dataesr/scientific-paragraphs-categorization huggingface dataset:
Automatic annotation and analysis of protein domains The automatic annotation software, AutoFACT (v. 3. 4),  36  was used for functional annotation of gene models. AutoFACTenables transitive annotation based on sequence similarity searches in several databases. We used the blastp algorithm (e-value 1 Â 10 -5 ) to model gene alignments against the following protein databases: non-redundant proteins (NCBI RefSeq database, downloaded 8/6/2010), Swiss-Prot (only manually curated proteins, downloaded 4/15/2010),  37  UniRef90, and UniRef100 (UniProt databases of clustered protein families, downloaded 4/15/2010), CDD (conserved protein domains, downloaded 4/15/2010),  38  PFAM ( protein domains, downloaded 4/15/2010),  39  and KEGG (metabolic pathways, downloaded 4/15/2010). 40 e set AutoFACT to consider the following order of importance for annotation: UniRef100, UniRef90, KEGG, non-redundant proteins, and CDD.
Orthologous gene analysis A data set composed of all the Leishmania spp. gene models was created and compared all-against-all using blastp (e-value cutoff of 1 Â 10 25 ). The results were submitted to OrthoMCL (v. 1. 4) software,  41  which clustered the proteins into orthologous and paralogous families. We applied the default software parameters, including an inflation index of 1. 5. The inflation index regulates the cluster tightness (granularity) associated with sensitivity and selectivity. 42 Clusters of proteins that presented bidirectional similarities between at least two Leishmania species were considered orthologs.
Pseudogene identification Leishmania and Trypanosoma spp. proteins were aligned against the L. (L. ) amazonensis genome using blastx with an e-value threshold of 1 Â 10 25. The coordinates of the first hit alignment for all proteins were converted to GFF file format using an in-house Perl script. The BEDTools package  43  was used to identify L. (L. ) amazonensis regions exceeding 200 bp that showed similarity with Leishmania or Trypanosoma spp. proteins without overlapping with the gene model predictions. These regions were compared against the NCBI non-redundant database using the blastx program and manual annotation.
Calculation of the codon adaptation index The CodonW v. 1. 4. 4 software (http://bioweb. pasteur. fr/ seqanal/interfaces/codonw. html) was employed to calculate the codon usage indices of each L. (L. ) amazonensis predicted gene. The codon adaptation index (CAI) estimates the extent of bias toward codons known to be preferred in highly expressed genes. 44 his index ranges from 0 to 1. 0 with higher values indicating stronger codon usage bias and a higher expression level. The frequency of codon usage in highly expressed genes defines the relative fitness values for each synonymous codon. These values were calculated from the relative synonymous codon usage rather than from the raw codon usage and therefore were essentially independent of amino acid composition. Because fitness values are highly species specific, we first identified a set of highly expressed genes in L.
Phylogeny of amastin surface proteins A phylogeny of Leishmania amastin proteins was built from a set of L. (L. ) amazonensis amastins predicted by our assembled genome and from all predicted amastin proteins in the T. cruzi, T. brucei, L. (L. ) major, L. (L. ) infantum, L. (V. ) braziliensis, L. (L. ) mexicana, and L. (S. ) tarentolae genomes. These amastin sequences [except L. (L. ) amazonensis amastins] were extracted from TriTrypDB  47  (accessed 6/28/2012) by searching for 'amastin' or 'amastin-like' entries. The 181 amastin/amastin-like protein sequences encoded by L. (L. ) braziliensis, L. (L. ) infantum, L. (L. ) major, L. (L. ) mexicana, and L. (S. ) tarentolae and 24 amastin/amastin-like protein sequences from L. (L. ) amazonensis were aligned using Geneious software (v. 5. 6. 3),  48 in which an embedded MUSCLE software was applied.
Interactome of mammalian proteins and Leishmania secreted proteins From the CDD and OrthoMCL analyses, protein families that were found to be expanded in L. (L. ) amazonensis [i. e. more gene/domain copies when compared with L. (V. ) braziliensis, L. (L. ) infantum, and L. (L. ) major] were also screened for orthologous in human protein databases. To identify human proteins that are orthologous to L. (L. ) amazonensis proteins, we performed a blastp search (e-value cutoff of 1 Â 10 210 ) against the human protein databases using the parasite's expanded proteins as query. These selected gene models were then analyzed using TargetP 1. 1 and SecretomeP 2. 0 prediction tools. 52, 53 Our objective was to identify proteins that could be secreted to the extracellular compartment or exposed for interaction with host cell components.
