dataesr/scientific-paragraphs-categorization huggingface dataset:
A white star indicates that every catalytic amino acid is present. Candidate BL02069 also possesses an additional known domain, AAA_12 (in yellow). https://doi. org/10. 1371/journal. pgen. 1007915. g001 RNA library preparation kit (Illumina, San Diego, CA, USA), following the manufacturer's instructions. Libraries were sequenced by the MGX sequencing facility (CNRS, Montpellier, France). Read sequences were aligned on the B. lanceolatum genome assembly  [43]  using bowtie2. A database of abundant non-coding RNAs was assembled by a search for orthologs for human and murine rRNAs, tRNAs, snRNAs, snoRNAs and scaRNAs; deep-sequencing libraries were also mapped on that database using bowtie2, and matching reads were flagged as "abundant ncRNA fragments". For pre-miRNA annotation, every B. lanceolatum locus with a Blast E-value �10 -6 to any of the annotated B.
Extragenomic contig assembly and annotation Small RNA reads that fail to map on the B. lanceolatum genome or transcriptome according to bowtie2 were collected and assembled using velvet  [45] , with k values ranging from 9 to 19 for better sensitivity  [46]. Contigs at least 50 bp in length were then compared to the NCBI non-redundant nucleotide collection (as of October 31, 2018) by megablast on the NCBI server with default parameters. Contigs with a detected similarity to known sequences in the collection were annotated with phylogenetic information using the NCBI "Taxonomy" database.
According to our HMMer-based search, the B. lanceolatum genome encodes 6 candidate RdRPs, three of which containing an intact active site DbDGD (with b representing a bulky amino acid;  [51] ) (see Fig  1B ). The current B. lanceolatum genome assembly contains a direct 1,657 bp repeat in one of the 6 RdRP genes, named BL09945. This long duplication appears to be an assembly artifact: we cloned and re-sequenced that locus and identified two alleles (with a synonymous mutation on the 505th codon; deposited at GenBank under accession numbers MH261373 and MH261374), and none of them contained the repeat. In subsequent analyses, we thus used a corrected version of that locus, where the 1,657 bp duplication is removed. In most metazoan species, siRNAs (as well as miRNAs) bear a 5 0 monophosphate and a 3 0 hydroxyl  [52, 53].
In summary, transcriptome-matching small RNAs in our Branchiostoma libraries contain miRNA and piRNA candidates, but they do not contain any obvious class of presumptive secondary siRNAs that would exhibit a precise size distribution, and possibly a 5 0 nucleotide bias. If Branchiostoma RdRPs generated secondary siRNAs by polymerizing mature short antisense RNAs (similarly to nematode 22G RNAs according to the prevalent model), then such hypothetical siRNAs should be detected in libraries #3. If Branchiostoma RdRPs generated long antisense RNAs, that would anneal to sense RNAs to produce a Dicer substrate (similarly to fungus and plant RdRP-derived siRNAs according to the prevalent model), then secondary siRNAs should be detected in libraries #1. As we did not observe candidate siRNA populations in either libraries #1 or 3, our data seem to rule out the existence of secondary siRNAs in Branchiostoma, regardless of the mechanistical involvement of RdRPs in their production.
At least these genes are transcribed: analysis of gene expression in long RNA-Seq data  [43]  shows a dynamic regulation, especially for the three genes with an intact predicted active site (see Supplementary S4 Fig). One could hypothesize that these RdRPs do not play any biological function. Yet at least two of them, BL02069 and BL23385, possess a full-length RdRP domain with a preserved catalytic site. The conservation of these two intact genes suggests that they are functionally important. It can therefore be speculated that Branchiostoma RdRPs play a biological role, which is unrelated to RNAi. Such a function may involve the generation of double-stranded RNA (formed by the hybridization of template RNA with the RdRP product), but it could also involve single-stranded RdRP products. Future work will be needed to identify the biological functionality of these enzymes.
Acknowledgments The authors are grateful to  Dr. Darryl Conte  for helpful discussions and to  Julie Claycomb ,  Kazufumi Mochizuki  and  Phillip D. Zamore  for critical reading of the manuscript. We thank the  B. lanceolatum genome consortium  for the assembly and annotation of the B. lanceolatum genome,  Dr. Manuel Irimia  for assistance in transcriptomics analyses, and  Dr. Ferdinand Marle  ´taz for sharing unpublished data. We thank the  MGX facility  ( Biocampus Montpellier ,  CNRS ,  INSERM ,  Univ. Montpellier, Montpellier, France)  for sequencing the Small RNA-Seq libraries.
