dataesr/scientific-paragraphs-categorization huggingface dataset:
The white dashed rectangle in the merge column represents the enlarged area used for colocalization analysis using ImageJ software. Pearson's colocalization coefficients (PCC) were determined with JACoP plugin  49  in cells from different experiments (n ⩾ 10). t-test was used to evaluate the difference between PCC for AFF3/SC35 colocalization analysis after 8 and 24 h of transfection (lines 1 and 2). (b) Protein lysates from H295R cells overexpressing AFF3-Flag or Ctr vectors were immunoprecipitated with Anti-Flag M2 Affinity Gel and analyzed by western blot using the indicated antibodies. Overproduced AFF3 associated with CDK9 and Cyclin T1. Protein lysates from H295R cells overexpressing AFF3-Flag or silenced for AFF3 by siAFF3 were immunoprecipitated with anti-CDK9 antibodies and analyzed by western blot using the indicated antibodies.
Correlation, expression and statistical analysis Pearson's r correlation test was used to evaluate the correlation between gene expression and AXIN2 expression in two, previously described, independent cohorts of ACC: Cohort 1 8 included 47 ACC and four NA (Gene Expression Omnibus data set GSE49280 and ArrayExpress data set E-TABM-311); and Cohort 2,  10  33 ACC and 10 NA (Gene Expression Omnibus data set GSE33371). All samples were normalized in batches, independently for each chip type using the RMA algorithm (Bioconductor affy package), and probe set intensities were then averaged per gene symbol. Both final matrices were row-centered using the NA median. Differential expression was measured with moderated t-test (limma R package) and survival curves were obtained by the Kaplan-Meier method.
Differences in survival were assessed with the log-rank test. Gene expression profiles for three H295R/TR/shβ and one control clones with and without doxycycline treatment were analyzed using Affymetrix Human Gene 1. 0 ST arrays (ArrayExpress data set E-MTAB-3330) and with the moderated paired t-test (limma R package). All P-values were adjusted using the Benjamini-Hochberg correction method. All in vitro data reported with statistical analyses represent means from at least three experiments. The error bars indicate the s. d. Control conditions were set as one and data were analyzed by one way analysis of variance followed by Fisher's LSD post hoc test. Gene set enrichment analysis  48  were performed with several data sets from breast cancers (ArrayExpress data set E-MTAB-365; Gene Expression Omnibus (GEO) data sets GSE6532, GSE4922 and GSE1456; and The Cancer Genome Atlas breast carcinoma data set) and with a data set with inhibition of CDK9 by Flavopiridol.
RNA-seq library preparation and analysis Total RNA from H295R cells was extracted, and complementary DNA library was created using Epicentre ScriptSeq strand-specific after a RNA depletion optimized with the Ribo-Zero kit (Epicentre, Madison, WI, USA). A pairedend 10-bp sequencing was performed on a HiSeq 1500 device (Illumina, San Diego, CA, USA). Reads were mapped to the UCSC (University of California, Santa Cruz) reference transcripts from hg19 genome using RSEM (RNA-Seq by Expectation Maximization) and the Bowtie alignment software. Wiggle files in genomic coordinate were generated for visualization.
ACKNOWLEDGEMENTS We thank  Dr B Bardoni  for the generous gift of the AFF3-flag vector and for helpful discussions. We thank all the members of the genomic platform and the imaging facility of the  Cochin Institute. This work was supported in part by the  COMETE Network  ( Programme Hospitalier de Recherche Clinique Grant   AOM95201 ), the  Seventh Framework Programme  ( FP7/2007-2013/259735 ), the  Association pour la Recherche sur le Cancer  ( SFI20111203542 ), the Ligue contre le cancer ( RS12/75-105 ),  Cony-Maeva  foundation,  Fonds de Dotation Patrick de Brou de Laurière , the  Weigand Trust Germany , the  Sander Stiftung  ( 2011. 003. 1 ) and France Génomique national infrastructure, funded as part of the  'Investissements d'Avenir' program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-0009 ).
