The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.Long non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC ( LNC genes act through regulatory actions at every levels of gene expression, from the epigenetic modification of the chromatin12 to the regulation of transcription13 and protein translation14.Since their description at the end of the 1990s15 that likely are regulatory variants modulating gene expression, the molecular chain of causality linking the DNA variant to the gene to the phenotype remains scarce. A better knowledge of LNC genes and of their regulatory functions could therefore be of help to identify their target genes, and possibly link them with the phenotype of interest. In model species such as human and mouse, LNC are well characterized with 15,137 and 10,177 LNC genes, respectively in Ensembl database 106; although these estimates are nevertheless bound to increase16 in the continuity of works like the GENCODE v7 annotation of human LNC that manually annotated 9277 LNC in 201217, while the human NONCODE (v5) integrated more than 96,000 LNC at the end of 201718. In non-model species, such as farm species, LNC knowledge is largely incomplete. In the Ensembl release 94 reference database used in the present paper (October 2018), 4,643 LNC genes were known in chicken, none in cow and 361 in pig, versus 18,346; 19,994 and 22,452 PCG, respectively16. LNC annotation is one of the priorities of the Functional Annotation of ANimal Genomes (FAANG) initiative20. We and others recently reported LNC transcript sets for cattle, sheep, horse, pig, goat21 and chicken22.While it is well known that complex traits are mostly driven by variants located outside the coding regions23 and is a valuable model in the developmental biology research field24. Moreover, chicken is a species of great economic value, being one of the most consumed in the world, and representing a 300 billion dollars market25.Among farm species, chicken represents an interesting species, both for fundamental and applied research. It is used in evolutionary studies to evaluate the level of conservation of genomics feature among species26, NONCODE27 ALDB28 and FR-AgENCODE29. This enriched gene catalogue can be useful for the scientific community working on chicken, especially for those aiming at analysing gene expression rather than modelling genes, and therefore use a reference annotation such as Ensembl or NCBI at the gene level.The first aim of this study was to enrich the chicken Ensembl gene catalogue in long non-coding genes. For this we used new LNC genes that were computationally predicted in this study using 364 RNA-seq samples, as well as LNC genes available in the public databases NCBI30, we provide the level of expression for each LNC in the tissue in which it is the most expressed, giving an indication of how easy it is to study experimentally. Since the configuration of a LNC and its close PCG , such as the close divergent and antisense, can be an indicator of a regulatory role of the LNC on the PCG34, and therefore of an involvement in a common biological function35, we classified the LNC based on their genomic configuration with respect to their closest protein-coding gene (PCG) and screened for LNC:PCG pairs showing a significant co-expression. This latter approach is based on the “guilt-by-association” principle. It consists in grouping together genes (of known and/or unknown function) with a high expression correlation, with the hypothesis that this high correlation could be due to a common regulation, meaning that the genes play a role in the same biological process. We further searched for LNC hosting miRNAs genes, since the transcription of these LNC may result in the co-expression of their host miRNA36 and highlighted interesting cases suggesting that the LNC and the miRNA could participate to the same biological process. Finally, starting from the premise that a gene expressed in one or a few tissues likely plays a role related to these tissue functions37, we performed an in-depth analysis of LNC and PCG tissue specific expression. We showed that LNC are clearly more tissue-specific than PCG and pinpointed some specific features for the tissue-specific and divergent LNC:PCG pairs. The extended catalogue, in coordinates corresponding to the Gallus_gallus-5.0 and the more recent GRCg6a assemblies, as well as all the information regarding the configurations of gene pairs and gene tissue-specificity are available in the Supplementary material of this article, and also on the FR-AgENCODE website (http://www.fragencode.org/). The files in this website will be regularly updated when new information or new version of the chicken genome assembly is available.The second aim of our study was the characterization of the identified LNC. We analysed their expression profiles in 25 distinct tissue types. Since LNC genes are generally very weakly expressed compared to the PCGINRAGALG models) with the ones from NONCODE27, NCBI26 and ALDB28 public databases, to further extend the reference chicken Ensembl catalogue. The combination of these different catalogues is relevant since they are complementary as indicated by the low percentage of 1 bp-or-more overlapping transcripts between catalogues (from 3.2% to 29.1%)  of the transcripts: we calculated the percentage of LNC transcript models having their 5′ extremity within ± 30 bp of a CAGE peak, suggesting a good modelling, at least in the 5′-end. The corresponding percentages were 7.3%, 5.5%, 5.3% and 4.2% for INRA, ALDB, NCBI and NONCODE respectively, giving the order of addition of these sources. For the ± 100 bp criteria, these percentages were slightly higher . These values are low when compared to the 41% and 50% for the PCG transcripts using the ± 30 bp and ± 100 bp criteria respectively. Overall, these results are consistent with those of Derrien et al.17 who found 15% for human LNC transcripts versus 55% for human PCG transcripts, using a ± 100 bp criteria. Lastly, we added to the catalogue, genes that we have recently produced in the multi-species FR-AgENCODE project29. The process leads to the generation of an extended catalogue of 52,075 chicken genes, with 30,084 LNC and 19,545 PCG genes, including all the Ensembl genes  , with a higher density on the micro-chromosomes compared to the macro-chromosomes: 32 LNC and 36 PCG per Mb for the micro-chromosomes (chr 11 to 33) versus 22 and 12 per Mb respectively for the macro-chromosomes (chr 1 to 5).The Ensembl gene catalogue  contained 24,881 genes . Among these, 18,346 were annotated as protein-coding genes (PCG) and 4,643 as lncRNA (LNC) genes. Here we enriched this catalog by combining four complementary data sources: NCBI, ENCODE and ALDB LNC databases and the INRA catalogue newly generated in this study using 364 RNA-seq samples from 3 tissues. Using the pipeline “STAR – Cufflinks – Cuffmerge” and the “FEELnc” LNC prediction tool as described in Material & Methods section, we modelled 14,760 new genes composed of 1,199 PCG and 13,009 LNC genes. Among the LNC gene models, 7,265 were mono-exonics and 5,744 were multi-exonic. The expression of these new models was well above the background noise . Out of the 52,075 genes of the extended annotation, on average 80% of the genes were expressed in at least one tissue of one dataset: 17,438 PCG (89%); 22,000 LNC (77%) and 777 other biotypes (32%). Interestingly, in the 21 T dataset similar numbers of expressed PCG were found across tissues  while the number of expressed LNC are more variable    and 94%PCG  with expressions ≥ 0.5 TPM and 41% LNC  and 90% PCG  with expressions ≥ 1 TPM. The hierarchical clustering using the LNC expressions from the 21 T dataset shows biologically meaningful relationships among tissue types . Splitting the intergenic genes in two classes based on distance22 (“close”: ≤ 5 kb and “distant”: > 5 kb) shows an enrichment of close divergent compared to the convergent or same strand genes . These two observations regarding the divergent pairs are supportive of widespread bidirectional transcription.LNC models were classified with respect to the closest coding gene in the genome, according to the international gold-standard lncRNA classification provided by the GENCODE consortiumρ ≥ 0.55, pFDR < 0.05) with only 39 pairs having a negative correlation  gene shows a high expression correlation with the new INRAGALG00000007244 LNC model , followed by sequencing.As an illustration, one example of positive correlation of LNC:PCG pairs is displayed in Fig. del Fig. c, which del Fig. b top. Th36. Similarly, for small RNAs, we identified 42 LNC  hosting 58 small RNA genes, of which 48 snoRNAs (19% of snoRNAs) and 10 snRNAs (9% of snRNAs). These results are provided in Supplementary Data S2. Focusing on the 185 LNC hosting miRNA, we studied their co-expression with PCG across the 21 tissues of the 21 T dataset. Out of these 185 host LNC, 126 were expressed in at least one tissue of the 21 T dataset, and had between 0 and 939 correlated PCG among the whole dataset , all positively. The selection process described in Material and Method left a list of 10 miRNAs cited in the literature and for which and 75% of the correlated PCG had a HGNC (Supplementary Tables S3–S13). Among these 10 host LNC, we found two cases in which the host LNC was conserved in human and mouse, and the correlated PCG were enriched in functions similar to those of the miRNA. The first example is reported in Fig. 43  and to a lesser extent in digestive system-associated tissues , as showed in Fig. LAT2 and ITK), identified in both miRTarBase (with support type indicated as “weak”) and mirDB, and one target of human hsa-miR-155-3p (TXK) using mirDB. ITK was also identified as a target of the chicken gga-miR-155 in mirDB (target prediction score = 98). The new chicken INRAGALG00000001802 LNC model was confirmed by RT-PCR through a clear amplification using spleen RNA samples , followed by sequencing. The second case concerned gga-mir-124a-2, hosted in sense of intron by NONGGAG008930, which was expressed in the optical lope and cerebellum, and was correlated with 89 PCG enriched in genes associated to the development of the nervous system. In human, MIR124-3, of which gga-mir-124a-2 is a one-to-many ortholog with the same synteny, is hosted in antisense of intron by a LNC expressed in brain parts and testis in the GTEx dataset. In mouse, Gm27032 and Mir124a-3 are located on the same strand and have a small overlap. This LNC was well correlated with the human 1-to-1 ortholog of some of the 89 chicken PCG correlated with NONGGAG008930. Using Chickspress data, we found that gga-miR-124a, in particular