dataesr/scientific-paragraphs-categorization huggingface dataset:
Data acquisition, correction and normalization Hybridized slides were scanned at 2μm resolution using an Agilent G2565BA DNA microarray scanner. Each slide was scanned two times at two different sensitivity levels: XDR Hi 100% and XDR Lo 10%. The two generated images were analysed together, data were extracted and background subtracted using the standard procedures provided in the Agilent Feature Extraction Software version 10. 7. 3. 1. To evaluate goodness and reliability of spot intensity estimates the software returns a series of spot quality measures. All control features (positive, negative, etc. ), except for Spike-in (Spike-in Viral RNAs), were excluded from subsequent analyses. The fluorescence values were normalized by performing a quantile normalization in R statistical software.
Statistical analyses were performed on 35,770 out of 59,824 probes with signal higher than background in at least 6 out of 14 target samples. A log base 2 transformation was applied to all expression values and finally the parametric Combat algorithm  [37]  was implemented in R in order to adjust for the known between-experiments batch effect (i. e. different microarray slides). Normalized data were deposited in GEO archive under accession number GSE90679.
Data analysis A T-test, implemented in TMeV, was used to identify differentially expressed probes between stripped and spawned oocytes. Only the differentially expressed probes showing a significant variation have been selected (Bonferroni-adjusted p-value <0. 05; Fold Change (FC) > 1. 5). To identify the transcripts whose expression was positively or negatively associated with the D-larval rate, a PMT template matching analysis (TMeV) was carried out on log2 fluorescence values and D-larval rates of released oocytes, setting a threshold p-value of 0. 05 and a minimum correlation value (R) of 0. 7. A more systematic, functional interpretation of significant genes was then obtained through enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) software  [38].
"KEGG Pathway", "Biological process" (BP), "Cellular component" (CC), "Molecular function" (MF) annotation categories were used by setting the gene count equal to 3 and the maximum p-value equal to 0. 05. Because DAVID database contains functional annotation data for a limited number of species, it was necessary to link the scallop transcripts with sequence identifiers that could be recognized in DAVID. This process was accomplished using UniProtKB/SwissProt feature identifiers corresponding to each probe. These identifiers were used to define a "gene list" (i. e. significant probes) and a "background" (i. e. all the probes represented in the array) in the bioinformatic tool DAVID, corresponding to differentially transcribed scallop genes and to all the transcripts that were represented on the array, respectively.
Correlation between gene expression profiles and D-larval rates The PMT template matching analysis allowed the identification of a total of 1,904 probes (S2 Table ) whose expression pattern was either positively (973) or negatively (931) correlated with D-larval rate values. Among these, a putative annotation against UniProtKB/SwissProt database was attained for 925 probes, corresponding to 848 unique proteins. The probes having the highest positive (R = 0. 98) and negative (R = -0. 96) correlation factors did not have any match against the considered databases. The list of the putative protein identity of the most significantly correlated (R > 0. 9) transcripts annotated against the protein database UniProtKB/ Uniprot has been provided in Table  1. In order to investigate the main biological processes that most likely affect oocyte quality reflected by D-larval rates of each female, a functional enrichment analysis was performed by using the 811 UniProt accession numbers recognized in DAVID as gene list (S3 Table  , Fig 2).
Transcriptional differences between stripped and spawned oocytes Pairwise comparison among stripped and spawned oocytes revealed a total of 1,682 probes differentially expressed (S4 Table ). Among these, 652 and 1,030 probes were more expressed in REL and STR oocytes, respectively. Putative annotation against UniProtKB/SwissProt database was attained for 597 probes, corresponding to 546 unique proteins. Enrichment analysis,.
Choice of comparator and production of material for the comparative analysis 11 Application EFSA-GMO-BE-2013-117 presents data on agronomic and phenotypic characteristics, as well as on forage and grain composition, of the three-event stack maize MON 87427 9 MON 89034 9 NK603 derived from field trials performed at nine sites in the USA during the 2010 growing season. Seven field trials were used to collect both agronomic and phenotypic characteristics and tissues for the compositional analysis, while the two remaining trial sites were used to collect only one set of data each (agronomic and phenotypic characteristics or composition) (Table  6 ). The three-event stack maize MON 87427 9 MON 89034 9 NK603 was obtained by conventional crossing of the three single events. Event MON 87427 was introgressed in the inbred line LH198, while events MON 89034 and NK603 were introgressed in LH287.
