dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Hermann Steller ,  Helmut Krämer ,  Giorgos Pyrowolakis , and the  Bloomington Stock Center  for fly strains. We thank  David Strutt  as well as the  Developmental Studies Hybridoma Bank  for antibodies. We thank  Magda Cannata Serio  for critically reading the article. The work has been supported by the  ATIP-Avenir program , the  Fondation Bettencourt-Schueller (Liliane Bettencourt Chair of Developmental Biology) , as well as state funding by the  Agence Nationale de la Recherche (ANR)  under the " Investissements d'avenir" program  ( ANR-10-IAHU-01 ) and a  NEPHROFLY  ( ANR-14-ACHN-0013 ) grant to M. S. and by  National Institutes of Health  grant  GM38006  to T. H. S.
Acknowledgements We thank  G. Chiosis  and  L. M. Neckers  for helpful discussions and providing  PU-H71. We thank  B. S. J. Blagg  for providing KU-32. We thank  P. Wortmann for Aha1  and  J. Thurn  for p23. This work was funded by the  European Research Council  through  ERC  grant agreement no. 681891  and by the  German Science Foundation  ( SFB863 ,  A4 ).
However, AFP-003 T reproducible matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) profile did not match any of the profiles entered in the Bruker database (version December, 2015, including M. lentiflavum), suggesting that AFP-003 T was not identifiable as M. lentiflavum and could indeed be representative of a hitherto undescribed species of Mycobacterium. AFP-003 T was then shown to be in vitro susceptible to ciprofloxacin, clarithromycin and rifampicin (Table  2 ). Furthermore, Biolog ® Phenotype MicroArray test showed that AFP-003 T grew under other 14 inhibitory chem- ical conditions including minocycline, lincomycin, guanidine HCl, Niaproof Anionic Surfactant, vancomycin, tetrazolium violet, tetrazolium blue, nalidixic acid, lithium chloride, potassium tellurite, aztreonam, sodium butyrate and sodium bromate; and was able to metabolize eight carbon sources including α D-glucose, glucuronamide, methyl pyruvate, α-keto-glutaric, α-keto-butyric acid, acetoacetic acid, propionic acid and acetic acid (Table  3 ).
The 16S rRNA gene sequence's (GenBank accession: LT797535) highest similarity was of 98. 1%, 97. 8%, 97. 5% and 97. 4% with M. lentiflavum ATCC 51985, Mycobacterium simiae, Mycobacterium triplex and Mycobacterium sherrisii, respectively. Partial rpoB gene sequencing was previously shown to be a useful marker to delineate new Mycobacterium species  3  and we sequenced a 619-bp rpoB gene fragment in AFP-003 T strain (GenBank accession: FR695853). This sequence's highest similarity was of 96. 43%, 95. 55% and 94. 95% with Mycobacterium florentinum DSM 44852, Mycobacterium stomatepiae DSM 45059 and Mycobacterium genavense FI-06288 respectively. These values being all below the 97% cut-off value previously proposed to delineate different species among Mycobacterium 3 enforced the suggestion that AFP-003 T was representative of a new species belonging to the M.
46% with M. genavense, 26% ± 3. 39% with M. sherresii, 25. 8% ± 3. 39% with M. simiae, 24. 7% ± 3. 39% with M. interjectum and 24. 2% ± 3. 39 with M. parascrofulaceum. Ori-Finder 6 was used to predict the origin of replication in the genome of strain AFP-003 T. We found three OriC regions separated by the dnaA gene and located in scaffold 1 (218, 312 and 391 bp) (Supplementary File 1). The three predicted OriC region showed no homology sequence with those of the DoriC database7  7. Contigs have been deposited (EBI accession number: FXEG02000000). Annotated genome is available at https://www. ebi. ac. uk/ena/data/view/PRJEB20293. To better describe AFP003 T , the mycolic acids were identified. The mass spectrometry analysis of Mycobacterium tuberculosis H37Rv strain (used as a positive control) showed the previously described mycolic acid pattern  8, 9  , including α-(C 74-84 ), methoxy-(C 80-90 ) and keto-(C 80-89 ) forms.
