dataesr/scientific-paragraphs-categorization huggingface dataset:
Determination of transcription levels To determine the transcription levels we decided to use Erange  [20]  software on Illumina data. After cleaning low quality reads, the remaining reads were mapped to the T. vivax genome (retrieved from GenBank) using Bowtie  [13]  and allowing up to 1000 multimatches and up to 1 mismatch. RNA-seq Erange pipeline was used with minor modifications. It is important to take into account that in genomes like this one, which contain several related paralogous genes, the use of computer applications that consider the unique regions of the genes to renormalize the assignment of multimatching reads (like Erange), is essential. This approach permits also determining which ones of the paralogous genes from a multigene family are really expressed at a given time. For 454 data (where the problem of multimatching reads is mitigated or simply eliminated, because of reads' lengths) transcription levels were computed using in house Bash and Perl scripts to parse Blast or Bowtie outputs.
Identification of splice-acceptor sites cDNA sequence tags (36 bp) that contained terminal Spliced Leader sequence (SL) were extracted from the Illumina output. The SL sequence was found in a 0. 5% (171200) of the reads and in the majority (94. 8%) of them in the sense direction, as expected because of constraints imposed by the cDNA size-fractionation and sequencing protocols. The Spliced Leader segment was trimmed from these sequence tags with a homemade python script. Sequences greater than 19 bases were used in downstream pipeline. Genomic matches were identified by mapping these reads with Bowtie against the T. vivax genome sequence. No mismatches were allowed. We used output of Bowtie (sam file), genomic information as given by the gff files and blockbuster software  [22]  to cluster the mapped reads in order to detect trans-splicing sites in the chromosomes and other genomic sequences from T.
vivax. Cluster information was parsed with gff information with homemade Perl script and a final table with gene information and transsplicing sites associated were obtained. A similar pipeline was used to analyze trans-splicing patterns in T. brucei and Trypanosoma cruzi. In the case of T. cruzi, the RNAseq data from three stages of the life cycle of the parasite (epimastigote, trypomastigote and amastigote) were obtained in our laboratory (further analysis on this data will be published elsewhere). Due to the sequencing strategy used in T. cruzi (stranded) the number of Spliced Leader containing reads was modest; this restricted the type comparisons that could be conducted in this species to only the determination the splicing motifs.
Results and discussion Assembling 454 reads and functional annotation of resulting contigs. Because 454 FLX sequencer yields long reads, it is possible conduct "de novo" assembling to obtain good quality contigs. This was done with two different computer programs, Mira and Newbler (Roche) using optimized parameters for RNA-seq assembling. The results obtained allowed us to conclude that Mira outperforms Newbler, since the contigs obtained represent better reconstructions of full length mRNA (i. e. the Q statistics is higher). Besides, more than 92% of the putatively expressed mRNAs are tagged (either by contigs containing several reads, or by individual reads), hence indicating that the 454 derived sequence dataset is a good picture of the transcriptional state of the parasite (Additional file 3: Table  S2 ).
Finally, it is worth mentioning that more than 1000 contigs that are transcribed at different levels and unequivocally correspond to protein coding genes, do not have homologs in other species, including other trypanosomatids such as Leishmania sp, T. cruzi and the African trypanosomes for which genome sequence is available (T. b. brucei, T. b. gambiense and T. congolense). This means that in all likelihood they are species specific. Among these T. vivax specific contigs, around 50 genes have not even been reported in the T. vivax genome available in GenBank, indicating that very probably they are specific of the strain LIEM-176. 564 species specific contigs for which it was possible to build a full cDNA were chosen for additional analysis. A preliminary characterization of these genes was carried out using a battery of informatics tools such as those that identify signals for sub-cellular localization and domain analysis.
