dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 1) (Bankevich et al. , 2012). SPAdes contigs were identified by BLAST (v2. 10. 1) (Altschul et al. , 1990)  and filtered according to their coverage. Filtered SPAdes contigs were extended with SSPACE (v1. 12) into long scaffolds (up 170 kb). Finally, contigs were manually assembled into single circular molecules. The code used for the assembly is available at github. com/ARNTET/Illumina_plant_mtDNA_assembly.
Annotation of the Genomes Lactuca sativa mtDNA was first automatically annotated using GeSeq (v2. 0. 3)  (Tillich et al. , 2017)  with default settings for circular mitochondrial genome and with Diplostephium hartwegii (KX063855. 1) as 3rd party reference. Then the annotation was manually reviewed with L. sativa RNA-seq data (accessions SRR080725 and SRR085107). RNA-seq reads were mapped on L. sativa mtDNA using Hisat2 (v2. 1. 0) (Kim et al. , 2015)  with reduced mismatch penalties (-mp parameter to "2. 0"), allowing the mapping of more reads. SAM format files were then converted to BAM format with Samtools (v1. 9)  (Li et al. , 2009). BAM files were sorted with Samtools for visualization on IGV (v2. 8. 12) 2 http://github. com/ExpressionAnalysis/ea-utils (Robinson et al. , 2011)  to review CDS border.
SNPs calling was made with Freebayes (v1. 3. 1) (Garrison and Marth, 2012) , using default parameters. SNPs were filtered to remove non-C-to-U (or G-to-A) ones. Finally, only SNPs in coding regions were conserved and annotated as RNA editing sites. Code available at github. com/ARNTET/RNAseq_mapping_hisat2. The L. saligna, L. serriola, and L. virosa mitochondrial genomes were automatically annotated using GeSeq (v2. 0. 3)  (Tillich et al. , 2017)  with default settings for circular mitochondrial genome and with the L. sativa mtDNA and the recently published L. serriola US96UC23 and L. saligna CGN5271 mtDNAs (MK820672. 1 and MK759657. 1, respectively) as "3rd party references. " The repeated regions among the Lactuca mtDNA sequences were identified by using the python script ROUSfinder (v1.
1)  (Wynn and Christensen, 2019). We annotated the sequences larger than 50 bp from the largest (R01) to the smallest (R17). The nomenclature in our dataset is based on the one of L. sativa, homologous repeated regions in the others Lactuca species mitogenomes are annotated with the same number. Visualization and identification of blocks of synteny between the mtDNA genomes assembled in this work was done with progressiveMauve (v2. 4. 0)  (Darling et al. , 2010)  with default options. The annotated mtDNA sequences have been deposited in GenBank under accession numbers MZ159953 (LsatUC12100), MZ159954 (LsatLACWENDEL), MZ159955 (LsatLAC004500), MZ159956 (LsalLAC008020), MZ159957 (LserCGN004799), MZ159958 (LserLAC005780), MZ159959 (LvirLAC006941), MZ159960 (LvirCGN013357), and MZ159961 (LvirCGN019045).
Phenotypic Diversity Within a Collection of Nine Accessions of Cultivated and Wild Lettuce For this study, we selected nine accessions of Lactuca spp. among those available at the Genbank of Enza Zaden (Enkhuizen, Netherlands): three of L. sativa, one of L. saligna, two of L. serriola, and three of L. virosa. The three L. sativa accessions represent two cultivars: LACWendel is a Butterhead lettuce (L. sativa var. capitata L. nidus tenerrima), and UC12100 and LAC004500 are Crisphead lettuce (var. capitata L. salinas). The accessions were selected to include the three wild Lactuca species believed to be at the origin of domesticated L. sativa, and covering a wide diversity of geographical origins (Supplementary Table  1 ). Accessions showed marked phenotypical variation according to the species, but also interspecies (Figure  1 ).
