dataesr/scientific-paragraphs-categorization huggingface dataset:
5 A ˚resolution to properly compare the anomalous signal in the two data sets. No indication of global radiation damage, such as an increase in the unit-cell volume or in the overall B factor, is observed in the data. Values in parentheses are for the outer shell. orientations) or orienting a specific unit-cell axis parallel to the ! rotation axis so that Friedel mates can be measured on the same diffraction image. To overcome this limitation, ESRF tuneable beamlines are equipped with MK3 minikappa goniometers  (Brockhauser et al. , 2013)  that allow crystal reorientation, while sample rotation is performed around ! (see Fig. 1  in  Brockhauser et al. , 2013). Truly redundant data can straightforwardly be obtained by combining data sets collected from the same crystal at different angles. In this way, equivalent reflections are recorded on a different area of the two-dimensional detector and with different X-ray path lengths through the sample.
MXCuBE2 facilitates such experiments by allowing the creation of a queue of data collections, from the same or from different positions in a crystal, at different, user-defined angles. A more complicated use of the functionality of the MK3 consists of reorienting the crystal so that an evenfold rotation axis of the unit-cell point group is oriented parallel to the ! rotation axis (Fig. 4 ). This allows the collection of both reflections in a Friedel pair on the same diffraction image, thus ensuring that they are measured at the same time and after the crystal has suffered the same X-ray dose. Calculating the and ' angles required for such a reorientation can be a complicated task that is beyond the scope of even experienced experimenters, as it requires knowledge of the beamline hardware configurations and limits.
Random Once the crystal has been reoriented, the user centres the sample in the X-ray beam, a datacollection strategy for the optimum collection of anomalous data is then calculated with EDNA and, upon the approval of the experimenter, executed. In  PDB entry 1gkk) obtained in space group P4 2 2 1 2. Two data sets were collected from different positions of a single crystal. The first data set was collected with the crystal in a random orientation, as harvested in the nylon loop, and the second with the c* axis aligned parallel to !. In both cases the datacollection strategies were as recommended by EDNA/BEST. As can be seen, reorientation along the c* axis results in a larger anomalous signal ('DelAnom' and 'Mid-Slope of Anom. Normal Probability' in Table  1 ), despite very similar total doses being used to collect data sets with very similar completeness and multiplicity.
Inverse-beam data collection Although the MK3 is an extremely useful device for aligning crystals during MAD/SAD experiments (see above) or for aligning crystals such that a long unit axis is parallel to !, a combination of unfortunate crystal orientation inside the sample loop and the limited (24 ) opening angle of the MK3 may mean that the reorientation required is out of reach. In such cases it may well be desirable in MAD/SAD experiments to ensure the collection of both reflections in a Friedel pair close together in time and in X-ray dose by resorting to inverse-beam geometry  (Hendrickson et al. , 1985; Dauter, 1997; Fig. 5). In an inverse-beam experiment the two reflections in a Freidel pair are collected at rotation angles ! and ! + 180 , respectively, and an inverse-beam geometry experiment consists of subdividing the total angular range to be collected into two data sets 180 apart in ! and collecting them, often broken down into 'subwedges', in sequence (Figs.
Interleaved MAD data collection In the early days, MAD experiments were complicated and required intervention from the local staff to properly prepare the beamline (i. e. change the energy and realign the beamline optical components and sample environment) at each wavelength at which MAD data were collected. The degree of automation achieved during the last decade at the ESRF MX beamlines  (Arzt et al. , 2005)  and at other synchrotrons worldwide  (Soltis et al. , 2008; Stepanov et al. , 2011; Cork et al. , 2006)    Effect of the alignment of a rod-like crystal along one of its evenfold symmetry axes, starting from (a) a random orientation, the result of the harvesting from the crystallization drop, to (b) a final reorientation with, in this case, c* parallel to !. diffraction data sets are alternately collected at different energies is 'interleaved MAD'  (Finke et al.