These results are presented in Additional file 5: Table  S4. Database web interface. A relational database (MySQL) was built to store and browse the data and results produced in this work. In fact the database contains raw as well as processed and annotated data as described in the previous section. A Pyhton web application was developed using the Django programming framework. This application provides user-friendly data querying, browsing and visualization through a web interface (http://bioinformatica. fcien. edu. uy/Tvivax/). In this web interface it is possible to search for, and retrieve reads, contigs as well as virtual translations. Besides, the database can be searched using different criteria such as length, depth of the contigs (i. e. expression level), GO terms, Enzyme Commission numbers, Blast e-values, keywords, etc.
or a combination of these criteria. Moreover any sequence can be blasted against the dataset. The annotated entries are linked to the reference databases used for their annotation, namely Amigo Gene Ontology  [23] , KEGG repository at EBI and NCBI. In addition the database offers the possibility to highlight on-the-fly the enzymes in the pathway image files downloaded from the KEGG FTP site. Expression of Variant Surface Glycoproteins in T. vivax, and the protein composition of the cellular surface. Because of the strategic evolutionary position of T. vivax, as the earliest branching African trypanosome, it is important to analyze in this species the expression patterns of Variant Surface Glycoproteins, as well as the organization of this gene family to help shedding light on diverse questions concerning the origin and evolution of antigenic variation.
It should be taken into account that T. vivax was introduced in America around 1850, in the French Guiana, by infected Zebus imported from Africa  [26] [27] [28] [29]. Since its introduction, T. vivax has been disseminated by horse flies (Tabanidae)  [4]  and stable flies (Stomoxys spp. )  [30] , and it was rapidly dispersed throughout South America. However, the degree of sequence similarity seems to be much higher than what we would have expected if account is taken to the fact that these genes normally diverges extremely fast. Indeed, the comparison of VSG genes among T. brucei strains reveals that even closely related subspecies (like T. brucei brucei and T. brucei gambiense and the so called Tororo isolates) have very divergent silent repertoires  [31]. In addition, it should be noted that this VSG gene was not identified in the draft genome deposited in GenBank corresponding to the Y486 strain.
The visual inspection of these troublesome points shows that they correspond to DNA segments having extreme compositional biases. On the other hand the comparison between the two technologies was also conducted by mapping their reads on genomic regions to assess the variability in sequencing depths estimated by each method. Again it is possible to observe that there is a good agreement between both methods (Figure  2B ). Estimation of transcription levels for 11886 CDS annotated in GenBank was done using the Erange software that corrects multiple matching reads considering unique parts of genes for their assignation. The gene expression levels (read count and RPKMs) are available in Additional file 4: Table  S3. An unexpected observation is that several genes and genomic regions appear to be non-transcribed at all in the bloodstream stage of T.
Finally, we would like to mention that in spite of the fact that these explanations may account in part for this atypical behavior displayed by trypanosomes, it is also evident that they do not apply to the case of ribosomal and other conserved proteins that exhibit low o none GC 3 biases and very high expression. Mapping transsplice sites. To identify trans-splice sites, we mapped 159395 miniexon containing Illumina reads onto the T. vivax draft genome that has been recently made available in Genbank. This allowed us to identify the trans-splice sites in 5959 genes. Among these genes, 3350 had only one bloodstream splice site and 2609 genes had two or more. The distribution of splicing sites per gene is presented in Figure  5A. The maximum number of sites per gene was 9 and the average 1.
A Gene Ontology enrichment analysis was carried out to explore this aspect, namely if the genes exhibiting this feature encode proteins belonging to some particular categories. Interestingly enough, this group contains a much higher than expected frequency of ribosomal proteins, elongation factors and other proteins related with the translation machinery. Other type of proteins over-represented in this group are heat shock proteins and proteins that interact with RNA (Figures  8A  and B ). Because the annotation of T. vivax genes available in GenBank is not precise in relation to the correct identification of start codons, and considering that this trouble can introduce serious biases in this analysis, the same ontology analyses were also conducted in T. brucei, whose annotation is expected to be much more depurated.
Conclusions In this work we conducted a RNA-seq analysis in T. vivax, a species of great importance for comparative purposes owing to its evolutionary location as the  earliest branching African trypanosome. To this aim we sequenced the bloodstream stage of its life cycle using two complementary sequencing technologies. The first of these technologies allowed us to obtain a high quality assembly without the restriction of a reference set. The annotation of the contigs thus obtained (using a battery of bioinformatic tools) permitted the identification of about 6500 protein coding genes and other non-coding RNAs. Noteworthy, more than 1000 genes were found to be species specific and about 50 exclusive of T. vivax LIEM-176. This information and the partial reconstruction of metabolic pathways, is publicly available through a searchable online database.