Samples were analyzed in the Sensitivity Negative ionization mode using a Vion IMS QTof high resolution mass spectrometer (Waters, Guyancourt, France). Samples were infused at 10 µL/min after fluidics wash with a chloroform/methanol solution (50:50) and monitored from 500 to 2000 m/z during 2 minutes. Ionization parameters were set as follow: capillary voltage 2. 5 kV, cone voltage 50 V, source and desolvation temperatures 120/650 °C. Mass calibration was adjusted automatically during analysis using a Leucine Enkephalin solution at 50 pg/µL (554. 2620 m/z). Mass spectra between 900 and 1400 m/z were used for subsequent data interpretation. Mycolic acids were described according to previously detailed structures  31.
5 m/sec) for 90 s. Relative intensity was calculated from the sum of detected monoisotopic peaks. For each run, global information of 5. 3 and 7. 2 Gb was obtained respectively from a 559 and 765 K/mm2 cluster density with a cluster passing quality control filters of 96. 3 and 94. 7% (10,450,000 and 14,162,000 passed filter clustersfor each sequencing run). Within these runs, the index representation for AFP-003 T was determined to be of 8. 51 and 7. 62%. The 888,760 and 1,079,096 paired-end reads. The three runs leaded to a total of 3,392,116 paired-end reads which were filtered according to the read qualities. The reads were assembled using the SPAdes software (http://bioinf. spbau. ru/spades)  34. Contigs obtained were combined by use of SSPACE  35  assisted by manual finishing and GapFiller  36.
Open reading frames (ORFs) were predicted using Prodigal 37 with default parameters. The predicted ORFs were excluded if they spanned a sequencing gap region (containing N). The predicted bacterial protein sequences were searched against the GenBank database and the Clusters of Orthologous Groups (COGs) database using BLASTP (E value 1e-03, coverage 0. 7 and 30% identity). If no hit was found, it searched against the NR database using BLASTP with an E value of 1e-03, coverage 0. 7 and 30% identity. The tRNAs and rRNAs were predicted using the tRNA Scan-SE and RNAmmer tools, respectively  38, 39. SignalP and TMHMM were used to foresee the signal peptides and the number of transmembrane helices, respectively  40, 41. For each selected genome, complete genome sequence, proteome genome sequence and Orfeome genome sequence were retrieved from the FTP site of National Center for Biotechnology Information (NCBI).
All proteomes were analyzed using proteinOrtho  42. An annotation of the entire proteome was performed to define the distribution of functional classes of predicted genes per cluster of orthologous groups of proteins (using the same method as for the genome annotation). The origin of replication was predicted using OriFinder  5, 6  (http://tubic. tju. edu. cn/ Ori-Finder/) and homology with other OriC regions was searched using blast algorithm in DoriC database7 (http://tubic. tju. edu. cn/doric/). The M. ahvazicum strain AFP-003 T genome was further incorporated into in silico DNA-DNA hybridization (DDH)  43  with reference genomes selected based on 16S rRNA gene proximity; and DDH values were estimated using the GGDC version 2. 0 online tool  44. For AFP-003 T genome comparison, we used the following species: of M.
Versions of the SCS Over the past two decades computational biology has transformed from being a misunderstood field  [8]  to being enthroned as the pillar of new biology  [9]. The availability of 'omics data at a growth rate faster-than-Moore's-Law  [10]  inspired the launching of increased numbers of scientific projects focusing computational biology, leading to an apparent explosion of career opportunities  [11]. The ISCB-SC recognised a growth in the number of RSGs across the globe and saw the need to host more than one SCS, preferebly in different geographic regions. This was also influenced by the schedule of ISCB meetings in different parts of the world. ISMB alternates its venue between Europe and North America each year, and in every two years both conferences take place together in Europe as 'ISMB/ECCB'.
Travel fellowships In many cases, students and early-career researchers struggle to attend conferences and symposia on a shoestring budget. Offering them fellowships to assist in their journey is much appreciated by participants, and, in some cases essential. At the SCS events, these are awarded through competitive reviews of submitted abstracts and a candidate's motivation for participation. The fellowships empower students to present at the symposium and also motivates to contribute back to the community. Some current and past leaders of the council were travel fellowship awardees. Financial support such as travel grants are particularly beneficial for students studying in developing countries. These opportunities allow students to build upon their academic experiences and give them a chance to participate in a networking environment through discussions with colleagues from the global community.