, 1998). We did it for our selected accessions, from our Illumina reads, and for the L. sativa and L. serriola accessions from which the mtDNA was recently published and from which sequence data is available (accessions SRR080725 and SRR085107). We aligned the corresponding sequences to the 89 additional ones of Asteraceae species available on the NCBI nucleotide database (Supplementary Table  2 ). The alignment of these 100 sequences was 723 nucleotides long, comprising 404 conserved positions. The polyphyletic tree built using Maximum Likelihood from this alignment is shown in Figure  2. In this tree, our sequences branched as expected, in the same clusters as others from the same species. It is possible to distinguish every individual species, at the exception of L. sativa and L. serriola, which are unresolvable with this marker, as previously described.
sativa domestication than previously suspected. Backcrosses would have cleaned the actual L. sativa nuclear genome from most L. virosa sequences, as observed at present  (Zhang et al. , 2017). However, this scenario does not explain the heterogeneity of mitogenomes observed among L. serriola accessions, with the one of L. serriola US96UC23 almost identical to the one of L. sativa. The cpDNA genome assembled from the same Illumina sequences data set is also virtually identical to the cpDNA of L. sativa. This observation calls for the hypothesis that this sequence data set originates from a L. sativa accession mislabeled as L. serriola. An alternative possibility is that L. virosa had previously been hybridized with L. serriola, leading to two different L. serriola groups; a wild one harboring a mtDNA distinct from the one of L.
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www. ncbi. nlm. nih. gov/genbank/, MZ159953; https://www. ncbi. nlm. nih. gov/ genbank/, MZ159954; https://www. ncbi. nlm. nih. gov/genbank/, MZ159955; https://www. ncbi. nlm. nih. gov/genbank/, MZ159956; https://www. ncbi. nlm. nih. gov/genbank/, MZ159957; https:// www. ncbi. nlm. nih. gov/genbank/, MZ159958; https://www. ncbi. nlm. nih. gov/genbank/, MZ159959; https://www. ncbi. nlm. nih. gov/genbank/, MZ159960; https://www. ncbi. nlm. nih. gov/ genbank/, MZ159961; https://www. ncbi. nlm. nih. gov/genbank/, ERS5267070; https://www. ncbi. nlm. nih. gov/genbank/, ERS 5267071; https://www. ncbi. nlm.
nih. gov/genbank/, ERS 5267072; https://www. ncbi. nlm. nih. gov/genbank/, ERS5267073; https://www. ncbi. nlm. nih. gov/genbank/, ERS5267074; https:// www. ncbi. nlm. nih. gov/genbank/, ERS5267075; https://www. ncbi. nlm. nih. gov/genbank/, ERS5267076; https://www. ncbi. nlm. nih. gov/genbank/, ERS5267077; and https://www. ncbi. nlm. nih. gov/ genbank/, ERS5267078.
To determine whether birds that winter in France originate from the known breeding area or from a previously undetected breeding population, we equipped seven individuals with global location sensors (GLSs) during winter 2019/2020 (Table  S2 ). The following winter, we re-captured three of these individuals (recapture is needed for data retrieval with GLS). All three birds spent the summer at the western edge of the known breeding range in a small area of the Novosibirsk Oblast in Russia (Figure  3 ) where light data indicate that at least one individual engaged in breeding behavior (Figure  3F ), supporting the hypothesis that these birds were regular migrants rather than vagrants. During the spring migration, the three individuals used three different stopover locations for more than 24 h; the second-year individual first headed north and stopped in Sweden before redirecting eastward to reach a likely breeding site where it lingered until the GLS stopped (Figure  3E ).
These three individuals thus traveled 5,750, 7,045, and 7,320 km during their spring migration between southern France and Siberia along a migratory route that crosses countries where the species is rarely sighted. To determine whether regular migrants are also present in other parts of the western non-breeding range (Figure  1 ), we tested for the presence of adult birds in these locations using pictures from citizen-science databases to estimate the age ratio. On good-quality pictures, first-generation wing coverts (grown in the nest) can easily be distinguished from second-generation feathers (Figure  S2 ), allowing safe aging of birds with first-generation coverts as young birds (i. e. , less than 1 year old). However, some young birds molt all wing coverts during the autumn, resulting in an adult-type plumage in winter that cannot be separated from adults without in-hand examination.
e. , overwinter survival) and on their ability to perform a successful return journey and transmit their migration route to their offspring (via genetic inheritance as migration direction is genetically determined in songbirds; e. g. , Helbig,  31  Lundberg et al. ,  32  and Delmore et al. 33  ). In Richard's Pipit, the main wintering grounds are in southern and Southeast Asia (Figure  1 ). 13 Thus, the most parsimonious hypothesis is that the species' ancestral wintering grounds are in Asia and that the European wintering range represents a derived state that was recently established, or at least recently increased. To investigate the factors that may have favored the observed change in migration strategy, we examined the wintering niche conditions in the ancestral and derived ranges. When citizen-science occurrences data were plotted in a twodimensional space defined by principal component analysis (PCA) axes on climatic and habitat variables (Figure  4A ),  13, 34  we found that the niche of the European wintering range is broadly similar but not identical to the niche used in Asia: niches overlapped with a Schoener's D value of 0.