Acknowledgements T. vivax bovine Venezuelan isolate (LIEM-176) was a kindly provided by  Laura Morón  and  Glenda Moreno , from  Universidad de los Andes ,  Núcleo Táchira ,  Venezuela. Cryostabilites of Trypanosoma  vivax (Y486 strain) were kindly provided by  Philippe Büscher  ( Parasite Diagnostics Unit, Department of Parasitology, Institute of Tropical Medicine Antwerp, Belgium ). We thank  Paula Tucci  for critical reading of the manuscript and  Daniel Ramón  and  Laia Pedrola  ( Life Sequencing S. L. , Valencia, Spain ) for technical assistance and helpful experimental suggestions on 454 sequencing. This work was supported by grants from  Fondo Clemente Estable (ANII)  and  CSIC  ( Universidad de la República, Uruguay ). DP, CR. and  FAV  are researchers from the  Sistema Nacional de Investigadores (ANII) ,  Montevideo Uruguay.
To minimize false-positive hits, two independent SILAC labelswap experiments were performed (Figures  1B  and S1B ). The list of factors preferentially associated with H2Bub1-rich chromatin (average log2 enrichment ratio > 1. 5) contains more than 90 proteins, greatly expanding the known H2Bub1 interactome (Table  S1 ). Of these, WDR82 is known to associate with chromatin in a H2Bub1-dependent manner  (Wu et al. , 2008) , whereas chromodomain helicase DNA binding protein 1 (CHD1) is crucial for maintaining high H2Bub1 levels  (Lee et al. , 2012). Remarkably, the enriched set includes multiple components of several protein complexes (examples are color coded in Figure  1B ), deduced from interaction relationships based on experimental or curated database evidence (Figure  S1C ). Ingenuity Pathway Analysis (Ingenuity Systems, http://ingenuity.
ACKNOWLEDGMENTS We are grateful to  Moshe Yaniv ,  Frauke Melchior ,  Steven Bergink ,  Gilad Fuchs ,  Eran Kotler , and  Lior Golomb  for helpful discussions. We thank  Tamar Unger  and  Shira Albeck  for preparing H2B and ubiquitin and  Uwe Plessmann  and  Monika Raabe  for expert technical assistance. This work was supported in part by grant  293438  (RUBICAN) from the  European Research Council  (to M. O. ), grant  R37 CA40099  from the  National Cancer Institute  (to M. O. ), the  Dr. Miriam and Sheldon G. Adelson Medical Research Foundation  (to M. O. ), the  Edmond J. Safra Foundation  (A. B. ), the  Lower Saxony-Israeli Association  (to M. O. and W. F. ), the  Max Planck Society  (to H. U. and W. F. ), and the  Mina  and  James Heinemann Foundation  (to W. F. ). M. O. is the incumbent of the Andre Lwoff chair in molecular biology.
V. NUMERICAL RESULTS AND COMPUTATIONAL DETAILS A. Numerical integration There are several software packages for carrying out sector decomposition and subtraction of divergences, and for integrating the resulting expressions numerically  [27] [28] [29]. We used the package FIESTA 2  [29] , written in a combination of Mathematica  [52]  and C++  [53] , and which can take advantage of multiple processors. However, a six-loop diagram is too complicated to evaluate directly. For example, the sector decomposition (which is not very parallelized) takes about a day per primary sector to run in Mathematica on a modern computer, and produces a total code for the integrands which takes up hundreds of gigabytes. Thus the computation must be split into smaller parts to make it feasible. To do so, we only need a small portion of the FIESTA 2 software, and we have extracted this portion and adapted it to our purpose by hand.
We did the sector decomposition on a small (10 node) cluster, and then performed the numerical integrations on a large (1000 node) cluster, using the adaptive quasi-Monte Carlo integrator Vegas  [54]. An important element in FIESTA 2 is the CIntegrate package (see ref. [29] , appendix F), which accepts a symbolic algebraic expression of the sort that can be produced easily by Mathematica, and compiles it into pseudocode that can be executed efficiently in C. Using this package, we were able to break down the computation by having Mathematica produce a file for each sector containing its integrand, which could be passed to a Vegas integration program. The integrals are of course completely independent, so this step is easy to parallelize. This approach also allowed us to keep the partial results for every sector, which helped in debugging and uncertainty analysis.
