dataesr/scientific-paragraphs-categorization huggingface dataset:
However, obtaining an optimal transcriptome from short-read RNA-Seq data is not a trivial task. Each de novo assembler suffers from different intrinsic error generation and no single assembler performs best on all datasets  11. To maximise diversity and completeness of potato cultivar transcriptomes, usage of multiple de novo transcriptome assemblers and various parameter combinations over the same input data was employed. Following this"over-assembly" step, we used tr2aacds pipeline from EvidentialGene  12  to reduce redundancy across assemblies and obtain cultivar-specific transcriptomes. Finally, we consolidated representative cultivar-specific sequences to generate a potato pan-transcriptome (StPanTr). These transcriptomes will improve high throughput sequencing analyses, from RNA-Seq and sRNA-Seq to more specific ones like ATAC-Seq, by providing a more comprehensive and accurate mapping reference.
Orphaned reads were re-assigned as single-end (SE) reads. Processed reads were pooled into cultivar datasets as properly paired-end (PE) reads or SE reads per cultivar per sequencing platform. For the Velvet assembler, SOLiD reads were converted into double encoding reads using perl script "denovo_preprocessor_solid_v2. 2. 1. pl"  15. To reduce the size of cv. Désirée and cv. Rywal datasets, digital normalization was performed using khmer from bbmap suite v37. 68  16  prior to conducting de novo assembly using Velvet and rnaSPAdes. PacBio long reads were processed for each sample independently using Iso-Seq 3 analysis software (Pacific Biosciences). Briefly, the pipeline included Circular Consensus Sequence (CCS) generation, full-length reads identification ("classify" step), clustering isoforms ("cluster" step) and"polishing" step using Arrow consensus algorithm.
Only high-quality full-length PacBio isoforms were used as input for further steps. PacBio Cupcake ToFU pipeline. Cupcake ToFU (github. com/Magdoll/cDNA_Cupcake) scripts  17  were used to further refine the Iso-Seq transcript set. Redundant PacBio isoforms were collapsed with "collapse_iso-forms_by_sam. py" and counts were obtained with "get_abundance_post_collapse. py". Isoforms with less than two supporting counts were filtered using "filter_by_count. py" and 5′-degraded isoforms were filtered using "fil-ter_away_subset. py". Isoforms from the two samples were combined into one non-redundant Iso-Seq transcript set using "chain_samples. py". De Bruijn graph based de novo assembly of short reads. Short reads were de novo assembled using Trinity v. r2013-02-25  18  , Velvet/Oases v. 1. 2. 10 19 , rnaSPAdes v.
3. 11. 1  20  and CLC Genomics Workbench v8. 5. 4-v10. 1. 1 (Qiagen). Illumina and SOLiD reads were assembled separately. For CLC Genomics de novo assemblies, combinations of three bubble sizes and 14 k-mer sizes were tested on PW363 Illumina dataset. Varying bubble size length did not influence the assembly statistics much (Supplementary Fig. 2 ), therefore we decided to use the length of 85 bp for Illumina datasets of the other two cultivars. Bubble size and k-mer length parameters used for Velvet and CLC are given in Table  1. The scaffolding option in CLC and Velvet was disabled. More detailed information per assembly is provided in Auxiliary Table 2  21. Decreasing redundancy of assemblies and annotation. 739 mio Désirée short reads were assembled into 3,765,661 potential transcripts, 284 mio PW363 short reads were assembled into 6,022,291 potential transcripts, and 710 mio Rywal short reads and 1.
4 mio Rywal PacBio sequences were assembled into 1,912,821 potential transcripts. While generation of several transcriptomes from diverse input data and various parameter combinations increases the likelihood of capturing and accurately assembling transcripts  22  , redundancy reduction without loss of information and error removal from the over-assemblies is required. All cultivar-specific transcriptome assemblies, compiled into cultivar-specific transcriptome over-assembly, were initially filtered with the tr2aacds pipeline (part of EvidentialGene v2016. 07. 11  12  ) which consists of four steps. First, all perfect redundant nucleotide sequences are removed using fastnrdb, part of the exonerate package  23  , leaving only the transcript with the longest coding region. Next, all perfect fragments of the remaining transcripts are removed using cd-hit  24.
These first two steps are important in reducing transcriptome redundancy, as true transcripts are expected to be assembled independently by multiple of the assembly methods. Keeping the transcripts with the longest and most complete coding region helps eliminate chimeric and misassembled transcripts, as these errors tend to occur more often in UTR regions or in a manner that causes frameshifts and long, incomplete coding regions  12  ). The third and the fourth step of the tr2aacds pipeline segregate transcripts that are likely isoforms, alleles, or other variations seen at a single locus. This is done through amino acid sequence clustering, which identifies putative transcripts that vary only in silent mutations, and through reciprocal BLAST, which detects high-identity exon-sized alignments (likely isoforms).
