dataesr/scientific-paragraphs-categorization huggingface dataset:
AtMTR4 and HEN2 have distinct intranuclear localization patterns To extend previous localization studies  [13] , we transiently expressed HEN2 and AtMTR4 GFP fusion proteins in Nicotiana benthamiana leaves, alongside with RFP-labeled XRN2 and Fibrillarin as nucleolar markers, and with SRP34 as a nucleoplasmic marker  [38] [39] [40] [41] [42] [43]. Similar to XRN2-RFP and Fibrillarin-RFP, AtMTR4-GFP was detected in the nucleus, strongly enriched in Phylogenetic tree of the MTR4/SKI family of RNA helicases. The basic branch that separates HEN2 homologues from MTR4 and SKI2 homologues is detected with 1000/1000 bootstraps. Protein sequences were retrieved from metazome, phytozome and JGI databases and aligned with ClustalX. Dark red, vertebrates; light red, other eumetazoa; pink, M. brevicollis (Choanozoa); blue, B.
AtMTR4 and HEN2 interact with different sets of proteins To investigate whether AtMTR4 and HEN2 are associated with specific proteins reflecting their distinct localization and to confirm that both helicases interact with EXO9, we performed IP experiments using plant lines expressing GFP-tagged versions of AtMTR4 or HEN2 in their respective mutant backgrounds. The list of proteins co-purifying with AtMTR4-GFP or HEN2-GFP was established by considering only proteins that were not identified in mock purifications and common to replicate IPs for AtMTR4-GFP and HEN2-GFP experiments, respectively (Tables 2 and 3; Tables  S2  and S3 ). All canonical nine subunits of EXO9 were identified in both AtMTR4-GFP and HEN2-GFP datasets, which confirmed that both RNA helicases interact with the Arabidopsis exosome complex.
edu/) are provided in Fig. S8. Steady-state levels of the six selected exosome targets were determined by quantitative RT-PCR using oligo-dT primed cDNA samples prepared from seedlings of wild type, mtr4-1, mtr4-2, hen2-2 or hen2-4 mutant plants. We included also samples from RRP41 RNAi lines in which depletion of the exosome core subunit RRP41 is triggered by an inducible RNAi construct  [5]. As shown in Fig. 4 , all exosome substrates tested in this experiment were over-accumulated in hen2 samples as compared to wild type samples. By contrast, no or only a mild accumulation was observed in mtr4 mutants. These data provided a first indication that HEN2 is involved in the degradation of nuclear exosome targets that are not substrates of AtMTR4.
The statistical analysis, based on a 4-state Hidden Markov Chain, classified probes into four clusters corresponding to over-expressed probes, under-expressed probes, probes with unchanged expression, and noise (not expressed probes), respectively. Interestingly, the analysis did not declare any probe as under-expressed in both biological replicates. This result is in line with the prediction that loss of HEN2 and AtMTR4 Table  2. Cont. impairs RNA degradation, and therefore results predominantly in an increased accumulation of RNA substrates. Indeed, signals for 1860 unique probes were significantly increased in both biological replicates of hen2 samples. 499 probes were identified as overexpressed in both biological replicates of mtr4 samples. A file allowing the visualization of the upregulated probes aligned to the Arabidopsis genome can be found in dataset S1.
Plant material With the exception of hen2-1  [12]  used for the S-PTGS assay, all Arabidopsis thaliana plants were of Columbia ecotype (Col-0). C ) reporter transgene in the indicated mutants and backgrounds. The color code indicates the intracellular localization of the mutated proteins: red marks the nucleolar proteins; green marks nucleoplasmic proteins, and blue mark cytoplasmic proteins. A. Loss of MTR4 has only a marginal effect on GUS S-PTGS. The effect of the mtr4-2 mutation on silencing of a GUS reporter transgene was tested in the Hc1/Col reporter system as described in  [51]. For comparison, we included previously published data from  [49]  that show the effects of compromised 59-39 exoribonucleases XRN2 (nucleolar), XRN3 (nucleoplasmic) or XRN4 (cytoplasmic) in the same reporter system.
Sequence analysis and phylogeny Sequences were retrieved from Phytozome (http://www. phytozome. net), Metazome (http://www. metazome. net) and JGI (http://genome. jgi. doe. gov) genome databases, using AtMTR4, HEN2 and AtSKI2 proteins as BLAST queries. Structures of AtMTR4 and HEN2 were modeled with MODELLER Software (http://modbase. compbio. ucsf. edu/ModWeb20-html/modweb. ht ml) using the crystal structures of S. cerevisiae MTR4 (PDB 3L9O and 2XGJ)  [34, 35]  as templates. Alignments were performed with Chimera (http://www. cgl. ucsf. edu/chimera, for structure-based sequence alignments) and ClustalX (http://www. clustal. org, for phylogenetic analysis). The phylogenetic tree was calculated with the neighbor-joining algorithm built in ClustalX and 1000 bootstraps, and drawn with Figtree (http://tree.
