dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, 137 plants from  19  BanMMV-infected accessions were used for in vitro testing (only IC-RT-PCR) of the plant tissue surrounding the meristem that went to meristem therapy. Moreover, the tissue surrounding the meristem was sampled for 41 plants, from 21 accessions, after thermotherapy when the meristem was further grown in vitro. Three banana accessions (9 plants) known to be healthy were also used as negative controls during tests. A detailed list of this plant material is provided in Supplementary File S1. All information about accessions was found in Musa Germplasm Information System (MGIS) database  [21]. In addition, seven banana accessions (23 plants) known to be infected with BanMMV were used for testing (RT-PCR and HTS) of either basal or leaf tissues. Four accessions (16 plants) that tested negative for BanMMV infection were also used as negative controls during these tests.
RNA Extraction Total RNA was extracted from in vitro tissues (100 mg in total) of either the base or leaves (separately) using RNeasy Plus Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. Purified RNA concentration was quantified by spectrophotometry, and quality was evaluated using the Agilent 2100 Bioanalyzer (2100 expert software, version B. 02. 07. SI532). RNA extracts from each in vitro plant were used for both targeted molecular test (RT-PCR) or high-throughput sequencing. In addition, two alien external controls were used. They corresponded to plant samples infected at high concentration by a plant virus, called the alien plant virus, which cannot infect banana. They were processed in parallel to the samples from sampling to bioinformatic analysis. The presence of sequences from alien plant viruses in the banana samples can give a useful indication of the cross-contamination level between samples.
Bioinformatics Analysis The obtained sequence reads from HTS were subjected to demultiplexing and removal of Illumina adapter sequences before all reads were paired, quality filtered, and trimmed by BBDuk. In this study, reads shorter than 35 bp and Phred score less than 25 on both ends of reads were trimmed. Later, the trimmed reads were further merged by BBMerge, and "normal" rate was used. All the paired, trimmed, and merged reads were mapped to the custom-built database with all the reference sequences of BanMMV, one reference genome sequence for barley yellow dwarf virus (BYDV, GenBank Accession No. KU170668), and one reference genome sequence for Pepino Mosaic virus (PepMV, GenBank Accession No. FJ457096). Two hundred and ten nucleotide sequences of BanMMV were downloaded from NCBI on 12 December 2020.
Among them, 54 sequences corresponded to the CP sequences of the virus (40 partial and 14 complete sequences) and 154 corresponded to partial RdRp sequences. A single complete genome of BanMMV (GenBank accession NC_002729) was available. GenBank accessions numbers and names of these sequences are listed in Supplementary File S3. The mapper "Geneious", which is a fast mapping method with high sensitivity, was selected. In order to improve the results by aligning reads to each other in addition to the reference sequence, the fine tuning for mapping was set to "Iterate 2 times". In order to save calculation time and improve mapping efficiency, "20% mismatches" tolerance and customer sensitivity were selected for the sensitivity option for BanMMV, PepMV, and BYDV mapping, respectively. The "map multiple best matches" option was set to "Randomly", under which the reads will be mapped randomly to one of the best hits presenting equal scoring.
A sample was considered positive by HTS if more than 10 reads were mapped to one of the BanMMV downloaded sequences  [23]. De novo assembly into contigs was carried out to reconstruct the genome sequence of a potential new viral species, using the SPAdes software embedded in Geneious with default parameters and a k-mer of 55  [22]. The full genome sequence was aligned and used for phylogenetic analyses with the MEGA software package version 7. 0. The phylogenetic relationships were inferred using neighbor-joining method embedded in the same version of the MEGA software. The stability of the topology was evaluated using bootstrap (1000 replications)  [22]. Accession numbers of virus sequences obtained from GenBank (http://www. ncbi. nlm. nih. gov/, accessed on 1 October 2021) have been integrated in the phylogenetic trees.
Validation of Detection of the New Species To confirm the sequence of the new species, its RdRp and CP genes were amplified by RT-PCR and sequenced. Firstly, nine primer pairs were designed using Geneious software (v11. 0. 4). Seven pairs were used to amplify the RdRp of the new sequence, and two pairs were used to amplify the CP of the same sequence. Primers sequences and corresponding PCR programs are detailed in Supplementary File S4. The Tm and dimer formation of the selected primers were also checked using Geneious. Then, Sanger sequencing (Macrogen Europe BV, Amsterdam, The Netherlands) was performed from purified PCR products.
