (see Table  2 ).
The relative scatter of the red portion (λ > 5500Å) of the spectrum is around 5. 7%, and it increases to 8. 9% at the blue end (λ < 5500Å). The  Moustakas et al. (2010)  spectra are typically redder than our spectra. The differences between our and the  Moustakas et al. (2010)  spectra reach ∼ 60 % at λ ∼ 4000 Å and 35 % at λ ∼ 7000 Å in HRS 205, while is generally 20 % and 15 % in the same spectral domain when our data are compared to  Moustakas & Kennicutt (2006)  and  Jansen et al. (2000). This large difference might result from the fact that the  Moustakas et al. (2010)  spectra are limited to the radial strip observed by Spitzer within the SINGS project. These strips cover the inner regions and are thus significantly less extended than the optical discs of the observed galaxies. Because of the colour gradient, these inner spectra are redder, as clearly evident in NGC 4254 (HRS 102).
We further test our spectrophotometric accuracy by comparing magnitudes synthesized from our spectra to the recently published broadband photometry from  Cortese et al. (2012a). Synthesized spectroscopic gr colours were obtained by deconvolving the continua with the profiles of the g and r SDSS filters. Figure  4  shows the comparison between the photometric and the spectroscopic gr colours synthesized for all the HRS galaxies with available photometric data. The mean offset is 0. 03 mag with a rms scatter of 0. 09 mag. Overall the agreement between the two sets of data is good, considering that the error on the photometric magnitudes is of the order of ∼ 10 %. A systematic difference is however present for the bluest galaxies, where the synthetic colours are bluer than the photometric ones by 0.
38 Å). Variations with the metallicity are at most of 0. 3 Å, thus 10 % of the E. W. Hα abs. Sandage model star formation histories give values of E. W. Hα abs in the range 2. 3-3 Å, slightly lower than those predicted by the Boissier & Prantzos models (2. 6 E. W. Hα abs 3. 5 Å) in the range of τ and M star expected for the HRS galaxies. With the data in our hand we are at present in the impossibility of identifying which between the two models (Sandage vs. Boissier & Prantzos) better reproduces the observations, making the choice of a constant value of E. W. Hα abs reasonable. Figure  5  shows that the adoption of a constant value of E. W. Hα abs might induce systematic effects in the dataset presented and analysed in this work. These effects, however, should be very minor. The mean equivalent width of the Hα line measured from our spectra is of the order of 25 Å (see sect.
Comparison with integrated spectra A few HRS galaxies have been observed using the same drift scan technique adopted in this work by  Kennicutt (1992a; 4 objects) ,  Jansen et al. (2000; 5) ,  Moustakas & Kennicutt (2006; 16)  and  Moustakas et al. (2010; 8). Their integrated spectroscopic data are given in Tables  10  and 11  and can be compared to those obtained in this work or in  Gavazzi et al. (2004). Figure  8  shows the relationship between the equivalent width (left) and the normalised flux (right) of the most important emission lines for galaxies in common. We consider here the data published in  Gavazzi et al. (2004)  together with those collected in this more recent observational campaign, as both datasets have been taken using the same telescope with the same instrumental configuration.
g. Elbaz et al. 2011; Smith et al. 2012b ). We thus use either of the two parameters in the following analysis. Gas metallicities, 12+log(O/H), are derived using the set of data described in this paper as indicated in Paper II  (Hughes et al. 2012). We follow the conversions of  Kewley & Ellison (2008)  for five different metallicity calibrations from the literature and adopt the PP04 O3N2 calibration on the [Nii] and [Oiii] emission lines  (Pettini & Pagel 2004)  as the base metallicity. We then determine the average oxygen abundance in units of 12+log(O/H) for each galaxy. Integrated HI gas data, available for almost the totality of the late-type galaxies of the sample, are used to detect those objects that might have suffered any kind of perturbation induced by the Virgo cluster environment.
Interferometric data, mainly taken in order of preference from VIVA  (Chung et al. 2009a) ,  Cayatte et al. (1994)  and  Warmels (1986) , are used to measure HI gas column densities (in M⊙ pc -2 ). For the VIVA galaxies, column densities are determined using the isophotal HI radius 14. For the sake of homogeneity, we normalise the HI column densities given in  Cayatte et al. (1994)  and in  Warmels (1986)  by calculating the mean ratio of the column densities measured for galaxies in common with the VIVA survey. This gives log ΣHI(VIVA) = 0. 92 (± 0. 58) log ΣHI(Cayatte) 15  and log ΣHI(VIVA) = 1. 04 (± 0. 45) log ΣHI(Warmels). 12 CO(1-0) imaging data are available for a small fraction of the HRS galaxies. We estimate H 2 gas column densities using the set of data of  Chung et al. (2009b).
Line diagnostic Spectral line emission is often used to characterise, using different diagnostic diagrams, the nature of the emitting source. The set of data in our hands allow us to construct two of the three mostly used diagnostic diagrams, the [Oiii]λ5007/Hβ vs. [Nii]λ6584/Hα and the [Oiii]λ5007/Hβ vs. [Sii]λ6717-6731/Hα (Fig. 11 ). To discriminate active galaxies from normal, star forming objects here we use the recent definitions of  Kewley & Ellision (2008). Despite the presence of a large fraction of active galaxies in the sample 16 , the diagnostic diagrams shown in Fig. 11  indicate that the integrated spectra of the HRS galaxies are mainly those of star forming objects (HII), with a few composite spectra (Comp). Only one galaxy, NGC 4388, has an integrated spectrum typical of a Seyfert galaxy.
The contribution of the nuclear emission to the integrated spectrum is thus more important in HI-deficient objects than in gas rich systems. Despite the contribution of the AGN to the integrated spectra is minor, objects hosting an active nucleus should be removed in any study of the physical properties of the ISM since their line emission can be partly contaminated by the nuclear activity. The Herschel Reference Survey, however, has been designed not only to gather a complete set of multifrequency data ideal for the study the physical properties of the ISM in nearby galaxies, but also for providing a well defined reference for high redshift studies. In these studies of distant, unresolved galaxies the spectra cover a large fraction of the galaxy disc and are thus directly comparable to the integrated spectra obtained in this work  (Kobulnicky et al.
Blue symbols are for galaxies with a normal HI gas content (HIde f ≤ 0. 4), red symbols for HI-deficient objects (HIde f > 0. 4). Filled symbols are for galaxies hosting an AGN. The black cross shows the typical uncertainty on the data. The blue and red lines indicate the best fit to the data for HI-normal and HI-deficient galaxies respectively whenever evident correlations are present. Solid lines indicate the best fit obtained including all galaxies, the dotted lines excluding those objects hosting an AGN. The black dotted line indicates the relation obtained by  Gilbank et al. (2010)  using SDSS data (increased by 13 % to take into account the difference between the two extinction laws used in their  (Seaton 1979 ) and our work  (Fitzpatrick & Massa 2007) ). The black short-dashed line indicates the relationships obtained by  Garn & Best (2010)  using SDSS, increased by 30 % to take into account the difference between our extinction law and that used in their work  (Calzetti et al 2000).
Spearman correlation coefficients ρ of the relations between the Balmer decrement and the underlying Hβ absorption and the physical parameters for all galaxies (Fig. 14     ). Blue symbols are for galaxies with a normal HI gas content (HIde f ≤ 0. 4), red symbols for HI-deficient objects (HIde f > 0. 4). Filled symbols are for galaxies hosting an AGN. The black cross shows the typical uncertainty on the data. not see any relation just because of the poor statistics. Indeed quite dispersed variations have been already reported in the literature between A(FUV) and the total gas column density  (Xu et al. 1997; Boquien et al. , in prep. ) , although based on data of much lower quality. The Balmer decrement can be compared to other standard tracers of the dust attenuation within galaxies. Among these, the most widely used are the far-IR to UV flux ratio  (Buat & Xu 1996 , Gordon et al.
2000 , Witt & Gordon 2000) , and the slope of the UV spectrum β  (Meurer et al. 1999 , Calzetti et al. 2000 , Calzetti 2001 ). The far-IR to UV flux ratio has been generally measured using FUV GALEX and either IRAS or Spitzer IR Fig. 16. The relationship between the Balmer decrement C(Hβ) and the UV slope β GALEX determined using GALEX data. Blue symbols are for galaxies with a normal HI gas content (HIde f ≤ 0. 4), red symbols for HI-deficient objects (HIde f > 0. 4). Filled symbols are for galaxies hosting an AGN. The balck cross shows the typical uncertainty on the data. The black solid line indicates the best bisector fit to our data when all galaxies are included, the dotted line excluding AGNs. The orange dotted line shows the fit obtained by  Hao et al. (2011) , the green dottedlong dashed line and the dashed line the best fit for normal and starburst galaxies by  Cortese et al.
