dataesr/scientific-paragraphs-categorization huggingface dataset:
AtRTD2 has greater transcript diversity than AtRTD1 AtRTD2 was generated from assembly of two RNA-seq datasets (8. 5 Bn paired end reads) and merger with transcript sets from Marquez et al  [14] , AtRTD1, TAIR10 and Araport11. Redundancy filters removed transcripts with the same intron co-ordinates which were shorter than other transcripts. The final AtRTD2 was made up of 33,674 unique transcripts from AtRTD1, of which 18,801 came from TAIR10 and 14,872 from Marquez et al. [14] ; 7,518 and 10,460 new transcripts from Datasets 1 and 2, respectively, and 30,538 transcripts from Araport11 reflecting the extended 5' and 3' UTR sequences generated in Araport11 compared to TAIR10. The overall number of transcripts in AtRTD2 was increased by ~18k transcripts in comparison to AtRTD1 (after removal of transcripts from AtRTD1 which were unsupported by splice junction readssee Methods and Supplemental Methods).
75) (Table  1 ). The increased transcript complexity in AtRTD2 compared to TAIR10 and AtRTD1 is also shown by the increased number of genes with higher numbers of transcripts (see Supplemental Figure  S3 ). A total of 13,524 protein-coding genes have at least one AS transcript in AtRTD2 (see Supplemental Figure  S3 ) such that 60. 23% of the 22,453 protein-coding, intron-containing genes in Arabidopsis undergo AS. Thus, AtRTD2 represents a non-redundant transcript dataset highly enriched in AS transcripts. Although different sources of transcripts have been used to generate the AtRTD2, all possible developmental stages and environmental conditions are not covered such that it is likely that some AS transcripts are missing. Therefore, new releases of AtRTD will be generated as other high quality RNA-seq data becomes available.
Quantification of transcripts with Salmon and AtRTD2 To demonstrate the utility of the AtRTD2, we have used it to quantify transcripts in RNA-seq data with the Salmon quantification tool  [41]. To validate the quantification of the resulting changes in AS transcript ratios, we have compared the RNA-seq data to HR RT-PCR data generated using the same RNA samples. Previously, HR RT-PCR  [52]  proved to be instrumental for validating transcript assemblies from RNA-seq data: HR RT-PCR and Sanger sequencing of amplicons confirmed >90% of 586 assembled AS transcripts for 256 genes in the Arabidopsis normalized library RNA-seq  [14]. Here, AtRTD2 transcript structures were compared to the amplicons in HR RT-PCR and the TPMs of individual transcripts used to calculate splicing ratios for each of the AS events or event combinations in that region (see Supplemental Figures S4, S7, S9-11).
To directly compare the output from Salmon with HR RT-PCR, we analysed a total of 762 data points derived from 127 AS events from 62 genes and three biological replicates of the two time-points (T1 and T2, see Methods). In some cases, HR RT-PCR detected relatively low abundance AS transcripts which were not identified in RNA-seq and, therefore, to make direct comparisons, we calculated a splicing ratio by comparing the abundance of individual AS transcripts to that of the fully spliced (FS) transcript, which is usually the most abundant transcript and codes for the full-length protein. The Pearson's correlation coefficient was 0. 722 and the Spearman's rank correlation coefficient was 0. 804 for AtRTD2 (Figure  2a ). These correlation coefficients were unexpectedly less than those observed with AtRTD1 -0.
905 and 0. 907 respectively  [43]. In general, the ratios using AtRTD2 tended to over-estimate the abundance of AS transcripts, and we observed that some genes with transcripts with variation in their 5' and/or 3' UTR lengths tended to show discrepancies between the splicing ratios obtained from HR RT-PCR and Salmon/AtRTD2. To show that the quality of AS change quantification depends on the completeness and diversity of transcript models in a reference transcriptome, we also compared the AS/FS splicing ratios obtained with HR RT-PCR to those derived from TPMs generated by analysing the same RNA-seq data using Araport11 as the reference transcriptome. We predicted that the smaller number of transcripts in Araport11 (ca. 48. 4k transcripts) would impact transcript quantification and AS. We observed a Pearson's correlation coefficient of 0.
