dataesr/scientific-paragraphs-categorization huggingface dataset:
the Exome Variant Server, the ExAC database, a series of 204 Belgian control chromosomes, and internal exome databases curated in Prague (~600 exomes and 140 custom gene panels) and Nijmegen (~500 exomes). Screening for SEC61A1 Mutations in Individuals with a Similar Phenotype The second SEC61A1 variant was identified in 1 out of 46 unrelated probands with an ADTKD-like phenotype using a custom gene panel sequencing developed at the Institute for Inherited Metabolic Disorders in Prague. The proband from family 2 carried a heterozygous missense variant c. 200T>G (p. Val67Gly) in SEC61A1. The variant is also conserved at the nucleotide (PhyloP score: 6. 32) and amino acid levels (Figure1C). The variant was also predicted to be damaging by SIFT and PolyPhen and disease causing by MutationTaster.
Acknowledgments B. L. L. is senior clinical investigator of the  Fund for Scientific Research ,  Flanders  ( FWO, Belgium ). This research was supported by funding from the  Fund for Scientific Research, Flanders (FWO, Belgium)  [ G. 0458. 09;G. 0221. 12 ], the  Fondation Leducq  ( MIBAVA 12CVD03 ), Starting grant (to B. L. L. ) of the  European Research Council  ( ERC-StG-2012-30972-BRAVE ), and the  University of Antwerp (Lanceringsproject). N. K. is a Distinguished Brumley Professor. This work was supported by grants  P50DK096415  and  P30DK096493  (to N. K. ). S. K. , A. P. , K. H. , and V. S. are funded by  Charles University institutional programs   PRVOUK-P24/ LF1/3 ,  UNCE 204011 , and  SVV2016/260148  and by the project  LQ1604   NPU II  from the  Ministry of Education of the Czech Republic.
David Tilstra  from  CentraCare Clinic, St. Cloud , who referred family 2 for evaluation and kindly provided medical records. We thank  Corinne Antignac  for critical revision of the manuscript. Received: November 2, 2015 Accepted: May 30, 2016 Published: July 7, 2016 Web References 1000 Genomes, http://www. 1000genomes. orgCHOPCHOP, https://chopchop. rc. fas. harvard. edu/Ensembl Genome Browser, http://www. ensembl. org/index. htmlExAC Browser, http://exac. broadinstitute. org/MutationTaster, http://www. mutationtaster. org/ NHLBI Exome Sequencing Project (ESP) Exome Variant Server , http://evs. gs. washington. edu/EVS/OMIM, http://www. omim. org/PolyPhen-2, http://genetics. bwh. harvard. edu/pph2/RCSB Protein Data Bank, http://www. rcsb. org/pdb/home/home. doRefSeq, http://www. ncbi. nlm.
ACKNOWLEDGMENTS We thank members of the '' Cell Survival and Tumor Escape in Breast Cancer '' laboratory for their technical advice, fruitful comments, and enthusiasm. We thank  C. Couriaud  for her technical help in the preparation of lentivirus particles and  Dr. F. Edlich  for the generous gift of BCL-xL D2 allele. We are grateful for technical support from the  Cellular and Tissular Imaging (MicroPICell)  and  Molecular Interactions and Protein Activities (IMPACT) Core Facilities of Nantes University. J. P. and C. V. are supported by grants from the  Ministere de la Recherche et de l'Enseignement Supe  ´rieur  Q1  , and J. L. P. was supported by a grant from  INSERM Region. This work was supported by  Region Pays de la Loire  ( CIMATH2 ),  Ligue contre le Cancer  ( R13137 ),  ARC  ( R15083NN ), and  INCA PLBio 2013  ( R12134NN ).
MATERIALS AND METHODS Chemical synthesis. The syntheses of the compounds are described in detail in the supplemental material. Expression, purification, and crystallization of the M. tuberculosis LeuRS editing domain. A DNA fragment coding for the region spanning G309 to I513 of M. tuberculosis LeuRS (UniProt accession number P67510) was cloned into pETM-11 by using the NcoI and XdeI restriction sites (EMBL). The protein containing an N-terminal six-histidine tag was prepared and purified according to a protocol similar to the one described previously for Escherichia coli LeuRS  (8) , except that nickel affinity chromatography was conducted at pH 8. 0. Protein was stored in buffer comprising 20 mM Tris-HCl (pH 7. 4), 100 mM NaCl, 5 mM MgCl 2 , and 5 mM 2-mercaptoethanol. Crystallization was performed at 20°C by the hanging-drop vapor diffusion method.