Accession Code Tested Plant Corm Results Leaf Results ( Greenhouse ITC0099 1 -- According to Table  2 , BanMMV was detected from either corm tissues (in vitro tissues) or leaves of greenhouse plants, even after heat treatment, meristem culture, and a greenhouse acclimatization step. A success rate of 73% (per plant) for banana sanitation has been recorded in this study, confirming that thermotherapy in combination with meristem culture does not have a 100% efficacy for eradicating the virus. Table  2. Comparison of BanMMV detection between in vitro corm tissues and greenhouse leaf samples of the same 41 banana plants (21 accessions) using IC-RT-PCR. "+" refers to a positive result; "-" refers to a negative result; the healthy accessions used as negative controls through this experiment were ITC0245, ITC0654, and ITC1120, and they tested negative.
Accession Code Tested Plant Corm Results Leaf Results (in Greenhouse) ITC1460 1 + - 2 - - ITC1564 1 - - 2 + - 3 + - ITC1664 1 + + 2 + - 3 + + ITC1681 1 + + 2 - + 3 - + ITC1734 1 - - 2 - - ITC1748 1 - - 2 - - 3 - + ITC1752 1 - + ITC1758 1 - + ITC1767 1 - - ITC1768 1 + - 2 - - 3 - - ITC1776 1 - + ITC1792 1 + - 2 + - 3 + - ITC1808 1 + + 2 - - ITC1831 1 - + 2 - - ITC1857 1 - - Tests from tissue surrounding the meristem were carried out directly after one month of heat treatment for the meristem excision. The percentage of in vitro plantlets that tested negative was 61% (25 plantlets out of 41 tested). We must take into account also that the false-negative rate when testing such tissue from in vitro plant is quite important. On the other hand, leaf tests from fully developed plants were carried out after thermotherapy, meristem culture, and a greenhouse acclimatization of these plants for at least three months.
This also underlined the importance of using positive controls at a low concentration in routine detection by HTS tests. Supplementary data from the sequencing are presented in Supplementary File S6. BanMMV reads were detected in all the infected samples. The DSE and DSP of HTS on pooled plant tissues were both at 100% from either the base or the leaves. In same line with the RT-PCR results, the average DSE from the base was the same as the one from the leaves of the same in vitro plant. This sensitivity was similar when comparing HTS to RT-PCR for the same samples. A BanMMV infection was also detected by HTS on the accession ITC0528, although the accession tested negative by RT-PCR. There were four mismatches between the Ban-MMCP2 primer sequence and the sequence of the isolate, explaining the false-negative result obtained by RT-PCR (unpresented results).
Identification of a New Betaflexiviridae Species Infecting Musa from ITC0528 A divergent Betaflexiviridae isolate with a reconstructed genome of 7364 bp was obtained by analysing the sequencing data from the corms and leaves of ITC0528, an accession belonging to Musa ornata Roxb. species. The pairwise alignment showed that the new genome sequence shared a pairwise identity of 62. 7% at the nucleotide level with theBanMMV reference genome (GenBank accession NC_002729). Sequences from the new virus were not detected in the other accessions sequenced during this study nor in the more than 40 other accessions of Musa sp. sequenced at high throughput (Rong Wei, personal communication). Through the targeted RT-PCR and Sanger sequencing of amplicons, RdRp and CP sequences were confirmed. They presented 99.
3% and 99. 7% of identity, respectively, with the RdRp and CP consensus sequences generated by HTS. The conserved domain assessment of the new species revealed the presence of all the plant viral domains of the Betaflexiviridae family (Table  5 ). Two non-coding regions were found at the genome ends, 5 UTR and 3 UTR of 51 and 126 nt, respectively. The comparison of these values with members of the genus Foveavirus, for instance, belonging to the Betaflexiviridae family (5 UTR of 33-72 nt and 3 UTR of 176-312 nt) or with the BanMMV reference genome (GenBank accession NC_002729) (69 nt 5 UTR and 77 nt 3 UTR and), showed that the genome reported lacks less than 50 nucleotides at its 3 end and a maximum of 200 nt at its 5 end  [25]. Further annotation revealed that the new contig has the typical genome organization of Betaflexiviridae members (Table  6 ).