Small RNA sequencing: data analysis Fragments that were sequenced with SOLiD run were mapped on hg19 for human samples and mm10 for mice samples within LifeScope 2. 5. 1 small RNA pipeline (Life Technologies) with default parameters;. bam files were then merged with MergeSamFiles. jar (picard-tools-1. 38) for each sample, keeping a read group per lane. Fragments that were sequenced with Ion Proton P1 Chip were first trimmed with cutadapt v1. 2. 1 for 5p adapter TTCTACAGTCCGACGATC and 3p adapter TGGA ATTCTCGGGTGCCAAGG. Reads with size <15 bases were discarded. Remaining reads were mapped to hg19 build for human samples and mm10 build for mouse samples with bowtie2 (v2. 2. 4) with "-local -very-sensitivelocal -k 10". All. bam files were processed with a java homemade class, based on Picard java classes to identify and quantify small RNAs.
Annotation files used for quantification correspond to miRbase release 21, tRNAs from UCSC web server (UCSC tables), RNYs and ncRNAs from ensembl ncRNAs release 75, and piRNAs from fRNAdb. All sequencing results were submitted to the GEO database under the superseries accession number GSE63802. For quantitative analysis of small RNAs, each count data table generated from SOLiD and Ion Proton experiments was merged based on feature IDs. When the sum of reads mapped on each gene was less than five reads in a specific experiment, they were discarded (low level of expression). The final datasets comprised 1,338 genes for mouse data and 1,435 genes for human data. Functions from the Bioconductor package limma  [13]  were used to normalize data and assess differential expression. For statistical analysis, count data were first normalized for sequencing depth differences and transformed into log2 count per million using the voom function  [14]  and differential expression was assessed using linear modeling.
Data collection Male participants with and without CAD were examined in the morning after an overnight fast. Patients with stable CAD (cases) were examined in the clinics of the Cardiology Department, Toulouse University Hospital, and underwent a coronary angiography. Control subjects were examined in the same health center. The medical examination started with standardized face-to-face interviews performed by a trained physician using standardized methods. During this interview, participants received oral and written information on the aim of the study and signed a consent form. Then, a blood sample was taken from each participant. Each participant then completed standardized questionnaires, covering age, socioeconomic variables, educational level (number of years spent at school), smoking status, alcohol consumption, physical activity, information on cardiovascular risk factors, and past medical (personal and parental) history.
Acknowledgements We are indebted to  Drs. V. Corcelle ,  A. Couvreux ,  A. Jacquel ,  K. Lebrigand ,  P. Lesnik ,  B. Mari ,  A. Paquet ,  K. Retailleau , and  B. Tanasa  for technical help. We are grateful to  CRB-TBR , the biobank of the  Toulouse University Hospital , for providing the sera collection. Funding M. T. is an investigator with the Avenir program of INSERM. This work has been supported by  FRM  (grant # DEQ20140329551  to MT and # DEQ20130326464  to  PB ),  ANR  through the " Investments for the Future " #  ANR-11-LABX-0028-01  (LABEX SIGNALIFE) to MT and  PB , by  ANR Emergence and la région Midi-Pyrénées  (RBIO # 14054337 ,  CK11004  - Appel à Projets Recherche BIOmédicale)  to LOM, and by  ANR-10-INBS-09-03  ( France-Génomique ) to PB. LL is supported by  Marie Curie IEF  and  Fondation Lefoulon Delalande  programs and by  FRM  ( ING20140129224 ) to ER.
In the same experimental conditions, a significant increase in bacterial adhesiveness was observed, independently of their own adhesive ability. The increase was reverted by treatment with anti-TF and anti-CEACAM6 antibodies. Interestingly, influenza virus was able to efficiently replicate in human primary intestinal cells leading to TF exposure. Finally, intestinal infected cells produced high levels of pro-inflammatory cytokines compared to control. Overall these data suggest that influenza virus infection, could constitute an additional risk factor in CD patients.
