dataesr/scientific-paragraphs-categorization huggingface dataset:
20. 0 using default parameters  (Chen et al. , 2018) , aligned to the reference genome of the Normal C. clupeaformis (see below) with bwa-mem  (Li & Durbin, 2009) , and filtered to keep mapping quality over 10 with samtools version 1. 8  (Li et al. , 2009). Duplicate reads were removed with MarkDuplicates (picardtools version 1. 119. ) We realigned around indels with gatk IndelRealigner  (McKenna et al. , 2010)  and soft clipped overlapping read ends using clipOverlap in bamutil version 1. 0. 14  (Breese & Liu, 2013). The pipeline is available at https://github. com/enorm andea u/wgs_sample_prepa ration.
5, default parameters) using overlap sizes of 8, 10 and 12 kb and the assembly with the best N50 was chosen (10 kb). The final assemblies were first polished with their respective long reads using marginpolish (version 1. 2. 0 https:// github. com/UCSC-nanop ore-cgl/Margi nPolish) for the Normal and pepperpolish (default settings, model: pepper_r941_guppy305_ human. pkl), a successor program with similar performance, for the Dwarf. In a second step, each assembly was polished with short F I G U R E 1 Overview of the study design. Sampling and sequencing design, which included 32 wild samples of Coregonus clupeaformis Normal sp. and Dwarf sp. from Cliff Lake and Indian Lake in Maine (USA), sequenced by Illumina short reads, as well as two samples from Cliff Lake (one Normal and one Dwarf), sequenced by Nanopore long reads to assemble genomes.
The insets represent the geographical locations of the two lakes sampled for this study and a schematic phylogeny of the different populations based on relationships inferred in  Rougeux et al. (2017) , the arrows representing ongoing gene flow (one migrant per generation in Cliff Lake, 1-30 migrants per generation in Indian Lake). The flowchart displays the main features of the pipeline of analysis performed to detect and genotype structural variants (SVs) with different data sets reads using pilon  (Walker et al. , 2014)  requiring a minimal coverage of 3× to polish (version 1. 23, --mindepth 3). busco (Benchmarking Universal Single-Copy Orthologs) scores were computed to assess gene space completeness by looking for the presence or absence of highly conserved genes (busco version 3. 0. 2, reference database: actinopterygii_odb9 -sp zebrafish).
Only positions with at least 3× coverage were kept. A linkage map was built using lep-map3  (Rastas, 2017)  following a pipeline available at https://github. com/clair emero t/lepma p3_ pipeline. With the Filtering module, markers with more than 50% of missing data, that were noninformative, and with extreme segregation distortion (χ 2 test, p < 10 -12 ) were excluded. Markers were assigned to linkage groups (LGs) using the SeparateChromosomes module with increasing values of the logarithm of the odds (LOD) from 8 to 11 and a minimum size of 20 markers. Markers unassigned to LGs, or released from LG correction, were subsequently joined to LGs using the module JoinSingle with decreasing values of LOD until LOD = 3 and a minimum LOD difference of 1. This procedure assigned 5188 markers into 40 LGs.
| Annotation for genes and TEs Gene content annotation of both genomes was made with the NCBI Prokaryotic Genome Annotation Pipeline using the following transcriptome sources available on NCBI: Dion-Côté: PRJNA237376; Rougeux: 72 liver RNA samples from 2018, NCBI: PRJNA448004; Carruthers: SRR6321817, SRR6321818, SRR6321819, SRR6321820, SRR6321821, SRR6321822, SRR6321823, SRR6321824; Pasquier: SRP058861 lake whitefish, SRP045143 European whitefish. We used repeatmodeler2  (Flynn et al. , 2020)  to build a library of TEs from the C. clupeaformis sp. Normal assembly. We had to slightly modify the repeatmodeler LTR pipeline because ltrharvest failed for an unknown reason. We instead substituted it with an equivalent program, ltrfinder-parallel  (Ou & Jiang, 2019) , to identify long terminal repeats (LTRs) in the genome.