Conclusions Experimental phasing by anomalous dispersion techniques are the most successful and common ab initio methods for structure determination. The success of an anomalous dispersion experiment strongly relies on the data quality, and this becomes more significant when small anomalous signals are expected. In such circumstances, experiment design and the tools to perform experiments play a major role. Here, we have presented a list of the most common 'best practices' to be used in the collection of high-quality diffraction data for use in MAD or SAD experiments. MXCuBE2, the experimentcontrol GUI available at the ESRF Structural Biology Beamlines, facilitates these by a number of different means. MXCuBE2 allows the use of EDNA, RADDOSE and BEST to propose data-collection strategies that take into account crystal decay caused by radiation damage, it allows the straightforward implementation of complex data-collection protocols such as inverse-beam and interleaved data-collection experiments and it allows the efficient use of minikappa goniometers and of multi-position and multi-crystal data collections.
Moreover, the MXCuBE2 data-collection queue modularity smooths the way for the integration of novel datacollection methods and data analysis, as already demonstrated with the MeshAndCollect pipeline. MXCuBE2 is constantly evolving to incorporate new experiment types and to hand them over to the MX user community. Data-collection parameters and processing statistics for the feruloyl esterase module of xylanase 10B from C. thermocellum  (Prates et al. , 2001; PDB entry 1gkk). Data sets were collected from a P2 1 2 1 2 1 crystal in the form of a 'classic' two-wavelength MAD experiment and a 'interleaved' MAD experiment, in which data are collected in 10 subwedges alternating between the two energies. Data were cut at 3. 5 A ˚resolution to properly compare the anomalous signal in the two data sets.
Acknowledgements The continuous development of the  Structural Biology Beamlines  has been possible thanks to the support from the  ESRF Beamline Control Unit , all Structural Biology Group members and the feedback from our user community. MXCuBE2 is the fruit of the collaboration of the partners of the  MXCuBE consortium , which includes  ESRF ,  EMBL , the  MAX IV Laboratory, Synchrotron SOLEIL, HZB-BESSY, DESY , the  ALBA Synchrotron and Global Phasing Ltd.
Background: Proximity ligation-mediated methods are essential to study the impact of three-dimensional chromatin organization on gene programming. Albeit significant progress has been made in the development of computational tools that assess long-range chromatin interactions, next to nothing is known about the quality of the generated datasets. Method: We have developed LOGIQA (www. ngs-qc. org/logiqa), a database hosting quality scores for long-range genome interaction assays, accessible through a user-friendly web-based environment. Results: Currently, LOGIQA harbors QC scores for >900 datasets, which provides a global view of their relative quality and reveals the impact of genome size, coverage and other technical aspects. LOGIQA provides a user-friendly dataset query panel and a genome viewer to assess local genome-interaction maps at different resolution and quality-assessment conditions.
Background Today massive parallel DNA sequencing is used not only to decrypt the digital nature of genomes but, in combination with a variety of molecular biology techniques, it provides functional insights into a plethora of regulatory levels and functions, including epigenomics and protein-genome interactions (e. g. , ChIP-seq, MeDIP-seq), global transcriptional activity (e. g. , RNA-seq, GRO-seq, Ribo-seq), protein-RNA interactions (e. g. , CLIP/RIP-seq), chromatin accessibility (e. g. , DNase-seq, FAIRE-seq, ATAC-seq, MNase-seq) and the 3-dimensional chromatin organisation [HiC  [1] , ChIA-PET  [2, 3] ]. While data acquisition is not anymore an issue, today's challenge is the availability of user-friendly computational solutions to interrogate and integrate -in a comparative manner -billions of data points from different types of functional genomics datasets.
In fact, large consortia, like ENCODE, modENCODE, IHEC, NIH Epigenomics Roadmap provide enormous amounts of functional genomics data  [4]. In addition, a great number of laboratories perform functional genomics studies in a diverse set of systems covering a large number of molecular targets, such that the number of genomics data linked to various cell/(patho)physiological functions increase exponentially in public repositories like the Gene Expression Omnibus (GEO  [5] ). However, despite the fact that these repositories contain huge amounts of functional genomics information their exploitation is seriously limited by (i) the lack of information on the quality of these datasets and (ii) the limited toolbox of exploratory computational resources. In this context, we have developed previously a quality control system dedicated to ChIP-seq and enrichmentrelated datasets  [6]  (www.