1% FA in Acetonitrile) for 60 minutes at a 300 nl/min flow rate. The TripleTOF 5600 was operated in highsensitivity data-dependant acquisition mode with Analyst software (v1. 6, ABSciex) on a 350-1250 m/z range. Up to 20 of the most intense multiply-charged ions (2+ to 5+) were selected for CID fragmentation, with a cycle time of 3. 3s (TOP 20 discovery mode). For protein identification, raw data were converted to Mascot Generic File format (mgf) and searched against a TAIR 10 database supplemented with a decoy database build from reverse sequences. Data were analyzed using Mascot algorithm version 2. 2 (Matrix Science, UK) through ProteinScape 3. 1 software (Bruker). Search parameters allowed N-acetylation (protein N-terminal), carbamidomethylation (C) and oxidation (M) as variable peptide modifications.
Mass tolerances in MS and MS/MS were set to 20ppm and 0. 5Da, respectively. 2 trypsin mis-cleavages were allowed. Peptide identifications obtained from Mascot were validated with a FDR ,1%. A second algorithm, PEAKS DB (version 5. 3, BSI Informatics) was used with the same search parameters to strengthen the identifications. Identified proteins were assessed by the total number of fragmented spectra per protein (spectral count). Data of three (RRP41, MTR4) or two (HEN2 50 mM NaCl, HEN2 150 mM) replicates were crossed. Protein partners were considered only if present in all co-IP replicates. All proteins observed in the corresponding control replicates, including sameset and sub-set proteins, were discarded. A second filter was set using controls from 15 independent other IP experiments in A.
Microarray analysis Wild type, hen2 and mtr4 plants were grown on MS agar supplemented with 0. 5% sucrose. Total RNA was extracted from two biological replicates using Nucleospin RNA plant columns (Machery & Nagel). cDNA synthesis and labeling with Cy3-dUTP or Cy5-dUTP (Perkin-Elmer-NEN Life Science Products) for fluorochrome reversal was performed as described previously  [73]. Samples were hybridized to NimbleGen whole genome microrrays (Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/), ac-cession no. GPL17057 and GPL11005) as described in  [73]. Topological positions and nucleotide sequence of the 1,434,492 strand-specific NimbleGen probes are available in the FLAGdb ++ database at http://urgv. evry. inra. fr/FLAGdb++ [45]. Two micron scanning was performed with an InnoScan900 scanner and raw data were extracted using Mapix software (Innopsys).
Statistical analysis Probes that mapped to repetitive sequences of the Arabidopsis genome were excluded from the analysis. Statistical analyses were performed with the software R. For each experiment, the raw data comprised the logarithm of median feature pixel intensities at wavelengths 635 nm (red) and 532 nm (green), respectively. The dye bias was corrected by a global intensity-dependent normalization for each chromosome and each array using the loess procedure  [74]  and then averaged over the technical replicates. The outputs of this procedure are two normalized intensity values per probe, one for each of the co-hybridized samples. Normalized raw data were deposited at Gene Expression Omnibus (http:// www. ncbi. nlm. nih. gov/geo/), accession no. GSE48178, and at CATdb (http://urgv. evry.
The emission distribution of the noise cluster was modeled by a spherical Gaussian. A Gaussian mixture, components of which were forced to be colinear along the main axis of the ellipse representing each cluster, modeled each of the three other clusters. Data projection on the main axes allowed us to work with unidimensional mixtures and to put a unique Gaussian distribution along the associated perpendicular axis for all components. Model parameters were estimated using an adapted version of the EM algorithm taking the model constraints and the spatial dependency between probes into account. A detailed documentation of the statistical analysis can be found in dataset S2. Probe classification into the four clusters was based on the conditional probabilities: a probe was assigned in the cluster for which the conditional probability was the highest (MAP rule).
Bioinformatics analysis A file allowing the visualization of the upregulated probes aligned to the Arabidopsis genome using seqmonk software (http://www. bioinformatics. babraham. ac. uk/projects/seqmonk/) can be found in dataset S1. Probes with a unique localisation in the genome and for which a significant over-expression was detected by the statistical analysis were sorted by genome coordinates to identify upregulated regions. Only regions with at least two consecutive probes were allowed. Bioinformatic analysis was performed with adapted perl scripts. Sequences of upregulated regions were annotated using TAIR10 genome database, FLAGdb ++ , and recent studies that identified snoRNA, miRNA genes and linc RNA genes  [45] [46] [47] 75]. Sequence alignments were performed with BLASTn and sim4 tools (for annotation of spliced transcripts)  [76, 77].