Pro-inflammatory cytokines analysis Cytokine and Chemokine levels in supernatants of Caco-2 cells and of InEpC were determined by a Bioplex multiplex assay; plates (Millipore) were read on a Bio-Plex MAGIPIX instrument. Data were analyzed using the Bio-pro software and the results were expressed as fold induction to control (= 1). All standards and sample were run in duplicate.
48±7. 54 and 15. 73±6. 80, respectively) (Fig. 6C ), suggesting that not only FimH adhesin could be involved in bacterial adhesion during IV infection. In mock-infected cells, no differences in the adhesion index of the two E. coli strains were observed between treated and untreated cells. Overall the data confirm that the exposure of TF and CEACAM6 antigens observed during IV infection plays a key role in increasing bacterial adhesion to intestinal cells, but we cannot exclude that other cellular receptors could be involved.
In the last two years the Pfam database (http://pfam. xfam. org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved.
INTRODUCTION While sequence databases are growing at exponential rates, protein family databases such as Pfam are growing at a roughly linear rate  (1). That Pfam scales better is primarily due to the ability to capture the diversity of a set of evolutionarily related sequences. For each Pfam entry (also known as a Pfam-A entry), a representative subset of the entire set of matching sequences are aligned to make the seed alignment. This seed alignment is used to construct a profile hidden Markov model (HMM) using the HMMER software (http://hmmer. org/; currently version 3. 1b2). The profile HMM is searched against sequence databases, with all matches scoring greater than or equal to a curated threshold (the gathering threshold, previously described in detail  (2) ) being considered as true members.
These members are aligned to the profile HMM to generate the full alignment. Previously, the main Pfam sequence database, termed pfamseq, was a derivative of the UniProtKB database (3), with non-continuous and spurious sequences (identified by AntiFam (4)) removed. This underlying database remains fixed for the lifetime of a release. Pfam entries that have been identified as being related are grouped into sets called 'clans'. Relationships are identified using sequence information (such as HMMER cross matches and SCOOP (5)), known protein structures and HMM-HMM comparison using HHsearch  (6). In the last NAR article describing the Pfam database  (7) , the use of representative proteomes (RP)  (8)  to provide non-redundant versions of the full alignments was described. RP is a grouping of similar proteomes, whereby a single proteome is selected as the best example of the group.
The selected proteome is chosen to best represent the set of grouped proteomes in terms of both sequence and anno-D280 Nucleic Acids Research, 2016, Vol. 44, Database issue tation information. The grouping of proteomes is derived from a clustering of all protein sequences from complete proteomes in UniProtKB (clustering based on UniRef50). The similarity of two proteomes is determined by considering just the UniRef50 clusters containing sequences from either of the two proteomes. The two proteomes are grouped when the fraction of clusters that contain sequences from both proteomes out of the subset of proteome-specific clusters exceeds a given threshold. This threshold is termed the co-membership threshold. The percentage threshold of comembership (or common clusters) can be adjusted down to produce larger groupings, and hence less redundant sequence sets (co-membership thresholds of 75, 55, 35 and 15% were used).
While this has helped users get access to smaller full alignments, it has no impact on the internal working of the Pfam database because the representative proteome alignments were produced as a post-processing step. In Pfam 28. 0, pfamseq contained over 80 million sequences. pfamseq is updated between each Pfam release, but the increasing size of UniProtKB, together with the computational and curation effort of ensuring that each Pfam entry conforms to our internal quality control measures (described in more detail below) have hampered our ability to produce frequent Pfam releases, with the time between Pfam 27. 0 and 28. 0 being close to two years. Over that time frame, not only did the sequence database increase by nearly 4-fold, but 1445 new entries were added to Pfam. The fact that these new entries, together with updates to pre-existing entries, reside internally for many months is unsatisfactory and frustrating both for us and for our users.