Structure determination and refinement. All diffraction data sets were collected at the European Synchrotron Radiation Facility (ESRF, Grenoble, France). Data were integrated and scaled with the XDS suite  (10). Further data analysis was performed with the CCP4 suite  (11). The structure of the LeuRS:AMP-compound 6 complex was initially solved by molecular replacement with PHASER (12), using the E. coli LeuRS editing domain structure  (13)  (PDB accession number 2AJG) as a model. The model was improved by automatic building using ARP-wARP  (14) , and manual adjustments were made with COOT  (15). The structures of the complexes with compounds 14 and 16 were solved by using the editing domain of M. tuberculosis LeuRS (described above) as a model. All models were refined by using REFMAC5 with anisotropic B-factors.
The heat evolved after each ligand injection was obtained from the integral of the calorimetric signal. The resulting binding isotherms were analyzed by nonlinear least-squares fitting of the experimental data to a single-site model. Analysis of the data was performed by using Micro-Cal Origin software (OriginLab version 7). Experiments were performed at least twice. The variability in the binding experiments was estimated to be 5% for binding enthalpy and 10% for both the binding affinity and the number of sites. Determination of MIC values for M. tuberculosis. MIC values were determined mainly by using resazurin (alamarBlue) as an indicator for cell growth  (17) , with additional determinations being performed as described previously by Ollinger et al. (18). Selection of Mycobacterium smegmatis ATCC 700084 single-step mutants.
In triplicate studies, the cytopathic effects of compounds were evaluated colorimetrically by using the 3-(4,5dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) cell proliferation assay (ATCC). IC 50 data were obtained from dose-response curves plotted by using GraphPad Prism 5. Mitochondrial protein synthesis assay. The human liver carcinomaderived HepG2 cell line was obtained from the ATCC (ATCC HB-8065). HepG2 cells were grown in Dulbecco's modified Eagle's medium containing 10% fetal calf serum, 1 mM sodium pyruvate, 0. 1 mM essential amino acids, and 50 U/ml penicillin-streptomycin at 37°C with 5% CO 2. HepG2 cells were seeded into 96-well plates at 3,000 cells/200 l/well in cell culture medium. Cells were then grown in the presence of the compounds at 37°C in 10% CO 2 for 7 days in at least duplicate concentrations, with the medium and compounds being replaced on the fourth day.
The experimental design for the acute-infection assay was previously described  (23). In brief, for the acute-infection assay, mice were intratracheally infected with 100,000 CFU/mouse (M. tuberculosis H37Rv strain). Products were administered for 8 consecutive days starting 1 day after infection. For the chronic-infection assay, mice were intratracheally infected with 100 CFU/mouse, and the products were administered daily (7 days a week) for 8 consecutive weeks starting 6 weeks after infection. Lungs were harvested 24 h after the last administration. All lung lobes were aseptically removed, homogenized, and frozen. Homogenates were plated onto 10% OADC-Middlebrook 7H11 medium and incubated for 21 days at 37°C. The viable CFU were converted to logarithms, which were then evaluated by one-way analysis of variance, followed by multiple-comparison analysis of variance by a one-way Tukey test (SigmaStat software program).
Drugs were administered 5 days per week by oral gavage for 4 weeks. Lungs were harvested 72 h after the last administration. All lung lobes were aseptically removed, homogenized, and frozen. Homogenates were plated onto 10% OADC-Middlebrook 7H11 medium and incubated for 21 days at 37°C. All animal studies strictly adhered to the protocols and regulations approved by the Colorado State University Animal Care and Use Committee. Accession numbers. Atomic coordinates and structure factors for compounds 2, 11, and 13 have been deposited with the Protein Data Bank (PDB) under accession numbers 5AGR, 5AGS, and 5AGT, respectively.
ACKNOWLEDGMENTS We thank  Ding Zhou  and his team at  PharmaResources ,  Michael Xu  and his team at  WuXi AppTec , and  James Nieman  and his team at  NAEJA Pharmaceuticals  for their contributions to medicinal chemistry. We thank the staff of the EMBL HTX (high-throughput crystallization) platform within the  Grenoble Partnership for Structural Biology (PSB)  and of the  ESRF-EMBL Joint Structural Biology Group  for access to ESRF beamlines. We thank  James Ahn  for technical assistance. A. J. L. acknowledges  NIH/NIAID  contract  NO1 AI-95385  ( Tuberculosis Antimicrobial Acquisition and Coordinating Facility ) for the efficacy testing in mouse models of acute and chronic of infections.
