dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Experimental design. Here we produce a complete transcriptome dataset, containing both cDNA-Seq and RNA-Seq, using the Illumina and Nanopore technologies. RNAs were sampled from brain and liver tissues of mice and were mixed with Lexogen's Spike-In RNA Variants (SIRVs) as a control for quantification of RNAs. We follow the protocols recommended by the manufacturers to generate the three following datasets on each tissue: Illumina cDNA-Seq, Nanopore cDNA-Seq and Nanopore RNA-Seq. The first was sequenced using the Illumina platform (TruSeq_SR) and the last two using the MinION device (PCS108_LR and RNA001_LR). From the brain tissue, we generated biological (two brain RNA samples, C1 and C2) and technical replicates (R1 and R2) for the three datasets (Fig. 1 ). Additionally, the second was also sequenced using the Illumina platform (PCS108_SR).
3c ). As an example, the Set gene contains an internal run of 20 T's and ONT cDNA-Seq reads are systematically interrupted at this location (Fig. 3c , tracks 2 and 3), while this is not the case for Illumina Truseq (Fig. 3d , track 4) and ONT RNA-Seq (Fig. 3d , track 1). We find that the magnitude of the bias is associated to the length of the internal run of poly(T) (Supplementary Fig. 1 ). The bias is very pronounced for transcripts containing at least 15 T's, but it is already detectable for transcripts containing at least 9 T's. This bias has remained unreported so far, but it is also present in other published Nanopore dataset 11 (Supplementary Fig. 2 ). It however concerns a large fraction of expressed transcripts. Indeed, transcripts containing at least 9 T's correspond to 27% of transcripts expressed with at least one read in mouse brain (resp.
Evaluation of the accuracy of the gene expression quantification using spike-in data. In order to assess which protocol was best to quantify gene expression, we analyzed the 67 spike-ins contained in the brain datasets. Since we exactly know which transcripts are present in the sample, the quantification is rather straightforward. We aligned reads to the reference transcriptome, used RSEM for short reads, and counted the number of primary alignments for long reads (see Methods). The best quantifications were obtained for the ONT RNA-Seq (Spearman ρ =. 0 86, Pearson r = 0. 85) and Illumina TruSeq (ρ =. 0 81, r = 0. 82) protocols (Fig. 4 ). In contrast, cDNA-Seq (sequenced using Illumina or ONT) produced more imprecise quantifications (ρ =. 0 54, =. r 0 57 and ρ =. 0 6, =. r 0 50). Importantly, we obtained very similar results on all our three replicates (Supplementary Figs  3  and 4 ), with ONT RNA-Seq consistently exhibiting the higher correlation with true quantifications.
The use of salmon either for short or long reads, as was done in  12  did not change our results. We then wanted to test if the number of ONT RNA-Seq reads was indeed a better predictor of the true transcript quantification, than the number of cDNA-Seq reads or the TPM measure derived from Illumina. Using 30 fold cross-validations, we found that the mean square error was ∈. which highlights that ONT RNA-Seq yields significantly better quantifications than Illumina TruSeq and ONT cDNA-Seq. Although the magnitude of the difference with Illumina TruSeq is small, we found it to be reproducible. We could further show that, for each technology, the errors made for each SIRV were reproducible across replicates (Supplementary Fig. 5 ) meaning that a transcript whose expression is over-estimated with one technology is consistently over-estimated with the same technology.
Quantification of the expression level of mouse transcripts. Given that with the spike in, the best quantification were obtained with ONT RNA-Seq, we compared the quantifications obtained with this protocol with the ones obtained with the ONT and Illumina cDNA-Seq protocols. Figure  5  summarizes the correlations in terms of transcript quantification of our datasets. Comparing the Illumina cDNA-Seq and the ONT RNA-Seq protocols we obtain a spearman coefficient of correlation ρ =. 0 51. The correlation is lower in the liver sample (Supplementary Fig. 8 ) probably because of a lower number of RNA-seq reads and a shorter read length. Comparing the ONT RNA-Seq and cDNA-Seq quantification, we obtain a higher correlation (ρ = 0 75), suggesting that read length strongly influences transcript quantification.
