dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, succeeding in getting a protein to crystallize is definitely not the final hurdle in the determination of its threedimensional structure. Molecular replacement (MR) remains the method of choice for structure determination, especially since the international structural genomics effort in the last decade has provided a tremendous increase in the number of structures deposited in the Protein Data Bank (PDB; http:// www. pdb. org; Berman et al. , 2000). Even if molecular replacement can sometimes be straightforward, it can also provide incorrect solutions or solutions that are not refinable. Here, we will discuss some of the most evident bottlenecks that occur when there is low sequence identity between the reference structure and the target or when the reference structure only provides a partial solution (multi-domain proteins or structures of complexes).
The accuracy of the model is essential This raises the question of how to generate accurate models. One way would be to use as much information as possible to retrieve the features that drive a sequence to fold in a specific structure. Given the number of available sequences and structures in the databases, multiple alignment is the first option to define the boundaries of secondary-structure elements. 3D-Coffee or Expresso  (Notredame, 2010; Poirot et al. , 2004; Armougom et al. , 2006)  are able to retrieve the best structural homologue for each of the sequences included in the alignment, including the target itself, thus allowing the structural information to be mapped onto the multiple alignment. It also provides a CORE index, which is a measure of the accuracy of the alignment for each sequence at every position in the alignment.
Consequently, the parts of the reference structures that are accurately aligned to the target sequence become obvious, as well as those which are inaccurate and could eventually be removed to optimize the models (Fig. 1 ). This provides the core structure most likely to provide a molecular-replacement solution. A set of models can then be generated by mapping the target sequence onto the best reference structures based on the Expresso CORE index. It can thus happen that the final models correspond to chimeric structures, where one reference structure is used to model part of the target sequence and another reference structure will help to build the rest of the model (PDB entry 1j2r;  Claude et al. , 2004). We implemented the CaspR server (http:// www. igs. cnrs-mrs. fr/Caspr2/index. cgi), which is an automated molecular-replacement procedure using a set of standard crystallographic tools together with multiple alignment.
The first step of the process is to produce a robust multiple alignment using the Expresso software, and the CORE index information is provided to MODELLER  (Sali & Blundell, 1993; Fiser & Sali, 2003)  with a random initial perturbation to generate a large number of different models. With properly selected sequences and structures, MODELLER will produce a set of accurate models. Fig. 2  illustrates the structural space sampled by the main chains of the models, with the width of the ribbon being proportional to the variability between the models and with the core structure deviating less than the loops. The CaspR suite will truncate unreliable parts of the alignment, thus doubling the number of models (truncated parts are represented in red in Fig. 2 ). Molecular replacement using the AMoRe software  (Navaza, 2001)  is performed for each model in search of a possible solution.
, 2007; Keegan & Winn, 2008; Adams et al. , 2011; Vagin & Teplyakov, 2010; Long et al. , 2008) ; however, few of them use multiple alignment to generate their models even if the pruning of unreliable parts of the structure can sometimes be proposed. The set of models based on multiple alignment generated by CaspR can always be retrieved and used to screen for a molecular-replacement solution using any other available software. It is now worth discussing a problem that arises when the divergence between the target sequence and the reference sequences/structures spans a large fraction of the target sequence. In such cases, the resulting truncations may make the refinement of true molecular-replacement solutions tedious. This can also happen when working with multidomain structures (or complexes of macromolecules) where there is a structural homologue available for only one domain  (or one molecule of the complex).
6 A ˚resolution (Fig. 4 ;  Abergel et al. , 2007). In recent decades, much effort has been invested in refinement and in automatic building programs, and their use would probably have reduced the time and effort that we invested to progressively build the Ivy structure. For example, Buccaneer  (Cowtan, 2006)  allows the Ivy dimer structure to be built from the phases provided by the molecular-replacement solution obtained with two lysozyme molecules and, given the resolution of the data, Buccaneer performed even better when the starting phases were run through ACORN  (Yao, 2002). Owing to the expected divergence between the model and the target structure, molecular replacement is usually performed using the low-resolution part of the data. However, once a solution has been identified, the refinement and building steps will become increasingly straightforward when the resolution of the data also increases.
Lysozyme molecules are coloured grey (first monomer), pale brown (second monomer, initial solution) and light blue (second monomer, final solution). An intermediary stage of the Ivy dimer construction using BUSTER for refinement is coloured yellow. The monomers in the final Ivy dimer structure are coloured red and blue. The figure was produced using the VMD software  (Humphrey et al. , 1996). DNA and RNA. We obtained a crystal form of this enzyme in which the asymmetric unit contained a dimer consisting of one monomer in an open state and the other in a closed conformation upon binding of a phosphate molecule at the active site of the protein. As a consequence, the active-site pocket underwent a 7 A ˚closure to give an overall 2. 1 A ˚C r. m. s. d. between the two monomers (Fig. 5 ; PDB entry 4bnq).
