dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the thermochemical disc modelling code ProDiMo based on the chemistry, radiation field, and temperature structure of a previously published model for HD100546. We calculated line fluxes and profiles for the whole set of observed CO ro-vibrational transitions using a large CO model molecule that includes the lowest two electronic states, each with 7 vibrational levels and within them 60 rotational levels. Comparing observations and the model, we investigated the possibility of disc asymmetries, the excitation mechanism (UV fluorescence), the geometry, and physical conditions of the inner disc. Results. The observed CO ro-vibrational lines are largely emitted from the inner rim of the outer disc at 10-13 AU. The line shapes are similar for all v levels and line fluxes from all vibrational levels vary only within one order of magnitude.
Spectroscopy The science data was reduced using the CRIRES pipeline recipes ver. 1. 11. 0 on esorex platform ver. 3. 8. 1 and was corrected for the detector non-linearity and response. The observations where done in nodding mode: The spectra are nodded between two positions (A and B) 10" apart on the sky in the pattern ABBA so that the source is in different positions on the chip. The sky emission was removed by subtracting the nodded A and B spectra. The spectrograms were registered and combined, and one dimensional spectra were extracted for each grating setting and slit position angle. The rectangular extraction method was used. The width of the extracted rectangle was 41 pixels = 3. 53. The data of the spectroscopic standard stars (HR 6556 and HR 6879) were reduced in the same way. The spectra of HD 100546 were divided by those of the standard stars after making small adjustments in the optical depth of the telluric lines, the wavelength, and the spectral resolution.
Confronting a model with the observations To compare our observations with current theoretical disc models, we have derived CO ro-vibrational lines from an already existing model of HD100546  (Thi et al. 2011 ) and produced CO line data cubes. We then run the line cubes through a custom made IDL filtering script to simulate a CRIRES observation of our model. The final 'observed' model line fluxes and profiles are then compared to our actual CRIRES observations.
ProDiMo/MCFOST A full model of the protoplanetary disc HD100546 already exists  (Thi et al. 2011; Tatulli et al. 2011; Benisty et al. 2010). The observed spectral energy distribution (SED) and near-and midinfrared interferometric VLT-AMBER and MIDI data have been used to constrain the dust properties. The disc structure has been fitted with a parameterized disc. The fitting with MCFOST is described in  Benisty et al. (2010)  and  Tatulli et al. (2011). MC-FOST is a three dimensional continuum and line radiative transfer code based on the Monte Carlo method  (Pinte et al. 2006 (Pinte et al. , 2009)). It calculates the dust temperature structure and radiation field, which can be used to produce images, polarization maps and SEDs with a ray-tracing method. Thi et al. (2011)  modelled the CH + chemistry in HD100546 using the thermo-chemical disc code ProDiMo on top of the above described dust model.
Line fluxes and band ratios To further visualize our model to observation comparison, we overplot modelled and observed fluxes as a function of wavelength. We select the full line dataset collected at P. A. =145°observed on the 29th (since this is the night with the best S/N and the position angle with least slit loss)(Fig. 8 ). The model line fluxes for the v=1-0 band are within a factor of two of the observed values. The model line fluxes of the higher v-bands fall a factor of 10 below the observed values. Hence the line ratio between higher and lower v-band lines is much higher in models than in observed data.
Conclusion In this paper, we present for the first time an extensive analysis of a large comprehensive set of observational data of the CO ro-vibrational emission lines from the disc around HD100546: Collected at eight different position angles covering six different grating settings at the CRIRES/VLT. The observations show line asymmetries and the line profile shapes vary with position angle. We also present modelled emission lines, using a ProDiMo model of HD100546 piped through a slit filtering tool. This model is in no way designed for the CO ro-vibrational lines, so the comparison provides us with a very strong test. We find from our observational analysis that large parts of the CO ro-vibrational emission is emitted at the disc wall (10-13 AU). The total line flux builds up out to 40 AU  (Goto et al.