22 Briefly, it was based on formative work with uninsured participants, health providers, community advisors, and health policy experts with attention to health literacy, numeracy, and graphic literacy. The algorithm used to estimate individual or family expected annual out-of-pocket costs included estimates from the Medical Expenditure Panel Survey (MEPS) with an additional risk-adjustment factor based on age, gender, and medical conditions. MEPS data estimate the probability of incurring health care-related costs during a calendar year and the size of these costs. Due to the plethora of similar plans and the need to adjust for potential risk of unexpected expenses, the tailoring algorithm compared plans pairwise until all plans were ordered to generate an appropriate set of recommendations for individuals.
Participants Eligible individuals were ages 18 to 64 years, English-speaking, not Medicaid-eligible, (therefore eligible for the ACA marketplace), and living in urban, suburban, and rural counties within 90 miles of St. Louis. A screening survey assessed eligibility using Missouri-specific criteria. The study was approved by the Human Research Protection Office at Washington University in St. Louis and registered with clinicaltrials. gov (protocol NCT02522624).
Data Collection Recruitment occurred between October 2015 and January 2016 via community events, online advertisements, social service organizations, and the recruitment/retention arm of our Center for Community-Engaged Research. Participants were computer-allocated to one of two study conditions: 1) SMHP or 2) HealthCare. gov (''See Plans and Prices'' section). Group assignment was meant to be random, but due to a programming oversimplification, was alternating. However, researchers had no ability to manipulate assignment order, participants were recruited in multiple locations simultaneously by different staff, and participants arrived at recruitment locations at random with no obvious patterns. The end result is therefore taken to be random for this analysis. Participants first completed a survey assessing federal poverty level (FPL) and health insurance literacy.
We examined data with and without participants who viewed 2015 plan information; because results did not change, results of the full sample are reported. Significance of a = 0. 05 was used and all tests were two-sided. The percentage of missing data in multivariable analyses ranged from 1. 2% to 5. 2%; number of observations included is displayed in Table  3. Statistical package SAS version 9. 3 was used for analyses.
Match With Preferences and Algorithmic Prediction The majority of SMHP participants selected plans that were good (85/162, 52. 5%) or moderate (71/162, 43. 8%) matches with their preferences and matched one of the algorithmic recommendations (134/162, 83. 2%). The metal level plan choice of those using SMHP matched the metal level of the ''good fit'' plans recommended by the algorithm 87. 6% of the time. Among participants using HealthCare. gov, we could only examine the preference match for the 60 participants covering themselves. We did not have data on health conditions of dependents in the HealthCare. gov group since they did not use SMHP. In that subset, 21/60 (35. 0%) selected plans that were a good preference  match, 29/60 (48. 3%) selected plans that were a moderate preference match, and 10/60 (16.
Background: The success of the CRISPR/Cas9 genome editing technique depends on the choice of the guide RNA sequence, which is facilitated by various websites. Despite the importance and popularity of these algorithms, it is unclear to which extent their predictions are in agreement with actual measurements. Results: We conduct the first independent evaluation of CRISPR/Cas9 predictions. To this end, we collect data from eight SpCas9 off-target studies and compare them with the sites predicted by popular algorithms. We identify problems in one implementation but found that sequence-based off-target predictions are very reliable, identifying most off-targets with mutation rates superior to 0. 1 %, while the number of false positives can be largely reduced with a cutoff on the off-target score.
Although published tools and scoring systems allow ranking sequences by specificity  [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]  and efficiency  [2, 8, [23] [24] [25] , they are usually limited to a handful of genomes and only few evidence-based recommendations exist to optimize offtarget search parameters and on-target efficiency. In this article, we compare existing scoring systems against published datasets and our own experimental data. The optimal selection parameters that we identified were integrated into a new CRISPR/Cas9 guide designer tool.