It is possible that it was a coincidence or weak correlation; however, this is supported by observations of recent years. The financial assistance requested by attendees is often higher than what the Student Council can cover. This is reflected in the fact that the number of applications received is much higher than the number of available fellowships. For example, a total of 40 applicants applied for travel fellowships to attend the SCS 2017, but there were only seven fellowships available to be awarded. Due to a large number of submissions, the fellowship applications need to be reviewed very carefully. To ensure the available funds have the most impact, the SCS travel fellowships reviewers consider the career level, motivation, abstract quality, current academic activity/exposure and community commitments.
Financial planning One of the significant factors of a successful symposium is fundraising. The ISCB and participant registration costs support some core costs of organising the event in the form of venue and refreshments. The Student Council relies on sponsorships to cover remaining costs. A critical cost item for organising the event are the travel fellowships awarded to students who are in need of financial assistance to attend the meeting. Beyond the travel fellowships, there are other costs including the awards for best talk and poster, costs associated with the printed material, symposium report publications and off-venue networking sessions. The fundraising practice is a vital aspect of the transferable skills development of students as the organisers learn various elements of commercial activities, such as creating a budget, designing promotional materials, budgeting, contract negotiation, and spending management.
Keynotes Professor Christine Orengo, from University College London, delivered the first keynote address at the SCS 2017. Prof. Orengo is renowned for her contribution to protein structure prediction work, particularly the CATH database  [29]. The main topics that the Orengo team focuses on is study of protein evolution and the design of algorithms to aid in protein studies. Prof. Orengo discussed an algorithm that identifies diverse superfamilies to trace back the last universal common ancestor (LUCA), even when relatives have diverged considerably to acquire modified structures and functions. She also highlighted that phylogenetic analyses of protein families can provide insight into the evolution of novel substrate specificities, and that functional studies can be combined with thermodynamic analyses to reveal the energetic considerations associated with functional divergence.
The second keynote was given by Dr. Johannes Söding who is known for his work on protein homology detection and protein structure prediction. At SCS 2017, Dr. Söding presented two newly developed tools in his lab which aim to solve some of the most prominent challenges in metagenomics  [30]. MMSeqs2 performs profile-based searches for homologous proteins in large metagenomic databases with more sensitivity than PSI-BLAST and at more than 400 times its speed. To build such profiles, clustering is needed first. LinClust, part of the MMSeqs suite, helps to cluster billions of protein sequences in linear time. As a working example, 1. 7 billion sequences from 2200 metagenomic and metatranscriptomic datasets were clustered by LinClust in 30 hours on 28 cores.
Acknowledgements The Student Council thanks the ISMB/ECCB 2017 conference chairs,  Professor Dame Janet Thornton  and  Professor Yves Moreau , for their support in organising the 13th  Student Council Symposium. Besides , the organisers extend gratitude to ISCB conference director  Steven Leard  and  ISCB Finance Team  for their crucial support and assistance in organising the SCS 2017. The Student Council would like to thank the  ISCB Executive Director Diane E. Kovats and President of the ISCB Alfonso Valencia  for their indispensable support and advice for the symposium. Finally, the organisers of the SCS 2017 and the  Executive Team of the ISCB Student Council  would like to thank all of the volunteers sincerely, reviewers and SC members, past and present, for their contribution and the countless hours spent in making this and all previous symposia successful.
Here, we aimed to broaden our understanding of the mechanistic links between ancestry-related differences in DNA methylation, genetic factors, and immune gene regulation. To do so, we build upon the EvoImmunoPop collection of primary monocytes originating from healthy individuals of African and European ancestry  [48]. We profiled the DNA methylome of 156 donors, including 78 of each ancestry, using the high-resolution Infinium Methyla-tionEPIC array, which captures methylation variation at more than 850,000 sites. This new dataset was combined with both genome-wide genotyping and whole-exome sequencing data, as well as with RNA-sequencing profiles from resting and stimulated monocytes with various immune stimuli, obtained from the same individuals. Such a system-level approach, integrating epigenetic, genetic, and transcriptional data, allowed us to assess the extent to which population-level variation in DNA methylation and its genetic determinants impact transcriptional activity related to immune responses.