We considered as putative secreted proteins those gene products for which TargetP returned a signal peptide value exceeding 0. 8 and for which other values, such as the mitochondrial targeting peptide and the chloroplast transit peptide, were below 0. 2. L. (L. ) amazonensis proteins implicated in non-classical secretion or ectodomain shedding were predicted using SecretomeP v. 2. 0 with the recommended threshold of 0. 5. Considering that amastigote is the developmental form which maintains a durable relationship with the mammalian host cell, an additional step was included in the screening. Using the screened gene models above mentioned, we performed a blastp analysis (e-value cutoff of 1 Â 10 25 ) against L. (L. ) mexicana proteins expressed by amastigotes and identified in proteomic databases.
54 L. (L. ) amazonensis gene models that attend to these four criteria were considered for hybrid interactomes: (i) expanded or exclusive in L. (L. ) amazonensis; (ii) orthologous to human proteins; (iii) possibly secreted (start codon was manually verified); and (iv) possibly expressed by amastigotes. Some of the screened gene products could allow for interactions and interferences with native mammalian interactomes. Interactome networks for parasitesecreted proteins and for human and mouse proteins were created using IPA software (Ingenuity Systems, Redwood City, CA, USA) which was configured to build interactomes considering only those proteins expressed by cells of the immune system.
Results and discussion 3. 1. Annotation of Leishmania (L. ) amazonensis gene models Approximately 37 million 76-bp paired-end reads (average insert size, 400 bp) were assembled into 4411 scaffolds (coverage, 94Â) using Velvet de novo assembler software. The 454 reads (179 112 reads; average read length, 312 bp) were assembled using Newbler software into 27 856 contigs (coverage, 2Â. The final assembly was performed using Zorro, which combined scaffolds from Solexa and contigs from 454 sequencing to generate 2627 scaffolds (!1000 bp in length) that specified an (L. ) amazonensis genome size of 29. 6 Mb. Ab initio gene prediction was conducted using Glimmer and Genemark. hmm programs, which gave 8032 and 11 641 gene models (gene lengths !150 nt), respectively. Comparative gene predictions consisted of proteins from L.
(V. ) braziliensis, L. (L. ) infantum, L. (L. ) mexicana, and L. (L. ) major aligned against the final L. (L. ) amazonensis genome assembly. The final set of 8168 gene models was created using a combination of ab initio and comparative gene model analyses, EVM software to identify consensus gene structures supported by these two approaches, and manual annotation. Incomplete genes and genes overlapping gap regions were discarded from further analyses. Following manual annotation, 8100 gene models remained with an average length of 1793 bp; this is consistent with other sequenced Leishmania species. These data are summarized and compared with other Leishmania genomes in Table  1. The final set of gene models were annotated automatically by comparing them against protein databases (blastp) and summarizing with AutoFACT software.
The results indicated that 55% of the gene models showed high sequence similarity (e-value 1 Â 10 210 ) to functionally annotated proteins, whereas 42% of the models were similar to unassigned proteins (i. e. proteins with no functional annotation). The remaining 3% yielded no hits with any databases and were considered unclassified (Fig. 2 ). In agreement with previous reports,  19,20,22 -24  our genome sequence analysis indicated that more than 90% of the 8100 L. (L. ) amazonensis genes are shared with other human pathogenic Leishmania spp. with little variation in orthologous gene content. Despite varying clinical manifestations and features of lesions, Leishmania spp. harbor a conserved genomic core encoding functions ranging from fundamental biological processes to complex host -parasite interaction networks.