Taking the uncertainties as 100% correlated and conservatively adding them directly (instead of in quadrature), the final result for the UV divergence is 15 6 10 × (0. 7631 ± 0. 0015) + 5 × (0. 8183 ± 0. 0024) 15 (68. 68 ± 0. 17) , A (6) 4 D=5, div. = -X 6 10 V (f) + 5 V (g) -V (h) = - 1 ǫ X (4π) -(0. 2762 ± 0. 0008) = - 1 ǫ X (4π) where X = -stuA tree 4 (1, 2, 3, 4). (5. 3) It is clear from this result that the coefficient of the UV divergence is nonzero, well within the integration uncertainty. This proves that MSYM is perturbatively ultraviolet divergent in D = 5. The data used to numerically verify the integral consistency relations, as a means of assessing uncertainties in the numerical integration. The columns labeled by "Value" and "Uncertainty" are multiplied by ǫ(4π) 15.
Acknowledgments We thank  Nima Arkani-Hamed ,  Radu Roiban  and  Edward Witten  for helpful discussions. We also thank  Paul Heslop  and  Gregory Korchemsky  for assistance in comparing our amplitude integrand to their form [20]. We thank the  Institute for Nuclear Theory in Seattle  and the  Banff International Research Station  for hospitality, and  Academic Technology Services  at  UCLA  for computer support. This research was supported by the  US Department of Energy  under contracts  DE-AC02-76SF00515  and  DE-FG03-91ER40662. JJMC acknowledges that this publication was made possible through the support of the  Stanford Institute for Theoretical Physics , and a grant from the  John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation.
Mean residual molar ellipticity values (h) were calculated as follows: h = 33006m6DA/(l6c6n), where m is the protein molecular mass, DA is the difference between right and left circularly polarized light, l is the path length (0. 1 cm), c is the protein concentration (between 0. 1 and 0. 2 mg/mL) and n is the number of residues. The experimental data obtained between 195 and 245 nm were analyzed using the CDNN software program  [24]  to calculate the percentage of each type of secondary structure in the proteins. Esterase and lipase assays using vinyl-esters and fluorescent triglycerides substrates. Enzymatic hydrolysis of solutions and emulsions of various (vinyl-) esters was monitored potentiometrically at 37uC for at least 5 min using a pH-Stat (Metrohm 718 STAT Titrino)  [25]. Assays were performed at pH 8 in 20 ml of 2.
In silico Protein Modeling and Ligand Docking Three-dimensional structural models of wild type Cut4 and wild type or mutant Cfp21 proteins were generated with the automatic protein structure homology modeling server using the I-Tasser software program  [29, 30]. Although the models start after the signal peptide  [7] , the residue numbering of the full length protein has been used in the text. The structural overlay and figure were drawn using the PyMOL Molecular Graphics System (version 1. 3, Schro ¨dinger, LLC). Molecular docking was performed with the Autodock Vina program, and the results were interpreted using its plugin for PyMOL under Windows XP  [31]. TAG and phospholipid ligand molecules were generated and their geometries were refined using the open source Avogadro software program (Version 1.
Three Dimensional Models, Site-directed Mutagenesis and Protein Purification Despite the high level of homology known to exist between their amino acid sequences, the biochemical data obtained on Cfp21 and Cut4 indicated that these two proteins have distinct substrate specificities  [7]  showing that Cfp21 is a lipase and Cut4 is a strict phospholipase. In order to understand this difference, Cfp21 and Cut4 structural models (Figure  1A-B ) were built, based on the crystal structure of the Fusarium solani cutinase (pdb code 1CEX)  [32] , using the I-Tasser Server software program  [30]. The superimposition of the cutinase 3D structure and Cfp21 and Cut4 models showed good alignment of the a/b hydrolase fold structural elements (a-helices located on either side of the central b-sheet) and the catalytic triads composed of Ser/Asp/His residues in each protein.
To explain this discrepancy, the lysophospholipase activity of Cut4 and Cfp21 mutants was assessed. Contrary to the Cfp21 mutants, a lysophospholipase activity was detected in Cut4 (data not shown). It was therefore concluded that during the 48-h incubation period prior to the radioactive assay, sn-1 and sn-2 cleavage occur with Cut4 and the three Cfp21 mutants, and radiolabeled lysophospholipids and fatty acids are released. In the case of Cut4, the lysoPLA 2 activity may induce the release of radiolabeled fatty acids from the lysophospholipid, which would explain why no radiolabeled lysophospholipid was detected after 48 h of hydrolysis. In addition, during this long incubation experiment, non enzymatic reactions such as trans-esterification are known to possibly/sometimes occur, which may transfer the radiolabeled fatty acid from the sn-2 position to the sn-1 position of the substrate, where it might be hydrolyzed by the lysoPLA 1 activity.