We combined the LTR-specific library with the general repeat library as done in canonical repeatmodeler2. After obtaining the TE library, we relabelled the fasta headers of sequences that were identified in the LTR pipeline but were assigned an "Unknown" classification due to lack of homology to database sequences, to broadly classify them as LTR elements. We then used repeatmasker to annotate the locations of each repeat family in both the Normal and the Dwarf genomes. We used parseRM. pl (https://github. com/4urel iek/Parsi ng-Repea tMask er-Outpu ts/blob/maste r/parse RM. pl) to summarize the genomic abundance of each TE subclass (LTR, LINE, SINE, DNA-TIR, Helitron), correcting for overlapping masking which sometimes occurs with repeatmasker. We also used parseRM. pl to produce a landscape plot of the genome composition, where the TE-subclass composition is shown in 1% divergence windows (compared to each TE copy's respective consensus sequence), where low-divergence sequences suggest more recent insertions and higher divergence sequences suggest older insertions.
| Synteny, map, chromosomes and genome analysis To analyse synteny with related species, we first compared the linkage map to the previously published maps of C. clupeaformis  (Gagnaire, Normandeau, et al. , 2013)   et al. , 2011)  to extract syntenic blocks along the genome. Syntenic blocks were visualized in R using the package Circlize  (Gu et al. , 2014). To investigate chromosome types (acrocentric, metacentric), we used phased information from the linkage map by applying a method developed by  Limborg et al. (2016) , which uses phased progeny genotypes to detect individual recombination events. The cumulative number of recombination events between the first marker and increasingly distant markers was computed from both extremities of each chromosome and this recombination frequency (RFm) is expected to reach a plateau over a chromosome arm (see  Limborg et al.
, 2019)  with standard parameters. After filtering, the three VCFs were joined using jasmine  (Kirsche et al. , 2021)  using the following parameters: "--ignore_strand --mutual_distance --max_dist_linear=0. 5 --min_dist=100," and we kept SVs detected by at least two approaches. All scripts are available at https://github. com/clair emero t/assem bly_SV.
| SV detection based on long reads We mapped long reads from both the Dwarf and the Normal samples to the Normal reference using winnowmap2 version 2. 0 with the "--MD" flag to better resolve repetitive regions of the genome  (Jain et al. , 2020). SAM files were sorted and converted into BAM files with samtools version 1. 3. 1  (Li et al. , 2009). SV detection was performed with three long-read-specific SV calling programs: sniffles version 1. 0. 12 (Sedlazeck et al. , 2018)  (-l 50 -s 7 -n -1), svim version 1. 2. 0  (Heller & Vingron, 2019 ) (--insertion_sequences) and nanovar version 1. 3. 9 (Tham et al. , 2020)  with default settings. VCF files were filtered using custom R scripts to remove excess information and read names were added to preserve insertion sequences in the final VCF. We kept SVs detected by at least two callers after merging with jasmine version 1.
1. 0 (Kirsche et al. , 2021)  including refinement of insertion sequences with iris "max_dist_linear=0. 1 min_ dist=50 --default_zero_genotype --mutual_distance min_support=2 --output_genotypes --normalize_type --run_iris iris_args=--keep_ long_variants. " All scripts are available at https://github. com/krist inast enlok k/long_read_SV.
| SV detection based on short reads SVs among the 32 samples sequenced with short reads were identified using three independent approaches: (i) manta  (Chen et al. , 2016) , (ii) the smoove pipeline (https://github. com/brent p/smoove) which is based on lumpy  (Layer et al. , 2014)  and (iii) delly  (Rausch et al. , 2012). All of the approaches rely on the filtered bam files resulting from the alignment of the short reads to the Normal reference (as described above). All SV callers were run with default parameters except for smoove which was run by subsets of chromosomes, and delly by subsets of individuals. VCF outputs were formatted and filtered with custom scripts called "delly_filter," "manta_filter," and "smoove_filter" to include full sequence information. The three VCFs were joined using jasmine  (Kirsche et al.