We performed a comparative annotation of tRNAs from the L. (L. ) amazonensis, L. (L. ) infantum, L. (V. ) braziliensis, and L. (L. ) major genomes. Using tRNAscan-SE software,  55  the annotation yielded very similar numbers among the studied Leishmania species (Supplementary data, Table  S1 ). Given the number of tRNAs for each codon in L. (L. ) amazonensis, we calculated the CAI for all annotated gene models (Supplementary data, Table  S2 ). The index measures 572 The Genome Sequence of L. (L. ) amazonensis [Vol. 20, the codon usage bias presented by highly expressed genes and can be comparatively employed to score native genes (higher CAI, more adapted to the pattern of codon usage) and possibly foreign or transferred genes (lower CAI, less adapted to the pattern of codon usage). 56 Ranging from 0 (non-expressing gene, likely pseudogene) to 1 (highly expressed gene), we found that L.
23, 24 We mapped the L. (L. ) amazonensis reads generated by Illumina against the L. (L. ) mexicana chromosomes using the software SSAHA2. 60 The median of coverage along each chromosome indicated a probable extra copy of chromosomes 7 and 26 and 3 extra copies of chromosome 30 in L. (L. ) amazonensis (Supplementary data, Fig. S1B ). In L. (L. ) amazonensis, the exact number of chromosomes has not been defined. Preliminary studies using pulsed field gel electrophoresis have reported that the L. (L. ) amazonensis karyotype consists of 25 chromosomal bands ranging in size from 0. 2 to 2. 2 Mb. 61  Some bands exhibited variable ethidium bromide staining intensities possibly due to co-migration of chromosomes of similar sizes. Further studies will be needed to define the number of chromosomes and ploidy in L.
Functional analysis of gene models In our functional analysis of gene models, we focused on the common factors, rather than the species-specific factors, predicted in L. (L. ) amazonensis and L. (L. ) mexicana genomes and not predicted in the genomes of the other species. We chose one genome data set for each disease outcome to compare with L. (L. ) amazonensis and L. (L. ) mexicana. Specifically, we chose leishmaniasis causative agents representative of cutaneous [L. (L. ) major], mucocutaneous [L. (V. ) braziliensis], and visceral [L. (L. ) infantum] infections. We searched for L. (L. ) amazonensis genes that could be expanded or contracted in terms of gene or domain copies compared with the other species, particularly L. (L. ) mexicana. We present a discussion of   24  Ivens et al. 22  Peacock et al.
19  Peacock et al. 19  Downing et al. 23  Raymond et al. 20  The number of L. (L. ) amazonensis chromosomes (*) was inferred by mapping against L. (L. ) mexicana chromosomes using the software SSAHA2 with all the L. (L. ) amazonensis reads generated by Illumina. N/A, not available. No   3A , Supplementary data, Table  S4 ). The five largest families identified across human pathogenic Leishmania spp. using OrthoMCL also were identified in L. (L. ) amazonensis: dyneins (Family 1), glycoprotein GP63 (Family 3, leishmanolysins), histone H4 (Family 4), ABC transporters (Family 8), and amastin proteins (Family 14; Supplementary data, Table  S4 ). We identified several families associated with 2-fold or more gene copies in L. (L. ) amazonensis than in any of the other human pathogenic species. These families include aminotransferases (family 256), 60S ribosomal protein L37 (family 216), and hypothetical proteins (families 323, 5508, and 7732).
The expansion and contraction of genes in tandem arrays could result in up-or down-regulation of gene expression associated with copy-number variation. In addition, extensive variation in aneuploidy frequencies within parasite populations has been reported for several Leishmania spp. and for different Leishmania isolates within the same species. 23, 24, 59 2. 2. Expanded and contracted conserved domains Another comparison between Leishmania genomes was based on the identification of predicted protein domains in CDD -PFAM databanks. The result of CDD -PFAM analysis, included in the AutoFACT annotation, was retrieved and manually evaluated. In total, 2509 protein domains were identified; 2186 of these domains were detected in at least one L. (L. ) amazonensis protein (Supplementary data, Table  S5 ).
83 e hypothesized that Leishmania secreted factors could mimic mammalian factors, thus perturbing native host protein interactions. To identify possible interactions between parasitic and mammalian host factors, we constructed hybrid protein interaction networks in which human and mouse databases were compared against the list of proteins that are potentially secreted by L. (L. ) amazonensis amastigotes. Our CDD -PFAM and OrthoMCL analyses identified nine conserved domains and three orthologous gene families that were exclusive to or expanded in L. (L. ) amazonensis and/or L. (L. ) mexicana and are also orthologous to human proteins (Supplementary data, Table  S6 ). The CDD 143803, an HSP 70 domain, exists as six copies in L. (L. ) amazonensis, four copies in L. (L. ) mexicana, and three copies in the other species' genomes.
