dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of BCL-2 homologous sequences and BH3-only sequences The FindBCL2 program (Figure  1A ) ensures sequence identification and provides two modes of execution: discovery and production. In the discovery mode, a profile HMM is computed (hmmbuild program of HMMER package 3. 0) for each reference sequence (of individual BCL-2 homologs or BH3-only proteins) from a multiple alignment of their closest homologous sequences extracted after a BLAST search against UniProtKB with a score threshold tailored for each reference sequence. Each profile HMM is then used to search UniProtKB (hmmsearch program), and an E-value threshold is defined for use during the annotation process to classify the sequences into orthology groups (for BCL-2 homologous proteins) or clusters (for BH3-only proteins).
The discovery mode is run periodically to improve the profiles sensitivity or when a new sequence is included in the seed set. The production mode is used to generate BCL2DB. The process starts by searching UniProtKB with the profile HMMs that were computed in the discovery mode. Then, for each selected sequence (E-value < 0. 1) the Ensembl (10), Ensembl Genomes  (11)  or ENA entry is retrieved from UniProtKB cross-references or after a BLAST search. UniProtKB sequences corresponding to identical Ensembl or ENA entry are merged into one single entry. Unwanted annotations (i. e. uncertain, poor quality or nonconformity to the vocabulary standards) retrieved from Ensembl/ENA entries are then deleted to create a BCL2DB entry template that will be enriched with standardized data during the annotation procedure.
Annotation procedure The annotation procedure (AnnotateBCL2 program; Figure  1B ) starts from the entry templates generated for sequences that belong to the group of BCL-2 homologs or BH3-only proteins. The annotation process automatically affiliates each identified protein to its closest orthology group or cluster based on a specific curated gathering threshold cutoff (different for each profile). Above the threshold, the entry (typically a sequence from a nonmammalian organism) is considered as 'unclassified'. Moreover, homemade BH1-4 motif profiles were developed (see below) for use in computational annotation of BCL2DB sequences to precise the positions of their respective BH region(s). Finally, the Protein Data Bank (PDB)  (12)  sequences are searched for known structures with the profile HMMs.
Entry content The text format of a BCL2DB entry is an extension of the ENA EMBL-Bank format  (14, 15). The BCL2DB accession numbers (AC line, repeated in ID line) are the UniProtKB accession numbers of the sequences identified by the profile HMM searches. In a BCL2DB Pearson/Fasta file, the accession number is associated with the gene/protein name and an isoform number (if needed) to compose the sequence identifier. The description (DE line) and keyword (KW line) fields of a BCL2DB entry contain information about the classification and the BH motifs of the sequence, as computed during the annotation procedure. The bibliographic references (RN, RC, RP, RA, RT and RL lines) are merged from the ENA and UniProtKB entries. Crossreferences (DR lines and db_xref and PRABI_prodft qualifiers) to ENA, Ensembl, Ensembl Genomes, Gene Ontology  (16) , Human Protein Atlas  (17) , Protein Data Bank, RefSeq  (18) , NCBI Taxonomy  (19)  and UniProtKB are also provided.
An additional cross-reference is provided to link each entry to the related BCL2DB reference sequence. The features (FT lines) retrieved from ENA, Ensembl and Ensembl Genomes entries are enriched in protein annotations through PRABI_prodft qualifiers under mat_peptide features. The PRABI_prodft qualifiers follow the feature table format of the UniProtKB database and describe information about proteins (e. g. chains, domains or sites). The protein annotation data added are the structural information and the presence of BH motifs. Known 3D structures, retrieved in DR lines of UniProtKB entries, or homologous sequences with an experimentally solved structure, retrieved by profile HMM searches on PDB sequences, are listed as PRABI_prodft of types PDB and MODEL3D, respectively. In both cases, information about PDB code, experimental technique used to solve the structure and the resolution in case of X-ray crystallography are provided.
BCL-2 menu The Nomenclature submenu describes the terminology and classification used in BCL2DB with three protein subgroups (BCL-2 homologous, BH3-only proteins and structurally related proteins) composing the BCL-2 protein group. The Domain and Motif submenus list the clades, orthology groups and reference sequences existing within the three subgroups. For each reference sequence, the recommended protein and gene names, their synonyms, their primary function with regard to apoptosis, their accession number in BCL2DB and a literature citation with a link to PubMed are provided in a table (Figure  2 ). The Community submenu offers links to other Web resources related to cell death and apoptosis, as well as pointers to the community working in the field.