43, 44. 54 E ± 0. 9 for the three birds, respectively), at the limit of the known breeding distribution represented by the pink polygon. 18 The change of pattern in light intensity data (F) suggests that this individual was likely incubating and thus engaged in breeding behavior. Other dots (with error bars corresponding to standard errors of longitude and latitude estimates) indicate stopover sites where the bird stayed more than 24 h. The sizes of these dots are proportional to the duration of the stopover. For each individual, the migration phenology is indicated alongside the map where colored bars indicate stopovers and white spaces between them represent movement periods between the stopovers. The equinox period (22 September ± 14 days) is indicated for the autumn migration (B-D). Since birds undertook most of their autumn migrations during this period, latitudinal positions have large errors.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following:   hosted the first and most of the wintering sites and includes several localities where observation pressure had remained strong over this period. d KEY RESOURCES In addition, to test whether the trend can be explained by the establishment of a new migration route, we collected flock size data in France over the same period since an increase in group size can only result from actual increase in number rather than increase in probability of detection of the species. To avoid double-counting, we considered, per season and on a given year, the maximal flock size recorded in sites known to host the species during winter and each flock size record in other sites. Finally, we reported the number of sites frequented by the species in winter between 1981 and 2019.
Assessing the geographical distribution of molt phenotypes from pictures We collected georeferenced pictures on citizen-science databases and Internet picture repositories across different European and North African countries that regularly host the species in autumn and/or during the wintering period. After February, young birds that have not finished molting in the autumn can resume their molt and become similar to adult birds, hence we only collected data associated with pictures obtained between 08 (2013) and 02 (2019). We selected good quality pictures of perched birds showing the secondary coverts and tertials correctly. When several photos were available for a single location where several birds were reported, we allocated photos to different individuals by comparing photos (individual birds usually differ in details of molt or plumage); when in doubt, only one picture was selected from the available sample.
Then, we used the TwGeos R package  45  to define twilights with the preprocessLight function, using a threshold of 2. 5 lux. We manually removed obviously erroneous twilights and applied an additional automated screening using the twilightEdit function (settings were window = 4, outlier. mins = 45 and stationary. mins = 25). We then used the FLightR package  46  to determine migration timing. Since, the package requires calibration periods during which the bird is stationary in a known location, we ran the model a first time using the month of March as calibration period and with a particle filter of 10 4 particles (color-ringed birds were observed throughout March on their wintering site, pers. obs). We refined the calibration period using the plot_lon_lat function (plot of latitude and longitude in horizontal layout).
Wintering niche comparison To compare environmental niches of birds wintering in their ancestral Asian areas and birds wintering in Europe, we collected occurrences of Richard's Pipit from the Global Biodiversity Information Facility (https://www. gbif. org/). We selected occurrence data for the wintering period alone (from December to February) and from countries known to host the species regularly during this period (Europe and North Africa: France, Spain, Portugal, Italy, Morocco; Asia: Taiwan, Thailand, Hong-Kong, China, India, Myanmar, Vietnam, Laos, Cambodia, Sri Lanka; see Alstro ¨m and Mild  13  ). We retained verified and georeferenced occurrences, resulting in 1,268 occurrences for Europe and North Africa and 6, 717 occurrences for Asia, derived from citizen science databases widely used by the ornithological community (mostly eBird.
org, iNaturalist. org, observation. org and the various Biolovision national reporting systems). For Europe, these databased are usually well curated by validation committees that operate at local (regional) or national scales. Data were then thinned prior to analyses to reduce the effect of biased species occurrence collections, using the spThin R package  48  with a thinning parameter of 0. 5 km, leaving a total of 1,638 occurrences. Since environmental conditions seem to play a key role in the selection of species' wintering grounds, particularly for finding the resources necessary for winter survival,  34  we collected several climatic and habitat variables related to the species preference. 13 We downloaded the temperature and precipitations of the winter months (between December and February), an aridity index (the ratio of the mean annual precipitation on the mean annual potential evapo-transpiration) and a discrete variable characterizing open and closed habitats.
