dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter problem is harder than the former, another embodiment of the role of dynamics in the emergence of function. Our parameters are of high interest for both problems. At the pose prediction stage, they provide filters to check that putative Ig-Ag complexes proposed by docking algorithms comply with our classification rules, as a function of the ligand type. In a similar spirit, these parameters are of direct relevance to predict the ligand type from the structure of the Ig VH + VL domains. At the affinity prediction stage, assuming a good quality (i. e. , resolution) putative structure for the complex, reliable affinity predictions can be made. These results also call for extensions, in particular to handle different ligand types (peptides and haptens). Since the quality of predictions owes in particular to a good coverage of the region of the model space targeted by predictions, this extension is likely to be successful assuming a database-identical in spirit to the SAB, providing sufficiently many cases to learn from.
To determine the mitoproteome we isolated ultrapure mitochondria from end-stage hearts of all knockouts and used label-free mass spectrometry to identify differentially expressed proteins between knockouts and age-matched controls (Figure  1B ). In parallel, we did RNA sequencing (RNA-Seq) on total RNA from end-stage hearts of the different knockouts. The reproducibility of the biological replicates was tested by Pearson correlation and visualized in a correlation heatmap for the transcriptome and proteome profiles (Figure  1 -figure supplement 2A). Overall, the transcriptomic and mitoproteomic data were highly reproducible with a correlation coefficient of >85% across all samples and >95% between biological replicates. In total, 1118 transcripts encoding mitochondrial proteins were quantified from RNA-Seq.
Delta-1-pyrroline-5-carboxylate synthase (Aldh18a1) and pyrroline-5-carboxylate reductases 2 (Pycr2); Figure  1 -figure supplement 4). The effects of imputed values for individual proteins can be assessed in the Supplementary file 8. Differentially regulated mitochondrial genes and proteins were manually classified into 19 functional categories. These datasets were complemented with a temporal mitoproteomic analysis of conditional Lrpprc heart knockout mice at 2, 3, 5, 7 and 10 weeks of age. In addition, we generated a third dataset comparing the RNA-Seq and proteomic data from all control hearts to evaluate post-natal changes in the transcriptome and mitoproteome of juvenile mice (3 weeks) at different time points until adulthood (24 weeks) (Figure  1B ).
Interestingly, the most enriched pathway was signaling via eukaryotic translation initiation factor 2 (eIF2), mediated by the cyclic AMP-dependent transcription factor ATF-4 (Atf4), myc proto-oncogene protein (Myc), C/ EBP-homologous protein (Chop/Ddit-3) and cyclic AMP-dependent transcription factor ATF-5 (Atf5). Furthermore, cis-regulatory sequence analyses using iRegulon  (Janky et al. , 2014)  predicted both, Atf4 and Myc, among the top regulators based on our datasets (Supplementary file 6). Atf4 and Myc have previously been associated with mitochondrial biogenesis  (Morrish and Hockenbery, 2014)  and we verified their expression levels via RT-qPCR. The Myc and Atf4 transcription factors showed a very strong increase in all five knockouts in contrast to more general transcription factors involved in mitochondrial biogenesis, such as nuclear respiratory factor 1 (Nrf1), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (Pgc1a) and GA-binding protein alpha chain (Gabpa) (Figure  3B ).
LC-MS/MS data analysis The raw data were analyzed with MaxQuant version 1. 4. 1. 2 (RRID:SCR_014485; Cox and Mann, 2008) using the integrated Andromeda search engine  (Cox et al. , 2011). Peptide fragmentation spectra were searched against the canonical and isoform sequences of the mouse reference proteome (proteome ID UP000000589, August 2015 from UniProt). The database was complemented with 245 sequences of contaminating proteins by MaxQuant. For the analysis methionine oxidation and protein N-terminal acetylation were set as variable modifications. The digestion parameters were set to 'specific' and 'Trypsin/P,' allowing for cleavage after lysine and arginine also when followed by proline. The minimum number of peptides and razor peptides for protein identification was 1; the minimum number of unique peptides was 0.
The pI and GRAVY score values were combined with the average log2 RNA reads count per L/L, cre genotype and with the number of genotypes that had quantification values in the proteomic analysis (see Supplementary file 7). The data was divided into two disjoint sets, namely proteins quantified in zero genotypes and proteins quantified in at least one genotype. The empirical cumulative distributions of the pIs, GRAVY scores and the average log2 RNA counts of the two sets were visualized using the stat_ecdf function of the ggplot2 package. Mitochondrial proteins were manually placed in functional categories based on literature and Uniprot (The UniProt Consortium, 2017). For the L/L comparison across different time points, Max-Quant's proteinGroups. txt file was combined with annotations from MitoCarta2.
0  (Calvo et al. , 2016)  as described above. The MitoCarta annotated data were imported into Perseus and filtered for at least nine valid LFQ intensity values across all samples (26 in total). Only proteins annotated as mitochondrial, according to the 'MitoCarta2_List' column, were kept for analysis. Missing values were imputed as described above. Limma's F-statistic was used to detect proteins with significant differences in protein expression across the time points. Testing was performed after the data were grouped per week (nine week groups in total). The fold changes of the average LFQ intensity for all time points, relative to week three were calculated, scaled and z-score normalized in R. Hierarchical clustering of the normalized data was performed with pheatmap, version 1. 0. 8 (Kolde, 2015) , using Euclidean distance as a metric.
2D annotation enrichment analysis 2D annotation enrichment analysis of the proteomic and transcriptomic data was performed with Perseus  (Cox and Mann, 2012)  using Benjamini-Hochberg FDR for truncation and a threshold value of 0. 2. For the evaluation of the protein abundances during the post-natal development, identification of overrepresented functional categories in separate clusters was performed using Fisher exact test in Perseus. Benjamini-Hochberg FDR was for truncation with a threshold value of 1. The top three overrepresented categories in each cluster, based on adjusted p value, are shown. RNA isolation, qRT-PCR and RNA sequencing RNA was isolated from cultured cells and frozen mouse heart using the miRNeasy Mini Kit (Qiagen, Germany) and the concentration, purity, and integrity were confirmed using a Bioanalyzer.
The alignment to the Mus musculus reference genome (GRCm38) was performed using HISAT2 version 2. 0. 4 (hisat2 -p 8 -dta;  Kim et al. , 2015). Alignment files were sorted and indexed with SAMtools version 1. 3. 1 (samtools sort -@ 8; samtools index -b;  Li et al. , 2009). The transcript abundances were estimated with StringTie 1. 2. 4 (stringtie -p 18 -e -B -G) using the Ensembl 81 annotation  (Pertea et al. , 2016). The raw reads count matrix at gene-level was extracted with prepDE. py script provided at http://ccb. jhu. edu/software/stringtie/dl/prepDE. py on Python version 2. 7. 6. Differential expression analyses were performed with DESeq2 package R version 3. 3. 2 according to the standard DESeq2 tutorial that is part of the package  (Love et al. , 2014). Genes with an adjusted p<0. 05 were considered statistically significant.
RNA-Seq data analysis RNA-Seq data were analyzed through the use of Ingenuity Pathways Analysis (Ingenuity Systems, www. ingenuity. com, RRID: SCR_008653). Log2 transformed expression values and adjusted p values were uploaded to the program. Genes from the data set that met the adjusted p value cutoff of 0. 05 in all the knockout strains of this study and were associated with a canonical pathway in Ingenuity's Knowledge Base were considered for the analysis. The significance of the association between the data set and the canonical pathway was measured in two ways: (1) A ratio of the number of molecules from the data set that map to the pathway divided by the total number of molecules that map to the canonical pathway is displayed, (2) Fisher's exact test was used to calculate a p value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone.
Genes from the RNA-Seq data sets that had a p value < 0. 05 for all the knockout strains were loaded into Cytoscape (version 3. 5. 0; Shannon et al. , 2003)  and used as queries to the iRegulon plug-in  (Janky et al. , 2014). The putative regulatory region was selected to be 20 kb centered on the transcriptional start site (TSS), the normalized enrichment score (NES) threshold was set at 3. 0, and the maximum false discovery rate (FDR) on motif similarity was set at 0. 001. Transcription factor enrichment analyses were performed based on motif or Chip-seq peaks enrichment using the 10K (9713 PWMs) motif collection and the 1120 Chip-seq track collection, respectively. Target genes for MYC and ATF4 were obtained from published Chip-seq experiments  (Han et al. , 2013; Seitz et al. , 2011).
1 mL and 8 ml. The sample manager was set to 8 ˚C and the source temperature to 150 ˚C, desolvation temperature was 500 ˚C and desolvation gas was set to 800 L/h, cone gas to 150 L/h. The following MRM transitions were used as quantifier for sarcosine m/z 89. 84 to 44. 02 cone 14V collision 18V, glycine m/z 75. 9 to 30. 08 cone 28V collision 6V and in a negative mode serine m/z 105. 9 to 42. 09 cone 72V collision 10V. The MassLynx (Waters) software was used for data management and TargetLynx (Waters) was used for data evaluation and absolute quantification. All compounds were freshly prepared and dissolved in 5 mM ammonium formiate + 0. 15% formic acid. An external standard calibration curve was calculated using 11 concentrations from 100 to 20000 ng/ml (all of them were prepared from stock solutions 100 mg/ml).
, 2012) ; RRID: AB_2716302), MRPL37 (#HPA025826, Sigma-Aldrich, RRID: AB_1854106), MRPL44 (#16394-1-AP, Proteintech, Germany, RRID: AB_ 2146062), MRPS35 (#16457-1-AP, Proteintech, RRID: AB_2146521), MTHFD1 (#ab103698, Abcam, RRID: AB_10862775), MTHFD2 (#ab37840, Abcam, RRID: AB_776544), NDUFA9 (#ab14713, Abcam, RRID: AB_301431),  POLRMT (Ku ¨hl et al. , 2014) ; RRID: AB_2716297), PYCR1 (#13108-1-AP, Proteintech, RRID: AB_2174878), SDHA (#459200, Thermofisher Scientific, RRID: AB_2532231), SHMT2 (#HPA020543, Sigma-Aldrich, RRID: AB_1856833), TFAM (#ab131607, Abcam, RRID: AB_11154693), Total OXPHOS Rodent WB Antibody Cocktail (#ab110413, Abcam, RRID: AB_2629281), Tubulin (clone 11H10, #2125, Cell Signaling, RRID: AB_2619646), TWINKLE  (Milenkovic et al. , 2013) ; RRID: AB_2716298), UQCRFS1 (#ab131152, Abcam, RRID: AB_2716303), VDAC1 (clone N152B/23 #MABN504, Millipore, Germany, RRID: AB_2716304).
The exact n and p values for metabolomics, qRT-PCR analyses, and enzyme activity measurements can be found in Supplementary file 5. Two to three technical replicates were performed for targeted metabolomics measurements, three technical replicates were performed for qRT-PCR and semiquantitative PCR analyses, three to seven technical replicates were performed for citrate synthase activity measurements. Proteomic, transcriptomic, qRT-PCR experiments were performed once. Metabolomic and semiquantitative PCR mtDNA quantifications determinations were performed two times. Western blot analyses were repeated >3 times with the exception of the data presented in Figure  5 -figure supplement 2. Sample processing for RNA-Seq, qRT-PCR, semiquantitative PCR, proteomics, enzyme activity measurements, and metabolomics analyses was performed blindly.
-G. Larsson; Ruzzenente et al. , 2012 Life technologies RRID: AB_2716302 Mm01190168_m1 (1:1000) Antibody Sequence-based reagent Taqman Assay -Mouse Ppargc1 Life technologies MRPL37 Sigma-Aldrich Cat#HPA025826 Mm00447183_m1 (1:500) Sequence-based reagent Taqman Assay -Mouse Polrmt Life technologies RRID: AB_1854106 Mm00553272_m1 Antibody Sequence-based reagent Taqman Assay -Mouse Tfam MRLP44 Proteintech Life technologies Cat#16394-1-AP Mm00627275_m1 (1:300) Sequence-based reagent Taqman Assay -Mouse Life technologies RRID: AB_2146062 Mm00467928_m1 Antibody MRPS35 Peo1/Twnk Proteintech Cat#16457-1-AP (1:500) Commercial assay or kit miRNeasy Mini kit Qiagen RRID: AB_2146521 Cat#217004 Antibody Commercial assay or kit MTHFD1 Ribo-Zero rRNA removal kit Abcam Illumina Cat#ab103698 RRID: AB_10862775 MRZH11124 (1:500) Antibody Commercial assay or kit MTHFD2 Tru-Seq Sample preparation Abcam Illumina Cat#ab37840 RS-122-2002 (1:500) Commercial assay or kit High Capacity cDNA Applied Biosystems RRID: AB_776544 Cat#4368814 Reagent type (species) or resource Antibody Commercial assay or kit Organism, C57BL/6N (Mus musculus) Antibody Commercial assay or kit Organism, C57BL/6N (Mus musculus) Antibody Chemical compound, drug EDTA-free complete Designation NDUFA9 revese transcription kit TaqMan Universal PCR Twnk LoxP/LoxP ; POLRMT mouse Master Mix, No Amperase UNG Citrate Synthase Assay Kit Twnk L/L Twnk LoxP/LoxP , +/Ckmm-cre; PYCR1 protease inhibitor cocktail Twnk L/L, cre Organism, C57BL/6N (Mus musculus) Tfam LoxP/LoxP ; Tfam L/L Organism, C57BL/6N (Mus musculus) Tfam LoxP/LoxP , +/Ckmm-cre; Antibody SDHA Chemical compound, drug PhosSTOP tablets Antibody SHMT2 Chemical compound, drug Percoll Chemical compound, drug Trypsin gold Tfam L/L, cre Organism, C57BL/6N Polrmt LoxP/LoxP ; Polrmt L/L Antibody TFAM Chemical compound, drug Standard Coenzyme Q9 Chemical compound, drug Standard Coenzyme Q10 (Mus musculus) Organism, C57BL/6N (Mus musculus) Polrmt LoxP/LoxP , Antibody Total OXPHOS Rodent WB Antibody Cocktail Chemical compound, drug Standard Glutamate (Glutamic acid) +/Ckmm-cre; Polrmt L/L, cre Organism, C57BL/6N Lrpprc LoxP/LoxP ; Lrpprc L/L Antibody Tubulin Chemical compound, drug Standard Glycine (Mus musculus) Chemical compound, drug Standard Proline Continued on next page Continued on next page Continued on next page Source or reference Abcam Applied Biosystems Milenkovic et al.
, 2013 N. -G. Larsson; Ku ¨hl et al. , 2014 Sigma-Aldrich Milenkovic et al. , 2013 Proteintech Roche Larsson et al. , 1998 Larsson et al. , 1998 Thermofisher Scientific Roche Sigma-Aldrich GE Heathcare Promega Ku ¨hl et al. , 2014, 2016 Abcam Sigma-Aldrich Sigma-Aldrich Ku ¨hl et al. , 2014, 2016 Abcam Sigma-Aldrich Ruzzenente et al. , 2012 Cell Signaling Sigma-Aldrich Sigma-Aldrich Identifiers Cat#ab14713 RRID: AB_301431 Cat#4324020 RRID: AB_2716297 RRID: MGI:5496889 Cat#CS0720 RRID: MGI:5496891 Cat#13108-1-AP Cat#05056489001 RRID: AB_2174878 RRID: MGI:2177633 RRID: MGI:2177634 Cat#459200 Cat#04906837001 RRID: AB_2532231 Cat#HPA020543 Cat#17-0891-02 Cat#V5280 RRID: AB_1856833 Cat#ab131607 Cat#27597 MGI:5704129 RRID: AB_11154693 Cat#C9538 RRID: MGI:5704131 Cat#ab110413 Cat#G1251 RRID: AB_2629281 RRID: MGI:5438915 Cat#2125 Cat#G7126 RRID: AB_2619646 Cat#P0380 (1:500) Additional information (1:200) (1:100) (1:500) (1:500) (1:1000) (1:1000).
Sequence accession numbers The following sequences were used in this study: X-TAS (L03284), 1 2L-TAS (U35404),  3  Canton-S cosmid (AL031884),  26  clone AF520587,  27  Invader 4 (FBgn0063427), 2R-TAS and 3R-TAS recovered from Lin and Yin, 2007  7  and y 1 ; cn 1 bw 1 sp 1 from FlyBase. org. 28.
DNA extraction and PCR amplification Flies were manually homogenized in a buffer containing 0. 1-M Tris pH9, 0. 1-M EDTA pH8 and 1% SDS and then incubated at 70 C for 30 min. Potassium Acetate 8 M and isopropanol were added prior centrifugation. Pellets of DNA were washed and resuspended in water and PCR was performed using primers (available upon request). This protocol was used for DNA extraction from single flies (Tables  2  and 3 , Supplementary Tables  S2  and S3 ) as well as from groups of twenty to fifty flies (Fig. 3  and Supplementary Table  S2 ). Since the absence of a PCR amplification product could be attributed to poor DNA quality, we confirmed the amplificability of DNA samples using independent sets of primers (data not shown).
Small RNA sequencing Total RNA extraction, small RNA library preparation and sequencing were performed as described in Hermant et al. 30  Sequence reads in FASTQ format were trimmed from the adapter sequence 5 0 -TGGAATTCTCGGGTGCCAAG-3 0 and matched to reference sequences (TAS, P element, or D. sechellia repeats) using Bowtie. 31 ultiple mappers from 23 to 29 nt matching the reference sequences with zero mismatches were retained for subsequent analysis. Annotation of small RNA libraries and normalization factors are described in Supplementary Table  S1. Sequence length distributions and small RNA mapping were generated from Bowtie alignments using Python and R (www. r-project. org/) scripts, which were wrapped and run in a Galaxy instance publicly available at http:// plastissipi. fr. Tools and workflows used in this study can be downloaded from this Galaxy instance.
TAS-R and TAS-L families are related We next investigated the origin of each of the "T"domains (T1-T5) identified above. Using several databases, the only sequence alignments recovered corresponded to those of a 158-bp region located in the T3 domain with sequences in subtelomeric TAS-L family repeats located on the 2L and 3L chromosomes (Fig. 1B  and E , Supplementary Fig. S4 ) as well with sequences in the two domains of the 0. 9 kb repeats next to X-TAS (Fig. 1B  and F  and Supplementary Fig. S5 ). The 2L-TAS and 3L-TAS are identical subtelomeric minisatellite repeats. 3 In the y 1 ; cn 1 bw 1 sp 1 reference genome (FlyBase. org), we found 11 repeats and 42 repeats of 458 bp on the 2L and 3L subtelomeres, respectively (Supplementary Fig. S6 ). Such similarities between TAS-R and TAS-L families have been suggested recently.
S1 ), providing us with an easy assay to systematically screen for the presence/absence of X-TAS. Using sequences available in databases,  1, 26, 28  absence of X-TAS repeats in Canton-S and y 1 ; cn 1 bw 1 sp 1 was confirmed and presence (Canton-S) or absence (y 1 ; cn 1 bw 1 sp 1 ) of the 0. 9 kb repeats was demonstrated (Fig. 1G  and H ). In order to extend this analysis, we performed in situ hybridization screen on polytene chromosomes and/or PCR amplifications on 11 unrelated fly stocks from diverse locations in the world and introduced into our laboratory or Stock Centers several decades ago. In situ hybridization results summarized in Table  1  showed that none of the strains are totally devoid of TAS-R loci, but that the majority are devoid of X-TAS (8/11) (some examples are shown in Supplementary Fig.
Distribution of TAS family in recently collected strains Year of capture T3 domain a X-TAS b 2R-TAS b 3R-TAS b TAS-L a Europe Paris 2013 þ þ þ À þ Combs-la-Ville 2013 þ þ þ þ þ Viry-Châtillon-3 2013 þ þ þ þ þ Chantemesle-iso 2013 þ þ þ þ þ Marsais-53Ja 2013 þ þ þ þ þ Marsais-80J 2013 þ þ þ À þ Marsais-86C 2013 þ þ þ þ þ Marsais-87C 2013 þ þ þ À þ Niort-A1 2013 þ þ À þ þ Niort-G3 2013 þ þ þ À þ Niort-P1. 3 2013 þ þ À þ þ Niort-P2. 1 2013 þ þ À þ þ Reth 2013 þ þ þ þ þ MauzeThouarsais-2 2013 þ þ À þ þ MauzeThouarsais-3 2013 þ þ À þ þ Sanguinet-1. 5 2014 þ þ þ þ þ Rouans1 2013 þ þ þ þ þ Gotheron, Valence-iso3 2012 þ þ þ À þ Africa Addis Ababa 2 2014 þ þ þ þ þ Kafountine 2015 þ þ þ À þ South America Saul, Cacao H2A2 2014 þ þ þ À þ Saul, Cacao H1C 2014 þ þ þ þ þ Ilha Grande 1, Rio Janeiro 2014 À À þ þ þ a The data were obtained by PCR amplification using specific primers testing the presence of T3 domain or TAS-L family.
Acknowledgements We thank Clement Carre,  Emile Civel , Aurelie  Hua-Van ,  Claude Lespinet ,  Gilbert  and  Micheline Lespinet ,  Pauline Marie ,  Valerie Ribeiro ,  Claude et Michele Teysset , and  Anne Laure Todeschini  for their help in collecting flies, the  Bloomington Stock Center  for providing stocks, FlyBase. org for providing valuable databases and  ARTbio/Bioinformatics  analyses platform at the IBPS for the development of valuable tools. We thank  Jean-Franc¸ois Gilles  and  Susanne Bolte  for the help with confocal microscopy carried out at the  Institute of Biology Paris-Seine Imaging Facility , supported by the  "Conseil Regional Ile-de France" , the  French national research council (CNRS)  and  Sorbonne University ,  UPMC Univ Paris 06. We thank  Ritha Zamy  and  Shang Nong HU  for technical assistance,  Robert W.
Funding This study was supported by fellowships from the  Ministere de l'Enseignement Superieur et de la Recherche  to A. A. -L. and by grants from the  Association de la Recherche contre le Cancer (Fondation ARC ,  SFI20121205921 ,  SFI20131200470 ), the  Fondation pour la Recherche Medicale , ( FRM DEP20131128532 ), the  Agence Nationale de la Recherche  ( ANR , project "plastisipi") to S. R. , and the  University Pierre et Marie Curie  (Emergence  EME1223 ) to L. T. Sampling of D. melanogaster in French Guiana was funded by a  CEBA grant  ( MONISPEC ANR-10-LABX-25-01 ) and a  CNRS Nouragues Travel Grant 2015  ( ACCISPEC NTG 2015 ) to W. J. M. The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.
Over the past two decades, the development of methods for visualizing and analysing specimens digitally, in three and even four dimensions, has transformed the study of living and fossil organisms. However, the initial promise that the widespread application of such methods would facilitate access to the underlying digital data has not been fully achieved. The underlying datasets for many published studies are not readily or freely available, introducing a barrier to verification and reproducibility, and the reuse of data. There is no current agreement or policy on the amount and type of data that should be made available alongside studies that use, and in some cases are wholly reliant on, digital morphology. Here, we propose a set of recommendations for minimum standards and additional best practice for three-dimensional digital data publication, and review the issues around data storage, management and accessibility.
Introduction Three-dimensional (3D) digital morphological data are commonly employed by palaeontologists and biologists in research. In palaeontology and anthropology, the widespread application of tomography (especially X-ray computed tomography, CT), laser and structured light scanning, and photogrammetry has revolutionized the study of morphology  [1] [2] [3] [4]. In biology, optical microscopy, magnetic resonance imaging (MRI) and contrast-enhanced CT are important tools for investigating soft-tissue anatomy  [5] [6] [7] [8] [9] [10]. The revolution brought about by these technologies has increased the amount and detail of anatomical information recovered from fossil and living organisms, transforming the nature of scientific enquiry in related fields. The resulting datasets are often reconstructed and presented as 3D digital models, which are themselves sometimes used in downstream analyses, including geometric morphometrics  [11, 12] , finite element analysis (FEA)  [13] , multibody dynamics analysis (MDA)  [14]  and computational fluid dynamics (CFD)  [15] , thereby facilitating quantitative tests of functional and evolutionary hypotheses  [3].