We noticed two outliers, VEGFA_site2 and HEK293_sgRNA4, from the study by Tsai et al. [3]. The two guides are responsible for 151 and 133 off-targets, respectively. Together they account for 44 % (284/650) of all off-target sequences in our dataset and 71 % (84/ 119) of the off-targets with five or more mismatches. They also have the highest GC content in the Tsai et al. data, 80 % and 75 %, respectively (Additional file 6: Figure  S2 ). A relationship between GC content and specificity is known from siRNA design  [30]  and would explain the previously observed difficulty to target GC-rich genes  [2, 8, 31]  and quadruplex-forming sequences  [32]. Of all four million unique -NGG guide sequences in human coding exons, the ones with a GC content >75 % constitute only 13 %, so they can usually be avoided.
Therefore, CRISPOR does not allow indels, ranks guides based on potential off-targets with up to four mismatches, and allows five mismatches for a detailed analysis of a single guide. It has been reported that the off-target predictors on the CRISPR Design website (http://crispr. mit. edu) and Ecrisp  [10]  failed to detect many off-target sites  [3, 34] , including off-targets with a single mismatch from the guide. In contrast, we confirmed that the BWA  [35]  sequence search algorithm used in CRISPOR as well as the novel algorithm in the recently published CasOff-Finder  [11]  were able to find all validated off-targets (Additional file 8: Table  S4 ), demonstrating that this is only a software issue and limited to certain tools. For example, in the case of the EMX1 guide, CRISPOR and CasOffFinder predict 1288 off-targets with up to four mismatches while the MIT site predicts only 334 and as a result does not find five out of 15 validated off-targets, one of which has only two mismatches and a >20 % modification frequency confirmed by two different assays (Additional file 4: Table  S2  and Additional file 5: Table  S3 ).
In order to rank potential off-targets, many prediction tools calculate a score based on the position of the mismatches to the guide sequence. Initially, systematic testing of the effect of mismatches led to a weight for each possible nucleotide change at each position and a formula to combine these into a score  [7]. The score of the MIT website (http://crispr. mit. edu/about) is based on these data but reduced to one weight per position. The off-target predictors CCTop  [36]  and CROP-IT  [37]  independently devised heuristics based on the distances of the mismatches to the protospacer adjacent motif (PAM). The more recent CFD score  [34]  is based on the biggest dataset to date, cleavage data obtained by infecting cells with a lentiviral library containing thousands of guides targeting the CD33 gene for all PAMs, including guides for all possible nucleotide mismatches and 1-bp indels at all positions.
Additional file 10: Table  S5  summarizes the studies and the different assay types. For datasets where replicates are available, the Spearman correlation is in the range 0. 71-0. 77 (Additional file 11: Table  S6 ; Hct116, mouse embryonic stem cells) for the same assay in the same cell type. This gives an indication of the quality of the data and suggests that a correlation of about 0. 7 constitutes an upper limit of any prediction. For some datasets, the assay was repeated in a different cell type. In these cases, the correlations were almost identical for some cell type combinations (e. g. , 0. 75 for Hl60/Kbm7  [38] ; Additional file 11: Table  S6 ) and lower for others (0. 53-0. 63 for Rpe1 cells  [41] ). If these lower correlations are due to differences in the chromatin state, this suggests that its influence varies and is relatively modest, at most 10-20 % of the rank correlation.
The heat map in Fig. 4  shows that on independent datasets, those not used to train any algorithm such as Hart et al. [41] , current predictions achieve Spearman correlations of 0. 341-0. 436 (see Additional file 12 for plots of individual data points). In cases when algorithms are applied to their own training dataset the correlations are higher, but this is an artifact, known as algorithmic overfitting; we show the corresponding correlation values in grey in Fig. 4. We observed that the quality of the assay is an important parameter. For example, for a dataset obtained with Surveyor Nuclease, we found no significant correlation between guide activity and any of the scores (see "Liu" in Additional file 13: Figure  S5 ). Another example is the Housden et al. score  [44] , which did not predict well the activity in any dataset, including its own.
This may be due again to the accuracy of the activity measurements or a result of the statistical model used by Housden et al. , a weight matrix. The dataset "Wang 2015" was designed with a scoring algorithm and shows very little signal. The dataset "Eschstruth" is very small and includes several guides that were selected based on very high Doench scores. In the Chari et al. study  [24] , the dataset from K562 cells was not correlated with two replicates of the same assay in HEK293T cells, so we only used the HEK293T dataset, like Chari et al. themselves. We do.