A tag is assigned to all transcripts providing detailed information on why they were discarded (e. g. perfect fragments, perfect duplicates, very high similarity, …) or why they were marked Fig. 1  Merging DM Phureja PGSC and ITAG gene models. (a) Decision tree for the merging of both genome models, with 6 possible outcomes: singleton genes ('keep gene as is'), manual curation ('gene cluster to manual curation' and programmatic solution (all remaining 4 options). Green solid lines represent a satisfied condition (Y: Yes), dashed red lines, an unsatisfied condition (N: No). (b) Example of manual curation in the merged DM genome GTF, region visualisation (chr12:11405699. 11418575) in the Spud DB (solanaceae. plantbiology. msu. edu) Genome Browser  13. ITAG defined Sotub12g014200. 1. 1 spans three PGSC defined coding sequences (PGSC0003DMT400005728, PGSC0003DMT400005745 and PGSC0003DMT400005726).
2 ). The de novo cultivar-specific transcript sets were first mapped to the DM reference genome by STARlong 2. 6. 1d 25 using parameters optimized for de novo transcriptome datasets (all scripts are deposited at FAIRDOMHub (fairdomhub. org) project home page  26  ). Aligned transcripts were analysed with MatchAnnot to identify transcripts that match the PGSC or ITAG gene models. Domains were assigned to the polypeptide dataset using InterProScan software package v5. 37-71. 0  27. For all transcripts and coding sequences, annotations using DIAMOND v0. 9. 24. 125  28  were generated by querying UniProt (www. uniprot. org) retrieved databases (E-value cut-off 10 -0. 5 and query transcript/cds and target sequence alignment coverage higher or equal to 50%; custom databases: Solanum tuberosum, Solanaceae, plants).
Initially assembled transcriptomes were also screened for nucleic acid sequences that may be of vector origin (vector segment contamination) using VecScreen plus taxonomy program v. 0. 16  29   Fig. 2  Bioinformatics pipeline for generation of potato transcriptomes. Software used in specific steps are given in bold. Input datasets (sequence reads) and output data (transcriptomes) are depicted as blue cylinders. Data upload steps to public repositories are shaded in orange. Abbreviations: SRA -NCBI Sequence Read Archive, PGSC -Potato Genome Sequencing Consortium, ITAG -international Tomato Annotation Group, CLC -CLC Genomics Workbench, PacBio -Pacific Biosciences Iso-Seq sequencing, Tr -transcriptome, StPanTr -potato pantranscriptome, tr2aacds -"transcript to amino acid coding sequence" Perl script from EvidentialGene pipeline.
MatchAnnot, InterProScan and DIAMOND were used as biological evidence in further filtering by in-house R scripts. Transcripts that did not map to the genome nor had any significant hits in either InterPro (www. ebi. ac. uk/interpro) or UniProt (www. uniprot. org) were eliminated from further analysis to obtain higher reliability of constructed transcriptomes  [30] [31] [32]. Pajek v5. 08  33  , in-house scripts, and cdhit-2d from the CD-HIT package v4. 6  24  were used to re-assign post-filtering representative and alternative classes and to obtain finalised cultivar-specific transcriptomes. The whole redundancy removal procedure reduced the initial transcriptome assemblies by 18-fold for Désirée, 38-fold for Rywal, and 24-fold for PW363. Completeness of each initial de novo assembly to cultivar-specific transcriptome was estimated with BUSCO (Supplementary Figs.
1 2 3 ). Individual contributions by various assembly methods were investigated in light of what contributed to the final, cleaned cultivar transcriptomes. SOLiD assemblies (Supplementary Fig. 1 : CLCdnDe1, CLCdnDe8, VdnDe8-10), produced by either CLC or Velvet/Oases pipelines, contributed least to transcriptomes, which can mostly be attributed to short length of the input sequences. Interestingly, increasing k-mer size in the CLC pipeline for Illumina assemblies produced more complete assemblies according to BUSCO scores and more transcripts were selected for the initial transcriptome (Supplementary Fig. 1 : CLCdnDe1-7, CLCdnDe9-14). On the contrary, increasing k-mer length in Velvet/Oases pipeline lead to transcripts that were less favoured by the redundancy removal procedure (Supplementary Fig.