Data menu The DATA menu provides access to sequence and structure data updated at the time of the database release computation by means of tables available in the Web pages. The tables allow simple and fast access to the data by biologists, Example of sequence analysis with BCL2DB sequences and the NPS@ server. (A) Results of a blastp search with the BCL-2 protein sequence (P10415) against the BCL2DB protein sequences. A first link (NPSA) is provided to extract the matching sequence from the sequence databank and perform further analyses with the set of tools available in NPS@. The second link on the sequence identifier is provided to view the BCL2DB entry (Figure  2B ). The pairwise sequence alignment between the query sequence and the matching sequence can be viewed, thanks to the link on the E-value.
(B) Partial view of the multiple sequence alignment of BCL2DB BCL-2 proteins in the region of the BH3 motif. The alignment was computed with sequences selected and extracted from the blastp results. The color code used is red, green, black for residues that are conserved, strongly similar, weakly similar and variable in the alignment column, respectively, as defined by Clustal W. Dashes indicate gaps. avoiding them to build and execute complex dynamic SQL queries. For the sequence data sets, the table cells can contain F, C and R letters that provide links to Fasta files, colorcoded multiple sequence alignments computed by means of MUSCLE  (20)  in Clustal W format and residue repertoires (Figure  3 ), respectively. A frequencies file is provided along with the repertoire that includes the Shannon entropy  (21) , a useful parameter to analyze conserved/variable alignment positions, and residue frequencies used to compute the repertoire and entropy.
Furthermore, the alignments can be interactively edited with the 'EditAlignment' applet developed by our team. The full-length sequence data sets are ordered according to BCL2DB classification scheme, orthology groups, species and gene/protein recommended names. The motif sequence data sets are ordered by protein recommended names and BH motifs. The All rows and columns give access to all sequences of a given species, gene/ protein or BH motif. The structure data sets follow the BCL2DB classification scheme, and the tables provide for a given protein the structures available in the PDB with links to download and view the structure file, the experimental method used to solve the structure, including the resolution for X-ray crystallography, the deposition year, the source organism and the reference with a link to PubMed.
Tools menu The analysis tools provided with the database are categorized either as generic or specialized. The generic analysis tools are available through the NPS@ server  (22) , our integrated resource for sequence analysis. For instance, BCL2DB nucleotide and protein sequences can be searched with BLAST  (23)  and selected sequences can be extracted and aligned with Clustal W (24) (Figure  4 ). The Annotate specialized tool permits users to annotate their own protein sequences with the set of programs used to feed BCL2DB. Users can determine whether their sequence is predicted as belonging to either the BCL-2 homologous or BH3-only group, its classification according to the orthology groups or clusters and its BH motif arrangement. The Annotate main result page contains a summary table showing each input sequence listed with its classification, its name and a link to access the detailed result page (Figure  5 ).
BCL2DB menu General information about the database is provided under this menu. The users have access to (i) the composition of the scientific advisory board (About submenu), (ii) a contact form to send messages to the BCL2DB team, (iii) the help about the Web interface, (iv) the news related to BCL2DB releases and changes, as well as Web site updates, and (v) the usage statistics. The main result page summarizes the submitted data and offers a table listing each input sequence (here, 18 sequences were uploaded) with its predicted protein name and its BH motif composition. A link to a detailed result page is provided on each sequence identifier when the sequence is annotated as belonging to the BCL-2 protein group. (B) The detailed result page for sequence MySeq02 displays the predicted protein name, the classification, the BH motif composition and the homologous known 3D structures.
Conclusion and perspectives BCL2DB is a collection of computer-annotated BCL-2related sequences. The automatic annotation process used to generate the BCL2DB entries guarantees updates of the data and standardized annotations. The latter allow efficient keyword searches useful to generate sequence data sets available through the Web interface. Sequences can be retrieved for further analysis with a set of bioinformatics tools available in the NPS@ server. The BCL2DB Web site also allows researchers to access up-to-date knowledge about BCL-2 family members and BH3-only proteins and to annotate their own sequences through the BCL2DB automatic annotation process. In its current implementation, BLC2DB offers a good template to integrate new annotation data that will enrich its content in the future (e.
g. gene expression, interaction data, information on posttranslational modifications) and will enhance its Web site with new analysis tools (e. g. to identify novel BH3-only proteins and splice variants) and a search tool to perform dynamic queries on the database to extract data sets of interest to the user. BCL2DB can serve as a reference for the analysis of data generated by means of high-throughput technologies. We put a lot of attention and rigor in developing BCL2DB to provide a helpful and powerful tool to both 'BCL-2-ologists' and researchers working in the various fields of physiopathology.
ß The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2014, Article ID bau013, doi:10. 1093/database/bau013.