They found that FA is the most comparable measure across different scanners and that nonlinear co-registration of FA maps reduced average inter-site CV  (Fox et al. , 2012; Vollmar et al. , 2010). A common way to reduce variability is to calibrate all scanners involved in a multicenter study, with the optimal procedure being to scan a number of participants as a reference group at each study center. However, this is a time consuming and expensive procedure, and not viable for many important but underfunded studies. Therefore, other equally valid ways of ensuring comparability of DTI results from different scanners should be explored. As a proof of concept, we used DTI data from early HD and control participants acquired from four different scanners. For comparison, we defined five ROIs based on previous findings in DTI studies with HD patients, i.
As the primary outcome variable the present study focused on FA data as the relevant DTI metric since FA differences between HD subjects and controls have already been reported  (Douaud et al. , 2009; Fox et al. , 2012; Rosas et al. , 2006). For analysis, we suggest a three-step procedure as outlined in Fig. 1. In the first step we tested whether the CV of FA values in predefined ROIs differed between centers. In this step we used data from control participants to estimate potential influences independent of disease related factors. In the second step we tested whether group differences between patients and controls differed between centers. In the third Fig. 1. Different sources of variability may contribute to the diffusion data of control subjects (CO) and patients with Huntington's disease (HD).
Although the contribution of each source of variation cannot be singled out, the coefficient of variance (CV) from the CO data of each center serves as an estimate whether one or more centers contributed systematically biased data. Investigating FA group differences between patients and controls on systematic between-center differences is a further criterion to decide whether pooling across centers is permitted. The calibration procedure as well as the inclusion of more subjects is an option and is an alternative if the various checks do not permit pooling the data. step patient and control data from different study sites were pooled and a voxel-wise whole-brain analysis was performed to assess whether putative group differences were present in the selected ROIs. This procedure may also help to monitor multi-center (DTI) studies.
Data collection and participants Data in this study were collected as part of the European PADDINGTON project at four study sites in Europe (Leiden, the Netherlands; London, UK; Paris, France; Ulm, Germany). The PADDINGTON project, which involves five partners (Siena Biotech SpA, University of Ulm, University College London, the London School of Hygiene and Tropical Medicine and KCR Poland), is an international initiative that aims to provide pharmacodynamic approaches for disease-modifying clinical trials. The work package 2 of this project entails the collection of volumetric 3 Tesla MRI and DTI scans acquired with standardized acquisition protocols from patients with HD in an early disease stage and from healthy control participants, with the objective of identifying biomarkers of disease progression.
DTI data from work package 2 were used to evaluate our three-step procedure. All participants gave written informed consent. Subjects were at least 18 years old. HD patients had a genetically confirmed diagnosis with a trinucleotide (CAG) repeat length of 36 or higher, and had clinical features of mild HD at least of stage I based on the Unified Huntington's Disease Rating Scale (UHDRS) with a total functional capacity (TFC) score of 11-13. All participants were ambulatory and agreed to volunteer for MRI scanning. Subjects with claustrophobia and conditions not permitting MR scanning were excluded from the study. In total 61 HD and 40 control subjects (CO) were scanned (DTI acquisition time is approximately 7 min per subject). The acquisition parameters for the different sites are listed in Table  1 , and sample sizes are listed in Table  2.
Discussion In order to comply with the requirements for increased statistical power and consistency of results in clinical imaging studies, it is necessary to scan increasingly larger sample sizes. This demands data to be collected across multiple study sites, since one single center is unlikely to be able to acquire such sample sizes in a reasonable timeframe. Ideally, measured diffusion metrics should be identical across different MR scanners after their calibration. In reality, however, differences between scanning parameters at different sites and with various scanner types can influence the homogeneity of diffusionderived dependent variables. Further sources of variation include environmental noise and inter-site and inter-subject variability. Also, since one single voxel covers multiple axon bundles, slight differences in positioning may result in averaging different FA values of the same voxel because even a slight shift in the voxel position could lead to a different coverage of axon bundles.
The listed values were obtained for differences between the HD and the control group. project. Therefore, the aim of the present study was to provide an alternative approach to evaluate the feasibility of pooling multicenter data in a valid and robust fashion. Within the framework of a three-step testing procedure we observed that in control participants CVs calculated in different a priori selected ROIs did not significantly differ between study sites. Although the specific contribution of any individual source of variation cannot be detected by this approach, the consistency of this measure across centers suggests that a systematic bias due to one or more centers is unlikely. Especially when controlling for influences of control participants' age all F-values in the present analyses of center effects on CV data were clearly below the critical threshold of F(3,35) = 2.