1 2 3 4 5 6 7 ). The Trinity assembly was comparable to the high k-mer CLC assemblies in transcriptome contribution and BUSCO score (Supplementary Fig. 1 ). It might seem that the PacBio Iso-Seq transcripts contributed less than expected to the cv. Rywal transcriptome (Supplementary Fig. 3 ), however it should be noted that a considerable number of PacBio transcripts was assigned to the EvidentialGene drop set because they had perfect or near-perfect CDS identity of transcripts assembled by CLC. The EvidentialGene pipeline also prioritised CLC-assembled transcripts over PacBio transcripts because the redundancy removal algorithm reorders the near-perfect duplicates by transcript name and only retains the first transcript listed (Auxiliary Table  6 34  ).
Potato pan-transcriptome construction. While the PGSC gene model defined transcripts as well as coding sequences, the ITAG gene model defined only coding sequences. Therefore, the potato pan-transcriptome construction was conducted at the level of CDS. Cultivar-specific representative coding sequences (57,943 of Désirée, 43,883 of PW363 and 36,336 of Rywal) were combined with coding sequences from the merged Phureja DM gene models (17,880 and 31,442 non-redundant PGSC and ITAG genes, respectively) and subjected to the cdhit-est  24  algorithm (global sequence identity threshold 90%, alignment coverage for the shorter sequence 75%, bandwidth of alignment 51 nt and word length of 9) to create potato pan-trancriptome. Sequences that did not cluster using cdhit-est were separated into tetraploid and DM datasets and subjected to the cdhit-2d 24 algorithm (local sequence identity threshold 90%, alignment coverage for the shorter sequence 45%, bandwidth of alignment 45 nt and word length of 5).
Sequences that are shared by the DM merged gene model and de novo assembled cultivar-specific transcriptomes were designated as "core" transcripts, and sequences that were assembled in only one transcriptome were designated "genotype-specific". The total pan-transcriptome includes 96,886 representative, non-redundant transcripts and 90,618 alternative sequences (covering alternative splice forms, allelic isoforms and partial transcripts) for those loci (Fig. 3 , Supplementary Fig. 4 , Auxiliary Table 7  35  ). The core subset of the pan-transcriptome contains 68,708 sequences, among which 12% are partial sequences. Polyploid crop pan-genomes generally consist of many cultivar-specific genes  36. Therefore we included all genotype-specific sequences in our potato pan-transcriptome (Fig. 3 , Supplementary Fig.
Data records Transcriptomic sequences of three potato genotypes, cv. Désirée, cv. Rywal and breeding clone PW363, were obtained from in-house RNA-Seq projects 37-41 and supplemented by publicly available cv. Désirée datasets retrieved from SRA 42-44 (Table  2 ). All generated files have been deposited to FAIRDOMhub  26  under project name _p_stRT (https://fairdomhub. org/projects/161). The largest quantity of reads, cca. 739 mio reads of various lengths, was obtained for cv. Désirée, using Illumina and SOLiD short-read sequencing platforms. For cv. Rywal and breeding clone PW363 only mature leaf samples were available. For cv. Désirée leaf samples were augmented with samples from stems, seedlings and roots. For cv. Rywal short-read sequencing was complemented with full-length PacBio Iso-Seq sequencing of independent samples.
Detailed sample information is provided in Auxiliary Table  1   45. Cv. Rywal NPR1-1 coding sequences, sequenced by the Sanger method, were deposited at NCBI GenBank under accession numbers MT210578, …, MT210585  [46] [47] [48] [49] [50] [51] [52] [53]. The GTF file with merged ITAG and PGSC gene models for S. tuberosum Group Phureja DM genome v4. 04 14 is also available at FAIRDOMHub project page, as well as the cultivar-specific and pan-transcriptome assembly FASTA and annotation files  54.
Technical Validation As a measure of assembly accuracy, the percentage of correctly assembled bases was obtained by mapping Illumina reads back to cultivar-specific initial transcripts using STAR v. 2. 6. 1d RNA-seq aligner with default parameters (Table  3 ). To assess the quality of transcriptomes via size-based and reference-based metrics, we ran TransRate v 1. 0. 1  55  on cultivar-specific transcriptomes (prior to and post filtering, Table  4 ), cultivar-specific representative transcript sequences and PGSC defined representative transcripts (Table  5 ). Comparative metrics for cultivar-specific coding sequences (CDS) were obtained using Conditional Reciprocal Best BLAST (CRBB)  56  against merged Phureja DM gene model coding sequences. To estimate the measure of completeness and define the duplicated fraction of assembled transcriptomes (prior and post filtering cultivar-specific transcriptomes and pan-transcriptome), BUSCO v3 57 scores were calculated using embryophyta_odb9 58 lineage data (Table  6 ).