Availability The Leishmania (Leishmania) amazonensis Genome Database is available at the URL http://www. lge. ibi. unicamp. br/leishmania. This Whole Genome Shotgun project was deposited at DDBJ/EMBL/GenBank under the accession APNT00000000 (SUBID SUB120161, BioProject PRJNA173202). The version described in this paper is the first version, APNT01000000. While revising this manuscript, we realized that another Brazilian group from Instituto Oswaldo Cruz (IOC)-Fiocruz, Rio de Janeiro, is sequencing the genome of L. (L. ) amazonensis.
Acknowledgements: F. R. and D. B. would like to thank  Dr Michel Rabinovitch  for stimulating advice and  Dr Andrew Jackson  for providing amastin alignments. The authors also thank  Dr Angela Kaysel Cruz  and  Dr Colin Bowles  for kindly revising the manuscript and response to reviewers and BioMed Proofreading (http://www. biomedproofreading. com) for English editing services. Supplementary Data: Supplementary Data are available at www. dnaresearch. oxfordjournals. org.
Funding The authors acknowledge the financial support of  Fundaça ˜o de Amparo aPesquisa do Estado de Sa ˜o Paulo ,  FAPESP  ( Jovem Pesquisador   07/50551-2 ). F. R. is recipient of a  FAPESP post-doctorate fellowship  ( 10/19335-4 ). D. B. , J. F. S. , R. A. M. , M. S. B. , G. A. P. are recipients of a  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico ,  CNPq , fellowship.
INTRODUCTION With the increasing need in modelling in all fields of science, and sometimes a lack of precautions in the way models are developed and used, some authors tried to define and promote good modelling practices for environmental sciences  (Van Waveren et al. , 1999)  or in physiology and medicine  (Carson and Cobelli, 2001)  by proposing different steps in the modelling process, from conceptual work to model applications. These steps include the use of statistical analysis tools like parameter estimation, sensitivity analysis, uncertainty analysis or model selection. Some existing numerical platforms like R, Scilab or Matlab propose existing algorithms belonging to these categories but no real standardization for the inputs and outputs of these tools. Moreover some existing modelling platforms like Modelica, Xcos or Simulink have a good modelling framework but are mainly deterministic and stream oriented which prevents implementing powerful estimation methods in a proper statistical framework.
(Campillo and Rossi, 2009)  and an adaptation developed by  (Chen et al. , 2012). The objective of this method is to estimate jointly the parameters and the hidden states of the system from online data i. e. data that comes from time to time improving the database. This kind of technique is known as data assimilation. The idea is to sample M particles (i. e. a parameter set and a state) and to propagate them through the model until the next available measurement to compare with the predicted states. Then we compute for each particle a weight, according to experimental data and prediction, which helps to classify and select the best particles that are closer to the real experimental case. The following results are based on the CPF method applied to the Log Normal Allocation and Senescence (LNAS) daily crop model with real experimental data  (Chen et al.
, 2013). The equations of the LNAS model are derived for sugar beet with three main processes during the plant growth period: biomass production, allocation and senescence. Two compartments are taken into account: foliage and root system. Based on the parameter estimation results from the 2010 dataset with an iterative version of the CPF method, we conducted the data assimilation approach with the CPF algorithm by recalibration the parameters and readjusting the hidden states of interest based on the data of early growth stage (five first data) of the 2006 dataset. The predictive capacity of the model for the last two dates of measurements is compared in two cases: with data assimilation and from pure uncertainty analysis (based on the calibration result, propagation of the uncertainty with the 2006 context).
WORKFLOW This framework was built with constant exchanges between the modellers, the mathematicians developing the methods, and software engineers. It helped us to understand the domain of course but also the way we were working on this domain. Most of the time a given model is associated to a given modeller and the transmission and integration in terms of code is quite complex if it does not follow a strict interface. Therefore we have defined a terminology and tool for managing this workflow. The tool is developed in python and is inspired by management tool frequently available with web framework like Rails or Symfony. The platform itself cannot be seen without its managing tool in order to establish a way of communication during the development of models and methods.