the − 3p transcript, was expressed in cerebellum, cerebrum and hypothalamus of adult chicken. Among these 89 PCG, we found only 3 targets of the chicken or human miRNAs. All these information are summarized in Supplementary Figure S3.We classified miRNAs and other small RNAs  genes with their closest LNC using FEELnc, in order to find LNC that host such genes. We found that 0.6% of the LNC  hosted one or more miRNA, and that 16% of miRNA  were hosted by one or more LNC. This is consistent with the literature in human in which 17.5% of the miRNA are located in LNC44, shown by Kryuchkova et al.45, to have a good correlation between datasets, a good biological relevance and to be robust to normalization methods, compared to other metrics such as the Gini coefficient46 or PEM47. The τ metrics associates to each gene a score ranging from 0 to 1. Zero corresponds to a gene that would be expressed at the same level in every tissue, while 1 corresponds to a gene that is expressed only in one tissue. Genes with a τ close to 1 usually show an important expression difference between the tissue with the highest expression and the other tissues in which it can be also expressed. It is noteworthy that the relevance of an analysis related to tissue specificity depends on the number of tissues studied. That is why we conducted this study using the 21 T dataset composed of a high number of tissues. Figure GAPDH, τ = 0.38). The second hump corresponds to tissue-specific genes, with for example the Aquaporin 7  or the Urocanate Hydratase 1 . Finally, the peak at τ = 1 corresponds to genes expressed in only one tissue (tissue-exclusive), such as the Phospholamban  expressed only in the heart, which is the principal regulator of the Ca2/-ATPase of cardiac sarcoplasmic reticulum48. The LNC . These distributions suggest a higher tissue-specificity for the LNC than the PCG: 6% of PCG with TPM ≥ 1 and 13.4% of LNC with TPM ≥ 1 had a τ value ≥ 0.95. We further characterized the genes having a tissue-specific expression, i.e. with a τ ≥ 0.95, a stringent threshold of tissue-specificity39 by analysing at the same time their expression across tissues and the number of tissues in which they are expressed. The Fig. i.e. their expression in Top2, and therefore in all the other tissues, is very weak (Supplementary Table S14). Overall, we found that 25.2% of the LNC  and 9.8% of the PCG  were tissue-specific in the 21 T dataset. Interestingly, the analysis of the 5 tissues in the 5 T dataset, revealed similar patterns for LNC and PCG, with 43.1%  and 8.4%  of tissue-specific genes, respectively . As expected, these percentages of tissue-specific genes are higher than what was observed in the 21 T dataset, due to the smaller number of tissues in the 5 T dataset. In each tissue, we found between 11 (proventriculus) and 375 (ovary) tissue-specific PCG with a median of 67 (Supplementary Table S14), and between 46 (harderian gland) and 972 (duodenum) tissue-specific LNC with a median of 156  or “sexual reproduction” . In the heart, the heart-specific genes  or “blood circulation” . The expressions of three tissue-specific and one ubiquitously expressed are shown in Fig. GAPDH (most left) clearly shows a tissue-ubiquist expression pattern, with overall similar levels of expression across all tissues, albeit with a peak in muscle consistently with the literature49. AQP and UROC1 have similar τ values, even though the former is expressed in four tissues while the latter is expressed in 10 tissues. The high difference in expression level between the Top1 and Top2 tissues of UROC1  explains its tissue-specific classification. Finally, PLN clearly shows a tissue-specific pattern, being expressed in the heart only. More generally, for the tissue-specific genes (τ ≥ 0.95), the mean expression fold change between the Top1 and Top2 tissues is equal to 4.We studied tissue-specificity using the τ metricspFDR ≤ 0.05) provided in Supplementary Table S18, all of them related to liver function.We then investigated the liver-specific genes, this tissue being present in both 21 T and 5 T datasets. We found 247  and 1,307  liver-specific genes in the 21 T and 5 T datasets, respectively. We found that 208 genes (71 PCG and 130 LNC) were common to both datasets, meaning that the liver-specific genes from the 21 T dataset are almost included in those of the 5 T dataset. These genes are available in the Supplementary Table S17. Keeping only the genes with a unique HUGO identifier, the KEGG term enrichment analysis of the 54 common PCG revealed six KEGG terms . Interestingly, these genes are enriched in the divergent configuration. In fact, in the 100 pairs list, only 12 genes belonged to the divergent class and six of them (indicated in the text with an asterisk) are related to the GO term abovementioned. Five out of the 6 LNC:PCG pairs seem to be conserved as antisense or divergent configurations in human and/or mouse as shown in Fig. LHX5, HOXD3, TBX4 and in Supplementary Figure S5 for EMX1 with the human ENSG00000278060 LNC, and Hmx1 with the mouse ENSMUSG00000055944 LNC. Note that for SOX1, the closest LNC of human and mouse SOX1 is an LNC-OT, i.e. an LNC in the same strand of the SOX1 gene whereas in chicken both members of the LNC:SOX1 pair are in opposite strands. Even if the LNC sequences are lowly conserved between distant species50, we found six significant blast hits of length 41 to 253 in human and one sequence of length 296 bp in mouse similar to the one of chicken SOX1 divergent LNC at 5380 bp and 4184pb, respectively, upstream of human and mouse SOX1 gene on the opposite strand  and PCG  from 21 T dataset with the classification of LNC:PCG pairs, we found 100 pairs for which both members were tissue-specific and had the same “Top1” tissue (Supplementary Table S19). The GO term analysis of these 100 genes (of which 45 PCG with a known identifier) revealed molecular functions related to “DNA-binding transcription factor activity, RNA polymerase II-specific” , supported by 14 genes: Gallus_gallus-5.0 genome coordinates, we also generated an extended version of the Ensembl v100 annotation of the new Gallus gallus genome reference (GRCg6a), comprising genes present in our present catalogue that did not overlap genes from the Ensembl v100 annotation. The methods used for this update are described in details in the Material and Methods section. As a result, we added to the 24,356 genes of the Ensembl v100 annotation  18,994 LNC gene models from the databases presented in this work, marked in the Supplementary Data S2. In addition, 3,179 models from the v94 annotation that were removed but that were mapped in the GRCg6a genome assembly. As a result, we generated an annotation comprising a grand total of 46,529 genes. Hence, users wishing to work on GRCg6a, can use this second GTF file available on the FR-AgENCODE website (http://www.fragencode.org/) to benefit from the Ensembl v100 enriched in LNC annotation.Since this work proposes a GTF build on the Ensembl v94 annotation in http://www.fragencode.org/), and they will regularly be updated, in particular when new assemblies are published. As a first update, we propose in this website an extended version of the Ensembl v100 annotation of the new Gallus gallus genome reference (GRCg6a), comprising genes present in this catalogue that did not overlap genes from the Ensembl v100 annotation, in GRCg6a coordinates.This work provides to the community an extended, LNC-enriched, gene catalogue of the chicken genome composed of 30,084 LNC, 19,545 PCG and 2,446 others genes, for a total of 52,075 genes. These data, in the form of a GTF file, are provided in Supplementary Data S1, and are ready to use by the community for RNA-seq expression analyses. We also provided a wealth of information in a table file . First, the configuration of the 30,084 LNC with respect to their closest PCG, plus the name of the PCG associated as well as the genomic distance between them. Second, for the 40,215 genes  expressed in one or both 21 T and 5 T datasets, we provide information relative to the tissue-specificity: the number of tissues in which the gene is expressed, the expression (in TPM) of the Top1 and Top2 tissues, the names of these tissues and the tissue-specificity values  in both datasets. For the LNC:PCG configurations, we also provide the spearman correlation of the expression for each gene pairs across the 21 and the 5 tissues of both datasets. All this information can be precious for researcher interested in one or several LNC to infer hypotheses about their expression and function. This GTF file, build on Ensembl v94 annotation, in Gallus_gallus-5.0 coordinates, and all related information are available on the FR-AgENCODE website 51, with a majority of intergenic genes (approximately 80%) of which 56% are in same-strand, the rest being in divergent and convergent configurations. Pairs in same-strand configuration should be considered with caution, since it is possible that the LNC is in fact a part of a not yet properly modelled PCG, especially when the gene couple is significantly co-expressed across tissues (as we show for 32% of the same strand pairs). Indeed, in non-model species, PCG isoforms are still poorly annotated. Only 38,118 isoforms are described in the chicken Ensembl v94 annotation for 24,881 genes in total, while more than 200,000 transcripts are modelled for 57,720 genes in total in the human Ensembl v94 catalogue. As an example, we recently showed that an LNC located at 978 bp in same-strand of SCD1 was in fact part of this gene52. However, this situation is also true for close, same-strand LNC, as exemplified in Fig. We then performed different types of analysis in order to provide some functional annotation to these LNC. The analysis of the LNC:PCG configurations revealed different types of pairs, in proportions that were consistent with the literature in human53 or even a regulation of the PCG by the LNC35. In both cases, we can hypothesize that the LNC is involved in the same biological processes as the PCG55. These hypotheses inferred by co-expression combined to gene configurations should allow to better orientate molecular biology experiments in order to better understand the biological role of the LNC of even of the LNC:PCG pair. Interestingly, we found a significant enrichment of co-expressed pairs among the divergent LNC:PCG configuration (14% of these pairs) and among the sense of introns configurations (49% of these pairs) compared to the convergent configuration (7% of these pairs). This enrichment is more limited but remains significant for the antisense configuration with 9% of these co-expressed