Indeed, in the comparison between Illumina cDNA-Seq and ONT RNA-Seq dataset, the lack of correlation comes from one main cause. Discriminating transcripts of a same gene that share common sequences with short reads is difficult. Longer reads are clearly helpful, however they do not always enable to discriminate transcripts. Indeed, in the case where a read only covers the 3′ end of a transcript, and not the full length, it may be ambiguously assigned to several transcripts. For example, for the Swi5 gene, although several rare (lowly expressed) transcripts are seen only with Illumina, the other ones are harder to quantify (red dots in Fig. 5a ). RSEM uses the unique part of each transcript to proportionally allocate the reads that mapped equally on the common part of the transcript. In the case where a transcript has no read which uniquely maps to it, its expression cannot be computed and is set to 0.
This is the case for the transcript ENSMUST00000050410 (Swi5-201, Supplementary Fig. 9 ) of Swi5, whose expression is underestimated (0 TPM). Conversely, some transcripts are underestimated by ONT RNA-Seq. This is the case of Swi5-204, whose unique region is located at the 5′ end of the gene, and is therefore poorly covered by long reads. 0 77, n = 54,532). Red points correspond to pseudogenes located on chromosome 1 within the NUMT, i. e. segment of the mitochondrial genome which has been copied and integrated in the nuclear genome and orange points correspond to the original mitochondrial genes. (d) Reads were mapped against the mouse reference genome and quantifications computed at gene level. We compared the ONT RNA-Seq and the Illumina cDNA-Seq protocols (Spearman's ρ =. 0 74, n = 54,532).
Processed pseudogenes originate from transcripts which contained a polyA tail, which was then integrated in the genome, downstream the pseudogene. Many of the ONT reads originating from the parent gene also contain this polyA tail, favoring the alignment at the processed pseudogene genomic location. The alignment will be longer thanks to the polyA tail. An example is shown in Fig. 6. The processed pseudogene Rpl17-ps8 differs from its parent gene by two bases (A to G at the position chrX:96,485,078 and A to G at the position chrX:96,485,267). These divergences are marked in red in the figure. At these two positions we observe that reads differ from the reference genome: they have a G instead of an A. This means that these reads come from the parent gene and we mistakenly aligned them onto the pseudogene because it is intronless and contains a polyA tail.
Discussion In this work, we generated a dataset which we think should be of general interest for the community. dataset consists of RNA and cDNA sequencing of the same samples using both Illumina and ONT technologies. Importantly, we also sequenced Lexogen E2 spike-in data, together with our mouse samples, which enabled us to assess which technology yielded the most accurate quantification. Although lexogen spike-in have been used to evaluate the quantification obtained with ONT cDNA-Seq  21  or ONT RNA-Seq 7 protocol separately, we are the first to compare the quantification obtained with ONT cDNA-Seq, RNA-Seq and Illumina cDNA-Seq. Using the spike-in data, we find that the ONT RNA-Seq protocol is the most accurate, slightly better than the widely used Illumina TruSeq protocol. In contrast, the cDNA-Seq data was more biased and yielded a poorer quantification.
We further found that transcripts with internal runs of poly(T) tend to be truncated and over-sampled when using the ONT cDNA-Seq protocol. Sequencing the same library preparation with the Illumina technology enabled us to confirm that the truncation issue was related to the sample preparation and not to the sequencing. We further show that this bias is not restricted to our dataset, and can be found in a human ONT dataset  11. Truncation biases associated to internal runs of poly(A) had been reported earlier and motivated the usage of anchored poly-dT primers (poly-TVN)  22. On the other hand, biases associated to internal runs of poly(T) had remained undetected, although they may affect more than 20% of expressed transcripts in mouse. This bias could also affect other long-reads cDNA-Seq data.
Although biases had been searched for in previous work  23  , it may have remained undetected because the authors were then focusing on internal runs of at least 20 A's. We then used our data to quantify mouse genes and found that ONT RNA-Seq quantification correlated well with Illumina cDNA-Seq quantification (Fig. 5c ) but when trying to quantify at the transcript level, the correlation was overall poorer (Fig. 5a ). A temptation could be to think that ONT RNA-Seq yields better transcript-level quantification as reads are longer and are, unlike short reads, unambiguously assigned to a single transcript. In practice, 70% of ONT RNA-Seq reads are assigned to a single transcript, while the remaining 30% are ambiguously mapped. This was particularly the case for transcripts which differed at their 5′end, like in Swi5.