The PhyloType software currently uses parsimony, but other approaches are possible. Figure  1  presents examples to illustrate the phylotype concept: Figure  1a  displays a phylotype that is as simple as possible. All taxa of the clade are marked by annotation A as well as the clade root and all intermediate nodes. Such a phylotype typically corresponds to the appearance of a new strain in a given country. Figure  1b  shows an ''intermediary'' A phylotype, included in a clade that comprises taxa not annotated with A. Some of these non-A taxa belong to a (simple) B phylotype. Some others seem to be randomly mixed with the members of the A phylotype. Such a phylotype is typically seen with epidemics that start to break out from their country of origin (A). In Figure  1c , we see a ''complex'' or ''ancient'' phylotype.
Reconstruction of ancestral annotations The PhyloType software uses parsimony to reconstruct ancestral annotations. To deal with incomplete information, we assume that non-annotated taxa are associated with the set of all possible annotations; other (informed) taxa must have a unique annotation. Annotations are treated as non-ordered discrete characters. Moreover, we assume that annotation changes are not oriented and thus use Fitch parsimony. Two standard options are available: ACCTRAN and DELTRAN  (Swofford and Maddison, 1987). Both perform two tree traversals associating each tree node with the set of most parsimonious annotations, and requiring O nv ð Þ computing time to analyze a tree with n taxa and v annotations. The first bottom-up tree traversal is common to ACCTRAN and DELTRAN; it recursively computes the most parsimonious annotation sets associated to tree nodes with respect to their descendants.
Assessing phylotype significance using shuffling Once a set of phylotypes has been selected from the data, an essential question is whether or not these phylotypes have some statistical significance and clearly depart from a random selection of taxon subsets within the input phylogeny. For this purpose, we use a shuffling procedure, a common statistical tool that is used for similar purposes in phylogenetic software packages (e. g. MacClade) and to study the phylogeography of virus epidemics (e. g. Wallace et al. , 2007). Here, we randomly shuffle leaf annotations and proceed with phylotype selection using the same procedure and selection criteria used for the original data. This shuffling procedure is repeated a number of times, typically 100 or 1000, and P-values are computed. The implicit null hypothesis is that the annotations are randomly associated with the leaves of the tree.
RESULTS WITH TWO HIV-1 DATA SETS In this section, we show the application of PhyloType software on two HIV-1 data sets, the first one (HIV-1 subtype A) with limited size to show the outputs and advantages of the method, the second one (HIV-1 subtype C) to demonstrate its efficiency to deal with large numbers of taxa and annotations. All input data and PhyloType results are available from www. phylotype. org/SupMat/.
The primary annotations in this data set are the countries in which the studied sequences were collected. These annotations were grouped into five geographic zones (as in Fig. 1a  of  Salemi et al. , 2008) : Africa, Western Europe, Eastern Europe, Greece and Albania (see Supplementary Table  S1  for details). The criteria chosen for the PhyloType analysis were Size !5, Persistence !1, Size/Different !1 and Support !0. 7. The ACCTRAN parsimony method was selected and 1000 shuffles were performed to test phylotype significance. Only those phylotypes whose P-value for Size is 1% were retained. These options, selection criteria and thresholds correspond to PhyloType's default parameter settings. Results are provided in Table  1  and Figure  3 , which shows two types of graphical outputs provided by PhyloType: (a) a phylogeny in which the selected phylotypes are represented by colored regions; (b) a phylotype map that indicates the succession of founder and migratory events.
Worldwide evolutionary history of HIV-1C To demonstrate the abilities and efficiency of PhyloType, we applied the software to a large amount of data related to the HIV-1 subtype C epidemic on a worldwide scale. This subtype is the cause of nearly half of all HIV-1 infections. It is highly prevalent in southern and eastern (mainly Burundi and Ethiopia) Africa, along with irregular distribution throughout the rest of the world, including some highly contaminated countries such as India  (Hemelaar et al. , 2011). We used the pol sequences of  (Jung et al. , 2012) , corresponding to all subtype C pol data available in the Los Alamos HIV database at the time of this study, to which 18 sequences from Senegal were added. We removed 45 sequences indicated as recombinant using the last version of SCUEAL  (Kosakovsky Pond et al.