Data Availability Statement: The nucleotide sequence of the new species was successfully deposited in GenBank under the following Genbank accession number, OK721096, and the partial sequences obtained by RT-PCR and Sanger sequencing were deposited under the following Genbank accession numbers OL412396 and OL412397, respectively, for RdRp and CP sequences. All raw reads produced and used in this study were submitted to the NCBI's Sequence Read Archive (SRA) under Bioproject PRJNA784940.
Splicing factors like SRSF1, a member of the phylogenetically conserved serine/arginine-rich RNA binding protein family, are involved in a plethora of biological processes  [21] [22] [23]  and are functionally essential genes. For example, SRSF1 knockout is embryonically lethal  [24] , and it is upregulated in many malignancies even though the level of SRSF1 is tightly controlled within the cell  [25] [26] [27] [28] [29]. Here, we investigated whether the global splicing landscape in MM cells is dysregulated compared to NPC. We evaluated the expression of splicing factors in a large dataset of MM patient samples and investigated the extent of AS events. We also investigated whether overexpression of SR (Serine/arginine) protein SRSF1 (previously known as SF2/ASF) drives aberrant AS in MM, thus impacting tumor growth and survival, which provides important biological insights with translational potential.
RNA-seq quantification and splicing analysis Paired-end 50 bp sequencing was performed for primary RNA samples and stranded 75 bp paired-end RNA sequencing for SRSF1 in-vitro samples. Raw files in fastq format were first evaluated with "FastQC v0. 11. 2" against any sequencing bias and error. All samples were evaluated after alignment with STAR (median reads per sample 79. 3 M [32. 2 M-168. 6 M], >90% of samples had 50 M or more reads, Supplementary Table  3  for alignment QC), and only samples passing QC were kept for downstream analysis. The reference human genome (GRCh38) was downloaded from the GENCODE project website. RNAseq samples in fastq format were aligned to the reference human genome using the STAR RNAseq aligner  [30]. Samtools  [31]  was used to convert, sort, and index alignment files.
Significantly spliced regions were used as the target regions for motif enrichment, and not significantly spliced regions were used for estimating background binding levels. We used 250 bp and 50 bp, respectively, as the length of the intronic and exonic regions to be examined and plotted. 50-bp sliding windows were used to count the motif occurrences, and the step size of windows sliding was set to 1. RSAT  [34]  was used to validate enrichment results from rMAPS. We converted the alternatively spliced regions identified with rMATS into a BED file and extracted hg38 sequenced using RSAT Sequence tools. FASTA files are generated with RSAT and then fed into matrixscan (full options) using ATtRACT (2017) database. Homo Sapiens specific background model estimation method was used to correct estimations and results were reported as Enrichment of hits in the whole input sequence set.
Differential expression and pathways analysis SRSF1 expression was compared between NPC, precursor conditions (MGUS, SMM), and newly diagnosed MM samples using the IFM 2009  [35] , Mayo Clinic (GSE6477)  [36] , and Arkansas (GSE2654 and GSE5900)  [37]  datasets. Transcript-level raw counts were estimated using the lightweight alignment tool Salmon  [38]  and converted to gene-level estimates by summing the estimated transcript counts. All genes with 0 counts were excluded from any downstream analysis before normalization. 26954 genes passed the filter and were used for normalization. Gene counts were normalized using DESeq2  [39] , and differentially expressed genes were identified. The Reactome Functional Interaction network analysis in Cytoscape  [40]  was used for pathway analysis. Microarray datasets were downloaded from canEvolve.
The landscape of alternative splicing changes in multiple myeloma To assess AS differences, we analyzed deep RNA sequencing data from CD138 + MM cells isolated from 323 newly diagnosed patients and CD138 + NPC from 16 normal donors. In total, we identified 448,488 AS events between MM and NPC and computed the percent-spliced-in (PSI) scores. Using stringent criteria for AS between MM cells and NPC (FDR < 0. 05 and absolute (ΔPSI) > 0. 1), we identified 1150 differential AS events in 715 genes (Fig. 1A , Supplementary Fig. 1A ). Global splicing analysis showed that events associated with exon usage alterations (mutually exclusive exons (MXE) (n = 510, 44. 3%) and exon skipping (ES) (n = 417, 36. 2%)) were the most frequent splicing events, while retained intron (RI) (n = 134, 11. 6%), alternative 5' site (A5) (n = 47, 4.