Acknowledgements The authors wish to acknowledge and thank the past, present and future members of their  Scientific Advisory Board  for their thoughtful comments and suggestions. Funding V. R. L. is the recipient of a doctoral fellowship from  La Ligue Contre le Cancer  (Comitede Saone-et-Loire). BCL2DB is developed on the  Pole Rhone-Alpes de BioInformatique (PRABI)  platform funded by the  Groupement d'Interet Scientifique Infrastructures en Biologie Santeet Agronomie (GIS IBiSA-AO 2009 ). We are grateful to  Region Rhone-Alpes  for a grant ( CIBLE program ). Funding for open access charge:  Region Rhone-Alpes  ( CPER 2006 , projet InterVir3D).
Page 1 of 7 ( 7. ß The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2014, Article ID bau013, doi:10. 1093/database/bau013.
g. Johns Hopkins Phenotyping and Pathology Core (http://www. hopkinsmedicine. org/ mcp/PHENOCORE/); Centre for Genomic Pathology (http://ctrgenpath. net/); CL Davis foundation (www. cldavis. org)]. These courses allow not only basic training and knowledge transfer but also access to the pathologists providing the training and their (often global) network of pathology contacts. Skills that need to be learnt include the accurate recognition of normal tissue variation, the cause and appearance of technical artefacts, understanding of pathological lesions (spontaneous and induced), and the use of standardised glossaries and semi-quantitative scoring systems  (Scudamore, 2014)  to allow data generation. Common background and spontaneous pathologies are well described (and illustrated) for rodents, including mice [INHAND (https://www.
In the high-stability regime, the AMIP configurations show lower, cloudier cloud layers indicative of stratocumulus, but this regime is not represented in the AQUA configurations. In both models, this low-level cloud layer in regions of large LTS becomes less cloudy with warming, and cloud amount increases at higher levels indicating a transition toward more cumuluslike conditions. Because this large LTS regime is not represented in the AQUA experiment, such a response is also absent. The AQUA SST?4K simulation has some data in the high-stability category; this occurs because the LTS distribution shifts by about 2 K with the warming, a feature common to all the models (Fig. 4 ). Most of the disparity between the AMIP and AQUA experiments for FGOALS-g2 and MRI-CGCM can thus be attributed to the strong positive DCRE in the stratocumulus regions-which the AQUA simulations do not represent-as the rest of the trade-wind regions show small CRE changes.
Acknowledgments We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (Table 1) for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Data analysis and figure creation were performed with NCL (2012). We thank  Benjamin Mo  ¨bis for performing the sensitivity runs with the  MPI-ESM-LR. B. M. acknowledges support from the  Regional Climate Modeling Program (RGCM)  of the  U. S. Department of Energy's, Office of Science (BER) ,  Cooperative Agreement   DE-FC02-97ER62402.
Results: We present TE-Tracker, a general and accurate computational method for the de-novo detection of germ line TE mobilization from re-sequenced genomes, as well as the identification of both their source and destination sequences. We compare our method with the two classes of existing software: specialized TE-detection tools and generic structural variant (SV) detection tools. We show that TE-Tracker, while working independently of any prior annotation, bridges the gap between these two approaches in terms of detection power. Indeed, its positive predictive value (PPV) is comparable to that of dedicated TE software while its sensitivity is typical of a generic SV detection tool. TE-Tracker demonstrates the benefit of adopting an annotation-independent, de novo approach for the detection of TE mobilization events.
Results and discussion The TE-Tracker pipeline TE-Tracker is divided into three independent modules: Eris, Leto and Metis (Figure  1 ). TE-Tracker starts with the Eris module detecting discordant read pairs (Figure  2a ), i. e. pairs that map in unexpected orientation or location with respect to the preparation and insert size, which can constitute evidence of a transposition event. First, alignments are filtered based on mapping quality and then a random sample of the read pairs is used to estimate the insert size distribution. Median and median absolute deviation (MAD) thresholds are used to mark as discordant the pairs for which the read mates map with an unexpected insert size (see Methods). Pairs mapping on different chromosomes or in an unexpected orientation with respect to the sequencing library are flagged discordant as well.
When multiple mappings are available for either mate of one pair, the pair is considered discordant only if all combinations of mate mappings match the aforementioned discordance criteria; in which case all potential mappings are recorded as if they were unique mappings from separate read pairs (see Methods). Once discordant read pairs are extracted, they are clustered using the Leto module. The aim of this step is to regroup discordant pairs that might support the same transposition event while discarding lone pairs that are most certainly due to mapping errors. Clustering is done using single-linkage clustering in the mate-position space. Pairs are classified according to read orientation as well as the chromosome each mate maps on; hence for every such couple of chromosomes, each discordant pair can be represented in a two-coordinate system, making it easy to compute the respective distance between the right and left mates of any two read pairs.