However, maize volunteers in the EU have been shown to grow weakly and flower asynchronously with the maize crop  (Palaudelm as et al. , 2009). As described in Section 3. 3. 2, field trials were carried out in the USA in the 2010 growing season to assess the agronomic and phenotypic characteristics of the three-event stack maize in comparison with the non-GM comparator. The data showed no changes in agronomic and phenotypic characteristics that would indicate altered fitness, persistence and invasiveness of the three-event stack maize. The GMO Panel considers that the persistence and invasiveness of the three-event stack maize MON 87427 9 MON 89034 9 NK603 compared to conventional maize remains unchanged. Considering the agronomic and phenotypic data on the three-event stack maize and the general characteristics of maize described above, there are no indications of an increased likelihood of establishment and spread of occasional feral GM maize plants harbouring any combination of the three events of which it is composed.
Risk assessment of the subcombinations The GMO Panel Guidance Documents establish the principle that where all single events have been assessed, the risk assessment of stacked events focuses on issues related to: (a) stability of the events; (b) expression of the events; and (c) potential interactions between the events (EFSA GMO Panel, 2011a). For the two subcombinations (maize MON 87427 9 MON 89034 and MON 87427 9 NK603) for which no specific data have been submitted and which have not been previously assessed by the GMO Panel, the risk assessment takes as its starting point the assessment of the single maize events, and uses the data generated for the three-event stack maize, as well as all the additional data available on subcombinations previously assessed by the GMO Panel.
Reference data sets are an incomplete representation of actual transcriptomes We first analyzed k-mer diversity in different human references and high-throughput experimental sequences. Thus, we extracted all 31-nt k-mers from sequence files using the Jellyfish program  [28]. Figure  1a , b compares k-mers from GENCODE transcripts and the human genome reference, with RNA-seq libraries from 18 different individuals  [29]  corresponding to three primary tissues (six libraries/tissue). To minimize the risk of including k-mers containing sequencing errors, for each tissue we retained only the set of k-mers appearing in at least six individuals. Measures of k-mer abundance show that k-mers are overwhelmingly associated with GENCODE transcripts (Fig. 1b1 ). However, when considering k-mer diversity, a large proportion of k-mers are tissue-specific and not found in the GENCODE reference (Fig.
B3 Fig. 1  The diversity of 31-mers in RNA-seq libraries exceeds that of reference sequences. a Intersection of k-mers present in GENCODE transcripts and RNA-seq data from three tissues: bone marrow, skin, and colon. The set of k-mers for each tissue was defined as the set of k-mers shared by all six individuals. b Intersection of k-mers present in GENCODE transcripts, the reference human genome (GRCh38), and RNA-seq data (same as in a). b1 Distribution of k-mer abundances for each tissue represented in a and b. k-mers shared with GENCODE are labeled as GENCODE. Among other k-mers, those shared with the human genome are labeled as GRCh38. The remaining k-mers are labeled as tissue-specific. The same procedure was applied in b2 and b3. b2 Repartition of k-mer diversity for each tissue. b3 Mapping statistics of k-mers labeled as tissue-specific in b2.
These k-mers were first mapped to GENCODE transcripts, and unmapped k-mers were then mapped to the GRCh38 reference using Bowtie1, with a tolerance of up to two mismatches in a 31-mer procedure. In this way, DE-kupl guarantees that no variation in the input sequence (even at the level of a single nucleotide) is lost at the initial stage of the analysis. Even unmappable k-mers from repeats, low complexity regions, or exogenous organisms are retained till the final stage and can, thus, be analyzed. The DE-kupl protocol is detailed in "Methods". We highlight here some of its key features. First, DE-kupl must accommodate the large size of the k-mer index. A single human RNA-seq library contains of the order of 10 7 to 10 8 distinct k-mers. We selected the Jellyfish tool for counting k-mers  [28]  as it has very fast computing times and allows the storage of the full index on disk for further querying.