The performance of HMMER3 is such that the limiting factor is not the calculation of matches, but rather other aspects of the database generation, particularly our internal quality control procedures. UniProtKB has also modified its data policy in the face of exponential growth and now employs methods to reduce bacterial proteome redundancy for a given species. Herein we describe approaches that we have taken to streamline database curation and production activities since the last paper describing Pfam, including the addition of over a thousand new entries, addition of many new clans and the classification of hundreds of entries into both new and pre-existing clans.
STREAMLINING THE PRODUCTION OF PFAM In 2015, UniProt made two significant changes to the organisation of the UniProtKB sequence database: (i) the removal of bacterial redundancy, substantially reducing the number of sequences (removing 46. 9 million (about twothirds) of the 71. 0 million bacterial sequences in April 2015); (ii) the establishment of a comprehensive reference proteome set (this was first released in March 2015, but the effort is on-going). The reference proteomes sequence set comprises a representative cross-section of the taxonomic diversity of complete proteomes found within UniPro-tKB, and includes proteomes of well-studied model organisms and proteomes that are of particular biomedical and biotechnological interest. Some species that are deemed especially important have several ecotypes or strains within the set.
Migration of pfamseq to reference proteomes Since the inception of Pfam, one of the underpinning quality assurance rules has been that all seed alignment sequences must be found in the underlying sequence database, pfamseq. Thus, when a sequence that is present in the seed is either made obsolete or is updated, the seed alignment must have the old sequence replaced by either the updated sequence or an alternative representative sequence if available. To generate seed alignments that are representative of the entry (particularly for entries that match many sequences), we typically make our seed alignments 80% non-redundant (i. e. if two sequences have ≥80% sequence identity over the entire alignment length, only one will be kept). When reducing the redundancy in the alignment, we do not preferentially keep sequences with 'better' evidence codes, or choose UniProtKB/Swiss-Prot sequences over UniProtKB/TrEMBL sequences.
This work began in Pfam 28. 0, and has been largely completed in Pfam 29. 0. Concomitant with this change to the seed alignments in Pfam 29. 0, pfamseq has been switched to being based on the reference proteome sequence set, with the same removal of discontinuous and spurious sequences as performed previously. To give perspective on the reduction in the Pfam sequence database size, UniProtKB 2015 08 contained 50 million sequences, while the reference proteome subset for this UniProtKB release (upon which Pfam 29. 0 is based) contained 12 million sequences, approximately one-fourth of the size of UniProtKB.
Reconstruction of seed alignments on reference proteomes 12 962 (80%) of the entries in Pfam 29. 0 have a seed alignment that is composed solely of sequences from reference proteomes. We have applied two approaches to migrating seed alignments to the reference proteomes. The first method was to take the existing profile HMM and search against the reference proteome sequence database, align all significant matches (excluding partial matches) and make the resulting alignment 80% non-redundant. This alignment was used to construct a new profile HMM which was searched against UniProtKB. We compared the sensitivity of the new profile HMM with the results of running the original profile HMM against the same version of UniPro-tKB. This approach was used as it was quick (in terms of software implementation, computation and biocuration) and our previous work has shown that iterating the seed alignments (a process whereby the entry full alignment is used to recreate a new seed alignment) can frequently improve the sensitivity of the entry.
This is because the sequence database contains more representatives of the entry since the original profile HMM was built. Those entries having similar or better sensitivity to that of the original seed alignment based on all UniProtKB sequences had their seed alignments updated to the new reference proteome seed alignment. This amounted to roughly half of all entries in Pfam. Of the 12 962 entries transferred to the reference proteomes, 70% were moved using this method. For the remaining entries where the sensitivity was reduced, we adopted a more precise method for transferring the seed alignment to the reference proteomes. In this second approach, each sequence in the original seed alignment was replaced by finding either an exact sequence match or a similar sequence with >95% identity from the reference proteomes set.