Population differences in DNA methylation profiles of primary monocytes To assess population differences in DNA methylation of a purified innate immune cell type, we characterized DNA methylation variation at > 850,000 CpG sites across the genome, in monocytes originating from 156 male healthy volunteers: 78 of African descent (AFB, median age = 30. 9 years) and 78 of European descent (EUB, median age = 25. 9 years), all living in Belgium. Note that AFB individuals moved to Belgium between the ages of 6-45 years old (median age = 29 years). After normalization and filtering (see "Materials and methods"), we retained a final dataset of 552,141 methylation sites in the 156 individuals (Additional file 1: Figure  S1 ). Principal component analysis (PCA) of DNA methylation clearly separated AFB and EUB along the first two PCs, which explained together 11.
6% of the total variance (Fig. 1a ). At a false discovery rate (FDR) = 1%, we identified 77,857 sites (14. 1% of the total number) that presented a significant difference between AFB and EUB in their mean level of DNA methylation, after adjusting for age and surrogate variables. When restricting our analyses to CpGs that presented a mean difference > 5% (measured by the β value  [68] , see "Materials and methods"), we identified a total of 12,050 differentially methylated sites between populations (DMS) that mapped to 4818 genes. Because the age distributions of AFB and EUB individuals significantly differ (Wilcoxon P value = 10 -4 ; Additional file 1: Figure  S2 ), and age might have a non-linear effect on DNA methylation  [69] , we also investigated with ANOVA the extent to which DNA methylation is non-linearly affected by age in our dataset.
[110, 111] ). Odds ratios were computed against the general distribution of the 552,141 CpGs of our dataset. c Proportion of DMS that are hyper-methylated either in AFB (red) or in EUB (blue) individuals. The density of β values of one CpG site by category is given as an illustration of the population differences, with red and blue lines representing the methylation density in AFB and EUB, respectively. d Gene Ontology (GO) enrichment analyses of AFB-and EUB-DMS. For both groups, the top-GO categories reaching 5% FDR are shown, together with the number of genes per category and the log 10 -transformed FDR-adjusted enrichment P values.
DNA methylation profiling and data normalization Genomic DNA was extracted from the monocyte fraction using a phenol/chloroform protocol followed by ethanol precipitation. The DNA was then bisulfite converted, and BC-DNA was then processed using the Illumina Infinium MethylationEPIC BeadChip Kit (Illumina, San Diego, CA) to obtain the methylation profile of each individual at more than 850,000 CpG sites genome-wide. In total, 184 samples were hybridized with the EPIC array, including 172 unique samples and 12 technical replicates. We removed any technically unreliable probes: (i) potentially cross-hybridizing probes (83,635 probes), (ii) those located on the X and Y chromosomes (17,229 probes), and (iii) probes overlapping SNPs that present a frequency higher than 1% in at least one of the studied populations (206,998 probes).
These SNPs were chosen based on our own genotyping dataset, as well as on the 1000 Genomes project  [99]. To control for the quality of the probes and samples, we filtered out individuals with > 5% of probes associated with a detection P value > 10 -3 , and then, probes with a detection P value > 10 -3 in one or more individuals (6833 probes). Following this filtering process, 552,141 of the original 866,836 sites on the array were retained. We calculated methylation levels from raw data, using the R Bioconductor lumi package  [100]. Given that the M value has been shown to provide better detection sensitivity than β values at extreme levels of modification  [68] , we used the M value to run all statistical analysis unless otherwise stated. Note that in some instances of the text and figures, β values are reported for ease of clarity and interpretation.
M values were then adjusted for background noise with the normal-exponential using out-of-band probes (noob) from the R Bioconductor minfi package  [101]. Next, normalization for color bias was performed using lumiMethyC with the "quantile" method, and for methylated/unmethylated intensity variation using the lumiMethyN with the "ssn" method  [100]. Finally, we corrected for technical differences between type I and type II assay designs, by performing beta-mixture quantile normalization  [102]. To correct for known batch effects and potential hidden confounders, we used the sva function from the sva Bioconductor package  [103]  with age as a variable of interest. Additionally, five EUB samples were removed because they presented an excess of hemimethylated sites, leaving 89 EUB and 78 AFB samples.
Mapping of methylation quantitative trait loci (meQTLs) All individuals were genotyped for a total of 4,301,332 SNPs on the Illumina HumanOmni5-Quad BeadChips and went through whole-exome sequencing with the Nextera Rapid Capture Expanded Exome kit, on the Illumina HiSeq 2000 platform, with 100-bp paired-end reads. Details of the processing of genotyping and whole-exome sequencing data, together with imputation using the 1000 Genomes Project imputation panel  [99] , are reported in ref. [48]. For the meQTL mapping, we filtered out SNPs with a minor allele frequency < 5% in the populations studied and kept a final dataset of 10,278,745 SNPs (i. e. , corresponding to the merged genotyping and whole-exome sequencing dataset after imputation; 8,913,090 SNPs in Africans and 6,178,808 SNPs in Europeans).