A central process in DE-kupl is k-mer filtering and masking. Filtering out unique or rare k-mers is relatively straightforward and considerably reduces k-mer diversity and the number of sequence errors. Masking entails the removal of k-mers matching a reference transcript collection. The rationale for this is that the bulk of k-mers in RNA-seq data comes from known exons, a form of canonical exon expression ignored in this study as it can be captured efficiently by conventional reference-based protocols  [7, 8]. Discarding these k-mers enables us to ignore the strong signal caused by known transcripts, allowing us to focus better on expressed regions harboring differences from the reference transcriptome. Depending on the application, masking can be performed using a full annotation such as GENCODE or a simpler transcriptome limited to major transcripts, or skipped altogether.
Two modes are available for the differential analysis of k-mers (Additional file 1: Figure  S1  and "Methods"). The t-test mode is fast and has low sensitivity, i. e. , it retrieves only the most significantly DE k-mers. The DESeq2-based mode  [33]  is slower, more sensitive, and is, therefore, recommended for small samples (fewer than six vs six samples). Finally, a k-mer extension procedure merges overlapping k-mers into contigs and stops as soon as a fork is encountered (i. e. , when a contig extremity is Fig. 2  Principal component analysis for non-reference k-mers discriminates tissues. Samples are labeled according to their tissues (bone marrow, colon, and skin). PCs were produced with normalized log-transformed counts. For genes and transcripts, counts were generated with Kallisto based on GENCODE V25.
80. 04% Fig. 3  The diversity of non-reference k-mers is greater for RNA-seq than for WGS. Intersection of k-mers between GENCODE transcripts, the human genome (GRCh38), RNA-seq, and WGS data. RNA-seq and WGS data originate from the same lymphoblastoid cell line (HCC1395). WGS whole-genome sequencing Fig. 4  The DE-kupl pipeline for the discovery and analysis of differentially expressed k-mers. First, Jellyfish is applied to count k-mers in all libraries. k-mers counts are then joined into a count matrix and filtered for low recurrence and matching to the reference transcriptome. Normalization factors are computed from raw k-mer counts and the differential expression procedure is applied. Finally, overlapping differentially expressed k-mers are extended into contigs and annotated based on their alignment to the reference and overlap with annotated genes further reduced counts to 40.
Unmapped contigs Finally, we analyzed DE contigs that did not map to the human genome. Unmapped contigs may result from transcripts produced by rearranged genes or by exogenous viral genomes and could, thus, be highly relevant biologically. In principle, DE-kupl is able to detect such events when levels of RNA vary across samples. In this test set, where all samples come from an in vitro cell line, we did not expect to observe this phenomenon. Indeed, out of 112 unmapped contigs of size >50 bp (Table  3 ), the vast majority (76 %) correspond to vector sequences overexpressed in the M condition (data not shown), indicating that these contigs come from the expression vector used for EMT induction. The remaining unmapped contigs correspond to a GA tandem repeat and several non-human primate sequences.
Comparison with specialized tools We compared DE-kupl events with predictions from two specialized tools. Since DE-kupl reports only events with DE, the protocols compared should involve an eventcalling stage combined with a differential filter. IRFinder  [19]  and KisSplice  [15]  predict intron retention and de novo differential splicing events, respectively. Both pieces of software report changes in relative inclusion, i. e. , variants whose proportions vary between conditions. Therefore, their results can be compared with differential (DU) introns and splice sites from DE-kupl. After running IRFinder on the EMT data set, we observed a strong enrichment in IRFinder predictions among the top DE-kupl intron retention events (Additional file 1: Figure  S11 ). Conversely, 68 % of IRFinder intron retention events were predicted by DE-kupl as intron DU (DU p value <0.