Funding: This research was supported by an  ATIP-Avenir grant  from  CNRS  and  Sanofi  (to HS) and a post-doctoral fellowship from  La Ligue  contre le cancer (to NP). HE's laboratory was supported by the  CNRS  and the  ANR16-CE12-0008-01  and SB by the  Institut Universitaire de France. The MGX facility acknowledges financial support from  France Genomique National  infrastructure, funded as part of the " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Deep-sequencing data has been deposited at NCBI's Short Read Archive under accession #SRP125901 (runs #SRR6334492 to SRR6334515 for Small RNA-Seq, SRR7278519 to SRR7278522 for adult RNA-Seq). Sequences of the re-sequenced B. lanceolatum BL09945 locus have been deposited https://www. ncbi. nlm. nih. gov/ genbank/) under accession #.
Identifying determinants of gene silencing dynamics with Random Forest modeling We noted that genes close to the Xist locus tended to be silenced earlier than distal genes (Fig. 2H ), in agreement with a previous study  (Marks et al. 2015). However, many genes did not follow this trend. To uncover additional factors that potentially determine the susceptibility to Xist-mediated silencing, we developed a machine-learning model to predict silencing dynamics based on genomic and epigenetic features. We collected 138 publicly available high-throughput data sets (ChIP-seq and bisulfite-seq) measuring chromatin modifications and DNA-binding factors, mostly in male mESCs. Because these data sets had been generated in undifferentiated mESCs, they correspond to the chromatin state before Xist induction.
4 ; Supplemental Figs. S2, S4, S5). Also a close proximity to LINE elements, low gene density, and enrichment for PRC1 (RING1B, H2AK119ub1, RYBP) and PRC2 (EZH2, H3K27me3) are associated with (early-) silenced genes in both models, with PRC1 playing a more prominent role in the XCI/escape model (MDA 8% for the not silenced class). (Early-) silenced genes are enriched for histone deacetylases (HDAC), involved in gene repression (MDA 1. 6%-2. 3% for the early-silenced class and 3. 1%-   (Marks et al. 2015) , in preimplantation mouse embryos (F)  (Borensztein et al. 2017) , and the classes used for Random Forest modeling (G): (blue) XCI/escape model; (red) silencing dynamics model. (H) Estimated half-times (black circles) for all genes in the PRO-seq data set along the X Chromosome. A fitted smooth curve of the half-times is displayed as a blue line, and the Xist locus is marked with a gray line.
(5) A maximum value of k = 5, corresponding to a half-time of 3. 5 d was set, because higher half-times cannot be reliably estimated from our data as a result of the limited range of time points from 0 to 24 h. The goodness of fit was evaluated via the square root of the sum of squared residuals sqrtRSS defined as sqrtRSS = t (norm t -N(t)) 2. (6) After filtering out genes without SNPs, with fewer than 10 reads for at least one time point and strong allelic skewing (f 0 B6 , 0. 2 or f 0 B6. 0. 8) and sqrtRSS > 1. 5, we obtained reliable half-times for 296 X-Chromosomal genes on mouse genome mm10 (Supplemental Tables  S1, S2 ). Those genes were mapped to the mouse genome mm9 with the liftOver tool from the UCSC Genome Browser  (Kuhn et al. 2007). Gene half-times from both differentiated and undifferentiated mRNA-seq time series were computed in the same way as described above.
For the undifferentiated mRNA-seq data set, we discarded replicate B because of insufficient read coverage and only used replicate A (Fig. 2A ), which resulted in computing half-times for 346 genes. For the differentiated mRNA-seq data set, we averaged replicate A and B for each time point and computed half-times for 379 genes. For 233 genes, half-time could be estimated from all three data sets.
Definition of model features The epigenetic and genomic features used for the Random Forest models are summarized in Table  1  and listed in detail in Supplemental Table  S7. In total, 138 ChIP-seq libraries and one bisulfite sequencing experiment on undifferentiated male mESCs (only HDAC3 was assessed in female mESCs) were collected from various sources. After performing ChIP-seq library quality control with the deepTools package  (Ramírez et al. 2014) , 58 ChIP-seq libraries and the bisulfite sequencing experiment were used in the model (Supplemental Figs. S15, S16; Supplemental Table  S8 ; Supplemental Text S1). Epigenetic features are defined as the average ChIP-seq signal in a predefined genomic region, normalized to the signal of a matched control experiment in the same region. Read counts of each feature were normalized to the control with the R package normR (Supplemental Fig.