pairs, compared to the convergent configuration. Divergent pairs suggests the presence of a bidirectional promoter that regulates both genes56, or even an effect of the LNC on the PCG expression32, even though the precise mechanisms remain unclear. We highlighted an example with the divergent LNC:PXDC1 pair, conserved between chicken and mammals. PXDC1 is a gene for which the function is still poorly known. It was found to be repressed in the liver of mice and rats exposed to a pollutant of the dioxin class 57. Furthermore, it was shown that transcriptional activation of PXDC1 using a CRISPR activation system increased the survival of an acute myeloid leukaemia cell line exposed to cytarabine, a molecule used in standard chemotherapeutic mix for this leukaemia, hence increasing the resistance of the cells to the chemotherapy58. The high expression correlation between PXDC1 and its divergent LNC suggests that the latter plays a role in the same biological processes as PXDC1. Antisense LNC, for their part, have also been shown to regulate their host gene expression, at both the transcriptional level, by the inducement of chromatin remodeling33, or at the post-transcriptional level34. Finally, sense intronic can arise from the splicing of a PCG or be produced from independent transcriptional unit59. They may regulate the expression of their host PCG, which are also associated to transcription regulation60, as shown by Guil et al., who observed a down-expression of the gene SMYD3 with the over-expression of a LNC from its intron61. In this study, we described 214 and 99 significantly co-expressed LNC:PCG pairs in antisense and sense intronic configuration respectively.The divergent or genic configuration for LNC:PCG pairs associated to their co-expression may allow to formulate hypotheses on the function of LNC. Indeed, a significant expression correlation between two genomically close genes is an argument for the existence of a common regulation17, for which 0.11% of the pairs LNC:PCG showed correlation lower than − 0.5, and in dog39, for which this percentage was 0.71%. Taken together, these results shared among different species suggest that LNC tends to act as positive, rather than negative, regulators or cofactors of the transcription of their closest gene, although there are well-known examples, such as HOTAIR62 or XIST63, that show that LNC can induce gene silencing. Interestingly, this trend was also found in PCG:PCG couples, for which only 0.15% (6 pairs) out of the 4,125 significant correlations were negative (3 divergent and 3 same-strand).Interestingly, across all the LNC:PCG configurations, we found only a small number of pairs for which the correlation was significantly negative with 1.2% (39 pairs) out of the 3,355 significant correlations (with 0 for sense intronics and 12 for both the divergent and convergent configurations). Such a result was also observed in human36. LNC hosting miRNA are thought to act as pri-miRNA , which are the precursors of pre-miRNAs, themselves precursors of miRNAs64. Among the 10 LNC hosting miRNA chosen here to be deeply analysed because of their functional annotation in the literature, we found two cases in which the LNC hosting a miRNA might be associated to the same biological process as the miRNA. Indeed, we observed that mir-155, which is involved in the normal immune function65 and also expressed in immune-related tissues in chicken is hosted by a LNC that we also found to be highly expressed in the immune-related tissues and correlated in expression with multiple immunity-related genes in both chicken and human. Such results are consistent with a recent work conducted by Maarouf et al.66 who showed in a human cell line that its host LNC, MIR155HG participate to the immune response to the influenza A virus (IAV). What is more, Maarouf et al.66 showed that MIR155HG deleted of the sequence of MIR155 still significantly suppressed the IAV replication, clearly indicating that this LNC had an action on its own, not only by harbouring MIR155 sequence. While the immunity-related function of miR-155 and now of the LNC hosting this miR are known, the relationships between these two genes is not clear. The low number of miR-155 targets detected among the correlated genes tends to suggest that mir-155 does not directly act on the immunity-related PCG co-expressed with its LNC. The second example concerned gga-mir-124a-2, the human ortholog of which is MIR124-3, which is involved in neurogenesis68. We observed that gga-mir-124a-2 was expressed in different brain-parts in adult chicken, and found that its LNC hosting gene in chicken and human was also expressed in the brain-related tissues. In both species, the LNC were also correlated in expression with genes involved in the nervous system. Supplementary experiments are needed to better understand mechanisms underlying such PCGs and miR155 host LNC or of miR124 host LNC co-expression across tissues.We found in chicken the same proportion of miRNA hosted by LNC than in human, 16% compared to 17.5%.17, using 16 tissues and counting the number of tissues in which each gene was expressed, found that approximately 27% of the LNC and 4% of the PCG were expressed in one or two tissues, while Cabili et al.30, using 24 tissues and cell types, found more extreme values with 78% of LNC and 19% of PCG found to be tissue-specific using an entropy-based measure. Melé et al.69, using the GTEx data composed of 43 healthy tissues and 13 brain subregions of cells lines also commented on the fact that PCG are generally ubiquitous while LNC are more typically tissue-specific. In dog, Le Béguec et al.39, using 26 tissues and the τ metrics with a threshold set at 0.95, found that 44% of LNC and 17% of PCG were tissue-specific. It is noteworthy that the absence of the testis in our analysis may be responsible for an under-estimation of the total number of tissue-specific genes, since this tissue is well known for having a specific expression. Indeed, Melé et al.69 found that ~ 95% of the approximately 200 genes expressed in only one tissue were expressed in the testis.Our analysis of the tissue-specificity of the gene expression showed that LNC are more tissue-specific compared to PCG: 25.2% of LNC versus 9.8% of PCG for the dataset with 21 tissues, using the τ metrics with a threshold set at 0.95. The tissue-specificity of genes depends on multiple factors, such as the number of tissues analysed or the tissue-specificity metrics used. Nevertheless, the higher tissue-specificity of LNC compared to PCG is consistent with previous works conducted on several species, even if the ratio of tissue-specific genes in each biotype is variable. In human, Derrien et al70, “Metabolic pathways”, “Histidine metabolism”71, “Tryptophan metabolism”72, “Steroid hormone biosynthesis”73 and “PPAR signalling pathway”74.Concerning the liver, which is the only tissue common to both independent 21 T and 5 T datasets analysed in this work, the high intersection of liver-specific genes detected between both datasets shows a good reproducibility of the results. This result shows the robustness of the gene tissue-specificity despite the technical and biological variations between the two datasets, from RNA extraction to sequencing, and from lines to ages or breeding conditions. These common genes were in enriched liver functions as “Complement and coagulation cascades”SOX1-OT), but for which blast hits were detected in divergent configuration, suggesting the existence of another LNC. Hence, it is difficult to conclude on the conservation of ENSGALG00000035091 even if a high co-expression and similar tissue-specificity between the human SOX1 and SOX1-OT genes was observed. Second, the tissue specificities observed in chicken are consistent with the one observed in human using GTEx data for LXH5 and SOX1 as brain-specific, TBX4 as lung-specific and EMX1 as kidney-specific. HMX1 is found to be skin-specific in chicken while it is testis and brain specific in human. Interestingly, we found HOXD3 to be kidney-specific in chicken while in human its “top tissues” are part of the female reproductive tract, just before the kidney  could to be a method of choice to discriminate between cell types and therefore minimize heterogeneity bias. However, scRNA-seq is currently limited in the number of detected transcripts, estimated to only 10–20% of the mRNA molecules actually present83, and seems to perform poorly in the detection of low expressed genes, which is the case of LNC. These technical limitations will therefore have to be alleviated for scRNA-seq to be used in LNC detection and modelling.Overall, the ability to obtain a comprehensive catalogue of the LNC in a species depends mainly on the number of tissues available, as shown in this work and in the literature.84 recently demonstrated the causative role of a point mutation in the exon of a LNC, located in divergent configuration with a PCG involved in neural development, in a form of neuropathy. Both genes appeared to be co-expressed, and the point mutation seemed to affect the binding of regulatory elements, leading to the reduction of their expression.As a conclusion, this study aims at providing an improved gene annotation enriched in LNC for the chicken species, information on their genomic configuration with respect to PCG and miRNA, and transcription patterns across tissues for the both PCG and LNC of the annotation. Such a catalogue and information associated will be useful to the community to work on LNC, for example, to unveil the molecular chain of causality that links variants located outside coding regions and phenotypes of interest. As an example, Plassais et alFor the gene modelling , we used 364 RNA-seq samples from three chicken tissues  with different physiological stages of broilers and layers, for which the raw RNA-seq data are available under the SRP079637 and PRJEB28745, PRJEB34310 and PRJEB34341.Gallus_gallus_5 reference genome, version v87 (December 2016) to v94 (October 2018). This dataset was composed of 21 tissues with 8 replicates per tissue with, on average, 15 M of reads per replicate representing one age (16–17 weeks old), one sex  of the same J-line strain. It is referred to as the “21 T dataset” in the “For the expression study, we used 5 tissues  from a Rhode Island Red line with a minimum of 24 biological replicates per tissue (data available under the PRJEB28745). Such a dataset with an average of 80 M reads per biological replicate is referred to as the “5 T dataset” in the Results section. We also used 168 RNA-seq samples from the PRJEB12891, used by Ensembl for annotating the chicken All the animal