Accession numbers (Arabidopsis Genome Initiative, AGI) ATMTR4: At1g59760; HEN2: At2g06990; RRP41: At3g61620. domains (blue), the winged helix domains (yellow), the arch domain (red, the black box depicts the KOW domain) and the Cterminal bundle domain (pink). Characteristic differences between plant MTR4 and HEN2 sequences are marked by stars. Athaliana, Arabidopsis thaliana (thale cress); Thalophila, Thelluniella halophila (Salt cress); Sitalica, Setaria italica (Foxtail millet); Mguttatus, Mimulus guttatus (Monkey flower); Pvulgaris, Phaesolus vulgaris (Common bean); Mtrunculata, Medicago trunculata (Barrel medic); Mesculenta, Manihot esculenta (Cassava); Ptrichocarpa, Populus trichocarpa (Poplar); Csativus, Cucumis sativus (cucumber); Egrandis, Eucalyptus grandis (Eucalyptus); Smoellendorfii, Selaginella moellendorfii (Spikemoss); Ppatens, Physcomitrella patens (Moss).
This work was funded by  Centre National de la Recherche Scientifique (CNRS, France)  and a  INRA  fellowship to CB. This work was realized in the frame of the  LABEX  ( ANR-2010-LABX-36  to DG) and ( ANR-2010-LABX-40  to HV) and benefits from funding from the state managed by the  French National Research Agency  as part of the  programme d'Investissements d'avenir. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The evidence is particularly strong for PIGQ, since a homozygous nonsense mutation in PIGA causes a similar phenotype  (35) , and we demonstrated that our mutation was loss of function. Further genetic and functional validation work is required to prove causality definitively, and this remains a challenge in a clinical setting, particularly for rare diseases. Nevertheless, this situation is expected to improve with greater adoption of these technologies and increased sharing of genetic data in public databases. In conclusion, our results have led to identification of novel genes for severe epilepsy phenotypes and, in addition, demonstrate the clinical utility of WGS as a means of providing comprehensive and rapid molecular diagnosis for patients with mechanistically complex genetic diseases, with concomitant implications for clinical management of these disorders.
Read mapping and variant calling WGS was conducted on the Illumina HiSeq platform to a coverage of at least 25×. The reads were mapped to the human reference genome (build 37d5) with Stampy (69), and SNVs and small indels were called with an in-house algorithm, Platypus  (70). Variants were annotated relative to RefSeq transcripts using ANNOVAR  (71)  and relative to all Ensembl transcripts using an in-house tool called VariantAnno.
PIGQ functional work We used a previously described human PIGQ expression vector  (77). The PIGQ mutant construct lacking exon 3 was made by site-directed mutagenesis. PIGQ-deficient CHO cells (10. 2. 1) (78) were transiently transfected with FLAG-tagged WT or mutant PIGQ cDNA, driven by an SRa promoter (pME FLAG-PIGQ) together with a luciferase expression plasmid for monitoring transfection efficiency. Two days later, to determine the restoration of GPI-AP expression, cells were stained with anti-CD59 (5H8) antibody followed by phycoerythrinconjugated anti-mouse IgG, and analyzed by a flow cytometer (Cant II; BD Biosciences, Franklin Lakes, NJ, USA) using the Flowjo software (Tommy Digital Inc. , Tokyo, Japan). To determine FLAG-tagged PIGQ protein levels, lysates of transfected cells were subjected to SDS -PAGE, and western blotting was performed using anti-FLAG antibody (M2, Sigma, St Louis, MO, USA), with anti-GAPDH (6C5, Life Technologies, Carlsbad, CA, USA) as a loading control.
We performed raw read processing as described (  79 ), but use a modified analysis pipeline for variant calling. SNV and indel calling and filtering was performed using the Genome Analysis Tool Kit (GATK version 2. 2) (http://www. broadinstitute. org/ gatk/, last accessed date on February, 2013). We excluded from further analysis any variants with allele balance. 0. 70, QUAL , 30, QD , 5 or coverage , 25×, and variants in clusters (window size 10 bp) or in homopolymer runs (5 bp). Variants were annotated with SeattleSeq (version 134; http://snp. gs. washington. edu/SeattleSeqAnnotation134/) and the Exome Sequencing Project dataset (see http://eversusgs. washington. edu/EVS/, last accessed date on February, 2013) used to assess variant frequency in the control population. For dominant (or de novo) models, we considered only variants not present in this control sample set.