We reported the overlap of SVs detected in more than one data set according to its type and its size. The sequences included in SVs (e. g. , the reference sequence in the case of a deletion, or the alternative sequence in the case of an insertion) were annotated for TEs using repeatmasker and the TE library of the Normal C. clupeaformis (see above). We explored the length of SV sequences covered by TE or simple repeats quantitatively (Tables  S4  and S5 ) and also categorized them as associated with TE or other kinds of repeats if more than 50% of the SV sequence was covered by a given TE family or other kind of repeats.
| SNP calling and genotyping To detect SNPs and genotype them, we analysed the short reads of the 32 samples, in bam format, with the program angsd version 0. 931  (Korneliussen et al. , 2014) , which accounts for genotype uncertainty and is appropriate for medium-coverage whole genome sequencing  (Lou et al. , 2020). Input reads were filtered to remove low-quality reads and to keep mapping quality above 30 and base quality above 20. Genotype likelihoods were estimated with the gatk method (-GL 2). The major allele was the most frequent allele (-doMajorMinor 1). We filtered to keep positions covered by at least one read in at least 50% of individuals, with a total coverage below 800 (25 times the number of individuals) to avoid including repeated regions in the analysis. From this list of variant and invariant positions, we selected SNPs outside SVs and with a minor allele frequency (MAF) above 5%.
| SV genotyping To genotype the identified SVs in the 32 samples, we used a genomegraph approach with the vg suite of tools  (Garrison et al. , 2018; Hickey et al. , 2020). Briefly, the full catalogue of SVs discovered (through assembly comparison and long-and short-read SV calling) was combined with the reference genome to build a variant-aware graph using the module vg autoindex -giraffe. Short reads from the 32 samples were then aligned to the graph with the module vg giraffe  (Sirén et al. , 2020). For each SV represented in the graph through a reference and an alternative path, a genotype likelihood was calculated with the module vg call. We then combined the VCFs of SV genotype likelihoods across the 32 samples. For population-level analysis, mirroring the filters applied for SNPs, we retained SVs covered by at least one read in at least 50% of samples, and with an alternative allele frequency between 5% and 95%.
68 Gb (Table  1 ). This reference genome had a high level of completeness, with 94% of universal single-copy orthologous genes in a busco analysis based on the actonipterygii database. A relatively high percentage of duplicated busco groups (44%) was observed, which is probably a consequence of the salmonidspecific whole genome duplication  (Allendorf & Thorgaard, 1984; Smith et al. , 2021). The genome of C. clupeaformis sp. Normal was composed of 60. 5% TEs (Figure  S5 , Table  S4 ). The greatest TE subclass representation in terms of total base pairs was DNA-TIR elements, taking up 24% of the genome. LINEs and LTRs were approximately equally abundant at about 13% of the genome each. Elements that were unclassified took up 9% of the genome. SINEs took up <1% of the genome, and rolling-circle/helitron elements were essentially absent.
Two classification models based on partial least squares discriminant analysis (PLS-DA) were built and compared. Moreover, Hierarchical PLS-DA shows a high proficiency in classifying the larvae, achieving a prediction accuracy of 84 % in validation. The scripts built along the work were compiled in a user-friendly standalone app (SETApp) freely accessible at https://github. com/UPV-EHU-IBeA/SETApp. The SETApp was tested in a real case scenario to fulfill the tedious requirements of a WWTP effect-directed analysis.
Image acquisition and processing Training and test image sets for the classification model construction were created using bright field pictures of sea-urchin larvae at 48 h postfertilization (hpf). Images of non-exposed control embryos were also taken. Cytation™ 5 (4x magnification objective, BIOTEK) image reader was used to automatically obtain an image mapping of 25 pictures per well all over the plates. The imaging conditions suggested for an optimal larva separation using Cytation5 are LED intensity = 2; IT = 115; CG = 4 and Focus Height = 2750 µm. The imaging conditions were optimized using GEN5 software (BIOTEK) to ensure an appropriate focus and contrast of the larvae. Raw images were saved as Tagged Image File Format (. tif). The images were processed to obtain a collection of up to 272 orientated and normalized larva pictures (Fig.
This way, the model was not biased towards any particular phenotype, and every larva was considered independent regardless of the experiment they came. The image of each larva was parametrized, calculating N = 14 image parameters concerning the shape of the larva (Table  1 ). Following this strategy, each larva was encoded to be the m th row of a matrix X (M x N), where the values of each parameter were placed in the N columns. The PLS-DA models were built using the Classification Toolbox built by  Ballabio and Consonni (Ballabio and Consonni, 2013)  working under a Matlab environment using randomized subset cross-validation to assure the independence of the validation subsets. The external validation was carried out by predicting the malformation level of the larvae belonging to the test set and comparing them to the level determined by the experts.