Acknowledgements. The authors would like to thank  Jonathan Smoker  for performing tests on the CRIRES spectro-astrometry and  Sean Brittain  for discussions of the CO ro-vibrational data and a careful and constructive referee report. IK, WFT, and PW acknowledge funding from the  EU   FP7-2011  under Grant Agreement no. 284405  ( PERG06-GA-2009-256513 ). GvdP acknowledges support from the  Millennium Science Initiative (Chilean Ministry of Economy ), through grant " Nucleus   P10-022-F".
Transposable elements (TEs) constitute the most active, diverse and ancient component in a broad range of genomes. Complete understanding of genome function and evolution cannot be achieved without a thorough understanding of TE impact and biology. However, in-depth analysis of TEs still represents a challenge due to the repetitive nature of these genomic entities. In this work, we present a broadly applicable and flexible tool: T-lex2. T-lex2 is the only available software that allows routine, automatic and accurate genotyping of individual TE insertions and estimation of their population frequencies both using individual strain and pooled next-generation sequencing data. Furthermore, T-lex2 also assesses the quality of the calls allowing the identification of miss-annotated TEs and providing the necessary information to re-annotate them.
Recently, several accurate computational approaches have been developed to analyze TE insertions using NGS data  (10, (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31). Designed for specific projects, all these tools were built with a limited number of features, e. g. some tools were designed to specifically call particular families of TEs  (10) , while others were designed exclusively for pooled NGS data  (26). Unfortunately, none of the currently available tools allows a complete and in-depth analysis of individual annotated TE insertions. With the idea of providing a tool to automatically call TE insertions in multiple genomes and to estimate their population frequency, we recently designed an integrated, flexible pipeline called T-lex  (31). While most of the available approaches only detect the presence of TE insertions, Tlex launches two complementary and distinct TE detection modules: one to detect the 'presence' and another to detect the 'absence' of a TE insertion.
Here, we present T-lex2, the version 2 of the original T-lex pipeline  (31). Besides improving the accuracy of the TE frequency estimate, T-lex2 now assesses the quality of the TE calls. To achieve this, T-lex2 uses information from (i) a new module specifically designed to identify target site duplications (TSDs) and (ii) the genomic context of each insertion, to identify putatively miss-annotated TE insertions highly likely to produce wrong calls. TSDs are short duplications, from 2 to 20 base pairs (bp), flanking the TE sequences as a result of the transposition mechanism of most TE families. While current TSD detection approaches for reference TE insertions require knowing the biology of the TEs and are limited to LTR or non-LTR elements, T-lex2 TSD detection module works without a priori knowledge, and for all type of TEs.
Each of these modules can be launched independently and provides a large number of options allowing the user to carry out flexible and customizable analyses. A detailed manual describing step-by-step how to run T-lex2 and listing all T-lex2 options is available online at http://petrov. stanford. edu/cgibin/Tlex2 manual. html. TE-analysis module. This module analyses the flanking sequences of each known TE insertion to identify features that might interfere with the presence and/or absence detection. The TE-analysis module (i) provides information about the presence of repetitive elements using Repeat-Masker (http://www. repeatmasker. org), (ii) identifies missannotated poly(A) tails and (iii) flags the TE insertions that are part of segmental duplications. To identify the presence of repetitive elements, the TEanalysis module extracts the flanking sequences of each TE insertion (125 bp by default; see option '-f') and launches RepeatMasker to annotate the TEs, simple repeats and lowcomplexity regions (http://www.
repeatmasker. org). The option '-s' allows specification of the name of the model organism and thus specifies the repeat library that needs to be used by RepeatMasker (http://www. repeatmasker. org). If at least one flanking region shows a repeat density greater than the pre-specified value (50% by default; option '-d'), the TE insertion is flagged as flanked by repetitive elements. By default, these TE insertions are filtered out. This filtering step can be bypassed using the option '-noFilterTE'. A new feature of the TE-analysis module identifies putatively miss-annotated poly(A) tails by searching for stretches of A's or T's (by default more than 5 bp) at the TE junctions, without a priori knowledge of the TE type. Non-LTR elements harbor poly(A) tails at their 3 -end that are known to be highly variable in length.