Importantly, applying this concept to long-range chromatin interaction assays provides a direct relationship between the sequencing depth and the confidence in predicting chromatin interactions. This confidence is herein referred to as the quality of the dataset under study. Technically, we first selected unique PETs (excluding potential PCR-generated "clonal" reads), which participate in intra-chromosomal interactions longer than 10 kb. We thereby excluded PETs resulting from short-range chromatin interactions, which dominate chromatin interactomes (forming the diagonal in interaction maps) and would bias the quality assessment due to their overrepresentation. Indeed, Removal of PETs spanning >10 kb or >25 kb led to a direct correlation between the amounts of PETs per dataset and their associated QCscores (Additional file 1: Figure  S1A ).
This correlated also with an improved visual quality and visibility of Topologically Associating Domains (TADs) in chromatin interaction maps (Additional file 1: Figure  S1B ). Next we established randomly sampled interaction PET subsets for defined fractions of the original population (90 %, 70 %, 50 %; described hereafter as s90, s70 or s50). After random sampling, intra-chromosomal interaction maps were reconstructed by assessing the number of PET counts within 5 kb or 25 kb bins. These two analytical windows enable quality assessment at two different resolutions and facilitate the comparison of different types of datasets; this concerns particularly HiC assays that are generated with different restriction enzymes or ChIA-PET assays involving sonication-sheared chromatin. Finally, global and local quality scores were computed by comparing the recovered PET counts per 5 kb or 25 kb bin after random sampling with those observed in the original dataset (Fig.
Computing local and global quality indicators Technically quality assessment is performed by first computing the recovered PET counts after random sampling as follows: recPETcounts ¼ samPETcounts oPETcounts Ã 100 where samPETcounts correspond to PET counts assessed after random sampling and oPETcounts correspond to those retrieved with the original dataset. Then it is used for computing the difference between the observed recovered PET counts after random sampling relative to that ideally expected (samd; which is equivalent to the random sampling density (90 %, 70 % or 50 %)): ∂PETcounts ¼ samd-recPETcounts The recovered PET count dispersion (δPETcounts) per genomic window is referred to as the local QC indicator, such that each evaluated genomic region (5 kb or 25 kb window) can be expressed by this quantitative readout assessed for a given random sampling subset analysis.
Importantly, representing genome interaction maps in the context of PET count dispersions (δPETcounts) transforms the display into a uniform scale for comparing datasets generated at variable PET sequencing levels (e. g. PET count dispersion: 5-50 %). Finally, while δPETcounts interaction maps provide a visual display of the quality associated to a given genomic region, they do not allow evaluation of the quality of the entire dataset. Therefore, we defined the following global quality descriptors: (See figure on previous page. ) Fig. 1  Principles in use for Quality Assessment. Total mapped paired-end tags (PETs) are first classified in intra-chromosomal and inter-chromosomal events. For quality assessment, only intra-chromosomal PETs spanning genome distances longer than 10 kb -referred here as filtered PETs -are considered.
Random sub-sampling generates PET subsets corresponding to 90, 70 and 50 % of the original filtered PETs and the numbers of PETs in 5 kb or 25 kb size genomic windows is quantified. By comparing each of the PET counts/window in the various random subsets with that observed on the original dataset, the fraction of recovered PET counts (recPETs) after random sub-sampling and the dispersion from the theoretically expected values are calculated. Note that the expected values correspond to a decrease in the number of PET counts per window that is proportional to the random sub-sampling (e. g. recPETs/window =50 % when 50 % of filtered PETs are random sub-sampled). By evaluating the fraction of genomic windows with recPET count dispersions lower than a defined confidence interval (default value 10 %) global quality descriptors like the density and similarity quality indicators (denQCi, and simQCi respectively), as well as the global QCscore are computed.
Density quality indicators (denQCi) The fraction of genomic regions (5 kb or 25 kb window) in the random sampled datasets presenting δPETcounts lower than a defined threshold; which in the context of this study has been fixed at 10 %. Specifically, LOGIQA presents denQCi values computed for 90 %, 70 % and 50 % random samplings (denQC. 90, denQC. 70 and denQC. 50 respectively).