Discussion In contrast to popular RNA-seq analysis software, DEkupl does not attempt full-length transcript assignment or assembly but focuses on local transcript variations instead. Indeed, we do not consider full-length transcript analysis to be realistic when screening for unspecified RNA variation, since the combinatorial nature of genomic, transcriptomic, and post-transcriptomic events would require an indefinitely expanding transcript catalog. In this sense, DE-kupl is closer in spirit to methods analyzing local RNA-seq coverage such as RNAprof  [39]  and DERfinder  [40] , with the notable difference that DE-kupl does not involve mapping and, thus, avoids mappingrelated pitfalls while considerably widening the range of detectable events. Another important benefit of the k-mer strategy is that k-mers representing events of interest can be used efficiently to assess the occurrence -log10(padj) (Human Protein Atlas).
GTEX events Fig. 8  Validation of DE-kupl events across independent data sets. Altogether, 550 differentially expressed contigs from six different event classes (intron with differential usage, lincRNA, polyA site, repeat, splice site, and unmapped) were identified using DE-kupl on GTEx libraries from two human tissues (skin and colon). A representative k-mer from each contig was then tested for differential expression in the skin and colon libraries in the Human Protein Atlas. Box plots represent distributions of DESeq2 adjusted p values for all k-mers in the different classes. The red line shows the adjusted p value cutoff of 0. 05. DU differential usage, lincRNA long intergenic non-coding RNA, padj adjusted p value, polyA polyadenylation of similar events in the huge public compendium of RNA-seq data.
Conclusion k-mer decomposition followed by filtering, masking, and DE analysis is a novel way of analyzing RNA-seq data. It can detect a wider spectrum of transcript variation than previous protocols. DE-kupl explores all k-mers in the input RNA-seq files (vs only k-mers from annotated transcripts in recent software  [7, 8] ), which potentially requires substantial computational time and memory resources. Using the Jellyfish k-mer indexing software and C-programming code for the key table manipulation, we achieved time/memory requirements on par with popular mapping-based software for similarly sized data sets. A key aspect of our protocol that rendered a full k-mer analysis tractable was the application of successive filters for rare k-mers, reference transcripts, and DE, which altogether resulted in a 200-fold reduction in k-mer counts.
These filters are not only useful for technical considerations (they reduce run times and enable us to get rid of most sequence errors), but also they allow the user to focus on k-mers that (i) vary significantly between the conditions under study and (ii) encompass events that would not be captured by conventional reference-based protocols. We showed that DE-kupl is able to detect a wide range of differential transcription and RNA processing events. Although specialized software may perform better at assessing specific event classes, such as differential splicing, no method known to the authors provides such a comprehensive screen. As differential RNA-seq analysis is often conducted with an exploratory spirit, we argue that it is preferable to cast a wide net with no preconceptions for target events, using DE-kupl along with a conventional gene-by-gene DE analysis.
Methods Characterization of k-mer diversity in human RNA-seq libraries RNA-seq data for bone marrow, skin, and colon from 18 individuals (six replicates per tissue) were retrieved from the HPA project  [29]  (E-MTAB-2836). We counted k-mers in each RNA-seq and reference sequence set using Jellyfish (2. 2. 6), with options k = 32 and -C (canonical k-mers). The k-mer list for each tissue (Fig. 1a, b ) was produced by merging counts for all six samples and conserving only those found in all replicates. For mapping statistics (Fig. 1b3 ), we extracted k-mers specific to each tissue and mapped them to the Ensembl 86 transcript reference using Bowtie (version 1. 1. 2). Unmapped k-mers were mapped a second time with Bowtie to the GRCh38 genome reference. Reads with three or more mismatches are not mapped by Bowtie and, therefore, are considered as unmapped.
k-mer counting Raw sequences (FASTQ files) are first processed with the jellyfish count command of the Jellyfish software, which produces one index (a disk representation of the Jellyfish hash table) for each sequence library. For stranded RNA-seq libraries, reads in the reverse direction relative to the transcript are reverse-complemented, ensuring the proper orientation of k-mers. At this point, for each library, only k-mers having at least two occurrences are recorded (a user-defined parameter). Once a Jellyfish index is built, we use the jellyfish dump command to output the raw counts in a two-column text file, which contains at each line a k-mer and its frequency of occurrence. Raw counts are then sorted alphabetically by k-mer sequence with the Unix sort command.