1C ). A3SS and A5SS events altered the coding potential of 60% and 42% of their target genes, respectively (Supplementary Fig. 1B ). Using the DepMap CRISPR dataset (21Q1)  [43, 44] , we identified 154 (dependency score < -0. 5 in at least 50% of MM cell lines) MM cell dependency genes with a significant alternative splicing event. These 154 genes constitute 21% of all MM dependency genes (n = 733). We then used a hypergeometric test to see if MM dependency genes are enriched in alternatively spliced genes (n = 715) and found a significant enrichment (hypergeometric test p-value 3. 62e-19). MM dependency genes with significant splicing events included MEF2C, IKZF3, IRF4, UBE2G2, PSMC1, and SMARCB1 (Supplementary Table  1 ). Overall, 20 tumor suppressors or oncogenes, 24 protein kinase genes, and 64 transcription factors were significantly differentially spliced between MM and NPC.
These critical genes were affected by different splicing mechanisms. For example, compared to NPC, MM cells preferentially expressed a longer IKZF1 transcript through alternative splicing involving DNA sequence recognition sites. Similarly, IRF4 had altered DNA binding domains by alternatively utilizing the second exon. The utilization of these spliced products was significantly higher in MM cells than in NPC (Fig. 1D, E ). To evaluate the pathways and network patterns affected by the splicing alterations, we performed Reactome Functional Interaction network analysis using 715 alternatively spliced genes (Supplementary Table  1 ) and identified 15 protein-protein interaction networks using Reactome Protein Interaction database. In total, 148 unique pathways were enriched in 15 networks (Supplementary Fig.
2B ). High SRSF1 expression was also significantly associated with shorter overall survival (OS) in three datasets (Fig. 2C ). We further confirmed that patients with high SRSF1 expression also had significantly more ES and MXE (Fig. 2D ). RBM analysis showed that ES events were typically accompanied by SRSF1 binding either within the exon skipped, or within 100 nucleotides upstream of the skipped 3′ splice site, whereas SRSF1-mediated ES involved binding of SRSF1 within the downstream intron (Fig. 2E ).
Alternative splicing database for multiple myeloma We have developed a R/Shiny-based web portal that allows the scientific community to access analysis results between MM, clinical subgroups, and NPC. A user-friendly interface can be accessed at http://rconnect. dfci. harvard. edu/mmsplicing for rMATS results and http://rconnect. dfci. harvard. edu/mmleafcutter for leafcutter results. Users can query splicing results generated by both tools using official gene symbols. Fig. 1  The landscape of alternative splicing (AS) events in multiple myeloma. A A volcano plot showing inclusion level (PSI) difference (xaxis) between MM (n = 323) and NPC (n = 16) and corresponding false discovery rate (FDR) (y-axis). Each point represents a splicing event. Events with ΔPSI > 0 (<0) indicate a higher (lower) utilization in MM compared to normal samples.
This would suggest that specific AS events have tissue-and condition-specific behaviors, similar to non-coding RNAs. However, short RNA sequencing platforms and alternative splicing detection methods tend to bias towards finding ES and MXE events. Further studies with long isoform sequencing platforms and more suitable techniques to detect broader event types without bias are needed to confirm these findings. Long isoform sequencing platforms like Oxford Nanopore would provide better resolution; however, their input sample requirements are usually much higher than Illumina-based methods, and it is often not possible to obtain enough plasma cells for every patient in large clinical studies. Although our study has a large RNAseq dataset, we would need to consider larger sets to measure individual variance in the future.
In addition, alternative splicing tools can affect the interpretation of the results. Certain tools, such as rMATS, specifically use predefined splicing models and identify alternative splicing changes by comparing groups, making interpreting data easy. Extensions of these primary approaches, such as LeafCutter, jointly analyze groups of splice events and detect changes within each group  [47]  but make interpretation more challenging. We have also run LeafCutter on our dataset and added the results on our portal, where you can search the gene-level data (https:// rconnect. dfci. harvard. edu/mmleafcutter/). The final comparison can be made on the isoform level. Techniques like isoform switch analysis utilize predicted isoform expression, and some try to explain them using AS events. Although the biological interpretation of these methods might be easier, they do not rely on direct evidence (reads covering the events).