At the cultivar-specific transcriptome level, the most diverse dataset in terms of tissues and experimental conditions, the cv. Désirée dataset, resulted in the highest BUSCO score as expected. The success in classification of representative and alternative transcripts is evident from the pan-transcriptome BUSCO scores (i. e. differences in single-copy and duplicated BUSCOs for representative and alternative dataset). The highest number of fragmented BUSCOs is observed for the breeding clone PW363, which we can probably attribute to the highest number of short-contig assemblies. Furthermore, the long-read assembly presumably contributed to the shift in favour of single-copy BUSCOs (Table  6 ) and uniquely mapped reads (Table  3 ) for cv. Rywal. To inspect the quality of paralogue cluster assignments, multiple sequence alignments (MSA) using MAFFT v7.
271  59  were conducted on representative and alternative sequences from paralogue clusters  35  containing sequences from each of the four genotypes (Désirée, PW363, Rywal and DM). Alignments were visualized using MView v1. 66  60  (Auxiliary File 2  61  ). These alignments were used to quality check de novo transcriptome assemblies  and helped us optimise the pipeline. The alignments within groups showed differences that can be attributed to biological diversity, e. g. SNPs and INDELS as well as alternative splicing  61. To estimate the proportion of transcripts originating from organisms other than potato, we performed a taxonomic classification of all cultivar-specific transcriptome sequences using Centrifuge v1. 0. 4  62  and the NCBI nt database. We used pavian  63  to generate classification summary reports (Auxiliary File 3  64  ).
Usage Notes Insights into variability of potato transcriptomes. Iso-Seq dataset in the case of cv. Rywal confirms that long reads contribute to less fragmentation of the de novo transcriptome. It is therefore recommended to generate at least a subset of data with one of the long-read technologies to complement the short-read RNA-seq. As it can be seen by the reduction rate in PW363 (24-fold), producing additional short-read assemblies does not contribute to the transcriptome quality as much as having several tissues or a combination of 2 nd and 3 rd generation sequencing (38-fold Rywal). From all four genotypes, cv. Désirée has the highest number of cultivar-specific representative transcripts, which can be attributed to having the most diverse input dataset used for the de novo assemblies in terms of tissues sequenced (stem, seedlings and roots) and experimental conditions covered.
Comprehensive protein databases, obtained from transcriptomic data, offer a higher chance of finding significant targets with peptide spectrum match algorithms, thus enhancing the detection and sensitivity of protein abundance measurements  68. Using transcriptomes to inform qPCR amplicon design. Aligning transcript coding sequences from a StPanTr paralogue cluster can be used to inform qPCR primer design in order to study gene expression in different cultivars or specific isoforms by selecting variable transcript regions (Fig. 5 ). On the other hand, when qPCR assays need to cover multiple cultivars, the nucleotide alignments can be inspected for conservative regions for design.
Limitations. Although the here presented transcriptomes are of higher quality than assemblies produced by a single assembler they are neither comprehensive nor perfectly accurate. Firstly, for cv. Rywal and breeding clone PW363 reference transcriptomes are leaf-specific. Additionally, some transcripts are represented by partial sequence(s) and some might be misassembled. Due to very low expression, some transcripts are missed. One also has to note that some true transcripts with very high similarity to the representative transcript, potentially expressed from distinct alleles differing in the SNP pattern, were removed by filtering. Furthermore, most long non-coding mRNAs are discarded in the EvidentialGene step. When mapping reads back to the initial transcriptomes a fraction of reads was spliced (Table  3 ), indicating that the transcripts to which these reads map might originate from splice variants that are not present in the transcriptome.
We would however like to point out that contiguity of this reads was not confirmed thus they might represent misassemblies. In Désirée 14,094 transcripts had uniquely mapped spliced reads (7% of the filtered transcriptome), and similarly 7,493 (9%) transcripts in Rywal and 10,220 (6%) in PW363. Low number of non-canonical sites was however confirmed by STARlong mapping: 118 for Désirée, 59 for Rywal and 101 for PW363. An indication of potential problems with some of the assemblies is that the number of transcripts with length less than 200 nt is 10x higher in constructed cultivar-specific transcriptomes than in DM Phureja PGSC representative transcripts (875, 1377 and 1310 vs. 87 for Désirée, PW363 and Rywal vs. DM Phureja) while the transcript length is about 300 nt shorter in average (Table  5 ).