e. not full-length) or to local variation or heterogeneity in transcription starts  [53]  or cleavage/polyadenylation sites  [54]. We reasoned that modifying transcripts so they had the same start and end co -ordinates should improve quantification of isoforms. Therefore, to demonstrate that UTR length variation impacted quantification of such genes, we took two approaches: 1) we trimme d transcripts from the 5' and 3' ends to the co-ordinates of the transcript(s) that covered the smallest region of the gene (see Supplemental Figure  S5a  and S5b) or 2) we padded genomic sequence from the ends of the shorter transcripts up to the co-ordinates of the end of the transcript(s) that covered the biggest region of the gene (see Supplemental Figure  S6a  and S6b ). These modifications were performed genome-wide on AtRTD2 to generate AtRTD2-trimmed and AtRTD2-padded, and the RNA-seq data were analysed with Salmon and the modified AtRTD2 datasets.
The impact on isoform quantification is illustrated for AT3G23280 (XBAT35) where trimming to the length of the shortest transcript (AT3G23280. s1) removes exons 1 and 2 of the other transcripts, generating new UTR variation and causing large changes in the TPM values of the transcripts (see Supplemental Figure  S7 ). We found an overall increased correlation of AS/FS ratios from HR RT-PCR with the AtRTD2-padded version (Figure  2 ). To examine whether the effect was due to quantification using Salmon, we also analysed our RNA-seq data with Kallisto using both AtRTD2 and AtRTD2-padded. We found that Kallisto generated very similar results and also showed an improved correlation of HR RT-PCR data with the AtRTD2-padded version (see Supplemental Figure  S8 ). We examined the effects of padding of AtRTD2 in detail for some genes/transcripts.
TPM values were similar using AtRTD2 and AtRTD2-padded for genes where transcripts had little or no differences in their 5' or 3' ends (for example see AT5G05550 (VFP5) -Supplemental Figure  S9 ). For genes with 5' and 3' end variation in the first and/or last exon, AtRTD2-padded gave more accurate TPM values. This was clearly shown by the three-way corroboration of AS/FS values from HR RT-PCR, from TPMs from analysing the RNA-seq data with Salmon and Kallisto using AtRTD2-padded, and from manual counting of splice junction reads in a read alignment viewer. For example, there is a 10-fold difference in the AS/FS splicing ratios of the Alt3'ss event in CRY2 between the HR RT-PCR data, Salmon/AtRTD2-padded and read counts when compared to analysis with AtRTD2 or Araport11 (Figure  4 ). Further examples are shown for genes encoding a RING/Ubox superfamily protein and HSF3 (see Supplemental Figures  S10  and S11 ).
The second aspect is unlikely to be affected by the different length of 5' and 3' UTRs in the transcript annotations. However the positional bias is a statistical measure that is estimated based on all transcripts, or a few transcript bins segregated by transcript length  [56]. For isoforms with different lengths of 3' or 5' UTRs, the positional bias at the same corresponding genomic location within the isoforms, which should be the same, could be characterised and quantified very differently such that, again, differences in 3' and 5' UTRs due to incompleteness or false extension are not well accounted for by the positional bias corrections. We therefore tested these functions to see their effect on the improved accuracy obtained with padding by analysing the RNA-seq data using Salmon with the EffectiveLengthCorrection function turned off and using Kallisto with the read bias correction turned off using both AtRTD2 and AtRTD2-QUASI as references.
Discussion In this paper we report a new Arabidopsis thaliana Reference Transcript Dataset (AtRTD2) for use in quantification of individual transcript abundances using the lightweight alignment programmes such as Salmon and Kallisto. The new AtRTD2 release contains around 82k non-redundant transcript isoforms where each transcript from a gene represents a different alternatively spliced transcript with at least one different splicing event. In addition to increased diversity of isoforms, stringent criteria have been applied to support the AtRTD2 transcripts. The AtRTD2 resource contains a significantly higher number of transcript isoforms than other current collections such as TAIR10 and Araport11. Accurate quantification of transcripts is essential for downstream differential expression analyses and depends on the diversity, quality and completeness of the reference transcripts (missing and mis-assembled can severely affect quantification) and thereby the transcriptome.
Here, we have shown the drastic effects on quantification of missing tr anscripts both at the individual gene and transcriptome reference levels. Although AtRTD2 is likely to still be incomplete, further short and long read data can be continually incorporated to eventually generate a full A. thaliana transcriptome. Finally, extensive validation of transcripts and their abundance is important in assessing the quality of RNA-seq data and its analysis. Previously, we developed HR RT-PCR for analysis of changes in alternative splicing  [5, 19, 24, 52, 58, 59]  and have used it to validate transcript assemblies from RNA-seq data  [14]. Here, we have performed extensive validation with HR RT-PCR and showed good correlation between splicing ratios calculated from TPMs from RNA-seq data analysed with both Salmon and Kallisto and the HR RT-PCR.