34 default parameters; http://www. soe. ucsc. edu/∼kent). When more than 50% of a flanking sequence matches at more than one location on the reference genome with more than 90% of sequence identity, the TE insertion is flagged as part of a duplication. TE insertions partially part of duplication (notified as 'sd left' or 'sd right') are distinguished from the TE fully part of a duplication (notified as 'sd'). When a TE is fully part of a duplication while another copies do not contain the TE insertion, this TE insertion is notified as 'sd noTE'. The analysis of the TE flanking sequences including the RepeatMasker output, whether the TE is flanked by a longer than annotated poly(A) tail, and the segmental duplication analysis results, are stored in a subdirectory called 'Tanalyses'. TE-presence detection module.
The presence detection module detects sequencing reads overlapping the flanking regions of the TE insertion (Figure  2A ). The junction sequences of each TE insertion are extracted and used as reference sequences. By default, each TE junction sequence encompasses the terminal TE sequence (60 bp from each end) and the flanking sequence (1 kb on each side). The lengths of the TE sequence and the flanking sequence can be changed using the options '-b' and '-j', respectively. The reads are mapped on these new extracted reference sequences (Figure  2A ) and are used to build a contig using Phrap (version 1. 090518; http://phrap. org). T-lex2 uses only the reads mapping with a minimum Phrep quality score (34 by default; this can be changed using the option '-minQ'). The pipeline also reports the number of reads mapped on the TE junction at the minimum Phrep quality score (Figure  2A ).
The absence module specifically looks for 'reads' spanning the TE junction with at least 15 bp (parameters by default; see option '-v') of overlap on each side. T-lex2 then runs RepeatMasker on the selected reads (see option '-noint') in order to test whether the mapping is due primarily to simple repeats or low-complexity regions (http://www. repeatmasker. org). After this step, T-lex2 selects the reads that have at least five non-repetitive (lowcomplexity or satellites) base pairs at each end that both map to the flanking regions. Because of the stringency of our approach, even a single read mapping to both flanking sides and thereby spanning the insertion site of the TE is sufficient for the absence detection module to classify the TE insertion as 'absent' (Figure  2B ). If no reads overlap the TE junction, two possibilities are considered: (i) the TE is present in the strain (or fixed in the pool sample) or (ii) the coverage of the data used is insufficient to detect reads providing evidence for the absence.
Taking all these expectations into account, we designed and verified a metric based on the local read depth coverage in the vicinity of the TE insertion to estimate TE frequencies from pooled data: total number of reads supporting the presence (NP) divided by the total number of reads supporting the presence (NP) and supporting the absence (NA), i. e. TE frequency = NP/(NP + NA). The file combining the TE calls from both TE detection modules is called 'Tresults'. The TE frequency estimates are stored in the 'Tfreq' or Tfreq pooled' if the option '-pooled' is specified.
TE-TSD detection module. To detect the TSD of each annotated TE insertion, this module analyzes the read alignments generated by the absence detection module (Figure  3 ). As all T-lex2 modules, this new module can be launched independently although it does require the read alignments from the absence detection module (see option '-tsd'). The TSD detection module looks for tandem duplications located on the absence reference sequence spanning the TE breakpoint. It starts by assembling all the selected reads supporting the absence for each TE using the Phrap program (http://phrap. org; Figure  3 ). Because Phrap requires a minimum of three sequences to build a contig, if fewer than three reads are selected to support the absence call, the reads are considered independently (Figure  3 ). Each contig (or read) is then re-aligned on the absence reference sequence using BLAT v.
34 program (33) (default parameters; Figure  3 ). Only the absence calls with a gap larger than two base pairs and located close to the TE breakpoint are selected and analyzed. If no gaps are observed, the TE-TSD detection module returns 'no Gap'. If a gap is observed, the motif present in front of the gap is called the putative target site or PTS (Figure  3 ). Using the FastaGrep program (available from bioinfo. ut. ee/download/dl. php?file=6 and executed with the default parameters), a tool that looks for short and conserved motifs, the TSD detection module looks for the copy of the PTS on the other side of the breakpoint. Based on the sensitivity of this program and the short length of the TSDs, we only trust the matches with more than 80% sequence identity (Figure  3 ). T-lex2 then sorts the FastaGrep hits to identify the sequence showing the closer and highly conserved motif.