The two Random Forest classification models were implemented with the randomForest R package and use 77 predictor variables (epigenetic and genomic features), which show a variable degree of correlation between each other (Supplemental Fig. S19 ;  R Core Team 2009). The error rate of the models is computed based on the out-of-bag (OOB) error, which is the mean prediction error over all the trees of the Random Forest. Importance of each feature is computed as "mean decrease in accuracy" (MDA) (Supplemental Text S2). Variables with large positive values of the MDA correspond to important features for the classification, whereas variables with MDA close to zero or negative correspond to noise. Feature importance (MDA) and classification performance (OOB error) measures were further averaged over a collection of five hundred Random Forests to obtain stable results.
Data access All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE121144. The Random Forest pipeline is available as Supplemental Code, and the scripts for all additional analysis are on GitHub (https://github. com/marsicoLab/xist_mediated_gene_ silencing).
(H) Estimated half-times (black circles) for all genes in the PRO-seq data set along the X Chromosome. A fitted smooth curve of the half-times is displayed as a blue line, and the Xist locus is marked with a gray line. (I ) Cumulative distribution of half-times of genes silenced (independent, light gray) or not silenced (dependent, dark gray) by Xist lacking the repeat A element (Sakata et al. 2017 ).
Acknowledgments We thank the  NGS  platform of the  Curie Institute  for sequencing,  J. Helmuth  and  Q. Deletang  for help with data analysis, and  S. Rocha  and  A. Bousard  for providing RNA-seq data of Xist mutants associated with a recent preprint. This work was supported by  NIH  grant  GM025332  to J. T. L. ;  Center for Vertebrate Genomics (Cornell University ) stipend to J. T. L. and I. J. ;  Helen Hay Whitney Postdoctoral Fellowship and Rosalind Franklin Fellowship  ( University of Groningen ) to I. J. ;  ERC Advanced Investigator award  ( ERC-2014-AdG no. 671027 ),  Labelisation La Ligue, FRM  (grant  DEI20151234398 ),  ANR   DoseX 2017, Labex DEEP  ( ANR-11-LBX-0044 ), part of the  IDEX Idex PSL  ( ANR-10-IDEX-0001-02 PSL ), and  ABS4NGS  ( ANR-11-BINF-0001 ) funding to E. H. ;  ARC Foundation for Cancer Research  ( France; post-doctoral fellowship  and grant  R17016KK ) and the  ATIP-AVENIR program  ( INSERM / CNRS, France ; grant  C16002KS ) to J.
Genomic studies and bioinformatics Whole exome sequencing (WES) was performed using SOLiDv4 (Life Technologies) or HiSeq2500 (Ilumina) sequencers after enrichment with the Agilent SureSelec-tXT Human All Exon 50Mb kit. Data analysis was done with BioScopeTM and pathogenicity calculations were performed using standard prediction tools. For P3, both tumors (osteosarcoma and AML) were sequenced, as germline material derived from blood was not available due to a hematopoietic cell transplantation (HCT). Specific consent for making the exome sequencing data public was not granted. However, any questions regarding the data may be directed to the clinical geneticist, Marojolijn Jongmans, at M. C. J. Jongmans-3@umcutrecht. nl. Variants that were present in both tumors and in one of the parents were considered to be germline variants.
The analysis focused on variants present in both tumors of the index patient (P3) and his affected mother (P2). Normal variation, defined as variants present in >10 cases of our in-house database and variants present with a frequency of more than 1% in db-SNP [build 144  (40) ], the ExAC or the gnomAD database  (41)  were excluded. Missense variants were filtered based on Call quality (≥10 total reads with ≥20% variant reads), conservation score (PhyloP ≥ 3. 0) and pathogenic prediction in two out of three computational prediction scores CADD PHRED (42) (version 1. 3, >20), SIFT (43) (damaging) and PolyPhen2 (44) (possibly/probably damaging) using Alamut Visual software (v2. 7. 1. , Interactive Biosoftware, Rouen, France). Multiple sequences alignments were performed using Clustal Omega online software (https:// www.