However, this detailed analysis also identified inaccurate quantification for genes with variation in the lengths of their 5' and 3' UTRs and we have, therefore, generated another resource, AtRTD2-QUASI, which greatly improves the accuracy of quantification for many genes (see below). RNA-seq is now widely used in transcriptome/expression analyses in plants to examine a wide range of developmental and environmental questions. In Arabidopsis, most RNA-seq data has been analysed with the TopHat/Cufflinks/Cuffdiff pipeline using TAIR9 or TAIR10 as references, despite the knowledge that these datasets are relatively poor in their coverage of AS transcripts. In addition, recent comparisons of different assembly and quantification programmes have pointed to the high degree of inaccuracy in transcript assembly of the most widely used programmes  [37, 38].
There is wide variation in the performance of different computational methods for detection of plant AS in RNA-seq data, and the quality of annotation can have a major impact  [60]. RNA-seq analysis using the combination of the AtRTD2 transcriptome with Salmon and extensive validation has allowed us to identify a major issue in the accuracy of quantification due to transcripts from the same gene having different lengths of 5' and/or 3' UTRs and that correction functions within quantification programmes do not appear to deal effectively with this problem. Thus, current methods of RNA-seq analysis in Arabidopsis are likely to be inaccurate at a number of different levels which represents a major problem for plant RNA-seq given that Arabidopsis has the most advanced genome and transcriptome annotation.
Variation of a few to tens of nucleotides may arise from artefacts of reverse transcription, priming of oligo-dT to internal sequences such that full-length cDNAs are not produced or different protocols of cDNA library preparation prior to RNA-seq. Finally, variation may be due to in vivo or in vitro RNA degradation. As much of the variation in UTRs is likely to be due to uncertainty of the 5' and 3' ends of a transcript and as the discrepancy in quantification using AtRTD2 suggests that many of the most widely used RNA-seq analysis programmes are unable to take UTR length variation into consideration effectively, we have also developed AtRTD2-QUASI for accurate quantification of AS isoforms. We propose that RNA-seq data is analysed with Salmon or Kallisto using AtRTD2-QUASI because quantification data agrees well with data obtained experimentally (HR RT-PCR) and data derived from manually counting splice junction reads.
The three-way corroboration of results suggests that AtRTD2-QUASI is a practical solution to obtaining good quantitative data on transcript isoforms. Although AtRTD2-QUASI improves the quantification markedly for the majority of genes, particular gene arrangements may not be reported on accurately and data should be treated with caution. For example, if quantification of genes with different bona fide transcription start sites and with different polyadenylation sites is performed by Salmon/Kallisto with AtRTD2-QUASI, experimental data will be required to decide on which output to use or whether either is appropriate. Finally, we have translated the >82k unique transcripts from the authentic translational start site to reflect the most realistic open reading frames in different AS isoforms (e.
Conclusions In this study we have developed a new Reference Transcript Dataset for A. thaliana (AtRTD2) for quantification of transcripts and changes in alternative splicing. We have demonstrated the impact that incomplete transcriptomes can have on quantification and that, therefore, transcriptomes need to be constantly refined with new data. Our analysis has also demonstrated the value of extensive experimental validation of RNA-seq data. In addition, we found that variation in the lengths of 5' and 3' UTRs of transcripts from the same genes perturbed accurate transcript quantification, and that current programmes appear unable to correct for such variation. This is likely to impact RNA-seq analyses of all organisms. We have generated AtRTD2-QUASI as a practical solution for quantifying transcript splice isoforms in Arabidopsis.
Experimental validation of the quantification of splicing ratios by High Resolution RT-PCR To validate the quantification of splicing ratios from transcript isoforms, HR RT-PCR  [52]  was performed on RNA from two different time-points (each with three biological repeats): dawn at 20°C (T1) and in the middle of the dark period four days after transfer to the low temperature (T2). T1 and T2 are from the same plant material as used for RNA-seq of Dataset 1. A total of 762 data points (127 AS events from 62 genes and three biological replicates of the two time-points) were analysed by HR RT-PCR using gene-specific primers (Table  S2 ). Primer pairs covering the AS events in these genes, where the upstream primer was end-labelled with a fluorescent tag, were used in RT -PCR reactions with 24 cycles of PCR as described previously  [5, 14, 24, 52]  and separated on an ABI 3730 automatic DNA sequencing machine.