Mapping of independent dataset to newly assembled cultivar specific reference transcriptome. Mapping statistics for Désirée leaf samples under drought stress to Désirée, ITAG/PGSC merged and PGSC representative transcriptome sequences is shown. RNA-seq data from Désirée leaf samples under drought stress retrieved from the GEO Series GSE140083 -"Transcriptome profiles of contrasting potato (Solanum tuberosum L. ) genotypes under water stress". No chimeric reads detected. # Relevant % of mapped reads: % of uniquely mapped reads + % of reads mapped to multiple loci.
Acknowledgements We thank  Robin Buell  for potato PGSC-ITAG gene model reference table,  Thomas Doak  and  Don Gilbert  for advice on using EvidentialGene scripts,  Henrik Krnec  for BLAST output parser,  Andrej Blejec  for FAIRDOMhub API usage in R,  Špela Baebler  for providing DSN Illumina-sequenced samples and  Karmen Pogačar  for providing Rywal cDNA samples for technical validation. This project was supported by the  Slovenian Research Agency  (grants  P4-0165 ,  J4-4165 ,  J4-7636 ,  J4-8228  and  J4-9302 ), COST actions CA15110 (CHARME) and CA15109 (COSTNET).
Code availability All used Bash, Perl, Python, and R/Markdown custom code and scripts complemented with intermediate and processed data (input and output files), and all other supporting information that enable reproduction and re-use are available at FAIRDOMHub under project name _p_stRT (fairdomhub. org/projects/161) under CC BY 4. 0 licenses. Data were locally stored in a ISA-tab compliant project directory tree generated using pISA-tree (github. com/NIB-SI/pISA) and uploaded to FAIRDOMHub repository using FAIRDOMHub API and R package pisar (github. com/NIB-SI/pisar).
The use of long reads were critical in achieving this contiguity and completeness. The complete genome is composed of 1. 7% (93 kbp) of genes with known function, 28. 0% (1,545 kbp) of 6,806 ORFs (each of at least 90 bp), 3. 7% (205 kbp) of repeats, and 66. 6% unclassified sequences. Of the ORFs, 1,039 are at least 300 bp (100 amino acids) in size and compose 7. 2% (400 kbp) of the genome. Aligning the ORFs with BLASTP, 63 ORFs (17 ORFs of at least 300 bp) have a significant (E < 0. 001) hit to the nr database. Plastid-derived sequences composes 0. 25% (14 kbp) of the genome spread across 24 segments. The nuclear repeats LTR/Gypsy compose 51% of the repeat sequence, LTR/Copia compose 7%, simple repeat sequences compose 34%, low complexity compose 3%, and 5% are other repeat sequences. The 36-bp Bpu repeat sequence is present in roughly 500 copies in Cycas taitungensis and roughly 100 copies in Ginkgo biloba  (Guo et al.
Data Availability The Oxford Nanopore MinION sequencing data has SRA accessions SRX5081713, and SRR8264752 through SRR8264769. The Illumina HiSeq sequencing data has SRA accession SRR5028199. The Sitka spruce (Picea sitchensis) genotype Q903 complete mitochondrial genome has NCBI GenBank accessions MK697696 through MK697708.
ACKNOWLEDGMENTS This work is supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement number  640891  ( DREAM  project ) , by  ANR-17-EURE-0017   FrontCog  and by  ANR-10-IDEX-0001-02 PSL. Simulations presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the  INRIA   ALADDIN development action  with support from  CNRS, RE-NATER , and several universities as well as other funding bodies (see https://www. grid5000. fr).
Dispersal is an essential process in population and community dynamics, but is difficult to measure in the field. In freshwater ecosystems, information on biological traits related to organisms' morphology, life history and behaviour provides useful dispersal proxies, but information remains scattered or unpublished for many taxa. We compiled information on multiple dispersal-related biological traits of European aquatic macroinvertebrates in a unique resource, the DISPERSE database. DISPERSE includes 39 trait categories grouped into nine dispersal-related traits for 480 taxa, including Annelida, Mollusca, Platyhelminthes, and Arthropoda such as Crustacea and Insecta,.
generally at the genus level. Information within DISPERSE can be used to address fundamental questions in metapopulation ecology, metacommunity ecology, macroecology and evolutionary ecology research. Information on dispersal proxies can be applied to improve predictions of ecological responses to global change, and to inform improvements to biomonitoring and conservation management strategies. The diverse sources used in DISPERSE complement existing trait databases by providing new information on dispersal traits, most of which would not otherwise be accessible to the scientific community.