The climatic data were collected from the CHELSA database v1. 2 (https://chelsa-climate. org/) at a resolution of 30 arc seconds, with monthly mean temperature and precipitation averaged over the period 1979-2013. 56 The aridity index was collected from the CGIAR-CSI Database (https://cgiarcsi. community/) at a resolution of 30 arc seconds for the period 1970-2000  57  and habitat variable from the Global Land Cover Map (GlobCover). 58 The habitat variable was coded as ''open'' and ''closed'' habitats based on GlobCover categories; crop and mosaic habitats with more than 50% of cropland or grassland were considered as open habitats whereas mosaic habitats with less than 50% of cropland or grassland and more than 50% of forest or scrubland were considered as closed habitats. We checked that variables were not collinear (correlation coefficients < 0.
ACKNOWLEDGMENTS We are grateful to  Loı ¨s Rancilhac  for his help in starting up this project;  Marc Illa ,  Albert Burgas ,  Oriol Clarabuch ,  Thomas Dagonet ,  Stephan Tillo , and other ringers for their invaluable assistance in the capture of birds;  Benjamin Long ,  Philippe Bailleul , and all the people who took part in the bird's monitoring;  Janne Aalto ,  Luca Bergamaschi ,  Christian Leth ,  Julien Mazenauer , and  Louis Sall  e for their help in the data collection;  Per Alstro  ¨m,  Yosef Kiat , and  Lukas Jenni  for their constructive comments on the ecology of the species and molt timing;  Anne Charmantier  for her help in logistical support;  James Fox  for help with geolocators; and  Christoph Haag  for his feedback and recommendations on the draft. For providing access to the study sites, we are grateful to  R eserve Naturelle de Crau ,  CEN PACA, ville de Fr ejus, Groupe Ornithologique du Roussillon (GOR) ,  Communaut e de communes La Domitienne, Departament de Territori i Sostenibilitat, Aiguamolls de l'EmpordaNatural Park, Montgrı ´, Illes Medes , and  Baix Ter Natural Park.
For collecting and sharing their data, we thank  Ligue de Protection des Oiseaux (LPO) ,  Comit e d'Homologation National (CHN) , and  Comit e des Migrateurs Rares (CMR). This work has been supported by a PhD grant to P. D. awarded by the  Labex   OSUG@2020 (Investissements d'avenir -ANR10LABX56 ) and a field grant for young researchers to P. D. awarded by the  Soci et e Francaise d'Ecologie et d'Evolution  ( SFE 2  ).
METHOD DETAILS Estimating trends in Richard's Pipit occurrence To check whether the number of Richard's Pipits occurring in Europe actually increased over the last few decades, we studied the trend in occurrence numbers in France between 1981 and 2019. We focused on France because the country hosted the first documented wintering record for Europe  16  and because data have been meticulously collected over a period of about 30 years. The French Birds Rarities Committee collected data between 1981 and 2005 when the species was still rare to scarce. From the 1990s onward, numbers of occurrences started to increase and data on the species were collected by another committee between 2005 and 2010, after which the citizen-science databases took over. To avoid double-counting, we used the maximum number of individuals simultaneously recorded on a given locality and on a given year.
To test whether the species increasing trend is entirely driven by an increase in observer effort, we compared linear regression coefficients of number of individuals over time for Richard's Pipit and other vagrants of eastern origin. As these data have always been collected using the same methodology by the French Birds Rarities Committee, we assumed no reporting bias over time and we considered these data as an approximation of observer effort. We excluded the rarest species from the dataset (less than 5 records over the 1981-2019 period; the list of species and numbers of individuals can be found in Table  S1 ). We repeated these analyses for the Mediterranean region of southern France (defined as departments bordering the Mediterranean Sea) as this region.