Similarity quality indicators (simQCi) The ratio between two denQCis is used to evaluate their degree of similarity. Specifically, LOGIQA presents simQCi values computed for denQC. 90 and denQC. 70 relative to denQC. 50 (simQC. 90/50 and simQC. 70/50 respectively). Note that denQCi aims at quantifying the proportion of genomic regions that fluctuates in less than 10 % for a given random sampling. In fact, an s90 random sampling presents generally less variation from the original dataset, while the s50 subset will have the highest deviation. The simQCi measures the relative difference between denQC indicators computed at different random subsampling conditions. For instance, simQC. 90/50 compares the denQC at 90 % to that computed at 50 % subsampling. In an ideal situation (saturation of the interactome readout), the fraction of genome interactions A C B D Fig.
2  Assessing quality descriptors over long-range genome interaction assays. a Scatter-plot illustrating the fraction of PET counts recovered after random subsampling (Y-axis) relative to the original PET counts in 5 kb genome windows (X-axis). Note that genome windows with high PET counts contain PET levels close to the expected value; in contrast, the lower the PET counts, the higher is the deviation from this theoretically expected level. b Recovery scatter-plots assessed from datasets with increasing PET count levels (from 100 to 500 millions). Note that we generated these datasets by random sub-sampling of a large metafile (>600 million reads). c QCscores computed from datasets presenting increasing PET count levels (from 100 to 500 millions). The illustrated QCscores, computed from five independent replicates, present variation coefficients below 3 % (see Additional file 1: Figure  S2 ).
d Local displays illustrating chromatin interactions (chromosome 6, mm9) evaluated in the context of PET count dispersion levels (percentage) per genomic window (5 kb) relative to the expected recovery levels. Note that short-range genomic interactions (diagonal) show the lowest dispersion levels affected by the random sampling is identical at 90 % and 50 % and would yield a simQC = 1. While none of the evaluated datasets are at saturation, the closer this indicator is to 1, the lower is the difference of the denQC indicators between the two random sub-samplings and the higher is the dataset quality. Intuitively, high quality datasets generally contain a high amount of genomics regions that are "robust" to the most severe 50 % random sub-sampling (i. e. , they will display high denQC. 50 levels); they will also show low differences between denQCis assessed at various random sub-sampling conditions (i.
Quality scores computed for a variety of long-range chromatin interaction assays Because of its universal principle, LOGIQA allows to compute quality scores for chromatin interaction datasets generated from a variety of techniques. Indeed, LOGIQA hosts currently QC scores for >250 publicly available HiC (including several variants of the original protocol, like in situ or capture HiC), but also several ChIA-PET (>50) and 4C-seq (>900) datasets.
Quality score validations One of the principal motivations for the development of the present quality score system was to provide a numerical quality descriptor that can predict the optimal sequencing depth for long-range chromatin interaction assays. In fact, even though chromatin interaction assays are expected to require high sequencing depth  [8, 9] , to date there is no quantitative approach that can compare multiple HiC or similar assays in the context of their relative sequencing depths. The QCscores computed by LOGIQA solve this problem. To illustrate this point, we have constructed a HiC metafile composed of more than 600 million PETs and established subsets by random sampling (100, 200, 300, 400 and 500 million PETs), which were used for calibration of a quality scale. This calibration system reveals a direct negative correlation between sequencing depth and the deviation of the recovered PET count levels from the original dataset after random sampling (Fig.
2b ; note the enlarged dispersions of the 100 million vs. the 500 million PET datasets) which translates into a gain of global QCscores for high PET counts (Fig. 2c ). Importantly, the reproducibility of the computed global QCscores has been validated from multiple independent random samplings, for which the coefficient of variation was systematically <10 % (Additional file 1: Figure  S2 ). This calibration revealed also the influence of the sequencing depth on PET count dispersion in a selected genome region, as illustrated for chromosome 6 in Fig. 2d , where the chromatin interaction maps reconstructed from different total PET counts are compared using a color-code for PET count dispersion. We next computed the quality scores for datasets that were reported to be of superior quality due to a modification of the technology, referred to as in situ HiC  [10].