Clusters are built by adding pairs that are close enough to any pair already in a cluster. Because the read pairs are sorted by position, and because only the first encountered mate is ordered when sorting paired-end reads, the distance requirements for the clustering differ for both dimensions. Intuitively, the distance requirement on the ordered mate side is smaller than on the unordered mate side, since it is determined by the coverage distribution, whereas in the latter case distance is influenced by the insert size distribution, which typically has a larger variance (Figure  2b ). These two values constitute the main parameters of the TE-Tracker software. In order to maximize the number of detected events, Leto will scan several values for both of these clustering parameters and merge clusters that are found more than once.
Calling. The four types of transposition events detected by TE-Tracker along with their associated cluster signatures, with an emphasis on the overlap condition used to assemble clusters with compatible signatures into bona fide events. the discordant read count will often be too low to allow clustering. Therefore, sudden drops in coverage can split large sets of discordant pairs into several clusters with identical signatures. Once these gaps are filled, knowledge of the dynamics of transposition and its influence on sequencing data  [23]  allow us to select only the combinations of cluster types that are likely to indicate a transposition event (Figure  2d ). Then the program considers every combination of clusters belonging to these specific types and determines whether they could underline a true event (see Methods).
Key differences are the fact that TE-Tracker incorporates information from all mappings of a given read pair including mismatches, and that the heuristic is based on overlap of clusters alone, rather than ploidy and previous knowledge of TE donor sites. For each pair of clusters passing the filter, TE-Tracker reports the acceptor and donor sites as defined by the cluster boundaries, the number of reads supporting the insertion event, the overlap size and whether the TE has been reversed during transposition. Finally, it is possible to annotate the output file with various data using the Metis module. If annotation data is available, both the acceptor and donor regions can be annotated; this is performed using the readily available BEDTools software suite  [25]. Metis is also able to read a discordant BAM file such as the one produced by the Eris module to perform donor-scoring.
Comparison with other software We compared TE-Tracker with RetroSeq  [10] , a popular program that detects novel mobilization among known TE families, as well as Delly  [20] , Hydra  [26] , VariationHunter-CommonLaw  [23]  and GASVPro  [21] , which are general-purpose structural variant detection tools that can be applied to the detection of TE insertions. We were not able to test other TE-dedicated software in a meaningful way, since only RetroSeq is generic enough to allow comparison. Indeed, it is not limited to TEs that exhibit a TSD, is not genome specific, and provides information about the family of the donor element. A comparison of the features, algorithms and input formats of all these programs is given in Table  1. This table illustrates a major pitfall when comparing SV detection programs, namely the variety of input file formats and level of output information.
Simulated reads were then aligned onto the Arabidopsis reference genome sequence. Results of the test runs are summarized in Table  2. Overall, they suggest that programs designed specifically for the detection of TE mobilization behave very differently from tools that were designed for a broader SV detection purpose. Indeed, RetroSeq, the only program in the first category, exhibits a high PPV (the number of true positives divided by the total number of events reported: 67% compared to an average 10. 5% for generic tools), which translates into a significantly lower number of false positives compared to programs in the second category. However, its sensitivity is also lower, with under half (43%) of the simulated insertions successfully detected. Programs in the second category perform better in that regard (64.
TE-Tracker algorithm The TE-Tracker algorithm comprises three Perl modules (Eris, Leto and Metis) that deal with preprocessing, clustering of discordant pairs and post-processing (annotation and scoring of results), respectively (Figure  1 ). Leto is the core of the pipeline, since it wraps the slclust single-linkage clustering program, written in C++ using the boost library  [43]. TE-Tracker s modular architecture allows to replace each module with custom ones, provided the command-line argument set is consistent with other elements of the pipeline. Similarly, another clustering program can be used in lieu of slclust. TE-Tracker uses one configuration file (SV. conf ) to manage the paths to the modules and slclust program, as well as all parameters used during execution. In the following sections we describe the most important steps of the pipeline, and the parameters we use on our test data.
Running TE-Tracker In order to maximize sensitivity, the Leto module should be run over a regular grid of increasing X and Y parameters and the results pooled by traversing the grid. A step size of 50 was chosen for X, while a step size of 100 was chosen for Y. X ranged from 50 to 1000, Y from 100 to 5000, which amounts to a total of 1000 clustering attempts. For traversing the grid, we build a dictionary of donors from the insertions found for the couple (X; Y) with the largest number of insertions. Then, we go through every point of the grid and add data to the dictionary: we perform the cartesian product of the dictionary and the output file and add events that do not overlap either on donor or acceptor site. This allows to build a comprehensive landscape of all insertions that appear at least once on the grid.