Quantifying transcripts and not genes is still challenging, and requires the development of dedicated bioinformatics methods. When trying to use salmon  24  on long reads, as in  12  , we did not obtain better results than when simply counting primary alignments. There should however be room for improvement in this direction, and our spike-in dataset could be a good training set for future methods. In this work, we chose to align reads to a reference transcriptome. Indeed, when trying to map reads to the reference genome, we observed a systematic over-estimation of the quantification of processed pseudogenes, at the expense of their parent gene. We further show that this biased quantification is due to alignment issues: 1poly(A) tails of pseudogenes are integrated in the genome and'attract' reads from the parent gene and 2-accurate identification of splice sites when mapping long RNA-Seq reads is challenging, which disfavors the parent gene.
There is currently a lot of interest for the potential of ONT RNA-Seq to identify and quantify genes and transcripts, as can be seen by the currently low but expanding number of datasets available with this technology. Here we proposed the first dataset on mouse with several interesting and unique features, as Lexogen E2 spike-ins, Illumina sequencing of ONT library preparation or Lexogen TeloPrime protocol. We think that ONT sequencing is promising for studying RNA, especially if the number of reads and full-length reads continues to increase. Improvements in the technology and library preparation protocol to obtain more reads and more full-length reads are also expected to be very helpful in obtaining precise quantification of all genes and transcripts. The recent launch of the PromethION device will allow a deep sequencing of transcriptomes which should enable to overcome the limitations of the MinION device.
Nanopore TeloPrime library. Three cDNA libraries were performed from 2 μg total RNA for each RNA sample according to the TeloPrime Full-Length cDNA Amplification protocol (Lexogen). A total of 5 PCR were carried out with 30 to 40 cycles for the brain sample and 30 cycles for the liver sample. Amplifications were then pooled and quantified. Nanopore libraries were performed from respectively 560 ng and 1000 ng of cDNA using the SQK-LSK108 kit according to the Oxford Nanopore protocol. Sequencing and reads processing. Illumina datasets. Illumina cDNA libraries, prepared with the TruSeq (TruSeq_SR) and Nanopore (PCS108_SR) protocols, were sequenced using 151 bp paired end reads chemistry on a HiSeq4000 Illumina sequencer (Table  1 ). After the Illumina sequencing, an in-house quality control process was applied to the reads that passed the Illumina quality filters.
The first step discards low-quality nucleotides (Q < 20) from both ends of the reads. Next, Illumina sequencing adapters and primer sequences were removed from the reads. Then, reads shorter than 30 nucleotides after trimming were discarded. The last step identifies and discards read pairs that mapped to the phage phiX genome, using SOAP  25  and the phiX reference sequence (GenBank: NC_001422. 1). These trimming and removal steps were achieved using in-house-designed software as described in  26. Nanopore datasets. Nanopore libraries were sequenced using a MinION Mk1b with R9. 4. 1 (PCS108_LR and RNA001_LR) or R9. 5 flowcells (TELO_LR). The data were generated using MinKNOW 1. 11. 5 and basecalled with Albacore 2. 1. 10 (PCS108_LR C1R1, RNA001_LR C1R1 and TELO_LR C1R1) or MinKNOW 3. 1. 19 and basecalled with Guppy 2.
Truncated reads analysis. For each transcript annotated in Ensembl94 containing an internal run of at least 9Ts, we computed the number of reads covering the following positions: 25, 50, 75 and 100nt upstream and downstream the internal run of poly(T). For each transcript t, the most covered position was retrieved, and the number of reads covering this position was noted max t. The coverage of each position was then divided by max t , so as to obtain a normalised coverage. Then for each position, we computed the mean of the relative coverage this position across all transcripts verifying max t > 10. This is the value plotted in Fig. 3a. The error bars represent the standard error around the mean. The same analysis was done for the human ONT dataset, using gencode27 annotations (Supplementary Fig.