Screening candidate genes in UK cases We carried out Sanger sequencing of CBL, CSNK1G1, PIGQ and KCNT1 in 11 patients with Ohtahara syndrome. Clinical details are given in Supplementary Materials, Table  S2. Genomic DNA from participating individuals was extracted from peripheral lymphocytes by standard techniques. All participants gave written informed consent and the study was performed in accordance with the Declaration of Helsinki. Primer pairs were designed for all coding exons with primer3 software (1,2) (http://bioinfo. ut. ee/primer3/, last accessed date on February, 2013) (Supplementary Materials, Table  S4 ). The exons were amplified by PCR using BioMix TM Red (Bioline Ltd). Two different PCR conditions were carried out to amplify exons: (i) an initial denaturation of 958C for 5 min, followed by 35 cycles of 45 s denaturation at 958C, 45 s annealing at 58-628C (depending on fragment) and 1 min extension at 728C with a final extension at 728C for 5 min, or (ii) a touchdown PCR program: an initial denaturation of 958C for 5 min, followed by 24 cycles of 30 s denaturation at 958C, 30 s annealing at 628C (minus 0.
ACKNOWLEDGEMENTS We thank the patients and their families for participating in this study, the  Genomics Core at the Wellcome Trust Centre for Human Genetics (WTCHG)  for generating the WGS data,  Elham Sadighi Akha  for running the SNP arrays for trio 2 and  Kevin Leyden  for doing the CD59 flow cytometry assay for trio 4. We are also grateful to  Adeline Nogh  and  Amy McTague  for their input in gathering the UCL cohort, and to  Helen Cross  and  Jozef Gecz  for facilitating initial contact between groups.
Drifts, trends and periodic variations were calculated from monthly zonally averaged ozone profiles. The ozone profiles were derived from level-1b data of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) by means of the scientific level-2 processor run by the Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK). All trend and drift analyses were performed using a multilinear parametric trend model which includes a linear term, several harmonics with period lengths from 3 to 24 months and the quasi-biennial oscillation (QBO). Drifts at 2-sigma significance level were mainly negative for ozone relative to Aura MLS and Odin OSIRIS and negative or near zero for most of the comparisons to lidar measurements. Lidar stations used here include those at Hohenpeissenberg (47.
44 to 0. 68 hPa. Beyond this, altitude-latitude distributions of the amplitude of periodic variations with period lengths of 6 (semi-annual oscillation -SAO) and 12 (annual oscillation -AO) months were assessed, as well as the quasi-biennial oscillation (QBO). These results were obtained using MIPAS data from July 2002 to April 2012. MIPAS measurement data exhibit a gap between March 2004 and January 2005, during which no data could be collected by the instrument due to technical problems. This also led to an altered operation mode for the second time period, with different spectral and horizontal resolution and tangent height pattern. In order to support the trend analyses, drifts are estimated via comparison with several coincident measurements of spaceborne instruments, namely ACE-FTS, Aura MLS and Odin OSIRIS.
MIPAS Envisat MIPAS was one of the core payload atmospheric chemistry instruments aboard the European satellite Envisat  (Fischer et al. , 2008). The satellite was launched into a Sunsynchronous polar orbit at approximately 800 km, with 98. 55 • inclination, by the European Space Agency (ESA) in March 2002. The instrument setup allows global coverage from pole to pole. Envisat crossed the Equator at approximately 10:00 and 22:00 local time and performed about 14. 4 orbits per day. However it ceased sending data on 8 April 2012 and was declared dead shortly afterwards; all in all, MI-PAS provided data for a period of about 10 yr. MIPAS is a Fourier transform limb emission spectrometer which measures the signatures of various trace gases within the spectral range of 4. 15 to 14. 6 µm. Originally, MI-PAS measured one limb scan consisting of 17 tangent altitudes from 6 to 68 km and about 75 profiles per orbit in its nominal mode, which results in approximately 1000 profiles per day.
The spectral resolution of MIPAS during this first time period  (2002) (2003) (2004)  was 0. 025 cm -1 unapodized (0. 035 cm -1 apodized). This period is referred to as the high spectral resolution period. The data version used in this study is associated with this time period is the V5H_O3_20 data set. The retrieval grid width in all MIPAS data versions is 1 km for altitudes below 44 km and 2 km above, independent of the tangent altitudes of the measurements. The vertical resolution of the V5H_O3_20 ozone data product varies between 2 and 6 km. For the second time period we chose to use the combination of the V5R_O3_220 (1541474 profiles) and V5R_O3_221 (331287 profiles) MIPAS ozone product, both of which were derived from version 5. 0 and later level-1b data. The V5R_O3_221 data ozone set consistently extends the V5R_O3_220 product after April 2011.