Region CS38-40 is a sub-TAD boundary of the HoxD locus Through series of deletions and inversions in vivo, we recently showed that region CS38-40, located within the large T-DOM TAD flanking the HoxD cluster, was capable to act as a topological border  [18]. We further evaluated the properties of this region by generating Hi-C datasets using mouse limb cells at embryonic day 12. 5 (E12. 5) in a mutant line bearing a deletion of CS38-40 (del(CS38-40))  [18, 29]  along with wild-type control cells. We identified topological domains (TADs or sub-TADs) using the insulation-based hicFindTADs algorithm and confirmed the partitioning of the T-DOM TAD into two sub-domains at the level of CS38-40 in wild-type limbs. This boundary however appeared relatively weak and was only identified with the smallest window size that was applied (  240   ), demonstrating the merging of the two T-DOM sub-TADs into one single structure.
Alteration of local chromatin structure upon integration of the TgN(38-40) construct We next investigated the conformational state of the region hosting the TgN  (38) (39) (40)  construct in chromosome 10 using our control Hi-C dataset and observed the presence of a 1. 2 Mblarge, well defined TAD wherein the transgene had integrated (Fig  3B ). This domain contains We then assessed whether and how the four ectopic CTCF-binding sites would interfere with the host chromatin landscape, knowing that all four had the same orientation. We performed circular chromosome conformation capture-sequencing (4C-seq) using the CTCF sites present in the CS38 and CS40 elements as viewpoints in TgN  (38) (39) (40)  transgenic samples (Fig 3D, CS38 and CS40 tracks). Both CS38 and CS40 CTCF viewpoints established strong interactions with regions of their new genomic environment.
TLA TLA was performed as in  [32]  with the following adaptations. Limb cells were dissociated using collagenase type XI (Sigma-Aldrich, C7657) and the cell suspension was strained. Transgene-positive (TgN(38-40)/-) samples were identified by PCR and two E12. 5 brains were used as starting material. TLA inverse PCR was performed using the viewpoint-directed inverse PCR primers listed in S2 Table. TLA library preparation was achieved using the Nextera DNA Flex Library Prep (Illumina) protocol, independently for each viewpoint. Libraries were sequenced as 100 bp single-end reads with an Illumina HiSeq 4000. TLA data analysis was performed using the custom pipelines described hereafter. In brief, all alignments were produced using Bowtie 2 (version 2. 3. 5) [45]  and were sorted with SAMtools (version 1.
Control-FREEC transgene quantification Copy number quantification was performed using Control-FREEC version 11. 5  [36]. The signal from the ectopic CS38-40 (test dataset) was computed based on the TgN  (38) (39) (40)  total input gDNA data of the ChIPmentation experiment (see ChIPmentation below). Next, the signal from the endogenous region CS38-40 (control dataset) was created by pooling total input gDNA data of four samples that were all TgN(38-40)-negative and wild-type for region CS38-40 in chromosome 2, each processed independently. For both test and control datasets, the Control-FREEC signal, expressed as the number of reads scored for non-overlapping genomic windows of given sizes  [49] , was calculated along a 7 Mb region of chromosome 2 including the HoxD locus (chr2:71000000-78000000).
The above analysis was carried out using two different window sizes: 1 and 2 kb (see S2B Fig). Then, the software calculated the test/control signal ratio, that is the number of reads from test divided by the number of reads from control, for each window, multiplied by two in order to obtain absolute allele counts. At last, the Control-FREEC software evaluated copy numbers along the 7 Mb chr2 region starting from the (test/control)�2 ratio by a maximum (log-)likelihood estimation  [36].
5 TgN  (38) (39) (40) / TgN  (38) (39) (40) ; del(CS38-40) -/-headless and tailless embryo. The sample was proteinase K digested in digestion buffer (50 mM Tris-HCl pH 8, 10 mM EDTA pH 8, 200 mM NaCl, 0. 5% SDS) at 55˚C while shaking for 48 hours. HMW gDNA was purified with two successive rounds of phenol-chloroform extraction, followed by ethanol precipitation. Size selection was carried out using 0. 8x SPRI magnetic beads. nCATS was performed in two independent Cas9mediated release reactions corresponding to the two different pools of sgRNAs and the products from both reactions were pooled together prior to sequencing on MIN106D flow cell. MinION output data in fast5 format were converted to fastq using the Guppy basecaller (version 3. 1. 5) (Oxford Nanopore Technologies) and the reads were mapped with minimap2 (version 2.