Saturation curve. For short reads, we kept only the best alignment as reported by RSEM and the primary alignment of each long read. Only protein coding transcripts (transcript_biotype = protein_coding) were taken into account. Quantification of TE-containing genes. Given that lncRNAs are lowly expressed, for this specific analysis, we restricted to cDNA-Seq and did not apply our 80% query coverage filter. Using annotated TEs from the RepeatMasker database  30  , we classified lncRNAs and mRNAs based on their TE coverage in four categories (with the "0%" class corresponding to genes without any exonic-overlapping TE and conversely, the class of ">66-100%" for genes highly enriched in exonic TE). For each expressed gene, we further computed the ratio between Nanopore cDNA versus Illumina TruSeq gene quantifications with respect to their TE categories.
Acknowledgements The authors are grateful to  Oxford Nanopore Technologies Ltd  for providing early access to the MinION device through the  MAP , and we thank the staff of  Oxford Nanopore Technology Ltd  for technical help, particularly  Botond Sipos ,  Daniel Turner ,  Michelle Hiscutt  and  James Platt  for insightful discussions about poly-T truncation phenomenon. We thank  Philippe Veber  and  Arnaud Mary  for their advice on statistical and bioinformatics analyses. This work was supported by  French National research agency  ( ANR  project  ANR-16-CE23-0001  '  ASTER '), the  INRIA , the  Genoscope , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) and France Génomique  ( ANR-10-INBS-09-08 ).
42 12. 36 13. 60 45. 88 Read Size (bp) 151 151 151 151 Accession number ERX2695239 ERX3387947 ERX3387948 ERX2695237 Liver Number of reads 49,270,153 - - 178,019,939 Cumulative size (Gb) 14. 16 - - 53. 43 Read Size (bp) 151 - - 151 Accession number ERX2695241 - - ERX2695242.
Sequence functional classification has become a fundamental bottleneck to the understanding of the myriad of protein sequences accumulating in our databases due to the recent progress in genomics and metagenomics. The large diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of living organisms and for biotechnological applications. ProfileView is a novel computational method designed to functionally classify sets of homologous sequences. Its architecture strongly relies on the structure of biological data, and answers to the challenge of automatically partitioning datasets of protein sequences in pertinent subfamilies based on meaningful conservation patterns.
Introduction The functional classification of biological sequences has become a fundamental bottleneck to the understanding of the ever-increasing genomic and metagenomic sequence data accumulating in our databases. This quest depends on the correct domain annotation of coding genes  [1] [2] [3] , which, in the past, was handled by sequence homology-, signature-and feature-based approaches. The first and most intuitive approach searches for homologous sequences to already known protein or domain sequences. However, sequence homology conveys a serious pitfall: it is defined on the basis of evolution, not function, and many homologues diversified their functions. More reliable methods use protein signatures, which are descriptions of protein or domain families derived from multiple sequence alignments.
These approaches use consensus sequences to produce a probabilistic model describing the conserved characteristics of a domain across sequences in the family and use it to evaluate sequence similarity. For protein families represented by very divergent sequences, these models might provide a very weak description and become of restrained use. The third class of methods selects an appropriate set of features (like short sequence segments or wavelet decompositions) but needs the availability of a large and very diversified dataset of sequences to perform classification successfully. Novel computational approaches classifying sequences by function and overcoming the limitations intrinsic to existing methods would help screening millions of sequences to design accurate experiments directed to functional testing and to discover new functions.
1A ). It is the usage of a multitude of probabilistic models that makes the distinctiveness of ProfileView in the context of sequence classification. This multiplicity of models demonstrated to be very powerful in the CLADE/MetaCLADE  [4, 5]  pipelines, where it produces very accurate domain annotations for full genomes and metagenomic/metatranscriptomic datasets, and allows for the discovery of new homologous sequences enriching protein families  [6, 7]. In ProfileView, the ensemble of probabilistic models plays the role of the "expert" in classification, in the same way as a biologist, looking at sequences, finds patterns by hand, learns about meaningful characteristics from subsets of similar sequences and then uses combined information to classify new sequences. Indeed, ProfileView codes the "biologist expertise" by using all models to look at sequences to be classified.