The sensitivity (estimation of the model ability to avoid false negatives), specificity (estimation of the model ability to avoid false positives), and the prediction error were calculated as outlined by Ballabio et al. (Ballabio et al. , 2018). An additional experiment to validate the size increase measurement was carried out by exposing the larvae to copper (II) chloride. The approach described by Carballeira et al. was implemented by two experts measuring the larvae under an inverted microscope coupled to an electronic camera and using NIS-Elements image analysis software v4. 30 (Nikon Instruments BV, Europe). Three larvae groups exposed to different concentrations of CuCl 2 were measured and compared to the size measurements obtained in the parametrization. The MATLAB App Designer was used to compile the image processing, analysis, and modelling scripts in a standalone app.
SETApp (v1. 0) Considering the obtained results, HPLS-DA was chosen to implement a MATLAB-based Application (SETApp) in the automatized sea urchin embryo test pipeline. This way, a standalone app (SETApp) has been released and is freely accessible at https://github. com/UPV-EHU-IBeA/ SETApp. Overall, the SETApp allows the user to measure and classify every sea-urchin larvae in a given digital microscope image and export the results and dose-response curves. The simplified graphical interface enables the user to select the path to folders, process, measure, and classify the images in a very intuitive manner. The SETApp includes two tabs, the Measurement, and the Classification tabs. The Measurement tab contains three folder browser buttons, egg group image selection (needed for growth calculation), control group selection, and sample selection (see SI.
The combination of levels 1 and 2 could result in a loss of information for interpretation purposes. Thus, the processed images are saved in the folders and can be checked for further studies. IT results are displayed in a bar plot and a table, and they can also be used to build a dose-response curve. Shortly, when the dataset is large enough, we will address the implementation of a new version based on deep convolutional architecture to improve the classification performance. Improvements in the classification model would bring the distinction between levels 1 and 2, and, at best, the classification between phenotypes, which would offer valuable information about the mode of action of the contaminants.
Acknowledgements Authors gratefully acknowledge financial support from the  Agencia Estatal de Investigación (AEI) of Spain  and the  European Regional Development Fund  through project  CTM2017-84763-C3-1-R  and the  Basque Government  through the financial support as a consolidated group of the Basque Research System ( IT1213-19 ). Iker Alvarez  is grateful to the  University of the Basque Country  and the  Université de Pau et des Pays de   L ′ Adour  for his cotutelle predoctoral scholarship.
Gene expression tends to reverse its developmental trajectory direction in ageing, a phenomenon previously linked to cellular identity loss. Our analysis of cortex, lung, liver and muscle transcriptomes of 16 mice, covering development and ageing intervals, revealed widespread but tissue-specific ageing-associated expression reversals. Cumulatively, these reversals create a unique phenomenon: mammalian tissue transcriptomes diverge from each other during postnatal development, but during ageing, they tend to converge towards similar expression levels, a process we term divergence convergence, or DiCo. We confirmed the DiCo pattern using data from independent mouse and human datasets. Further, using publicly available single-cell transcriptome data, we showed that DiCo could be driven both by alterations in tissue cell type composition and also by cell-autonomous expression changes within particular cell types.
Results We generated bulk RNA-seq data from 63 samples covering the cerebral cortex (which we refer as cortex), liver, lung, and skeletal muscle (which we refer as muscle) of 16 male C57BL/6J mice, aged between 2 to 904 days of postnatal age (see Methods). As mice reach sexual maturity by around two months  (Tacutu et al. 2018) , we treated samples from individuals aged between 2 and 61 days (n=7) as the development series, and those aged between 93 and 904 days (which roughly correspond to 80-year-old humans  (Flurkey, M. Currer, and Harrison 2007 )) (n=9) as the ageing series (Figure  1 figure supplement 1). The final dataset contained n=15,063 protein-coding genes expressed in at least 25% of the 63 samples (one 904 days old mouse lacked cortex data). Tissues diverge during postnatal development.