The masking process uses the same Jellyfish-based procedure to create the set of k-mers appearing in the reference transcriptome and to subtract this set from the experimental k-mers. Masking can be performed using any reference transcriptome. Here, we use either GEN-CODE V. 24 or a simplified transcriptome containing one major transcript per gene, built as follows. Principal transcripts for protein-coding genes are extracted from the APPRIS database  [43]. When several isoforms have the same principal level, the longest one is selected. All noncoding RNA transcripts are extracted from GENCODE and the longest transcript is retained when isoforms are present. The lightweight transcriptome, referred to as 1 transcript/gene, is produced by merging the proteincoding and non-coding RNA transcript sets.
Differential k-mer expression Prior to differential analysis, we compute normalization factors (NFs) using the median ratio method  [44]  with the table of k-mers after the recurrence filter. For each sample, the NF is the median of the ratios between sample counts and counts of a pseudo-reference obtained by taking the geometric mean of each k-mer across all samples. To avoid dealing with the complete table of k-mers, we extracted a random subset of 30 % of the k-mers and computed NFs for this subset. Computing NFs for the complete table of k-mers, for the table of k-mers after the recurrence filters and reference masking, or for the table of transcript abundances produced by Kallisto v0. 43. 0  [7]  resulted in similar values (Additional file 1: Figure  S15 ). Two options are implemented for the differential analysis (Additional file 1: Figure  S1 ).
The first option is to apply a t-test for each k-mer on the log-transformed counts, normalized with the previously computed NF. Transformation of raw counts in conjunction with linear model analysis has been successfully used for differential analysis of counts  [45]. We perform the t-test independently on each k-mer and avoid complex variance modeling strategies to reduce the execution time of the analysis. The t-test option has been implemented in C in the dekupl-TtestFilter binary. Note that this t-test option is not appropriate for small samples  [46]. To increase the power of the analysis, in particular for small samples (typically less than six vs six libraries), we strongly advise the use of the second option based on a generalized linear model, implemented in the R package DESeq2  [33].
Contig annotation Finally, DE contigs are annotated to facilitate biological event identification. Contigs are first aligned using BLAST  [48]  against Illumina adapters. Contigs matching these adapters are discarded. Retained contigs are further mapped to the reference Hg38 human genome using the GSNAP short read aligner  [49]  (v2017-01-14), which provided the best speed/sensitivity ratio for aligning both short and long contigs in internal tests (data not shown). GSNAP is used with option -N 1 to enable identification of new splice junctions. Contigs not mapped by GSNAP are collected and re-aligned using BLAST. Alignment characteristics are extracted from GSNAP and BLAST outputs. Alignment coordinates are compared with Ensembl (v86) annotations (in GFF3 format) using BEDTools  [50]  and a set of locus-related features is extracted.
The final set of annotated features (Additional file 1: Table  S5 ) is reported in a contig summary table. The annotation procedure generates two additional files: a per locus summary of contigs (one line per genic or intergenic locus), and a BED file of contig locations that can be used as a display track in genome browsers. In the per locus table, a locus is defined as an annotated gene, the genomic region located on the opposite strand of an annotated gene, or the genomic region separating two annotated genes. The table records the number of contigs overlapping each locus as well as the contig with the lowest false discovery rate for this genomic interval. Parallel to k-mer counting, filtering, and masking, we analyze the RNA-seq data libraries using a conventional DE protocol. Reads are processed with Kallisto  [7]  to estimate transcript abundances.
DE-kupl run on EMT data DE-kupl was run using RNA-syeq libraries from reference  [34]. The DE-kupl parameters were kmer_length 31, min_recurrence 6, min_recurrence_abun dance 5, pvalue_threshold 0. 05, lib_type stranded, and diff_method Ttest, with the GEN-CODE reference. Output files are provided in Additional file 1. The DE-kupl contig summary table was analyzed interactively using R commands to extract lists of contigs based on the filtering rules described in Table  3. Visualization of selected contigs was performed with IGV  [51] , using the BED file produced by DE-kupl and read mapping files produced by STAR  [52]. For comparison with KisSplice and IRFinder, DE-kupl was used with the same parameters as above, except for diff_method DeSeq2 and the 1-transcript/gene reference. KisSplice scripts were run in the following order: kissplice (v2.