The abundance of RT-PCR products was analysed with GeneMapper software and splicing ratios were calculated from peak areas of each product. We analysed the RNA-seq data from the same regions of these 62 genes and used the transcripts per million (TPM) values of the transcripts generated by Salmon on the RNA-seq data to calculate splicing ratios. For comparison with HR RT-PCR, Spearman and Pearson correlations were computed on splicing ratios.
AtRTD2-QUASI The AtRTD2 was modified to examine the effects of transcript 5' and/or 3' end length variation in genes on isoform quantification. Transcripts were trimmed at the 5' end and 3' ends. Trimming was to the end co-ordinates of the transcript that covered the shortest region of the gene and was achieved using in-house scripts (see Supplemental Figure  S5 ). Alternatively, transcripts in AtRTD2 were padded to give the transcripts of each gene the same 5' and 3' ends. Shorter transcripts were extended to the co-ordinates of the transcript that covered the longest region on the gene by adding the cognate genomic sequence by inhouse scripts (see Supplemental Figure  S6 ). The AtRTD2-padded version was called AtRTD2-QUASI.
Translation of AtRTD2 and new peptide database The main consequences of AS are either to alter the protein-coding sequence to generate protein variants or introduce premature stop codons/long faux 3'UTRs which can target transcripts to the NMD pathway. In order to assess the consequences of an AS event(s), translation of the transcript from the authentic translation start site is required  [57]. We therefore developed an algorithm which defined the position of the translation start AUG in the transcripts of a gene (usually that of the TAIR reference gene model) and used this translation start site as the reference point for translation of all of the transcripts. This generated realistic translations which were used in in silico trypsin digestions to generate a peptide database consisting of nearly 400k peptides of which half could be assigned to specific AS isoforms.
Funding This research was supported by funding from the  Biotechnology and Biological Sciences Research Council (BBSRC)  [ BB/K006568/1  to JWSB;  BB/K006835/1  to HGN], the  Scottish Government Rural and Environment Science and Analytical Services division (RESAS)  (JWSB, RZ), the  Austrian Science Fund (FWF)  [ P26333 ] to MK and [ DK W1207 ] to AB and the  LABEX Saclay Plant Sciences. We acknowledge the  European Alternative   Splicing Network of Excellence (EURASNET ),  LSHG-CT-2005-518238  for catalysing important collaborations.
Plant material for RNA-seq and datasets Two different extensive RNA-seq datasets were generated for a range of diverse genetic lines and treatments in the Arabidopsis thaliana Col-0 background (see Supplemental Methods: Table  S1 ). These two combined datasets included 285 RNA-seq runs obtained from 129 libraries. Dataset 1 was from samples of a time-course of adult Arabidopsis plants (5 weeks old) transferred from 20°C to 4°C (unpublished data). Plants were sampled every three hours for the 24 h period at 20°C directly before transfer to 4°C, and for the first and fourth days following transfer (26 time-points in 78 libraries -a total of 234 total biological/sequencing repeats) (see Supplemental Methods: Table  S1 ). Dataset 2 consisted of RNA-seq data from 51 libraries generated from samples of over-expression and mutant lines, some of which were treated with flagellin 22 (flg22) or mock -treated with water (unpublished data) (see Supplemental Methods: Table  S1 ).
The genetic lines were overexpression lines and knockout mutants of the serine-arginine-rich (SR) splicing factor genes, At-RS31 (AT3G61860) and At-RS2Z33 (AT2G37340); the mutant of the DNA cytosine methyltransferase MET-1 (AT5G49160), met1-3; and mutants of three MAP kinase genes (AT3G45640, AT4G01370 and AT2G43790), mpk3, mpk4 and mpk6. The latter were treated with flg22 or mock-treated, and wild type Col-0 controls were included for all of the above. Dataset 2 also included transcripts from TAIR10 and from RNA-seq from a normalised library of wild-type flowers and 10 day old seedlings  [14]  used in the construction of AtRTD1  [43]. The total number of 100 bp paired-end reads generated in the two datasets of RNA-seq was 4. 76 and 3. 73 Bn pairs of reads, respectively, such that a total of ca.