A B Fig. 3  Cross-study analysis of MIT specificity scores as calculated by CRISPOR versus the number and strength of off-targets found. a For 31 guide sequences, CRISPOR guide specificity scores are shown (x-axis), as well as the number of off-targets (y-axis) and sum of off-target modification frequencies (circle size). The guide with a specificity score of 88 has no single detected off-target. b The specificity scores of 31 tested guide sequences (blue) versus the specificity scores of all unique guides (unique 20mers followed by NGG) in human coding regions (green). Specificity scores were calculated using the CRISPOR website. For a version of this figure with specificity scores calculated by the MIT site, see Additional file 9: Figure  S4  not show these five datasets in Fig. 4  but instead in Additional file 13: Figure  S5 ; the raw data are included in Additional file 14.
[54]  but were used as processed by Xu et al. [23]. From the dataset by Hart et al. [41] , only the cell line Hct116/repeat2 was used, as it gave the highest correlation value; for this study efficiency was averaged over all time points. Data on human cell lines for the two datasets by Doench et al. [8, 34]  are not shown here but gave an almost identical correlation profile. All data points are shown as scatter plots in Additional file 12; for assay background information on the datasets see Additional file 8: Table  S4  predictor (Spearman P value 0. 019; see Additional file 12 for P values and Additional file 14 for all frequencies and prediction scores where these data sets are called "Schoenig", "Concordet", "Shkumatava", and "Teboul", respectively). Correlation of the prediction score with observed absolute activity may disadvantage some algorithms.
We therefore performed precision-recall curve analysis (Additional file 16: Figure  S6 ) and also calculated precision/recall based on the overlap of the top quartile of the predictions with the top quartile of measured activity (Additional file 17: Figure  S7 ). For the latter, we added two heuristics described by  [6, 25] , GC content in the last four base pairs and whether the guide ends with -GG. The results overall correspond to the performance as measured by correlation values; the Fusi/Doench and Moreno-Mateos scores perform best on the large datasets and depend on the expression system. Two prediction schemes can reach a relatively high precision: the Wong score  [45]  for cell cultures (U6 promoter) and the -GG rule for T7 in vitro transcription. However, their recall is relatively low; in the Doench 2014 dataset, for example, only 12.
8 % of guides have a Wong score that is not zero and 13. 2 % end with -GG. CRISPOR calculates all currently available scores and lets the user select the most suitable one for the particular assay/model organism. Based on Fig. 4 , we recommend the Fusi/Doench score for guides expressed from a U6 promoter and the Moreno-Mateos score for experiments where guides are produced by T7 in vitro transcription. As an additional ranking criterion, when there is a large set of possible guides to pick from, the Wong score and -GG rule predict well efficient U6-and in vitro-transcribed guides, respectively. Are correlations of around 0. 4 high enough to reduce the number of guides in practice? To demonstrate that the efficiency scores are useful not only when designing thousands of guides for genome-wide screens  [38]  but also in a more common genome editing project of just a few loci, we evaluated the prediction performance on the data from our labs shown in Fig.
5. For two datasets, we have screened multiple guides per locus to select the most efficient one and evaluated post hoc how much time could have been saved by using the appropriate prediction algorithm. In the K562 cell culture dataset, three guides each from eight loci in human, mouse, and rat were tested with an in vitro assay  [46]  (dataset "Schönig" in Additional file 14). For six out of eight loci, the highest Fusi/Doench score did predict the guide with the strongest cleavage (P = 0. 032). In another set of 104 guides from 11 zebrafish loci ("Shkumatava" in Additional file 14), taking only two guides with the highest Moreno-Mateos score from each locus would have reduced the number of injections from 104 to 22 and still identified one of the top two guides for nine out of 11 loci (P = 0.
and 5 indicate that the results Fig. 5  Heat map of Spearman rank correlation coefficients for the best two efficiency scores from Fig. 4  and four novel datasets from this study. Correlations are shown as in Fig. 4  of guide injections into mouse oocytes, the most expensive experiment in this field, despite being the organism where thousands of cell culture data points are available, are currently best predicted by an algorithm trained on injection results from a non-mammalian organism, zebrafish. Our summary of all publicly available data, the predicted scores, and source code to calculate these should simplify future computational work on CRISPR/Cas9 off-and on-target predictions. For wet lab experimentalists who want to integrate the current state of the art into their experimental design, our website (http://cris por.