Erythroid cell culture assays Erythroid cell culture assays were performed as previously described  (25, 48). Antibodies and stains for FACS analysis were PC7 or PE conjugated CD34 (Beckman coulter, Brea, CA, USA), APC conjugated CD36 (BD Biosciences San Jose, CA, USA), APC conjugated Band 3 (kindly provided by Mohandas Narla's lab, NYBC, New York, USA), PE/Cy7 conjugated IL-3R (Miltenny, Paris, France), PE/Cy7 (PE)-coupled GPA (Life Technologies Carlsbad, CA, USA), and DAPI (Sigma). Antibodies for western blotting were directed against TP53 (Sigma #5816), CDKN1A/p21 (Cell Signaling #2947), actin (Sigma #Ac-15), and uL6 (Abcam #ab102011). FACS analysis was conducted on a BD Biosciences Influx flow cytometer (BD Biosciences San Jose, CA, USA). Data were analyzed using Kaluza software (Beckman coulter, Brea, CA, USA).
Translational fidelity cell-based assays Deidentified LCLs were transfected by electroporation using an Amaxa™ Nucleofector II apparatus and Cell Line Nucleofector ® Kit V (Lonza), per the manufacturer's instructions. For each transfection, 1. 5 × 10 6 cells were mixed with 1. 5 g of plasmid DNA. All assays were performed in triplicate four independent times. Cell lysates were prepared using passive lysis buffer and luciferase activities were determined using the Dual-Luciferase ® Reporter Assay System and a GloMax-Multi+ Detection System (Promega), per the manufacturer's instructions 24 hours post-transfection. Data were analyzed by dividing the ratio of firefly luciferase to Renilla luciferase for each of the experimental plasmids by the same ratio for the readthrough plasmid control in the same experiment.
The nano-LC method consisted of a 120 min multi-linear gradient at a flow rate of 300 nl/min, ranging from 5 to 33% acetonitrile in 0. 1% formic acid. The spray voltage was set at 2 kV and the heated capillary was adjusted to 270 • C. Survey full-scan MS spectra (m/z = 400-1600) were acquired with a resolution of 60 000 after the accumulation of 10 6 ions (maximum filling time 200 ms). The 20 most intense ions were fragmented by higher-energy collisional dissociation after the accumulation of 10 5 ions (maximum filling time: 50 ms). MS and MS/MS data were acquired using the software Xcalibur (Thermo Scientific). Data were processed automatically using Mascot Distiller software (version 2. 7. 1. 0, Matrix Science). Peptides and proteins were identified using Mascot (version 2. 6) through concomitant searches against Uniprot (Homo sapiens taxonomy, June 2019 version), the mutated RPL9 protein sequence, classical contaminants database (homemade) and their corresponding reversed databases.
Trypsin/P was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerance were set, respectively, to 20 ppm and 25 mmu. Peptide modifications allowed during the search were: carbamidomethylation (fixed), acetyl (protein N-terminal, variable) and oxidation (variable). The Proline software (http://proline. profiproteomics. fr) was used to filter the merged results: conservation of rank 1 peptide-spectrum match (PSM) with a minimal length of 7 and a minimal score of 25. PSM score filtering is then optimized to reach a False Discovery Rate (FDR) of PSM identification below 1% by employing the target decoy approach. A minimum of one specific peptide per identified protein group was set. Proline was then used to perform MS1-based label free quantification of the peptides and protein groups from the different samples.
9 Build 2926). Data analysis was performed using Thermo Scientific Xcalibur software (Version 4. 1. 50, Build date Monday, 9 October 2017), and processed using in house developed statistical package in R (version 3. 3. 1, 2016-06-21). Enrichment analysis was performed with MetaboAnalyst 3. 0 online software (https://www. metaboanalyst. ca).
Gene variants in RPL9 link to DBA and multiple cancer incidences Whole exome sequencing (WES) of patient-parent trios identified an individual within the EuroDBA consortium registries with the heterozygous variant c. -2+1G>C (P1) in the 5 UTR of the RPL9 gene (NM 000661. 4) (Table  1  and Figure  1 ). The c. -2+1G>C variant appears to be de novo as both parents tested wild-type for RPL9. Sanger sequencing of cDNA from EBV-immortalized lymphoblast cell lines (LCLs) derived from these individuals confirmed the presence of double peaks, indicating that the variant allele is expressed (Figure  1B ). The c. -2G+1>C variant is predicted to be a splice site variation affecting the donor splice site of exon 1 (Figure  1C ) and has previously not been reported in SNP databases. This individual, in addition to anemia, presented with an abnormal thumb and colitis, the pathology of which was marked by apoptotic bodies, CD3 + lymphocyte infiltration, and TP53 stabilization (Table  1  and Supplementary Figure  S1 ).