Considering the whole transcriptome without a significance cutoff, we found a weak correlation of age-related expression changes in tissue pairs, both during development (⍴=[0. 17, 0. 39], permutation test p<0. 05 for all the pairs, Figure  1 -source data 1), and ageing (⍴= [0. 23, 0. 33], permutation test p<0. 05 in 4/6 pairs, Figure  1-source data 1 ). The difference between inter-tissue similarities within the development and those within ageing was not significant (Wilcoxon signed-rank test, p=0. 31). Moreover, the number of genes with the same direction of change across four tissues was consistently more than expected by chance (p<0. 05), except for genes upregulated in ageing (Figures 1e, Figure  1 -figure supplement 4). This attests to overall similarities across tissues both within the development and within ageing datasets, albeit of modest magnitudes.
1; Supplementary File 2). Contrary to widespread expression change during development (13-41%), the proportion of genes undergoing significant expression change during ageing was between 0. 0001-15% (Figure  1d ). This contrast between postnatal development and ageing was also observed in previous work on the primate brain  (Somel et al. 2010; Işıldak et al. 2020). In terms of the number of genes with the significant ageing-related change, the most substantial effect we found was in the lung (n=2,319), while close to no genes showed a statistically significant change in the muscle (n=2), a tissue previously noted for displaying a weak ageing transcriptome signature across multiple datasets  (Turan et al. 2019). Not unexpectedly, we found no common significant ageing-related genes across tissues (Figure  1 -figure supplement 5a).
Considering the similarity between the ageing and development datasets (Figure  1c ) and the similar samples sizes in development (n=7) and ageing periods (n=9), the lack of overlap in significant genes in ageing might be due to low signal-to-noise ratios in the ageing transcriptome, as ageing-related changes are subtler compared to those in development (Figure  1 -figure supplement 5b). Gene expression reversal is a common phenomenon in multiple tissues. We then turned to investigate the prevalence of the reversal phenomenon (i. e. an opposite direction of change during development and ageing) across the four tissues. We first compared age-related expression change between development and ageing periods in the same tissue (Figure  1c ). This revealed weak negative correlation trends in liver and muscle (though not in the lung and cortex), i.
(Supplementary File 5). We then studied this pattern at the single-gene level. We tested each gene for significant CoV change in their expression levels (i. e. divergence or convergence) in development and ageing (Spearman correlation test with FDR<0. 1). We found that the ratio of divergent and convergent genes differed significantly between development (70% divergence among 2,581 significant genes) and ageing (68% convergence among 62 significant genes) (Figure  2d-e ). The same pattern was also observed without using significance cutoff (Figure  2 -figure supplement 5). To our knowledge, inter-tissue convergence during ageing is a novel phenomenon. In order to verify this trend, we used a second approach to test it in our mouse dataset. For each individual, we calculated correlations between pairs of tissues across their gene expression profiles.
Under the divergence-convergence pattern, we would expect pairwise correlations to decrease during development and increase during ageing. Among all pairwise comparisons, we observed a strong negative correlation during development (⍴=[-0. 61, -0. 9], nominal p<0. 05 in 5 out of 6 tests), while during ageing, 4 out of 6 comparisons showed a moderate positive correlation (⍴=[0. 16, 0. 69], nominal p<0. 05 in 1 out of 6 comparisons, Inter-tissue convergence during ageing is a reproducible trend in another mouse dataset and in humans. We investigated inter-tissue convergence during ageing in another mouse ageing transcriptome dataset that comprises 5 tissue samples (brain, lung, liver, kidney, spleen) collected from the same mice (n=18)  (Jonker et al. 2013). We confirmed the decrease in mean CoV during ageing (⍴ = -0.
58, p = 0. 01) as well as the increased pairwise tissue correlations in 7/10 tissue pairs (Figure  2 -figure supplement 8). We next investigated inter-tissue convergence in the GTEx dataset, which includes transcriptome data from humans covering a large set of tissues during the ageing period  (GTEx Consortium et al. 2017). We first confirmed the results using 47 individuals with samples in the same four tissues as those studied in mice. We first noticed a tendency for an age-related increased similarity between tissue samples of the same individual using the mean Euclidean distance between PCs ( ⍴ = -0. 23, p = 0. 12, Figure  2 -figure supplement 9). We then calculated mean and median CoV values (⍴meanCoV = -0. 12, pmeanCoV = 0. 41; ⍴medCoV = -0. 18, pmedCoV = 0. 22) and pairwise inter-tissue correlations (5/6 comparisons positive, none was significant); which also followed a trend compatible with intertissue convergence during ageing, albeit not significant (Figure  2 -figure supplement 10).