If FastaGrep fails to detect a match, the TSD detection returns 'noTSD' as a result, otherwise, the TSD detection module returns 'detected'. In the latter case, the PTS and its closest copy sequences are also returned. Because several contigs can be generated for the same TE insertion, several TSDs can also be returned. The results of the TSD detection process are stored in a file called 'TSDannot'. TE-TSD detection module flags the TE insertions for which the TSDs cannot be clearly defined. TSD detection may fail for old TE insertions for which the TSD is too divergent, for truncated TEs, and when the boundaries of the TE insertions are not well annotated (Figure  4 ). The latter TEs can be re-annotated by the user after manual inspection.
In such situation, the module could return present as a call (Figure  4B. l). The Tfreq combine file of T-lex2 provides information from both the TE-analyses and TE-TSD detection modules, i. e. poly(A) tail longer than the official annotation and TSD detection failures, that allows classifying these TE insertions as putatively miss-annotated TEs. This information can be used to perform manual curation of these insertions and reannotate them.
Statistical analysis All the statistical analyses were performed using R (http: //cran. r-project. org/). Sensitivity and specificity were estimated using individual PCR frequencies as we have the T-lex2 frequencies estimates using the same exact strains. Sensitivity and specificity were computed such as: (i) Sensitivity = number of correct presence calls/total number of presence calls in the validation data set and (ii) Specificity = number of correct absence calls/total number of absence calls in the validation data set. For the human data set, the number of correct absence calls is not known because both polymorphic and absence calls are detected as absent in the validation data set. Thus, we used the positive predictive value (i. e. , number of correct presence calls based on T-lex2/total number of presence calls) as a proxy for specificity and the false discovery rate (i.
For 11 out of the 14 strains, we observed more than 40% of no calls, and for the other three strains (RAL-379, RAL-362 and RAL-313), more than 70% of TE insertions were detected as present. As we suspect these 14 strains to be problematic (most likely a combination of poor sequence quality and extensive residual heterozygosity), we decided to remove them from our analysis and analyze the calls in the remaining 72 strains (Supplementary Figure  S1 ). Out of the 755 TE insertions, TE analysis module detected six that were flanked by other TE insertions, and three that were part of segmental duplications (Supplementary Table  S2 ). We removed these nine insertions because they are likely to produce wrong calls (see Materials and Methods). We then filtered out seven TEs for which T-lex2 failed to return any calls, and 30 TE insertions for which T-lex2 fails to return a call for more than 75% of the strains (Supplementary Table  S2 ).
Overall, we identified for the first time TSDs for ten TE families: six DNA transposon families and four non-LTR families (Table  3 ). To assess the quality of the TSD annotation using T-lex2, we compared 148 TSD results obtained by T-lex2 with the annotations obtained with LTRharvest software (42) (Supplementary Table  S4 ). For 92 out of the 148 TE insertions, the TSD annotations are identical in length and motif. For 7 TEs, TSDs have the same length but differ in motif (Supplementary Table  S4 ) and for the other 49 TEs TSD motifs are identical but differ in length: 7 TEs have shorter motifs and 42 TEs have longer motifs. All the longer TSDs correspond to shifts of only 1 bp except for one TSD that is 3 bp longer than previously annotated. For 13 of these TEs, corresponding to Copia elements, T-lex2 results are supported by experimental studies indicating that Copia TSD are 5 bp long  (47, 48)  and not 4 bp as detected with LTRHarvest (Supplementary Table  S4 )  (42).
T-lex2 allows re-annotation of TEs in D. melanogaster Combining outputs from the TE-analysis module and the TE-TSD annotation, T-lex2 allows manual curation and re-annotation of TE insertions. To facilitate the identification of the putatively miss-annotated TEs, information on TEs for which the TSD detection failed and TEs with poly(A) tail longer than annotated, are also given in 'Tfreq combine' file. TSD detection fails for TEs that are longer (Figure  4A. II) and shorter (Figure  4A. III) than annotated. We thus manually curated the 197 TEs for which TSD could not be detected. For 65 out of the 197 TE insertions, manual curation confirms the miss-annotation. All these 65 TEs are detected experimentally and by T-lex2 at very low frequency (Supplementary Table  S5   S5 ). The only TE insertion detected as shorter than annotated is an LTR (FBti0020118; Supplementary Table  S5 ).