The results of our analysis demonstrate that multicenter pooling of DTI data can be performed in a valid manner. The resulting voxelwise pattern of FA group differences between patients and controls showed a high consistency with previous reports performed at single centers  (Douaud et al. , 2009; Rosas et al. , 2006; Weaver et al. , 2009)  and also encompassed all ROIs from which previous test statistics had been derived. Rosas et al. (2006)  compared FA maps of HD subjects against controls on a single center level, and they found decreases in the internal and external capsules, the corpus callosum and the thalamic region, whereas an increase was found in the basal ganglia. This increase in the basal ganglia appears to be a main feature of HD pathology probably reflecting a decline of medium spiny neurons.
Conclusion Patterns of changes in fractional anisotropy associated with early HD need to be reproduced at larger scales in order to improve reliability and to meet the requirements of clinical trials, which imposes the necessity of multicenter studies. In the present study we present a new approach for assessing feasibility and validity of pooling DTI data from different centers if time or cost limitations do not permit scanning of a reference sample at each study site. This study may serve as a reference for future multicenter trials employing MRI to measure disease-specific progression independent of site specific conditions. Another technical application of this methodological framework may be the interim quality control of DTI data during an ongoing multicenter imaging trial.
Material and methods (a) Datasets and non-homogeneous models Boussau et al. [7]  built a concatenate of small-and largesubunit rRNAs from 456 organisms (2239 sites) and used the sites restricted to stem regions (1043 sites) to infer the ancestral G þ C contents over the tree of life. From these alignments, we selected 125 species covering a broad taxonomic diversity without redundancy in the taxonomic sampling. Regarding the concatenation of proteins, the 56 gene families and 30 species considered in Boussau et al. [7]  were used here, and increased to 38 species, with the addition of Archaea species in particular, which were poorly represented in the first set of species. We reconstructed ML phylogenetic trees for rRNAs (on the 2239 sites dataset) and proteins with PHYML  [8]. A three-domain tree was obtained and the root was placed on the branch between the ancestors of Bacteria and Archaea/Eukaryotes.
As in  [9]  and  [7] , the branch-wise equilibrium frequencies were estimated along these universal phylogenetic trees. The stem dataset was analysed with the BPPML program  [10]  assuming a discrete gamma distribution with eight categories to model rate variation among sites and the non-homogeneous Galtier & Gouy (GG) substitution model  [11]. The GG model specifies branch-wise equilibrium G þ C contents, as well as an independent G þ C content at the root. For proteins, we used a new branch-wise non-homogeneous model implemented in the maximum-likelihood (ML) framework, named COaLA  [12]  that we recently designed. See the electronic supplementary material for a description of the COaLA model and an evaluation of the fit to data of the non-homogeneous models in comparison with homogeneous models.
(b) Molecular thermometers OGT highly correlates with the G þ C content of the stem regions of rRNAs (r ¼ 0. 76, p-value , 0. 001; see the electronic supplementary material, figure  S2 ) and with the second axis of the COA computed on amino acid compositions of the protein dataset restricted to prokaryotic species (r ¼ 0. 88, p-value , 0. 001; see the electronic supplementary material, figure  S3 ). We controlled for phylogenetic inertia with the phylogenetic independent contrast approach  [13]  using the R package APE  [14]  and observed that those correlations were still strongly significant. Linear regressions between OGTs and compositions were then computed to obtain the molecular thermometers.
Results and discussion We first confirm results obtained in  [7]  with the present rRNA and protein datasets and the non-homogeneous GG  [11]  and COaLA  [12]  substitution models in ML. Figures 1 and 2   rsbl. royalsocietypublishing. org Biol Lett 9: 20130608 show that LUCA is estimated to have lived in colder environments than the ancestors of Bacteria and Archaea (Wilcoxon test, p-value , 0. 001), which were hyperthermophiles. Electronic supplementary material, figure  S1  shows that this pattern is also recovered when an alternative tree topology is used, in which Eukaryotes branch within Archaea (Eocyte hypothesis  [15] ) but is less pronounced with the homogeneous LG model, which infers a thermophilic LUCA. The phylogenetic signal that informs a non-hyperthermophilic LUCA and yet two hyperthermophilic descendants is currently unknown.
However, several points suggest that the variation in evolutionary rate among sites plays a role. First, Fournier & Gogarten  [16]  highlighted that amino acids that are found in higher proportions in hyperthermophilic species are rarer at slow-evolving sites. Such amino acids notably include charged residues  [6]. Second, the signal for a parallel adaptation to high temperatures is partially lost when COaLA is employed without a gamma distribution to model the variation in rate among sites (see the electronic supplementary material, figure  S1 ). To highlight the influence of rate variation among sites in the differential recording of ancestral compositions, we partitioned the rRNA and protein datasets according to the site evolutionary rates. Figure  2a ,b shows that with slow-evolving sites, all ancestors are inferred to be mesophilic organisms, LUCA being adapted to lower temperatures than its two descendants.