Blue symbols are for galaxies with a normal HI gas content (HIde f ≤ 0. 4), red symbols for HI-deficient objects (HIde f > 0. 4). Filled symbols indicate galaxies hosting an AGN. The black cross shows the typical uncertainty on the data for a galaxy of equivalent width of 5 Å. of the observed galaxies. The correlation with stellar mass is significantly steeper than that determined in other works based either on integrated spectroscopy or SDSS data. Although dust is tightly associated to the gaseous phase of the interstellar medium, we do not observe any relation between the Balmer decrement and the column density of the atomic and molecular gas. On the contrary, the underlying Balmer absorption looks almost independent from stellar mass, stellar surface density, birthrate parameter, star formation rate and gas metallicity.
We want to thank the OHP telescope operators J. Taupenas et J. P. Troncin for their invaluable support during the observations and the PNCG for their generous allocation of time at the OHP telescope. A. B thanks the ESO visiting program committee for inviting him at the Garching headquarters for a two months staying. We are also grateful to P. Anders, S. Boissier, M. Boquien, L. Ciesla and M. Fossati for their help during the preparation of the figures. We thank R. Jansen for providing us with the fits files of HRS galaxies in common. TMH acknowledges the support of a Kavli Research Fellowship. The research leading to these results has received funding from the European Community's Seventh Framework Programme (/FP7/2007-2013/) under grant agreement No 229517. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration and of the GOLDMine database (http://goldmine.
For crystallization, we reconstituted and purified telomeric DNA complexes with all three binding constructs, but only one (G233-A345) yielded crystals when reconstituted with a duplex telomeric DNA (5 0 -cTGTTAGGGTTA GGGTTAG-3 0 and 3 0 -ACAATCCCAATCCCAATCt-5 0 ) similar to the one present in the crystal structures of the TRF1 and TRF2 homeodomains bound to telomeric DNA  (Court et al, 2005). The optimized crystals diffracted to 2. 9 A ˚resolution and we could solve the structure by molecular replacement using the NMR model of the human HOT1 homeodomain (residues 268-343, PDB entry 2CUF, unpublished data from RIKEN Structural Genomics/Proteomics Initiative). In the orthorhombic crystals, the DNA forms an infinite double helix via a C-T nonWatson-Crick base pairing of the single-base overhangs (not shown).
Again, depletion of both HOT1 and TCAB1 led to shorter telomere length with a shortening of on average 700 and 600 bp, respectively. Given the short nature of telomeres in this cell line (on average, 5 kb measured by STELA) and the substantial contribution of variant repeats and subtelomeric DNA to this value  (Aubert et al, 2012) , our quantitative FISH and STELA data mutually confirm HOT1 as a positive regulator of telomere length that quantitatively behaves similar to the established telomerase pathway member TCAB1. Next we performed telomere length measurements after HOT1 depletion at different time points. We reasoned that if HOT1 is influencing telomere length in a mode dependent on telomerase activity, its depletion should lead to a gradual loss of telomeric tracts. To test this, we performed telomere length measurements upon HOT1 depletion as a time course over several days.
MS data acquisition In-gel digestion and MS analysis was performed essentially as previously described by Butter et al, 2010. Peptides were desalted on StageTips and analysed by nanoflow liquid chromatography on an EASY-nLC system from Proxeon Biosystems coupled to a LTQ-Orbitrap XL or a Orbitrap-Velos (Thermo Electron). Peptides were separated on a C18-reversed phase column (15 cm long, 75 mm inner diameter, packed in-house with ReproSil-Pur C18-AQ 3 mm resin (provided by Dr Maisch)) directly mounted on the electrospray ion source. We used a 107-min gradient from 2 to 60% acetonitrile in 0. 5% acetic acid at a flow of 200 nl/min. The LTQ-Orbitrap XL was operated with a Top5 MS/MS spectra acquisition method in the linear ion trap per MS full scan in the Orbitrap, while for the Orbitrap-Velos a Top10 acquisition method with HCD fragmentation was used.
MS spectrum and data analysis The raw files were processed with MaxQuant  (Cox and Mann, 2008)  and searched with the Mascot search engine (Matrix Science) against IPI human v3. 37 protein database concatenated with a decoy of the reversed sequences. Carbamidomethylation was set as fixed modification, while methionine oxidation and protein N-acetylation were considered as variable modifications. The search was performed with an initial mass tolerance of 7 p. p. m. mass accuracy for the precursor ion and 0. 5 Da for the MS/MS spectra obtained with CID fragmentation and 20 p. p. m. for the MS/ MS spectra in the HCD fragmentation mode. Search results were processed with MaxQuant filtered with a false-discovery rate of 0. 01. Before statistical analysis, known contaminants and reverse hits were removed.