experiments used in the present study were approved by the local ethical committee in animal experimentation of Val de Loire, France  and by the French Ministries of Higher Education and Scientific Research, and of Agriculture and Fisheries . Animal experiments were conducted at the experimental farm PEAT under license number C37-175–1 for animal experimentation, in compliance with the European Union Legislation..22 and Jehl et al.85. Briefly, tissues were sampled immediately before (blood) or after slaughter  and stored appropriately. RNAs were extracted following the kits or reagents manufacturer’s instructions and stored at − 80 °C. Total RNA was quantified using a NanoDrop ND-1000 spectrophotometer . The A260/280 and A260/230 ratios were greater than 1.7 in all samples ensuring the RNA purity. The RNA quality was controlled using an Agilent 2100 bioanalyzer . The RNA integrity numbers were ≥ 7 for the adipose and the whole blood tissue, ≥ 8 for the hypothalamus, ≥ 8.5 for the liver and embryos. The sequencing was conducted in a stranded, paired-end 2 × 150 bp reads, on a HiSeq2000 or HiSeq3000 (Illumina).RNA extraction and RNA sequencing were performed as described in Muret et alGallus_gallus_5 reference genome using STAR86 v.2.5.2b, following the multi-sample 2-pass mapping procedure87, with the Ensembl v92 GTF file as input file for the generation of genome indices as described in Muret et al.22. The new transcript models were constructed as described in Foissac et al.21. Briefly, after read mapping and CIGAR-based softclip removal, each sample alignment file (BAM file) was processed with Cufflinks 2.2.1 with the max-intron-length  and overlap-radius (5) options, guided by the reference gene v92 annotation (–GTF-guide option). All cufflinks models were then merged into a single gene annotation using Cuffmerge 2.2. with the –ref-gtf option. Using the 364 RNA-seq samples, we modelled 25,085 putative new genes in addition to the Ensembl genes. To ensure reliability of the models, loci were selected based on their expression and their reproducibility across samples. First, 21,520 genes were selected with an expression greater than or equal to 0.1 TPM (Transcripts Per Million), a common threshold when working on lncRNA genes89, known to be lowly expressed. However, we observed that some models exceeding this threshold were supported by one or two reads at most, whichever the replicate. Hence, in order to discard such models, we applied a more stringent criterion, consisting in keeping only the models supported by a least five reads in the samples of a given tissue, similarly to de Goede et al.89. These selection steps resulted in a dataset of 14,760 models, hereafter called INRAGALG.RNA-seq reads were trimmed using cutadapt version 1.8.3. Reads were then mapped on the Ensembl 90, as described in Muret et al.22. Briefly, FEELnc codpot module calculates a coding potential score (CPS) for the assembled transcripts based on several predictors (such as multi k-mer frequencies and Open Reading Frame coverage) incorporated into a random forest algorithm91. In order to increase the robustness of the final set of novel lncRNAs and mRNAs, the option –spethres = 0.98 was set. The FEELnc model was trained with the chicken PCG and LNC transcripts from the chicken Ensembl v92 annotation.The discrimination between coding and long non-coding genes was realized using the FEELnc codpot module of the FEELnc  tool92. These artificial loci had the same length distribution as the LNC genes and were positioned at least 5 kb out of the closest known transcribed regions.The background noise corresponds to the expression of a set of artificial loci randomly distributed across chicken chromosomes 1 to 33 using the bedtools shuffle function from the BEDTools suiteEnsembl source: the Gallus_gallus_5 Ensembl v92 reference annotation was downloaded from the Ensembl FTP website106 as a GTF file. INRA source: the INRA GTF file was obtained and filtered as described in the two previous sections. ALDB source: the ALDB28 v1.0 database containing 5,752 LNC genes was downloaded from the FANTOM project Zenbu viewer93 page, as a BED file in Gallus_gallus_5 version. NONCODE source: the NONCODE v5.0 database containing 9,322 LNC genes was downloaded from the appropriate website94, the form of a BED file being in Galgal4 version. The file was therefore translated into Gallus_gallus_5 version using the LiftOver tool from UCSC95, and the chromosome names were converted into Ensembl chromosome names. NCBI source: the NCBI database v103 containing 5,738 LNC genes was downloaded from NCBI FTP website96 in the form of a GFF3 file in Gallus_gallus_5 version. Chromosome names were converted into Ensembl chromosome names. Finally, the GFF3 file was converted into a GTF file as well as the BED files from ALDB and NONCODE sources. FR-AgENCODE source: the FR-AgENCODE annotation containing 6,089 LNC genes was obtained from Foissac et al.21.Aggregation of the sources: we sequentially added the gene models from each database to a growing catalogue, keeping only the gene models that had no overlap with a gene already present in the growing catalogue. Two gene models were considered as overlapping if one or more of their transcripts were on the same-strand and had one or more nucleotides in common. The gene model overlap between sources was assessed using the bedtools intersect function from the BEDTools suite92. The different sources were aggregated to the Ensembl annotation in the order presented in Results, which was determined by calculating the percentage of LNC transcript models having their 5′ extremity within a CAGE peak38, suggesting that these transcripts were properly modelled, at least in their 5′-end. Note here that this sequential strategy was chosen since we observed that total aggregation of the overlapping models using the cuffmerge tool tended to create chimeric models composed of one or more known PCG Ensembl genes by dubious LNC models from another database.90 with default settings . Briefly, using (i) the LNC transcript models from the extended annotation and (ii) the PCG transcript models, the tool uses a 100 kb sliding window and classifies each LNC transcript using its location and orientation relative to the closest PCG transcript. The results distinguish LNC types (whether genic or intergenic), then subtypes  and finally locations . If no PCG were found within the sliding window, the LNC is considered as “unclassified”. From this transcript level classification, we generated a gene level classification. Generally, the different transcripts of a given LNC gene have the same classification relative to PCG. However, for the rare cases (4%) in which transcripts of a given LNC gene have different classifications relative to one or more PCG, we indicated these conflicts. The FEELnc classifier module was also used to classify the miRNAs present in the Ensembl annotation with respect to their closest LNC for identifying LNC hosting one or several miRNAs.Long non-coding transcripts were classified relatively to the closest protein-coding transcript using FEELnc classifier toolGallus_gallus_5 reference genome using STAR86 v.2.5.2b, following the multi-sample 2-pass mapping procedure, with the extended GTF file as input file for the generation of genome indexes step as described in Muret et al.22. Samples were analysed by tissue. FASTQ files were previously trimmed for Illumina adapter using TrimGalore version 0.4.5. Expression was quantified with RSEM97 v.1.3.0, using the extended GTF file at the gene-level39. This workflow is part of a snakemake98 pipeline, available at Ref.99. Each gene was considered as expressed in at least one tissue of 5 T or 21 T dataset using the criteria TPM ≥ 0.1.FASTQ files were mapped on the Ensembl For the 21 T dataset, the 21 tissues and their four letters abbreviations are: bursa of Fabricius (burs), cecal tonsils (cctl), cerebellum (crbl), duodenum (duod), adipose tissue around the gizzard (fatG), harderian gland (hard), heart (hert), ileum (ileu), kidney (kdny), liver (livr), lung (lung), breast muscle (mscB), optical lobe (optc), ovary (ovry), pancreas (pcrs), proventriculus (pvtc), skin (skin), spleen (spln), thymus (thym), thyroid gland (thyr) and trachea (trch). For the 5 T dataset, the 5 tissues, blood, adipose tissue, liver, hypothalamus and embryos, were abbreviated as blod, adip, livr, hypt and embr respectively.https://gtexportal.org/home/). Each gene expression was normalized as log10(TPM + 1), and the mean for each of the 53 tissue was calculated. The list of the 53 tissues is available in Supplementary Table S22.The version 7 RNA-seq TPM data was downloaded from the GTEx website  to 1 (gene expressed in exactly one tissue), using the following formula: let Tissue-specificity was assessed using the tau (τ) metric39, and corresponding to a ratio of 4 between the first and the second tissues with the highest expressions.A gene was considered as tissue-specific for τ ≥ 0.95, as done in previous studiesρ) between the expression values across tissues. P-values were corrected for multiple testing using the Benjamini–Hochberg method100. The false discovery rate was set to 0.05, corresponding to an absolute ρ value of 0.55 using the 21 tissues of the PRJEB12891 dataset, referred to as the “21 T dataset” in the Results section.For each LNC:PCG pairs detected, we computed the Spearman correlation (101 (https://string-db.org). Only the 1-to-1 human orthologous genes with a standardized HGNC name were submitted for the analysis. To investigate biological functions, we favoured KEGG terms when available, GO terms related to “Biological Process” otherwise. If no such enriched terms were found, we focused on the molecular functions of the genes using the GO “Molecular Function” terms.The enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) terms in each set of genes of interest was performed using the STRING tool43 in which 55 tissues and conditions are available, the list of which is provided in Supplementary Table S22. We further selected the LNC for which at least 75% of the correlated PCG had a HGNC and for which the hosted miRNA gene(s) were cited in the literature following a PubMed request of their name. Targets of the miRNAs were screened for in miRTarBase102 and mirDB103. For the latter, we only considered targets with a target prediction scores ≥ 80, which indicates a high confidence of the prediction.For each expressed LNC containing one or more miRNA gene, we calculated the expression correlation between the LNC and all the expressed PCG. The correlation threshold was set at | ρ |≥ 0.8, corresponding to a false