The ancestral compositions of fast-evolving sites tend to favour hotter ancestral environments, even for LUCA with proteins. But LUCA is still inferred to live at lower temperatures than the ancestors of Bacteria and Archaea. As expected, the quantitative estimates of past temperatures inferred by both slow-and fast-evolving sites are different from those obtained with the complete dataset. Indeed, although slow-evolving sites conserved reliable signals for ancestral compositions, they carry less phylogenetic information for the early parallel adaptation to high temperature, which explains why this pattern is less pronounced than that with the complete dataset. However, both the G þ C content (rRNAs) and the third axis of a correspondence analysis (proteins) computed from the slow-evolving sites of extant sequences correlate with OGT (r ¼ 0.
Figure1. Evolution of OGT along the universal tree of life obtained with the protein dataset. Branches have been coloured according to temperature estimates at nodes, following a linear interpolation from node to node. OGTs for Eukaryotes are not available, their branches are therefore grey coloured. The branch length scale is in substitution/site. The colour scale is in 8C. Mean estimates of temperature for LUCA and the ancestors of major domains are given above branches. Confidence intervals (95%) for estimates of ancestral OGTs are given between square brackets.
As shown in Figure  3A , very few of the transfected cells had atypical connections (see also quantitative data presented in Panel C). The modest increase in the number of cells with connecting chromatin in Myc-Rab22 transfected cells was not statistically significant. To investigate whether the final abscission of the daughter cells was affected, we performed time-lapse videomicroscopy experiments on cells transfected with GFP-Rab24. As control, cells transfected with the vector GFP alone were used. Cells undergoing cytokinesis were analyzed and images were collected every 20 min for up to 15 h. Strikingly, during the whole imaging period, cells overexpressing GFP-Rab24 remained connected by a narrow cytoplasmic bridge containing lagging chromatin which did not undergo abscission even after 15 h and usually the cells then finally died (Figure  S4B  upper panel).
Immunofluorescence studies Transfected and not transfected cells were grown on glass chamber slides as monolayers. After 1 day in culture the slides were washed with PBS and fixed in methanol (2 min at -20 • C) or in 3% paraformaldehyde (PFA) for 10 min at room temperature, washed with PBS and blocked with 50 mM NH 4 Cl in PBS. Subsequently, cells were permeabilized with 0. 2% Saponin plus 1% BSA, in PBS for 15 min. Slides were then incubated for 1 h with the primary antibody diluted in the same media (1% BSA, 0. 2% Saponin in PBS). After six washes, detection was revealed using the secondary antibody for 1 h. Nuclei were counterstained with Hoechst (Invitrogen) or TOPRO (Invitrogen), after that coverslips were mounted in Mowiol and analyzed by fluorescence microscopy using an inverted microscope (Nikon Eclipse 2000), Nikon C1 Confocal Microscope System and with the EZ-C1 program (Nikon) or Olympus FV1000 Confocal microscope, controlled by the Olympus software (Universal Imaging).
Time-lapse imaging For live imaging, HeLa cells were plated on 35 mm dishes containing cover slips, transfected the next day with GFP or GFP-Rab24wt and 10 h later placed in a temperature controlled chamber, in complete medium with 2 μM Hoechst at 37 • C. Cells were imaged every 20 min for up to 15 h with an Olympus FV1000 microscope, controlled by the Olympus software (Universal Imaging). Rab24 KD cells were analyzed at 37 • C with an inverted microscope (Nikon Eclipse TE 300, Germany). Images were processed using Nikon software (Universal Images Corporation). As a control HeLa cells were transfected with an irrelevant siRNA. The images are representative of at least four independent experiments.
Acknowledgments We are indebted to  Dr. Jerold Chun  ( The Scripps Research Institute, La Jolla, CA, USA ) for critical reading of this manuscript and helpful suggestions. We also thank  Dr. Luis Mayorga  for useful comments. We thank  Alejandra Medero  for valuable assistance with tissue culture,  Marcelo Furl  án for technical support and  Norberto Domizio  for excellent assistance with confocal microscopy. We are grateful to  Annick Boulet  for the construction of the myc-tagged Rab24 plasmids and to  Dr. William Maltese  for the gift of the Rab24 mutant ( D123I ). This work was partly supported by grants from  Agencia Nacional de Promoci ón Científica y Tecnol ógica  ( PICT2004  #  20711 ,  PICT2008 # 0192 ),  SECTyP  ( Universidad Nacional de Cuyo ) and  PIP 5943 (CONICET)  to M. I. C.
Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis.