dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION Our study proposes a solution for enriching the gene atlas of the two "RefSeq" and "Ensembl" chicken reference databases. This involves initially gathering these databases and then, supplementing them with four additional multi-tissue gene model resources, after determining a successive order of addition based on gene model quality criteria. While the use of a unique gene modeling pipeline including all raw sequencing data would be the best solution, our approach offers a good alternative. Indeed, i) it unifies the two most used genome annotations as the MANE (Matched Annotation from NCBI and EMBL-EBI) project which currently focused on the human  32  ii) it retains the identifiers of both "RefSeq" and "Ensembl" for common gene loci, iii) it is faster than a de novo annotation, and is adaptable to major changes in successive versions of the reference databases.
Moreover, to facilitate the comparison between studies associated to different genome assemblies and genome annotations, we provided an identifier correspondence between galgal5 and GRCg6a to that of GRCg7b based on our previous gene-enriched model atlases anchored on the "Ensembl" genome annotation (v101 for GRCg6a; v94 for galgal5)  13, 33. This atlas increases the completeness of the chicken genome annotation, especially for lncRNAs, which are more difficult to identify than PCGs due to their low tissue-and condition-specific expression  8, 34, 35. However, as the vast majority of current gene databases for livestock species are based on short-read data, transcript models are poorly described, regardless of gene biotype, even if this tendency is greater for lncRNAs than for PCGs  10. As an example, across the six databases used in our study, the maximum median number of transcripts per lncRNA and PCG was one and three, respectively.
These numbers are lower than those observed in human, with three and seven transcripts in average per lncRNA and PCG, respectively  10, 34. On the other hand, the overlap rate between transcript TSS and CAGE peaks, which are far from 100%, even for PCGs, underlines incomplete transcript modelling. These models will be clarified with long-read technologies, whose shortcoming today is the ability to obtain sequencing depths comparable to short-read technologies, thus limiting their massive use for studies focusing on gene expression  36. Surprisingly, whereas the "Davis" database is the only one mainly based on long-read RNA-seq, we can note that this database has a poor overlap rate between TSS and CAGE for PCGs compared to other databases. Moreover, these lncRNAs, mainly mono-exonic, are generally located in the same strand of PCG introns, as for the "Inrae" ones.
Indeed, 30. 5% and 21. 1% of the lncRNAs of "Davis" and "Inrae", respectively are in this case, a higher proportion compared to the other databases which oscillated between 4 and 7% (Sup. Table  9 ). One interpretation could be that the low sequencing depth makes it difficult to build a full transcript model. Another limitation is that some gene loci can be erroneous, as illustrated in the manuscript, especially for lncRNAs that are on the same strand to a close PCG, and highly co-expressed. These lncRNAs could be in practice an untranslated transcribed region (UTR) of the PCG which are, as lncRNAs, challenging to model and need some complementary analyses [40, 41]. Therefore, PCR validation is required to verify the existence of such lncRNAs (i. e. , on the same strand to a close PCG) before further analyzing their functions using time-consuming molecular biology studies.
Nevertheless, gathering genome annotations from multiple databases gives access to numerous new lncRNAsprecisely 44,428 lncRNAs including all the 5,789 and 11,944 loci from "RefSeq and "Ensembl" -since these datasets cover various tissues and conditions. We then provide a gene annotation based on the expression across 47 tissues using 1400 samples from 36 datasets and found 81% of the gene models expressed in at least one tissue. As reported in the literature in cross-species analysis, lncRNAs are preferentially expressed in sexual tissues such as testis, potentially associated to a pervasive chromatin environment facilitating transcription of putatively non-functional elements enabling the emergence of new genes  39, 40  , and in a second time by tissue related to brain  1, [41] [42] [43].
Thus, the 40 tissues and 7 cell populations used in our study represent a strong resource. As an example, using a chicken dataset of 21 tissues, we showed in 2020  13  a tissue specificity rate of 25% for lncRNAs vs. 10% for PCGs, against 68% and 23% observed respectively in this study. Tissue specificity also depends on the relationship between analyzed tissues, which explains why some genes are specific to several tissues, often sharing a similar functions. We also provided a list of 4,206 genes with a sex-biased expression within six tissues corresponding to 19. 8% of the total expressed PCGs, a lower percentage than reported by the human GTEx consortium due to the higher number of analyzed tissues (37% of all genes with 44 tissues,  44  ). Interestingly, 80% of sex biased genes are tissue-specific (sex DE observed in a single tissue), suggesting tissue-dependent regulation, even if this percentage is likely over-estimated in our study due to the low number of analyzed tissues (n = 6).
In our study, we observed an overevaluation of intragenic lncRNAs, which may be explained by the use of a long-read sequencing database, limited in depth. Focusing on intergenic genes and as shown in the literature  1, 54  , an enrichment in "same-strand" is observed. LncRNAs involved in such configurations should be considered with caution, since, as illustrated in the manuscript, some of them are part of a not well-modeled PCGs. Indeed, a lot of PCG isoforms are still poorly annotated, especially for non-model species. For example, as shown by Lagarrigue et al. , 2021  34  , for a stable number of gene models, the number of PCG transcripts oscillates between 28,000 and 50,000 for farm species while it exceeds 100,000 for mouse and 150,000 for human. A very high co-expression value across tissues (or intra tissue according to the study) and a low distance between gene models can be considered as a distrust indicator.
Gene-enriched atlas construction Origin of the six genome annotations. Gene models used to build the enriched genome annotation come from 6 genome annotations, all based on multi-tissue resources: i) both reference genome annotations according to the GRCg7b assembly: "RefSeq" v106  67  and "Ensembl" v107  68  , this latter has integrated the GENESWitCH project data; ii) both gene model datasets from FAANG pilot projects  69, 70  according to the GRCg6a assembly (GCF_000002315. 5): the FR-AgENCODE project  71   73. For genome annotations produced on a previous assembly, a remapping to GRCg7b was performed using the NCBI genome remapping service  74. Prioritization criteria. CAGE data used to prioritize the different gene models come from the FANTOM5 project  75. Peaks coordinates considered as robust according to the project were converted from galgal5 (GCF_000002315.
4) to GRCg7b using the NCBI genome remapping service  74. The transcript is then considered to be well modelled in 5' if its TSS overlap a peak within +/-30bp. All genome annotations previously presented were added sequentially considering gene model quality characteristics, the concordance with CAGE peaks, and the popularity of each databases as presented in the "Results" part, namely: 1-"RefSeq"; 2-"Ensembl"; 3-"FrAg"; 4-"Davis"; 5-"Inrae"; 6-"Noncode" (Figure  6A ).
Biological sample used for gene expression 36 datasets including a total of 1400 samples were used to represent the 47 tissues composing the atlas. As these datasets are publicly available (on SRA and/or ENA), the project numbers and the number of samples are available in the Sup. Table  4. The 47 tissues and their respective four letter abbreviations are: adipose tissue (adip), blood (blod), bone marrow derived macrophages (bmdm), brain (brai), bursa of Fabricius (burs), caecal tonsil (cctl), cecum (cecm), chorioallantoic membrane of an embryo (chor), colon (coln), cerebellum (crbl), cortex (crtx), dendritic cell (denC), duodenum (duod), embryon (ember), feather (feat), gizzard (gizz), Harderian gland (hard), heart (hert), hypothalamus (hypt), ileum (ileu), isthmus (isth), jejunum (jeju), kidney (kdny), liver (livr), lung (lung), lymphocyte B (lymB), lymphocyte T CD4 and CD8 (lymT), magnum (magn), monocyte, (mono), breast muscle (mscB), IEL-NK celles (nkil), optic lobe (optc), ovary (ovry), pancreas (pcrs), pineal gland (pine), pituitary (pitu), proventriculus (pvtc), retina (rtin), skin (skin), spleen (spln), testicule (test), thrombocyte (thro), thymus (thym), thyroid gland (thyr), trachea (trch), uterus (uter) and utricle (utri).
Differential gene expression between sexes First, the genes "expressed" in each tissue of each project for which at least 8 birds per sex were available were identified. The tissues and projects concerned were "hard -PRJNA484002", "burs -PRJEB23810", "bmdm -PRJEB34093", "bmdm -PRJEB22373" and 'livr, blod, adip -PRJEB44038". A gene was considered as expressed if the normalized TPM and TMM expressions were ≥ 0. 1 and if the read counts was ≥ 6 in at least 80% of the samples of one sex. Then, the differential expression (DE) analysis using the raw counts of the expressed genes previously selected was performed using the R package edgeR (v3. 32. 1)  80  based on a generalized negative binomial model for model fitting. The "edgeR-Robust" method was used to account for potential outliers when estimating per gene dispersion parameters  83.
miRNA expression in human The "miRNATissueAtlas2" database was exploited to quantify the expression of miRNA for human [51]. Because of the difficulty in associating the orthologous miRNAs between the chicken and the human, the expression of the miRNA precursor was used. Classification according to the closest feature PCG, lncRNA, miRNA and snRNA transcripts were classified relatively to their closest PCG and lncRNA transcript using the "FEELnc_classifier" function of FEELnc v. 0. 2. 1 with a maximum window of 100 kb (default setting)  85. The classification for gene models was performed by combining the transcript results and the "tpLevel2gnLevelClassfication" function from FEELnc.
DATA AVAILABILITY RNAseq data are publicly available at https://www. ebi. ac. uk/ena/browser/home and the corresponding project accession number are provided in the Sup. Table  4. Genome annotation files are publicly accessible as referenced in the "Methods" section. Data generated during this study are included in this published article (and its Supplementary Information files), on the https://www. fragencode. org/lnchickenatlas. html website and interactively using the https://gega. sigenae. org/ tool.
involving 11 tissues represented by 2 males and 2 females by tissue and the FarmENCODE project including 15 tissues with 1 male and 1 female; iii) and two other datasets including gene models from the previous atlas as presented in Jehl et al. , 2020 13  produced according to the galgal5 assembly (GCF_000002315. 4) and NONCODE v6. 0 72  including only non-coding gene models from the literature and other public databases according to the galgal4 assembly (GCF_000002315. 2). Contrary to all projects which used short-read sequencing, FarmENCODE includes samples sequenced with Oxford Nanopore long read Technology as presented inGuan et al. , 2022.
(a-b) Conservation of the genomic configuration (left) and expression profile in TPM (right), between the 47 chicken tissues (top) and the 53 human GTEx tissues (bottom). Mir expression is shown in the yellow rectangle. (a) MIR122HG gene, host of mir122 identified in human, has an equivalent locus in the chicken reference databases but is unnamed. (b) MIR219A2HG gene, host of mir219a2 and mir219b identified in human, has an unnamed equivalent locus in the extended chicken annotation but not in the reference databases. (*) indicates the old gene identifier for the human "RefSeq" database which is no longer used, the gene model being removed. (c) Classification of lncRNAs (top) and PCGs (bottom) according to their closest PCG and co-expression. SS. up: Same strand up, SS. down: same strand down.
Acknowledgements We are grateful to  M. -L. Parmentier  and  V. Van De Bor  for sharing fly stocks. This work was supported by the  Association Française contre les Myopathies (AFM)  ( Trampoline  grant  17683  and MyoNeurAlp network). This research was also financed by the  French government IDEX-ISITE  initiative  16-IDEX-0001. We also thank the  CLIC facility (Clermont Imagerie Confocale).
Generation of contact maps Contact maps were constructed as described previously  (Lioy et al. , 2018). Briefly, each read was assigned to a restriction fragment. Non-informative events such as self-circularized restriction fragments, or uncut co-linear restriction fragments were discarded, as in  (Cournac et al. , 2012). The genome was then divided into 5 kb units, and the corresponding contact map was generated and normalized through the sequential component normalization procedure of SCN (based on the sequential component normalization; https://github. com/koszullab/E_coli_analysis)  (Lioy et al. , 2018). Contact maps were visualized as logarithmic matrices to aid in visualization.
Search strategy/methods A thorough search for the scientific literature on mushroom bioactive compounds against cancer was undertaken in electronic scientific databases, such as PubMed, Embase, Scopus and Web of Science databases. The search was performed using the keywords such as 'mushroom', 'cancer', 'mitochondria', 'tumor', 'bioactive compound' and 'mushroom-mediated mitochondrial apoptotic pathway'. Studies from the last 10-12 years were taken into consideration. The criteria for the selection of specific studies were as follows: (a) mitochondrial-mediated anticancer mechanisms of mushroom compounds; (b) mushroom bioactive components that were tested in vitro (cell models) and in vivo (mice and rat models, case studies, randomized controlled trials) models (Fig. 1 ). Results obtained with only full versions of English-language articles were included.
gov = 56 Antrodia Aqueous extract NCT01287286 Ming-Yen Tsai 2016 2016 Metastatic Randomized Completed ClinicalT cinnamomea adenocarcinoma clinical study rials. gov n = 37 Coriolus Extract NCT00647075 P. Gascon 2008 2010 Breast Phase IV Unknown ClinicalT versicolor cancer n = 60 rials. gov Extract NCT00680667 C. Torkelson 2008 2017 Breast cancer Phase I n = 11 Completed ClinicalT rials. gov NCT02568787 J. E. Lewis 2015 2017 Breast cancer Phase I n = 23 Completed ClinicalT rials. gov Grifola frondosa Extract, NCT02603016 S. Jiao 2015 2017 Lung and Breast Phase I n = Completed ClinicalT (Dicks. ) Gray Polysaccharides neoplasms 480 rials. gov Ganoderma Spore vs NM X. Jin 2012 2012 Various types Randomized, Completed ClinicalT lucidum mycelium cancer clinical trial rials. gov n = 373 Lentinula edodes Genistein NCT00269555, R.
Hackmanand NM NM Prostate cancer, Open pilot Completed ClinicalT Combined NCT01018381 M. H. Bang Hepatocellular study rials. gov Polysaccharide carcinoma and n = 10 (GCP), hepatitis B and C Arabinoxyl infection Omphalotus A semisynthetic NCT00124527, E. Inc. , W. Yeo, NM NM Thyroid cancer, Phase 2 Completed ClinicalT illudens derivative of NCT00062257, G. A. Sarosy Metastatic gastric n = NM rials. gov illudin S NCT00019552 cancer, Persistent epithelial ovarian cancer Omphalotus Illudin S NM C. Munoz 2022 2022 Prostate cancer Phase II Completed ClinicalT nidiformis (Irofulven) n = NM rials. gov Pleurotus Oyster extract NM D. 2016 2016 Various types Double-blind, Completed ClinicalT cornucopiae Krishnamoorthy cancer placebo rials. gov controlled study n = 20 Schizophyllum Schizophyllan NM H.
U. 1985 1985 Gastric cancer Phase I n = Completed ClinicalT commune (SPG) Nakao 154 rials. gov Trametes Krestin, PSK, PSP NCT00680667 C. NM NM Breast cancer Phase 1 Completed ClinicalT versicolor Torkelson n = NM rials. gov Soy Powder+ Extract NCT00269555 R. Hackman 2005 2010 Prostate cancer NM Completed ClinicalT Grifola frondosa n = 62 rials. gov (Dicks. ) Gray Rice bran+ Extract NCT01018381 M. H. Bang 2009 2009 Liver cancer NM Completed ClinicalT Grifola frondosa n = 130 rials. gov (Dicks. ) Gray n participants; NM Not mentioned.
Numerical study design We used the two-parameter logistic growth model introduced in section 2. otherwise. This setting corresponds to defining a burn-in phase of 1300 iterations aimed at converging quickly to the neighborhood of the maximum likelihood estimator, followed by a refining phase in which the estimation is smoothed by decreasing the step sizes. Note that we use here a large number of iterations to guarantee good convergence of the algorithms on all simulated datasets; however, in practice, we observe that the convergence is reached far before the end of the 1500 iterations. 3 Results.
Prediction of genetic effects The results obtained for the predictions of the genetic effects under both experimental scenarios are listed in Table  1. To quantify the effect of the parameter estimation error on the quality of the prediction, we computed both the predictions obtained using the true values of the parameters (column θ known) and those obtained with our complete June 7, 2023 13/45 methodology, which requires estimating the parameters by SAEM in the first step (column θ estimated ). In both cases, the average squared differences between true genetic effects and their predictions were computed for the 50 simulated datasets. We observed that the quality of predictions is always better when θ is known, that is, when the prediction is not affected by any parameter estimation error.
Real data analysis The dataset was composed of N p " 752 plots of N v " 188 varieties observed at 10 time-points. Some data transformation was required before applying the NLMEM approach to the 2017 Tottori UAV data. The growth dynamics represented in In the growth model, because it is applied to the logarithm of height measures, φ i1 represents the maximum expected log height of the plant, and expp´φ i2 q is the growth rate. The effects of irrigation conditions and genotypes (varietal differences) on the growth mechanisms were studied using model (4) on pφ i1 , φ i2 q. In this model, µ A and µ B are the averages of φ i1 and φ i2 , respectively, under drought conditions, and δ A and δ B are the average effects of irrigation on these two quantities, respectively. As mentioned in Section 2. 2.
As shown in the simulated data study, by varying the number of varieties and plots and the data collection scheme, observing enough varieties, plots per variety, and points on the growth curves is essential to ensure accurate parameter estimations and predictions of the genetic effects. While one can reasonably trust the results obtained on the soybean growth data analyzed in this paper, as suggested by the simulated results on datasets with similar structures, the data collection must be streamlined beforehand to be able to collect data of sufficient quality and ensure that the NLME approach provides reliable and robust results. Note that during the simulations, data from 2000 plots were used simultaneously to estimate parameters. This also demonstrates the scalability of the proposed method for large data-sets.
June 7, 2023 20/45 Recently, remote sensing has enabled the highly efficient measurement of plant and animal growth over time. Furthermore, the determination of genome-wide markers has become more efficient, making it possible to measure both genome-wide polymorphisms and growth for a large number of genotypes. Combining the measurements obtained from such technological advances with the methods proposed in this study could genetically model the growth patterns of plants and animals, and help to understand, control, and predict their inheritance. The method proposed in this study is scalable to larger data sets owing to its computational speed. The codes for the method are available at https://github. com/madelattre/Scripts-soybean-paper and can be extended according to the conditions of the application in each research project.
Supporting information Computer codes The codes are available at https://github. com/madelattre/Scripts-soybean-paper S1 Appendix. Algorithmic details. The objective of this appendix is to provide the main technical elements for implementing the SAEM algorithm in model (  1 )-(  2 ). First, we introduce the compact matrix notations: • φ " Using that in model (  1 )-(2), y ij |φ i , u " N pgpt ij , φ i q, σ 2 q, June 7, 2023 of Metropolis-Hastings rather than one, and define φ rks i as the last accepted value after these iterations. • Choosing q i,θ p¨q as pp¨|u; θq allows to simplify the expression of the acceptance ratio α i,k. • In some situations, in particular when the number of observed plants per variety is small, the convergence of the algorithm can be improved by using with probability 1 ´αi,k.
Acknowledgments This study was supported by  JST CREST  (grant no. JPMJCR16O2 ) and  JSPS KAKENHI  (grant no. JP22H02306 ). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. We are grateful to the technical staff of the  Arid Land Research Center, Tottori University , and  Izumi Higashida  for the management of the field experiments. We would like to thank  Yoshihiro Ohmori ,  Yuji Yamasaki ,  Hirokazu Takahashi ,  Hideki Takanashi ,  Mai Tsuda ,  Yuji Sawada ,  Hisashi Tsujimoto ,  Akito Kaga ,  Mikio Nakazono , June 7, 2023.
Scenario 2 is used to study the effect of sampling frequency along the curves. We use for N v and N p a similar value as for the real data, N p " 800 and N v " 200. We simulate regularly spaced observations along the plant growth curves, with a total of either 45 (rich design) or 10 (sparse design) observations per curve. 50 datasets are simulated for each scenario and for each combination of N p , N v , and sampling frequency along the curves. The SAEM algorithm was applied to each of them to estimate the population parameters and then predict the genetic values of the N v varieties. Both algorithms are run for 1500 iterations in total, with step sizes for the stochastic approximation defined as γ k " 1 if k ă 1300 and γ k " pk ´1300q ´2{3.
LD-based recombination landscapes In order to achieve a fine resolution at a gene scale in LD-based recombination maps, we gathered highdensity polymorphism datasets in eleven flowering plant species (Table  1 , Table  S1 ). We identified species of interest, representing the diversity of plant genomes (small/large chromosomes, eudicots/monocots) and broad-scale recombination patterns (low/high genome-wide recombination rates), from a previous study  [1]. We also used a human dataset  [45]  to compare the results with a species with hotspots targeted outside genic regions due to the PRDM9 mechanism. To ensure the comparability of the results we run the same pipeline on this human dataset instead of directly using published recombination maps. After filtering SNPs (minor allele frequency < 0.
05, missing data per site < 0. 1, bi-allelic SNPs, genotype quality score ≥ 30), we kept only datasets with at least 0. 5 SNP/kb (range 0. 6-11. 8 SNP/kb, Table  1 ). Fine-scale recombination landscapes have been estimated with LDhat  [42]  using a custom pipeline (https://github. com/ThomasBrazier/ldhat-recombination-pipeline. git v1. 0). We checked for hidden population structure and sampled individuals within a single consistent genetic group with the highest polymorphism level, accordingly. We considered haploid genomes for selfing species. The population size parameter (θ π ) was estimated from the sampled population. For computational limits a maximum of forty diploid genomes per species (80 haploid genomes for selfing species, autosomes only) were sampled for LDhat analyses since the quality of estimates does not improve much over twenty individuals  [46].