discovery rate inferior or equal to 0.05 with the 21 tissues of the PRJEB12891 dataset, referred to as the “21 T dataset” in the Results section. MiRNA expression data were obtained from the Chickspress website2 solution (25 mM), 0.125 μl of GoTaq DNA Polymerase (5u/µl) , 0.5 μL of dNTPs 10 mM, 12.5 µl H20 and 1.25 µl of specific reverse and forward primers at 10 µM. Reaction mixtures were then incubated in a T100 Thermal cycler . The amplification products are then deposited on a 2% agarose gel and sent for sequencing (Genoscreen) to verify their location in the chicken genome. LNC were considered as validated if a PCR amplification was observed and their location in the genome was the expected one.The existence of different LNC models was assessed by RT-PCR and sequencing using RNA extracted from different chicken tissues. Each LNC RNA was validated using the tissue in which it was the most expressed. The PCR primers and hybridization temperature used are indicated in Supplementary Table S23 for each analysed LNC. Reverse transcription (RT) was carried out using the high-capacity cDNA archive kit  according to the manufacturer's protocol. Briefly, reaction mixture containing 2μL of 10 × RT buffer, 0.8μL of 25 × dNTPs, 2μL of 10 × random primers, 1μL of MultiScribe Reverse Transcriptase (50 U/μL), and total RNA (2 μg) was incubated for 10 min at 25 °C followed by 2 h at 37 °C and 5 min at 85 °C. Dilution RT reaction was further used for PCR. 5 µl of cDNA samples were mixed with 5 μl of GoTaq Flexi Buffer 5 × , 2μL of MgCl104 v0.11.0 and mapped to the GRCg6a assembly sequence using GMAP105 2015-11-20 with default parameters. If none of a gene's transcripts had a unique position (mapped on different chromosomes or on different strands), the gene and its transcripts were removed from the annotation. The genes from our catalogue in Gallus_gallus-5.0 coordinates for which even one transcript overlapped an Ensembl gene from the v100 annotation were removed. This insures that the annotation that we provide in GRCg6a positions is composed of the full Ensembl v100 annotation, enriched in LNC gene models from the present catalogue. This GTF generated in coordinates GRCg6a, as well as the GTF in Gallus_gallus-5.0 coordinates can be found the FR-AgENCODE website (http://www.fragencode.org/overview.html).Transcripts sequences were extracted from the Gallus_gallus-5.0 FASTA file using gffreadSupplementary Information.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Background: The addition of motivational strategies to a rehabilitation program is thought to enhance patient adherence and improve outcomes. However, little is known about how rehabilitation professionals motivate stroke patients during rehabilitation. The primary objective of this study was to provide a comprehensive and quantitative list of motivational strategies for stroke rehabilitation. In addition, we aimed to examine (1) whether professionals with more clinical experience used a higher number of motivational strategies, (2) the purpose for using each strategy, and (3) the information considered when choosing strategies.Methods: This descriptive, cross-sectional study was conducted using a web survey with a convenience sample of 407 rehabilitation professionals including physicians, nurses, physical therapists, occupational therapists, and speech-language-hearing therapists.Results: We received data for 362 participants. Fifteen strategies were found to be used by more than 75% of the respondents to motivate their patients. Almost all of the respondents reported that they actively listened to and praised their patients to increase patient adherence to rehabilitation programs. Respondents with more clinical experience tended to use a higher number of motivational strategies . For 11 of the 15 strategies selected by more than 75% of the respondents, the highest percentage of respondents reported that they used the strategies to make rehabilitation worthwhile for their patients. The majority of respondents reported that they decided which motivational strategy to use by considering comprehensive information regarding the patient health condition, environmental factors, and personal factors.Conclusions: The comprehensive list of motivational strategies obtained may be useful for increasing patient adherence to rehabilitation, especially for professionals with less clinical experience. Furthermore, our findings regarding the purpose for using each strategy and the information considered when choose strategies might help rehabilitation professionals to optimally utilize the motivational strategy list. Studies on stroke rehabilitation have recommended the use of intensive and repetitive task-specific practice, as well as aerobic exercise . Given tMotivational strategies such as feedback , 10, couMaclean et al.  conducteAs opposed to semi-structured interviews, the findings from quantitative surveys are generalizable to a larger population . TherefoThis study had a descriptive cross-sectional design. We used a convenience sampling web-based survey to obtain quantitative results from the participant perspective. The study protocol was approved by the appropriate ethics committee at the Hamamatsu University School of Medicine . Informed consent was obtained from all participants.Eligible participants were rehabilitation professionals including physicians, nurses, physical therapists, occupational therapists, speech-language-hearing therapists, or clinical psychologists currently working in rehabilitation. Participants were recruited with the cooperation of the 33rd conference of the Comprehensive Rehabilitation Ward Association and the 48th Annual Meeting of the Nagano Physical Therapy Association. To recruit participants, we set up a booth with laptops at each of the above-mentioned conferences. Professionals who were interested in participating in the study were able to access the survey website using the provided laptops. In addition, we distributed leaflets to professionals and displayed posters containing a brief description of the study and a hyperlink to the survey in front of the booth. Respondents were able to access the survey website using their own laptops, tablets, or smartphones. The first page of the survey informed participants about the total number of questions, the approximate time required to complete the survey, and the aim of the study. Participants were asked to report their professional category and years of experience working in stroke rehabilitation. Those who met the eligible criteria proceeded to the next page.We consulted existing guidelines and followed a checklist when preparing the survey , 16. We The survey items regarding motivational strategies were developed based on the clinical experience of the authors, the findings of related literature , 17–20, In the second section, for each motivational strategy that a respondent reported using, they were asked to state their aim when using that strategy. Specifically, they were asked to select from the following four purposes: (1) to increase the patient's interest in rehabilitation; (2) to make rehabilitation more worthwhile for the patient; (3) to help the patient gain confidence in performing a rehabilitation task; and (4) to increase patient satisfaction with the rehabilitation program. These purposes were based on the four sub-components of motivation proposed by the Attention, Relevance, Confidence, and Satisfaction (ARCS) model , 24. TheWe used the Cochran sample size formula to calculate the sample size. We set an alpha level a priori at 0.05, an acceptable error level of 5%, and a confidence interval of 95% . Consequp values < 0.05 were considered statistically significant.We used descriptive statistics to characterize the study sample and summarize the participant responses. The normality of distribution was tested using the Shapiro-Wilk test. We used Pearson's product-moment correlation coefficient or Spearman's rank correlation coefficient to test whether respondents with more experience used more motivational strategies. In addition, we used a stepwise multiple regression analysis to examine the relationship between the years of clinical experience and the number of motivational strategies used by each respondent, while controlling for potentially confounding variables. We included the respondents' gender, professional category, primary affiliation, and the patient phase of stroke recovery in the model as covariates. Dummy variables were used to incorporate categorical variables such as gender and professional category into the regression model. For the motivational strategies that were used by at least 75% of respondents , we condThis study was reported according to the recommended best practices and guidelines in the literature for the reporting of survey research , 22.The survey was conducted from February to July 2019. Approximately 4,150 rehabilitation professionals attended the two conferences. Among them, 407 professionals accessed the survey. Therefore, the response rate was ~9.8%. Forty-five respondents were excluded because they did not meet the eligibility criteria, withdrew consent to participate, or had missing data. Consequently, 362 respondents completed the survey. The flow of participants is shown in p < 0.001) .The percentages of respondents who used each of the presented motivational strategies are shown in < 0.001) . In addiFor 11 of the 15 motivational strategies that were used by at least 75% of the respondents, the highest percentage of respondents reported that they used the strategies to make rehabilitation worthwhile for their patients (51.3–31.6%) . For “coThe hierarchical cluster analysis produced three motivational strategy clusters . The firEach of the presented 11 items was selected by more than 75% of respondents as information that they considered when deciding which motivational strategy to use . Almost As a result of the present study, we generated a comprehensive list of motivational strategies used during stroke rehabilitation in Japan based on quantitative survey data. We identified 15 motivational strategies used by more than 75% of the rehabilitation professionals who participated in the study. The results support our hypothesis that rehabilitation professionals acquire skills to motivate stroke patients via clinical experience. Furthermore, our survey generated data regarding the purpose of using each motivational strategy and the information that professionals consider when they choose motivational strategies. To our knowledge, the present study is the first comprehensive survey-based investigation of strategies used to motivate