A highlight of ProfileView Our methodological approach to sequence classification, named ProfileView, is presented for the analysis of the cryptochrome/photolyase family (CPF). It bases the analysis on the FAD binding domain, occurring in all CPF sequences, and considers the set S CPF of 397 CPF sequences spanning the whole phylogenetic tree, of which 69 are functionally characterized CPF homologs and the remaining ones are known functionally uncharacterised sequences (Fig. S1 , top). ProfileView comprises four main modules (Fig. S1 ): (i) the model library construction for the FAD binding domain, (ii) a sequence filtering, (iii) a multidimensional representation space construction and (iv) a functional tree construction. We detail these steps below. ProfileView starts with the construction of a library, M FAD , of probabilistic models  [30]  for the FAD binding domain (Fig.
ProfileView functional organisation of these two families perfectly agrees with what is known. By constructing multiple probabilistic profiles characterising different conserved motifs in CPF/WW domain homologous sequences, ProfileView captures functional signals and, by combining them, is able to successfully classify large datasets. It obtains a striking improvement compared to previous attempts. The main advantages of ProfileView approach compared to those developed before are as follows: (i) it is alignment-free and avoids errors due to the difficulty of comparing distant homologues; (ii) several probabilistic models represent more precisely than a single consensus models the functional variability of protein families; (iii) large quantities of data are not needed to learn features and run the classification; (iv) functional annotation of many sequences does not need to be known to explore with precision the space of sequences and classify them.
To construct CCMs, we considered the full set of sequences S i associated to a domain D i of Pfam  [44]  and, for some representative sequences s j ∈ S i , we constructed a clade-centered profile HMM, in short CCM, by retrieving a set of homologous sequences close to s j. Such a model displays features characteristic of s j and that might differ from other domain sequences s k ∈ S i. The rationale is that the more s j and s k are divergent, the more clade-centered models are expected to highlight different features. It has been shown that CCMs significantly improve domain annotation (either for full genomes  [4]  or for metagenomic/metatranscriptomic datasets with MetaCLADE  [5] ) and, due to their closeness to actual protein sequences, they are more specific and functionally predictive than the canonical global-consensus approach.
CPF protein sequences dataset The set S CPF of CPF protein sequences was retrieved from publicly available databases such as UniProt, JGI projects (genome. jgi. doe. gov), and OIST projects (marinegenomics. oist. jp). It is comprised of 397 sequences, and was manually constructed following two main criteria: 1. it contains 69 CPF sequences known to have a specific function according to experimental evidence reported in the literature (see Supplemental File for bibliographical references); 2. it contains CPF sequences that span the whole tree of life; they belong to 146 species, 74 classes, and 40 phyla (see Supplemental File for the detailed list). The distance tree constructed from the 307 CPF sequences is reported in Fig. S3. In the text, a "CPF sequence" refers to the full length CPF sequence comprising the PHR domain, including the FAD binding domain, and possibly the C-and N-terminal extensions, while a "FAD sequence" refers to the FAD binding domain sequence exclusively.
WW domain sequences dataset The set of WW domain sequences was constructed by combining the datasets of natural sequences analysed in  [27] [28] [29] 38]. It is comprised of 349 sequences, 60 of which have been experimentally classified  [27] [28] [29]  and the remaining ones have been randomly selected, in comparable proportion, from the three sets classified in Tubiana et al (types I, II/III, IV)  [38].
Model library construction To construct a library of models for a domain, we considered sequences from the Pfam v31  [44]  database and, for each sequence, we built a CCM  [4]  by searching in UniProt (version 2017_10) for highly significant matches of homologous sequences having at least 60% identity with the query domain sequence and covering at least 70% of it. More precisely, a multiple sequence alignment is built using the command hhblits of the HH-suite  [43]  (with parameters -qid 60 -cov 70 -id 98 -e 1e-10 and database uniclust30_2017_10) and subsequently converted into a pHMM with HMMER  [30]  in order to perform a sequence-profile comparison. Moreover, a pHMM is considered only if it is trained with a minimum number of 20 sequences. The set of Pfam sequences used to construct the ProfileView's model library for CPF is mainly different from the set of classified sequences: among the 240 models taken into account for the classification of the 307 sequences, just 17 of these models were built from a (Pfam) sequence in S CPF and only one is a representative model (for the (6-4) PL subtree) in the ProfileView tree.