Since the identified Leu20 and Pro20 containing peptides are very close in sequence, we used label-free quantification to estimate and compare the abundances of the two uL6 peptide versions in ribosomes purified from P2 LCLs. As shown in Figure  7D , it revealed that these ribosomes from P2 LCLs contain about the same quantity of uL6 p. Leu20 and of uL6 p. Pro20 peptides compared to ribosomes purified from healthy control LCLs that exclusively contain uL6 p. Leu20 peptides. These data suggest that the uL6 proteins generated from the wild type and mutated alleles of RPL9 contribute about equally to the structure of mature ribosomes in LCLs carrying the uL6 p. Leu20Pro variant. In order to visualize how the incorporated uL6 p. Leu20Pro protein might alter the ribosome we used Pymol software to model the uL6 structure and its surroundings with either the WT or variant version of the amino acid residue 20.
FUNDING Specific  E-Rare  grants funding EuroDBA researchers [ZonMW # 40-44000-98-1008  in the Netherlands to A. W. M. ; # BMBF 01GM1609  in Germany to M. W. , # ANR-15-RAR3-0007-04  to P. E. G. and  M. O. D. , # ANR-15-RARE-0007-03  to L. D. C. in France]; P. E. G. , M. F. O. D. and L. D. C. are also funded by  ANR-15-CE12-0001  ( DBA-Multigenes ); L. D. C. is additionally supported by the  Laboratory of Excellence for Red Cells  [(LABEX  GR-Ex ) -ANR Avenir-11-LABX-0005-02 ];  French National PHRC OFABD  (DBA registry); J. D. D. and C. M. dS. V. are supported by a grant from the  United States National Institutes of Health/National Heart Lung and Blood Institute  [ R01HL119439-01A1 ]; G. Z. and A. N. O. are supported by  Jim  and  Diann Robbers Cancer Research Grant for New Investigators ;  I.
J. D. is funded by the  KiKa Foundation  [127]; M. C. J. J. and I. D. are supported by  Stichting Loeka  (http://www. stichtingloeka. nl/);M. P. and L. M. are supported by  Pallotti Legacy for Cancer Research. Funding for open access charge: ZonMW; DBA UK. Proteomic experiments were partly supported by  ProFI  [ ANR-10-INBS-08-01  grant].
D • • • • The aim of the present research was to evaluate the taxonomic position of these species, which are closely related due to their likeness, testing also their relationships to other Onthophagini groups, while employing phenotypic characters of the external and internal morphology of adults. In the Insects 2019, 10, 64 3 of 50 present research, it was also stressed whether the species should be regarded as a closely related cluster to which the generic rank should likely be assigned. Furthermore, the biogeographical diversity was tested by the evaluation of the present distribution data of these species.
Material The ingroup dataset comprises 26 Afrotropical species: 20 were formerly included in the speciose Onthophagus genus and six were identified here as new taxonomic entities to which specific rank was assigned (Table  1 ). The outgroup method was chosen to root the trees, selecting 10 representatives from Afrotropical and Palearctic Onthophagini (Table  1 ) to be included in the outgroup dataset. The choice of the outgroup taxa was done under the following considerations:  (1)  the well known, superficial resemblance within many "Onthophagus" species has greatly affected the taxonomic arrangements in the past;  (2)  the systematics of the majority of the Afrotropical Onthophagini species is far from being settled and their present knowledge has only been partially improved since the d'Orbigny Synopsys  [2] ; and,  (3)  no definition can be provided at present for Onthophagus due to the extreme heterogeneity of the species that are included in the genus.
The morphological dataset included:  (1)  the geometric morphometric partition, with 12 landmark configurations (Figure  1 ), chosen according to Palci and Lee  [21] ; and, (2) the discrete partition with 53 characters scored from external and internal anatomical traits of adult specimens (Supporting Information Data S1). Insects 2019, 10, x 4 of 49 acquired by the software Leica Application Suite (LAS, Leica ® Microsystems, Leica Microsystems AG, Wetzler, Germany), placing them in the same position in order to avoid error due to malpositioning. Since it was emphasized that the quality of phylogenetic analyses improves by increasing the number of landmark configurations that were used in the analysis  [22, 23] , here we employed many different structures (head, eye, and pronotum of both sexes, female head carinae, hindwing, right elytron, epipharynx, phallobase, and right paramere) that were analysed applying the geometric morphometric approach, as already done in other Scarabaeidae taxa  [24].