DISCUSSION We present here the new version of the T-lex pipeline: T-lex2 (Figure  1 ). 1 ). Besides individual strain NGS data, T-lex2 can now estimate TE frequencies from pooled NGS data. This is an important new feature of T-lex2 since sequencing pooled samples is an inexpensive and efficient means of obtaining population genome data  (26, 39). Another important new feature of T-lex2 is the ability to identify TEs likely to return wrong calls, i. e. miss-annotated TEs, TEs part of segmental duplications and TEs flanked by other TEs. This has been accomplished by the expansion of the TE-filter module of T-lex and by the incorporation of the TSD detection module (Figure  1 ). Being able to identify TEs likely to give wrong calls is especially important for the analyses of TE population frequencies in genomes that do not have a high-quality TE annotation.
Note that even in an extremely high quality genome as the D. melanogaster one, we re-annotated ∼10% of the analyzed TEs. This result also supports the opportunities offered by the NGS data to improve TE annotations  (49). We tested the reliability of T-lex2 calls and frequency estimates using the fly and the human genome. Indeed, the pipeline can be used for any species for which NGS data and TE annotation is available. T-lex2 can be run for all type of TEs in any species because (i) it is a flexible pipeline that allows to customize runs for NGS data sets of different qualities, (ii) it is able to identify putatively wrong calls and reannotate miss-annotate TEs and (iii) no information about the biology of the TE is needed to get accurate frequency estimates. There are other available tools that can be used to estimate TE frequencies  (10, (21) (22) (23) (24) (25) (26) (27) (28) (29) (30).
However, T-lex (version 1 and 2) is to date the only tool that (i) combines presence and absence detection analysis that allows the identification of heterozygotes and/or polymorphic TE insertions (31); (ii) can be used for individual and pooled samples; (iii) provides the user with an output file with the frequency of the analyzed TEs; (iv) can be easily customized to be run with different NGS data sets in any organism; and (v) assess the quality of the calls allowing to re-annotate miss-annotated TEs. Thus overall, T-lex2 is the most broadly applicable and flexible tool available to date. The flexibility of T-lex2, exemplified by the incorporation of the TE-TSD detection module in this new version, will allow us to easily add new modules in near future. We are currently working on a new module designed to identify TEs not annotated in reference genomes.
Dataset and preprocessing Four datasets dedicated to lung adenocarcinoma were used: one exploration dataset and three external validation datasets (table  1 ). The exploration dataset consisted of normalized RSEM (RNA-Seq by Expectation Maximization) gene expression profiles for 55 paired uninvolved and tumoral lung samples (downloaded on March 15, 2013). All measurements were log2 transformed. If several alternative transcripts referring to the same HGNC gene name were present, only that with maximum expression was considered for further analysis. In our efforts to minimally transform or bias the data, we processed all the mRNAs transcripts regardless of their expression levels. In other words, while mRNAs with low expression levels are routinely filtered out in expression analysis pipelines,  24  our proposed method is inclusive of all mRNA expression.
N-of-1-pathways framework Within-patient analyses N-of-1-pathways was performed on the exploration dataset independently for each patient, using gene expression profiles from two paired samples: uninvolved and tumoral lung tissues, in the context of lung adenocarcinoma. The proposed statistical analysis method consisted of a non-parametric paired Wilcoxon test (Wilcoxon signed-rank test) performed within each patient on the paired gene expression profiles restricted to a given pathway. Wilcoxon statistics, W + and W -, provided a direction of deregulation for each geneset, as overall 'up-regulated' or 'down-regulated', respectively. Both false discovery rate (FDR) and Bonferroni corrections were applied to adjust p values for multiple comparisons. In each paired sample, only deregulated pathways with adjusted p values with FDR ≤5% and Bonferroni ≤1% were retained for further analysis.