stroke patients during rehabilitation. We prepared the survey in accordance with existing guidelines  and condActive listening is one of the core communication skills used in counseling , 28. A sOur results suggest that the majority of the surveyed rehabilitation professionals recognized that the communication skills of the medical professional, rehabilitation environment, setting of goals, provision of information, and the presence of family members during practice could affect patient motivation regarding rehabilitation. These findings are consistent with those of Maclean et al. . CommuniRespect for self-determination, control of task difficulty, and provision of a goal-oriented practice were motivational strategies that were selected by more than 75% of the respondents. Self-determination contributes to the facilitation and maintenance of intrinsic motivation . The difWe found that the following four strategies were reportedly used by less than half of the respondents to motivate their patients: providing practice with game properties, proposing conditions for exchange, using progress-confirming tools, and providing group rehabilitation. The reasons why these particular motivational strategies were not used remain unclear. Environmental and time constraints, professional lack of confidence regarding the practice, and lack of perceived effectiveness may prevent rehabilitation professionals from using specific motivational strategies . In addiIn clinical settings, rehabilitation professionals may be required to select strategies according to the cause of a patients' lack of motivation. Therefore, we examined the reasons for using each motivational strategy. For 11 of the 15 strategies used by the majority of respondents, the highest percentage of respondents reported that they used the strategy to make rehabilitation worthwhile for their patient. Lack of knowledge about the potential benefits of training may decrease patient adherence to rehabilitation . TherefoOur hierarchical cluster analysis revealed three groups of motivational strategies. Strategies that center on the value of rehabilitation for patients, such as explaining the necessity of a practice and exercise, are expected to motivate patients with poor understanding regarding the benefits of rehabilitation. For patients with low confidence regarding their practice tasks, strategies focused on increasing patient confidence, such as control of task difficulty and praise, may be effective for increasing patient motivation. Moreover, engaging in enjoyable conversation and applying patient preferences to practice tasks are likely to increase patient interest and prevent patient boredom during rehabilitation. There were no strategies that were used by the majority of respondents to increase patient satisfaction with rehabilitation. However, more than a quarter of the respondents reported that they used goal setting, goal-oriented practice, and praise to increase patient satisfaction with rehabilitation. These strategies may be effective in motivating patients who are not satisfied with rehabilitation. Thus, our findings may help rehabilitation professionals to choose strategies from the list according to the cause of a patients' lack of motivation.Maclean et al.  reportedWe also examined the participant responses by professional category . We obseAs mentioned above, our results showed that the different strategies were used for different purposes . For exaSeveral limitations to this study should be mentioned. First, the actual response rate of the present survey was low , and we utilized a convenience sampling method. Therefore, the results of this survey may not accurately represent the actual use of motivational strategies for stoke rehabilitation in professionals in Japan. Unfortunately, the characteristics of the nonparticipants in this survey were unavailable. We recruited participants by distributing leaflets and displaying posters, and participation was voluntarily. As a result, the nonparticipants in this study may have been less interested in the motivation of patients compared with the participants. Accordingly, the results should be interpreted with caution because the rate of use of each motivational strategy may be higher in our sample than in the general population of rehabilitation professionals. We recommend that further studies employ a stratified random sampling method to increase the range of occupations of the respondents and the generalizability of the results .A secondary limitation is a weakness of generalizability of the study. Maclean et al.  reportedThird, there may have been a perceived lack of differentiation between the motivational strategies. As our list of motivational strategies comprised specific examples about motivating a patient, some of the presented motivational strategies may have overlapped with one another , potentiWe generated a comprehensive and qualitative list of motivational strategies used for stroke rehabilitation in Japan, and found that our 15 motivational strategies were used by the majority of rehabilitation professionals. In addition, we obtained data regarding the purpose for using each motivational strategy and the information considered when choosing motivational strategies. These findings may enhance the effective utilization of our motivational strategy list in stroke rehabilitation.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by Ethics committee at the Hamamatsu University School of Medicine. The patients/participants provided their written informed consent to participate in this study.KO contributed to the study concept and design, development of the survey, acquisition of data, analysis and interpretation of data, obtaining of funding, and drafting and revising the manuscript for content. MS contributed to the development of the survey, acquisition of data, and revising of the manuscript for content. YO contributed to the study concept and design, development of the survey, and revising of the manuscript for content. ST contributed to the study concept and design, development of the survey, acquisition of data, analysis and interpretation of data, study supervision, obtaining of funding, and drafting and revising of the manuscript for content. All authors reviewed and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Adjusted plus minus (APM) measures have redefined our understanding of player value in basketball and hockey, where both are team games featuring player productivity spillovers. APM measures use seasonal play-by-play data to estimate individual player contributions. If a team's overall score margin success is figuratively represented by a pie, APM measures are well-designed to slice the pie and attribute individual contributions accordingly. However, they do not account for the possibility that better players can increase the overall size of the pie and thus increase the size of the slice  for teammates. Herein, we use data from NBA player-season Real Plus Minus (RPM)—a leading APM measure—for all recorded player-seasons from 2013–19 and player lineup data to test whether RPM is related to teammate quality. We run sets of linear fixed effect regression models to explain variation in RPM across player-seasons. We also employ a two-stage least square (2-SLS) method for robustness check. Both empirical approaches address potential endogeneity in the relationship of interest. We find strong evidence that RPM is related to on-court teammate quality. Despite adjusting for teammate and opponent quality, RPM does not control for complementarity effects. As such, RPM is not suited for out-of-sample prediction. Adjusted plus minus (APM) measures have redefined our understanding of player value in basketball and hockey, where both are team games featuring player productivity spillovers . In baskIn an ESPN article introducing the real (adjusted) plus minus measure, Steve Ilardi  highlighconsistent estimators of player value toward an understanding of marginal player contributions from the season that occurred. From this, however, can we interpret the measure as providing teammate-independent player contribution values? Let us think of the value that a player creates—along with his lineup teammates—as a pie whose size represents (score margin) performance. APM measures can divide each such pie into constituent slices that represent consistently estimated player contributions. Due to the asymptotic property of consistency, that is, APM is capable of dividing credit at the player level based on the lineups and performances in which the player actually played. Is this division reliable when considering the player’s  productivity “out of sample”? Consider a teammate all of whose lineup teammates improve in ability . In this case, we know that the overall pie with which the player is associated has become larger, while the individual player retains the same player attributes. Do we expect this player to maintain the same expected APM value? More generally, do we expect player APM values in general not to be influenced by out of sample changes in playing conditions? If we are to treat the APM measure as teammate independent, then such an outcome must obtain. We present this analysis herein.In a related paper, Brian McDonald points out that APM values seek to disentangle player marginal effects from one another by using lineup variation across a season of play and are thus commonly interpreted as teammate independent . It is tThe remainder of the paper is organized as follows: Section II provides a summary of the data and their visualizations; Section III describes the estimation approach; Section IV presents the empirical results; Section V discusses the implications of the results; and Section VI concludes.real plus minus data and corresponding lineup data from NBA.com. The two databases are available at https://www.espn.com/ and https://stats.nba.com/ respectively. We also control for time variant features of the panel data . To the authors’ knowledge, ESPN’s real plus minus represents the only publicly available dataset of NBA player regularized APM values, where regularized adjusted plus minus estimation has become a standard methodology for NBA player analysis [real plus minus and APM interchangeably, as real plus minus is, in fact, an APM measure.For out of sample shifts in teammate quality that cause changes in the size of a figurative lineup performance pie, do we expect the size of a player’s slice to remain the same? Rather than simply a theoretical consideration, this question has substantial empirical bearing in the modern NBA, which features a high level of player entry and player