Generation of AtRTD2 A detailed description of the transcript assembly and parameters used, merging with the original AtRTD1  [43]  and Araport11 is given in Supplemental Methods and shown schematically in Figure  1. Briefly, RNA-seq reads from Datasets 1 and 2 were mapped to the genome using STAR and TopHat2 respectively, and transcripts for both datasets were assembled with both Cufflinks and StringTie. Transcripts supported by non-canonical junctions or low abundance splice junction reads were removed. Transcripts from unknown genes, antisense transcripts and low abundance transcripts were also filtered out. The resulting Cufflinks and StringTie transcriptome assemblies were merged and redundant transcripts were removed. AtRTD1 was re-assessed using the splice junction sequence set generated here and 10,397 transcripts deriving from Marquez et al.
Authors contributions AJ, CC, NF, NT, YM harvested material and extracted RNA; RZ, YM, PV carried out RNAseq transcript assemblies and data analyses; RZ, CC, JB, NT, WG, MK participated in RNAseq data analyses, interpretation and generation and analysis of modified AtRTD2; CC , NT performed the HR RT-PCR and analyses; MS developed the translation tool and performed the translation analyses; YM carried out the PWM analyses and generated the peptide database; JB, CC, RZ drafted the manuscript and all authors participated in its correction and have read and approved the final manuscript; AB, JB, HH, HN, MK acquired the funding.
Such a theory would be needed to make quantitative predictions about the stability of a system with estimates of only few state variables. Our food web dataset provided a great opportunity to study the effect of interspecific interactions on the relationship between complexity and stability and to demonstrate the existence of a negative correlation between S, C and s in empirical ecosystems. We had, however, no information about the strength of intraspecific interactions, which is a strong stabilizing mechanism. Our analysis thus focused on the radius of the distribution of eigenvalues in the complex plane, ignoring the location of the centre (Fig. 1c ). It is possible that the absence of relationship between complexity and stability results from a positive correlation between the strength of intraspecific interactions d À Á and complexity s ffiffiffiffiffi ffi SC p À Á.
Other mortality sources, M 0i (per year), can be expressed as (1 À EE i ) Â (P/B) i , where EE i is called the ecotrophic efficiency of i, corresponding to the fraction of the production that is used in the food web. The Ecopath model assumes mass-balance, meaning that all species biomass are at equilibrium (dB i /dt ¼ 0). Input parameters (that is, biomass, production/biomass and consumption/ biomass ratios, fishery yields, and diet composition) can have different origins: field sampling (for example, trawl survey), derived from similar Ecopath models, or known empirical relationships. Ecopath with Ecosim software includes routines that estimate missing parameters based on the mass-balance hypothesis and the generalized inverse method for a system of n linear equations and m unknowns (see Christensen et al.
22  , p. 12-15). In general, the biomasses, production/biomass and consumption/biomass ratios are entered for all groups to estimate ecotrophic efficiency, which is difficult to measure in the field  32. The Ecoranger module, also included in Ecopath with Ecosim software, can be used to explore the effect of uncertainty in input data on estimated parameters. This module calculates probability distributions of output parameters based on the confidence intervals of input parameters specified by the users  32. Full details of the Ecopath modelling approach and the Ecopath with Ecosim software can be obtained from www. ecopath. org. Parameterization of Lotka-Volterra interaction coefficients. We used the method from De Ruiter et al  13. to derive the community matrices from Ecopath models (Fig.
We therefore analysed the complexity-stability relationship on a subset of the best resolved Ecopath food webs. Overall, we found the same qualitative results than our main study. We conclude that our findings are robust to (i) input parameter uncertainty, (ii) addition of non-zero diagonal elements in community matrices and (iii) differences in food web resolution level. Interspecific interaction terms of the community matrix. We ran sensitivity analyses to determine how uncertainties in parameter estimates could affect the results of the study. For each input parameter, we tested if uncertainty biases (that is, overestimates or underestimates) food web stability and the variables determining complexity, and if our findings are qualitatively affected by this bias. We used the following parameters from Ecopath data to determine the interspecific terms of a community matrix: (i) biomass B, (ii) consumption/biomass ratio (Q/B), (iii) production/biomass ratio (P/B) and (iv) diet composition DC.