Impact of reference proteomes on full alignments As mentioned previously, in Pfam 29. 0 pfamseq is based on the reference proteome sequence set, rather than the whole of UniProtKB. As a result of only using a subset of UniPro-tKB, the full alignments are typically smaller, and substantially smaller for the ubiquitously found protein families. However, for most Pfam entries these full alignments still reflect the sequence diversity of the entire family and, due to the inclusion of model organisms, still provide sufficient annotations for key information. Of the 16 295 entries in Pfam 29. 0, 260 (1. 6%) do not have a match to anything in the reference proteome set. In the previous release, Pfam 28. 0, in which the underlying sequence database was UniProtKB, the taxonomic root of these entries was: Eukaryota (115 entries), Bacteria (60), Viruses (82), Archaea (1) and unclassified  (2).
Pfam annotations of UniProtKB Despite the move of pfamseq to using the reference proteomes sequences, we still wish to provide access to Pfam data for all of UniProtKB. To do so we also make the full alignments against UniProtKB, and the four different RP co-membership levels available for download. These alignments have been 'competed' to remove overlaps between clan members. Furthermore, the searching and storing of the UniProtKB matches in the database allows the website to provide users with the Pfam match information on D282 Nucleic Acids Research, 2016, Vol. 44, Database issue a per sequence basis using a UniProtKB accession or identifier. Another advantage is that we can use our pre-existing method to map Pfam entries to known structures using the SIFTS mapping (9) between UniProtKB and PDB accessions.
Within Pfam, we group sets of entries that we believe to be evolutionarily related into 'clans'. Entries within a clan are treated as a special case, and we resolve overlaps within a clan using a competitive post-processing step (based on lowest E-value), such that each region within the clan belongs to a single entry. Another exception to the rule is in the case of nested domains, where one domain is inserted with another. For example, CBS domains (PF00571) are often found inserted within the IMPDH domain (PF00478). In this case, the discontinuous domain has the inserted domain excluded from the profile HMM match state positions, and the inserted domain is represented by a separate Pfam entry. Each new entry or update to an entry is checked for overlaps, prior to commitment to the database.
At each release, after the seed alignments have been updated, we determine and resolve overlaps between entries. This is a very timeconsuming step (typically one to two months) and requires a biocurator to manually look at each overlapping region and decide on how best to remove the overlap. Methods for resolving overlaps include raising the gathering threshold on one or both of the entries involved, and changing the boundaries of either or both entries. If we believe the overlap is between two related entries, the entries may be added to the same clan. On looking at the overlaps each time we updated the sequence database we were aware that, in many cases, the overlapping region between entries was small and affected only a very small proportion of sequence members. As the burden of looking at each overlap and resolving it is timeconsuming, we have updated our overlap rule, as of Pfam 28.
PFAM STATISTICS Since the last Pfam NAR paper in 2014  (7) , we have added 1464 entries and 44 clans to the database. The current release of Pfam (version 29. 0) contains 16 295 entries and 559 clans. Of the 16 295 entries, 16 035 entries match 73. 5% of sequences and 47. 0% of residues in pfamseq (260 entries have no matches to this sequence database, yet match against UniProtKB). The corresponding values for matches against UniprotKB are 76. 1% and 54. 8%. Both the sequence and residue coverage have fallen (≈5% for each coverage measure), due to the removal of redundancy in UniProtKB. Much of the recent biocuration effort has been focused on expanding the classification of Pfam entries into clans. In Pfam 29. 0, 5282 entries belong to a clan (32% of all entries), an increase of 14% (719 entries) since Pfam 27.
CLAN RELATIONSHIPS Clans were first introduced in Pfam in 2005 to enable the grouping of related Pfam entries  (14). The Pfam website previously included a pre-calculated image of a relationship graph to visually represent the relationships between different Pfam entries within a clan. Each Pfam entry was represented by a labelled box, and the boxes were linked based on HHsearch results  (6). However, the images became very large and difficult to navigate for the larger clans. Indeed, our largest clan, the HTH clan (CL0123), contains 254 entries in Pfam 29. 0. As these images did not scale well, they were only produced for clans with fewer than 40 members. With 20 clans now containing over 40 members, we have revisited this graphical representation. In the latest release of Pfam, the relationship graph is generated on demand as an interactive, dynamic JavaScript graph view in the web browser (client).