movement from year to year. Often, players change teams and are presented with near wholesale upgrades or downgrades in terms of lineup-teammate quality. To test whether APM is teammate-independent, we take advantage of frequent player movements in the NBA to determine whether a representative player’s season-level APM values are related to the (minutes-weighted) average APM value of his lineup teammates in the same season. We do so using a six-year panel data of NBA player-season level ESPN analysis , 5–10. IThe present paper considers whether APM measures  are teammate dependent. It does so by considering player movement within and entry into the NBA. The following plots summarize the level of mobility within the NBA over the data period. Specifically, Within the directed graph , the dirNBA teams have relied heavily on rookie players in recent years. In each of the last three NBA seasons, more than one-fifth of the league players have been in their rookie season. Hence, player entry into the league has also been a substantial source of lineup disruption from season to season over the sample period.While there is substantial player movement in the NBA, this matters only if players exhibit significant heterogeneity in terms of productivity . The following plots represent density plots of player-season real plus minus values for the sample. As these plots demonstrate, there is substantial player ability heterogeneity at each position in the NBA. While each plot is roughly bell-shaped, we observe substantial dispersion in player productivity levels. In terms of range, the most productive players have an estimated +10 point score margin effect per 100 possessions, while the least productive have an estimated -7 point score margin effect. Moreover, substantial heterogeneity is observed at each position. The following real plus minus summary  further Note that the average player RPM is substantially negative because player-season observations are not weighted by minutes played within the sample. However, OtherPlayersRPM is weighted by teammate minutes played and is therefore closer to zero. Such a weighting would impose a zero-average constraint upon the variable. Even if a player is able to maintain a stable set of lineup-teammates across seasons, aging effects can change teammate ability levels fairly quickly in the NBA. To answer our key question as to whether team member’s RPM affects an individual player’s RPM, we specify a baseline linear regression model with player RPM as a function of the player’s individual characteristics such as age, age-squared, and minutes-weighted average RPM of lineup teammates.Difficulties exist in estimating the relationship between player APM and lineup-teammate APM due to potential endogeneity. Just as a player’s lineup-teammates may improve his APM value, so too might a player improve the APM values of his lineup-teammates. Moreover, players are often selected into lineups that potentially form a cluster of players with similar abilities. For example, starters disproportionately play alongside other starters, and backups disproportionately play alongside other backups. This latter issue also exacerbates endogeneity between player APM and lineup-teammate APM values. If we attempt to estimate the direct, uncontrolled relationship between these variables, we may actually pick up ability-clustering in lineup selections. We use the following techniques to address the endogeneity issue.RPMi,t is the real plus-minus measure for player i in season t; playeri and seasont capture the player specific and time fixed effects, and OtherPlayersRPM represents the RPM for the rest of the team members. We also control for the player’s individual characteristics by age and its squared term. This information is available for 810 unique NBA players (i = 1 to 810) over 6 different seasons (t = 2014 to 2019) giving us 2852 total observations. This provides us the opportunity to control for individual specific as well as time specific fixed effects. There are 262 instances in which a player participated in more than one team in the same season. In that case, we average the player’s RPM for that season. We perform a separate set of analyses using three-dimensional panel data , but this dramatically reduces the total number of observations included in the final results. However, the final results do not change. To include the maximum possible observations in our analysis, we ignore the latter approach.One manner by which to treat endogeneity is through the use of fixed effects . In the Player fixed effects control for unobserved baseline player ability such that the endogenous effect of the player’s ability upon teammate performance is then isolated within the fixed effects model. With the specification of player fixed effects, one is implicitly regressing the performance of a player relative to his baseline  against the APM level of lineup-teammates.Given that we have specified player fixed effects herein, the fixed effects model specification in (1) represents an exogenous test as to whether RPM, a leading APM measure, is influenced by teammate productivity .Another approach we follow to subdue the problem of endogeneity is the use two-stage least squares (2-SLS) instrumental variable estimation using the lagged values of the minutes-weighted average APM of lineup teammates as an instrument. While it may be that lineup-teammate ability affects the player performance residual, there is a possibility that a player may influence the team’s current period performance. To address such reverse causality, we use previous season performance of present season lineup teammates as an instrument for present quality of lineup teammates and use the estimated values in the regression as shown in model (2).ogeneity . The valWe regress model (1) and (2) for all NBA players and also in separate sub-samples by position-of-play for the Point Guard, Shooting Guard, Power Forward, Small Forward, and the Center positions. As such, we obtain six sets of regression results for each of the two estimation techniques explained above. We report the results in the subsequent section.Estimation results are presented in Tables i.e. 1.44 * 0.174) RPM units. Moreover, the estimated coefficient for OtherPlayersRPM is positive for each position-of-play in the position-of-play specific regressions, where the relationship is significant at standard p-values for the Small Forward and Center positions.In the fixed effects regression results shown in In the 2-SLS regressions results shown in Based on the estimations above, for each unit average gain in the teammate’s RPM, a player’s RPM is overestimated by a range of 0.17 to 0.66 points according to point estimates. We find that RPM is not context-independent. Rather, positive spillover effects from more capable teammates improve a given player’s RPM value, ceteris paribus. This result has implications upon player trade value. Namely, a player’s trade value is estimated to be a function of where he is traded. Indeed, basketball is a game of complementarity and no measure—not even a measure such as RPM—has demonstrated the ability to isolate these complementarity effects toward an understanding of player value in the abstract. While this is perhaps the holy grail of basketball data science, it is not assured that such a measure is possible. As perhaps similar to quantum mechanical information, it may be impossible to understand the contribution of the basketball player and that of the player’s environment in some absolute sense. Indeed, basketball leagues are not natural experiments in which players are randomly paired and resampled. Rather, players are organized into often stable team environments and resampling occurs infrequently such that players have often aged by the time they receive a new set of teammates with whom to play. In such an environment, counterfactuals concerning player value will go largely unobserved.The results provide strong evidence that regularized adjusted plus minus player productivity measures are not, in fact, “teammate-independent.” Rather, we find evidence that lineup-teammate productivity positively influences a given player’s real plus minus value. As this result is conditional upon a given player’s baseline productivity via player fixed effects and age, we interpret this as a significant and fairly strong complementarity effect that is uncontrolled in adjusted plus minus measures such as real plus minus. While real plus minus may control for in-sample teammate effects well, it appears that the measure does not control for out-of-sample lineup-teammate quality effects. We find this within a model that accounts for teammate quality changes from season to season. We note that basketball leagues are not natural experiments in which players are randomly paired and resampled. Rather, players are organized into often stable team environments and resampling occurs infrequently such that players have often aged by the time they receive a new set of teammates with whom to play. In such an environment, counterfactuals concerning player value will go largely unobserved. From this estimation, further (out-of-sample) adjustments to the APM estimation methodology can be explored in future work. 25 Jun 2020PONE-D-20-05517Measuring Individual Worker Output in a Complementary Team Setting: Does Regularized Adjusted Plus Minus Isolate Individual NBA Player Contributions?PLOS ONEDear Dr. Ghimire,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.plosone@plos.org. 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The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdfhttp://journals.plos.org/plosone/s/supporting-information.2. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: Additional Editor Comments (if provided):The identification of a causal effect in the paper requires a valid instrument. The use of lagged values as instruments is a bit out of fashion for several reasons proposed in the panel-data literature, which the authors should discuss. The authors should make an effort to provide additional arguments in support of their identification approach, also keeping into account the comments of the referees.Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The paper is well-written and discusses the endogeneity issue appropriately, but I think some changes have the potential to improve the manuscript. Here are a few comments.The data reported in Figure 3 show that there have been less rookies in 2016. Rookies increased by 7.5 percentage points the year after, representing a 50% raise. Is there a reason for this raise?I would be cautious in deriving a general formula for adjusted APM measure, i.e., equation 2. While the empirical analysis is performed appropriately, the estimates are based on data restricted to 4 time periods.The validity of the instrument could be supported by the results of the Sargan statistic and the Ftest.Figures should be self-explanatory. I suggest adding notes to describe what is plotted. Also, the legend in Figure 5 is not clear as the acronyms C, PF, PG, SF, and SG were not defined before.Abbreviations should be defined the first time they appear.On a minor note, it may be interesting to see what happens if the authors exclude the observations closer to the maximum age, for which the productivity is much lower.Reviewer #2: This paper contests the claim that "Adjusted plus minus (APM)" measures are independent from teammate productivity. In order to show that APM player productivity measures are indeed dependent from the one of other team players, the authors perform an empirical analysis using open data from NBA player-season, Real Plus Minus for all recorded player-seasons from 2013-19, and player lineup data.- The research question is clearly explained and contextualized.- As it regards the estimation approach, I suggest showing the equation for both model specifications (a and b).- In the “Two-stage Least Squares (2-SLS) Estimation” the goodness of the proposed instrument is not discussed. Conversely, it is important to show that the suggested instrument is not a weak one. Hence, I suggest including the F-statistic at least in the results’ table.- In the fifth section I would suggest a more general discussion of the results than the derivation of a new measure of the individual player performance.- I do not agree on the derivation of the adjusted APM measure (equation 2). The coefficient of the variable “OtherPlayersRPMi,t” is roughly the mean of the previous "OtherPlayersRPMi,t"’s estimated coefficients in the two separated models (with different underlying assumptions). I have serious doubts on its validity.Minor commentsNotes about the tables are missing. The abbreviations used in the tables should be explained below the tables.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  17 Jul 2020Editor's Comment: In the paragraphs below, we address the concerns raised by the reviewers on a comment-by-comment basis. The changes are highlighted in red within the manuscript. With respect to your own feedback on the use of instrument, the validity of the instrument is supported by the Cragg-Donald Wald F-statistic for the weak-identification test, as evidenced by the large enough F-statistics from the first stage regression. We agree with your comment that it would be ideal to use and external instrument, but we resort to the lagged-productivity technique in the absence of external instrument while also meeting our objective of checking the robustness of our results from the fixed effects estimation. The NBA is a singular league in terms of competitive level that does not feature a great deal of in-season roster movement. Therefore, the best and perhaps only counterfactual measure of current player productivity in this case is lagged productivity of the same player. This discussion has been added in the estimation section (page 10). Reviewer #1: The paper is well-written and discusses the endogeneity issue appropriately, but I think some changes have the potential to improve the manuscript. Here are a few comments.Response: Thank you for your comments. We appreciate your feedback. Below, we address your concerns on a comment-by-comment basis. The changes are highlighted in red within the manuscript.1. The data reported in Figure 3 show that there have been less rookies in 2016. Rookies increased by 7.5 percentage points the year after, representing a 50% raise. Is there a reason for this raise?Response: In 2016 there was roster expansion from 15 to 17 per team given the advent of 2-way contracts. This allowed additional rookies to gain limited NBA exposure during 2016. Specifically, several second round draft picks and undrafted free agent rookies who might have otherwise spent the season in the G-League were signed to two-way contracts for the first time in 2016. 2. I would be cautious in deriving a general formula for adjusted APM measure, i.e., equation 2. While the empirical analysis is performed appropriately, the estimates are based on data restricted to 4 time periods.Response: Upon review of your valuable comment, we agree and have striken this from the analysis. 3. The validity of the instrument could be supported by the results of the Sargan statistic and the F-test.Response: Table 3 has been updated to reflect the test statistics. Our estimation has only one endogenous regressor and one external instrumental variable. Accordingly, we report the F-statistic. The validity of the instrument is supported by the Cragg-Donald Wald F-statistic for the weak-identification test, as evidenced by the large enough F-statistics from the first stage regression. Additional explanation has been added in the estimation section (page 10). 4. Figures should be self-explanatory. I suggest adding notes to describe what is plotted. Also, the legend in Figure 5 is not clear as the acronyms C, PF, PG, SF, and SG were not defined before.Abbreviations should be defined the first time they appear.Response: The Figure 5 caption has been updated to reflect the acronyms used in the paper. 5. On a minor note, it may be interesting to see what happens if the authors exclude the observations closer to the maximum age, for which the productivity is much lower.Response:We ran the regressions excluding those players that are above the age of 40. There are only six such players and they do not significantly alter the results. Table 3: Instrumental Variable Regression: Lag of other player's RPM as an instrument Age  All PG SG SF PF COther's RPM 0.669*** 1.187*** 0.695** 0.670** 0.743** 0.531* (0.134) (0.257) (0.334) (0.273) (0.316) (0.289)Age 0.739*** 1.372*** 0.726** 1.245*** 0.731* 0.149 (0.179) (0.477) (0.359) (0.448) (0.423) (0.363)Age Squared -0.0130*** -0.0246*** -0.0126** -0.0217*** -0.0131* -0.00278 (0.00309) (0.00828) (0.00622) (0.00769) (0.00734) (0.00628)Constant -10.93*** -19.29*** -11.44** -18.31*** -10.30* -1.685 (2.537) (6.774) (5.102) (6.386) (6.025) (5.174)N 1723 339 371 300 356 357Note. PG = Point Guard; SG = Shooting Guard, PF = Power Forward, SF = Small Forward, C = Center positions. Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.  Reviewer #2: This paper contests the claim that "Adjusted plus minus (APM)" measures are independent from teammate productivity. In order to show that APM player productivity measures are indeed dependent from the one of other team players, the authors perform an empirical analysis using open data from NBA player-season, Real Plus Minus for all recorded player-seasons from 2013-19, and player lineup data. The research question is clearly explained and contextualized.Thank you for your comments. We appreciate your feedback. Below we address your concerns comments by comments. The changes are highlighted in red within the manuscript.1. As it regards the estimation approach, I suggest showing the equation for both model specifications (a and b).Response:Section III has been re-organized to include the regression equations separately for the fixed effects model and the 2SLS model. 2. In the “Two-stage Least Squares (2-SLS) Estimation” the goodness of the proposed instrument is not discussed. Conversely, it is important to show that the suggested instrument is not a weak one. Hence, I suggest including the F-statistic at least in the results’ table.Response: Table 3 has been updated to reflect the test statistics. Our estimation has only one endogenous regressor and one external instrumental variable. Accordingly, we report the F-statistic. The validity of the instrument is supported by the Cragg-Donald Wald F-statistic for the weak-identification test, as evidenced by the large enough F-statistics from the first stage regression. Additional explanation has been added in the estimation section (page 10).3. In the fifth section I would suggest a more general discussion of the results than the derivation of a new measure of the individual player performance.a. I do not agree on the derivation of the adjusted APM measure (equation 2). The coefficient of the variable “OtherPlayersRPMi,t” is roughly the mean of the previous "OtherPlayersRPMi,t"’s estimated coefficients in the two separated models (with different underlying assumptions). I have serious doubts on its validity.Response: Upon reflection, we agree and have stricken equation (2). Moreover, we now provide an extended general discussion of the results and their importance/interpretation as follows: Added passage (page 13-14): “We find that RPM is not context-independent. Rather, positive spillover effects from more capable teammates improve a given player’s RPM value, ceteris paribus. This result has implications upon player trade value. Namely, a player’s trade value is estimated to be a function of where he is traded. Indeed, basketball is a game of complementarity and no measure—not even a measure such as RPM—has demonstrated the ability to isolate these complementarity effects toward an understanding of player value in the abstract. While this is perhaps the holy grail of basketball data science, it is not assured that such a measure is possible. As perhaps similar to quantum mechanical information, it may be impossible to understand the contribution of the basketball player and that of the player’s environment in some absolute sense. Indeed, basketball leagues are not natural experiments in which players are randomly paired and resampled. Rather, players are organized into often stable team environments and resampling occurs infrequently such that players have often aged by the time they receive a new set of teammates with whom to play. In such an environment, counterfactuals concerning player value will go largely unoberserved.”4. Minor comments: Notes about the tables are missing. The abbreviations used in the tables should be explained below the tables.Response: Notes added at the bottom of Table 2 and Table 3 to reflect the full forms of the abbreviations. 6 Aug 2020Measuring Individual Worker Output in a Complementary Team Setting: Does Regularized Adjusted Plus Minus Isolate Individual NBA Player Contributions?PONE-D-20-05517R1Dear Dr. Ghimire,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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Dear Dr. Ghimire:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofProfessor Corrado Andini Academic EditorPLOS ONE