Age, PC1 and PC2 of the genotype matrix, and two surrogate variables, as identified with the sva R package, were used as covariates in the linear model. We mapped meQTLs using the statistical framework implemented in the MatrixEQTL R package  [70]. For local associations (i. e. , distance SNP-CpG ≤ 100 kb), we performed two independent mappings using (i) the direct linear model from the MatrixEQTL pipeline and (ii) a Kruskal-Wallis rank test. Associations were considered significant when passing the 5% FDR threshold in both mappings. Two models were considered: merging all individuals and including a binary variable adjusting for ancestry or keeping the two populations separately. To detect all possible independent SNPs regulating methylation at a single CpG site in cis, we regressed out genotypes of all primary cis-meQTLs and then performed cis-meQTL mapping on the regressed methylation data to find secondary cis-meQTLs.
We repeated this process in a stepwise fashion until no additional independent cis-meQTLs were detected. This allowed us to refine our local meQTL mapping by detecting all possible independent SNP-CpG associations. For distant, trans-acting associations (i. e. , distance between SNP and CpG ≥ 1 Mb or on different chromosomes), we restricted our analysis to SNPs located in the vicinity of transcription factor (TF) coding genes, to limit the burden of multiple testing. Specifically, we selected (i) all SNPs located less than 10 kb to the TSS of any expressed TF in our dataset and (ii) SNPs detected as cis-eQTLs for these TFs. For each SNP, we only investigated CpG sites that mapped at least 1 Mb from the SNP or located on other chromosomes, using a Kruskal-Wallis rank test. For both cis-and trans-meQTLs, FDR was computed by mapping meQTLs on 100 datasets with the M values permuted within each population.
GWAS enrichment analyses We used the NHGRI GWAS catalog  [105]  to first select all significant SNPs that were significantly associated with a complex trait or disease at a P < 1 × 10 -8. Using this set of GWAS hits, we next extracted all SNPs in LD with each of these hits (R 2 > 0. 8) and classified the resulting final set of 166,248 SNPs according to their parental Experimental Factor Ontology (EFO) term  [75]. We then selected all meQTLs in our dataset that passed the P value threshold corresponding to FDR 5% in our initial mapping, and filtered out meQTLs that were in LD (R 2 > 0. 8) keeping one SNP per independent loci (56,574 independent SNPs). For the resampling set, we considered all SNPs that were initially used for the meQTL mapping and pruned them for LD (R 2 > 0. 8), yielding a final set of 921,466 SNPs.
Expression quantitative trait methylation (eQTM) analysis To identify associations between DNA methylation levels and gene expression of nearby genes, we leveraged RNA-sequencing data obtained from the same individuals, both at the non-stimulated state (NS) and in response to four immune stimuli  [48]. Briefly, RNA-sequencing was performed on the Illumina HiSeq2000 platform with 101-bp single-read sequencing with fragment size of around 295 bp, and outputs of around 30 million single-end reads per sample were obtained. A total of 763 RNA-sequencing samples from our filtered dataset of 156 donors were analyzed for gene expression profiling, including 156, 151, 153, 148, and 155 samples for the NS, LPS, Pam3CSK4, R848, and IAV conditions, respectively. Details of cell culture, immune stimulation conditions, and RNA-seq processing can be found in ref.
[48]. Using the RNA-sequencing data from the NS condition, we mapped eQTMs (i. e. , CpGs whose variation is associated with gene expression) in a window of 100 kb around the TSS of each gene (12,578 expressed genes in primary monocytes). The associated P values and the coefficients of correlation between methylation profiles and gene expression were obtained using Spearman's rank correlation. FDR was computed by mapping eQTMs on 100 datasets with the M values permuted, and kept, after each permutation, the most significant P value per gene (gene-level FDR). We selected the P value threshold that provided a 5% FDR (P = 5 × 10 -5 ). We also mapped eQTMs in the context of the response to the various stimulations, namely response-QTMs (reQTMs). To do so, the same procedure explained above for the eQTM mapping was followed, using the fold change of expression upon stimulation as a measure of the host response to infection.