Specifically, these assays involve cell in situ proximity ligation, which reduces the frequency of random intermolecular ligation. In this context, we compared QC scores computed for 126 HiC and 87 in situ HiC datasets in the context of their total sequenced PETs. The QC scores of the in situ HiC datasets were generally among the top for a given PET range (Fig. 3a, e ) even though there was no clear separation in the quality of HiC and in situ HiC. Rather, it appears that the quality of HiC is more variable than that of in situ HiC, which were generally performed with lower total PETs (Fig. 3b ). Our comparative analysis supported also the notion that there are less inter-chromosomal PETs in in situ HiC, as we observed on average more than 70 % intrachromosomal PETs for in situ HiCs, while significantly less were seen in HiCs (Fig.
3c ). Given that LOGIQA computes QC scores on the basis of intra-chromosomal PETs that span a genomic distance of above 10 kb (referred to as "filtered PETs"), we compared the two HiC technologies in the context of filtered PETs. We noted that in situ HiC assays generated on average significantly higher amounts of filtered PETs (~40 %) than HiC (~25 %) assays (Fig. 3d ). Albeit increasing the PET coverage can compensate for reduced QC scores, we were rather interested in comparing the QC scores of HiC and in situ HiC at comparable PET coverage (and thus similar sequencing costs). Notably, mean QC scores around -30 were attained by in situ HiC at a total PET coverage of 50 M to 100 M, while for HiC 100 M to 200 M PETs were required to reach this score (Fig. 3e ; dashed green line). To demonstrate that the global QC score is a meaningful value also for local quality assessment we generated local genome interaction maps (chromosome 6, hg19) generated from two datasets with similar numbers of filtered PETs (~120 million) but significantly different global QC scores (Fig.
4 ). Importantly, the in situ HiC data formed clearly defined topological domains (TADs) for the illustrated region, which corresponds to the human histone gene cluster 1, while the dataset generated by classical HiC appeared less well defined. The visual perception of this difference is further enhanced when the graphic displays were generated from randomly sub-sampled fractions of the two original PET datasets. In fact, when 50 % of the PETs were used for reconstructing the chromatin interactomes, the TAD pattern was readily detectable by visual inspection in the in situ HiC assay for PET dispersion levels <10 %, while the classical HiC assay had PET dispersion levels >20 and a very blurred graphical presentation, in which no TADs could be identified. Taken together, in situ HiC generates higher amounts of intra-chromosomal PETs and delivers at similar PET coverage better QC scores than HiC.
LOGIQA provides a unique web access interface In contrast to other computational solutions dedicated to visualize HiC or related datasets  [10] , users do not require to install any software to use LOGIQA. Furthermore, while a few other databases that host publicly available HiC and related assays became recently available  [11, 12] , LOGIQA is to our knowledge the first database of quality descriptors for a large collection of publicly available datasets. LOGIQA is a fully functional web-based system, which provides to users the quality scores for currently more than 900 publicly available datasets covering mouse, human and drosophila on a variety of long-range chromatin interaction assays. Specifically, global QC scores for all evaluated datasets are available in a scatter-plot format relative to their related PET counts, revealing the impact of genome size, sequencing-depth, and technical performance on the robustness and thus, quality of the data sets (Fig.
6  and Additional file 1: Figure  S3 ). To facilitate the retrieval of datasets, LOGIQA provides a user-friendly query panel covering items like species, type of experiment (e. g. in situ HiC), use of restriction enzyme for chromatin fragmentation, target molecule for ChIA-PET assays, name of (an) author(s), minimal/ maximal PET counts to be retrieved, as well as a keyword search for the abstract of the corresponding publication(s). Finally, LOGIQA provides a dedicated genome viewer, in which users can either select a defined gene (with user-defined upstream and downstream extensions), or provide genome coordinates (Fig. 6  and Additional file 1: Figure  S4 ). The visualisation module displays either local QC dispersion readouts (for 70, 50 or 90 % random sampling conditions) or PET counts.