This work and the post-doctoral fellowship to L. Dedieu were funded by the  Agence Nationale de la Recherche  in the framework of the  ANR-Foamy-TuB grant  ( ANR MIEN 2009-00904 ) to Stephane Canaan, by the  LISA Carnot Institute  (Convention  ANR nu07-CARN-009-01 ) and by the  CNRS  and the  Aix-Marseille Universite. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Fluorescence microscopy and image analysis Wide-field fluorescent images were captured with an Olympus IX-81 microscope equipped with Polychrome V monochromator, Coolsnap HQ camera (Rooper) controlled with Metamorph acquisition software V6 (Universal Imaging). Confocal Microscopy was performed with an Andor Revolution Nipkow-disk confocal system installed on an Olympus IX-81, featuring a CSU22 confocal spinning disk unit (Yokogawa) and an EMCCD camera (DU 888, Andor). Pixel size was 65 nm. For 3D analysis, Z-stacks of 41 images with a 250 nm Z-step were used. Exposure time was 200 ms. Confocal images were processed and analyzed with a Matlab script nucloc, available at http://www. nucloc. org/ (MathWorks)  (32).
ACKNOWLEDGEMENTS The authors thank  M. F. O'Donohue  for plasmids pEYFP-UBF1 and pEYFP-UBF2. The authors also thank  Steven J. Brill  for his gift of antibodies against Hmo1. They acknowledge  Nacer Benmeradi  and  Ste´phanie Balor  for their help in EM acquisition. This work also benefited from the assistance of the electron microscopy facility of the IFR 109 and from the  Imaging platform of Toulouse TRI. O. G. dedicates this.
FUNDING ATIP grant from  CNRS , by  Agence Nationale de la Recherche  ( Nucleopol and ODynRib -Jeune chercheur programme ) and Jeune e´quipe from FRM; PhD fellowship from  FRM  (to B. A. ); Work in the McStay laboratory is funded by PI grant number  [07/IN. 1/B924 ]from  Science Foundation Ireland. Funding for open access charge:  ANR ODynRib.
Acknowledgements Thanks to  P. Darlu  ( MNHN, Paris ) who drove me to the central problem of this work, the congruence of gene trees. I would also like to thanks  C. Brochier  ( LBBE, Lyon ) and  P. Gambette  ( IGM, Marne-la-Vallée ) who sent me several tree sets to test, allowing me to improve this method and also  C. Chapple  ( TAGC, Marseille ) and two referees for their helpful comments on the manuscript. Received: 4 July 2012 Accepted: 11 January 2013 Published: 9 February 2013.
In addition to this variety of measurements, there are multiple units of comparison used to identify splicing differences. Classification of splicing differences between isoforms is non-trivial for complex gene models, and incomplete identification of these differences leads to ascertainment bias. We developed a suite of tools called the Splicing Analysis Kit (Spanki) to model, analyze, and improve junction detection, and to enable a complete splice-junction centric analysis of RNA-Seq data (Table  1 ). This software is available at http://www. cbcb. umd. edu/software/spanki and https://github. com/dsturg/Spanki. Spanki analyzes and mitigates error profiles, based on simulations that closely mimic real data. Uniquely, the Spanki read simulator combines robust empirical modeling with detailed reporting that is geared toward evaluating splicing detection performance.
Since junction detection is the foundation of our analysis, we undertook simulations to quantitatively assess splice junction detection performance so that we could characterize and then filter out dubious junctions. We built simulated datasets in two steps: modeling and read generation (Figure  1B ). The first step in the analysis of junction-based splicing detection is to identify and quantify the junction spanning reads (where part of the read aligns to one exon and another part to another exon Figure  1A ). We performed this analysis using an annotation (Figure  1C ) or without. We then merged junction coverage data and estimated the relative abundance of the alternative forms (Figure  1D ). In the next step we classified splicing events from annotated transcript models to obtain sets of junctions that define mutually exclusive "paths" (Inclusion and Exclusion) that interrogate each path specifically.