These versions of the ozone product are closely linked with version V4O_O3_202 (deduced from version 4. 67 level-1b data) as characterized by von Clarmann et al. (  2009 ) and validated by  Stiller et al. (2012). The thorough validation of the V5R_O3_220 (and V5R_O3_221) is currently in progress  (Laeng et al. (2012)  and paper submitted). Differences in the setup from version V4O_O3_202 to both V5R_O3 versions are major changes in level-1b data used (calibrated spectra) and the preceding temperature retrieval of which the results are used for the subsequent ozone retrieval. An empirical continuum emission signal was retrieved up to altitudes of 50 km instead of only 33 km in the preceding version. From version V5R_O3_220 to V5R_O3_221 only minor technical changes of the retrieval setup were applied which do not appreciably change the results for ozone in the investigated altitude range.
The vertical resolution of the version V5R_O3_220 and V5R_O3_221 data product ranges from about 2. 5 to 5 km, exhibiting the worst vertical resolu-tion around 30 to 35 km. Recent investigations of this feature showed that the vertical resolution in this altitude range would be significantly improved by activating microwindows in the MIPAS AB band (1020-1170 cm -1 ) at and above 33 km instead of 36 km and above as performed for the data set used here, at the cost of a higher positive bias, however. The combined data set of V5H_O3_20, V5R_O3_220 and V5R_O3_221 contains 2359706 ozone profiles in total.
Mainly focusing on tropospheric and stratospheric processes concerning ozone chemistry, Aura MLS provides data on several atmospheric chemical species linked to ozone destruction, including various reservoir gases. With approximately 240 profiles per orbit (nearly 3500 profiles per day) Aura MLS provides a large number of profiles (day and night) and is thus a very good candidate for drift estimation by means of coinciding profiles. In this study the ozone version v2. 2 of the Aura MLS data was used. The standard ozone product, which is used in this study, is derived from the 240 GHz region (main line at 235. 7 GHz and lines at  243. 45 and 244. 16 GHz; Froidevaux et al. , 2008). This Aura MLS data set is recommended for pressure levels between 215 and 0. 02 hPa, after necessary data screening is applied  (Froidevaux et al.
, 2008). Since the application of the MIPAS averaging kernel, as described in Sect. 3. 3. 2, was originally performed on the MIPAS altitude grid, we interpolated the Aura MLS data from its pressure grid to an adequate altitude grid using coincident ECMWF temperatures and pressure to calculate pressure-altitude relations. However, using this approach is prone to the risk that the temperatures used for conversion (here from ECMWF analysis) map onto the ozone drift. In order to avoid the related artefact, the drift analysis has in addition also been performed on the Aura MLS pressure grid as explained in Sect. 3. 3. 3.
Odin OSIRIS The Optical Spectrograph and InfraRed Imaging System (OSIRIS) is a Canadian instrument aboard the Swedish satellite Odin and has been collecting data since February 2001  (Degenstein et al. , 2009). It orbits Sun-synchronously at about 600 km with northward Equator-crossing time at 18:00 local time and 98 • inclination. Since OSIRIS is an instrument which measures scattered sunlight, measurements are only possible during June, July, August and September (November, December, January and February) in the Northern Hemisphere (Southern Hemisphere) because sunlight is imperative for the instrument's observations. While the optical spectrograph (OS) records spectra of limb-scattered sunlight from 280 to 800 nm and has a spectral resolution of about 1 nm, the second part of the instrument, the infrared imager (IRI), measures scattered sunlight as well as airglow emissions  (Llewellyn et al.
The first subset of data is missing in the plot compared to Fig. 1 , because Aura MLS and MIPAS have no overlap time within the first period of the MIPAS mission (cf. Table  1 ). The bottom panel shows the residual of the fit and the data points. altitude. The bias between the two data blocks is quite obvious and it is reasonably accounted for 220 during the fit process. A semi-annual oscillation and a QBO signal can also be identified in the data, which are satisfactorily reproduced by the fit. Before analysing derived trends a potential drift in MIPAS ozone data must be identified and accounted for. To estimate the drifts against other instruments the difference of MIPAS minus the reference instrument was calculated for each data point and the related time series were fitted by the 225 same method as just introduced.