Bone marrow samples from normal donors are critical pieces in plasma cell cancers like MM. In our study, we used 16 samples; however, this caused a disproportional sample size that eventually affected the statistical power. Although this is the largest RNAseq dataset with normal plasma cells profiled similarly to MM cells, an increased normal sample pool would definitely help in future studies. Therefore, comparing the two conditions would benefit from increased sample size. One of the key findings from our analysis was that specific treatment target genes, such as IRF4 and IKZF1, had a significant alteration in splicing patterns compared to NPCs. This confirmed our previous data showing two separate isoforms of IKZF1  [48]. This suggests the need to evaluate specific IKZF1 isoforms in future studies to understand the impact of treatments like lenalidomide, for which IKZF1 is a primary target.
Intronic regions are 250-bp flanking sequences, and exon regions are 50-bp sequences from the start or end of the exon where splicing factors often bind. B Boxplots of SRSF1 expression (y axis) by MM stage (x axis) in three datasets (3 panels). Panel corresponds to datasets: IFM 2009 (left), Mayo Clinic-(GSE6477) (center), and Arkansas-(GSE5900 and GSE2658) (right). Colors correspond to MM (red), MGUS (green), SMM (purple), and NPC (blue). p values for each dataset are given on the top with corresponding test names. C Hazard ratio (y axis) shown with blue boxes and 95% CI (lines) of overall survival (OS) for SRSF1 expression in three datasets. p values were calculated using the cox proportional hazard model, and the summary table is on the right. D Boxplots of the number of exon skipping and MXE (y axis) events by SRSF1 expression (x axis).
G Fig. 3  SRSF1 expression impacts MM cell growth and viability. A Genetic depletion of SRSF1 was achieved using four different shRNAs containing the target sequence or scrambled control in MM1S (x-axis). MM1S cells were infected with either scrambled (pLKO. 1) or 4 SRSF1targeted shRNAs and selected with puromycin for 72 h. qPCR analysis (right panel) was performed to confirm decreased SRSF1 mRNA levels (red line, right y-axis) in cells expressing SRSF1 shRNAs compared to scrambled cells (red line). Transduced cells were analyzed for effect on cell growth by 3(H) thymidine uptake and presented as fold change from cells infected with pLKO. 1 (left y-axis and bar plots). Data are shown as the mean values ± s. d. of triplicates. B SRSF1 protein levels and cell proliferation were evaluated three days from puromycin selection by WB and CTG.
E A panel of 13 MM cell lines (circles in left bar plot) and PHA-activated PBMC from seven healthy donors (triangles) were tested with different concentrations of TG003. IC50 analysis and a non-parametric t-test were performed. Data are shown as the mean value ± SD. F KMS20 MM cells were stably transduced with retroviruses expressing empty vector or T7-tagged SRSF1 mutants (xaxis). These cells were analyzed by western blotting with an anti-T7 antibody (right panel) to confirm transduction efficiency. Cell proliferation was evaluated after seven days of culture and represented as fold change from an empty control (y-axis). G SCID mice were injected with empty or SRSF1-expressing KMS20 cells (n = 3/cohort) and evaluated for overall survival. Log-rank Mantel-Cox test was utilized to assess statistical significance (p = 0.
The color coding of splicing types is as in panel H. Shared events between mutant types are indicated with the connected black points and lines below the vertical bar chart. C The functional association analysis of spliced genes obtained by building a network using the Reactome functional interaction database. Genes are clustered (red boxes) by protein-protein interaction networks provided by Reactome FI with pathways assigned by their functions (blue arrows). D Total numbers (upper panel) and percentage (bottom panel) of SRSF1 regulated AS events, by type (color codes), in overexpressed (upper two rows) and knockdown (bottom two rows) samples compared to their respective control cells. E Venn diagram showing the overlap between SRSF1 overexpression and knockdown experiments for genes with significant alternative splicing events.
The horizontal bars at the bottom left represent the total number of events induced by each mutant type (black bars on the left). The vertical bars above indicate the number of events shared between mutant experiments. The color coding of splicing types is as in panel H. Shared events between mutant types are indicated with the connected black points and lines below the vertical bar chart. C The functional association analysis of spliced genes obtained by building a network using the Reactome functional interaction database. Genes are clustered (red boxes) by protein-protein interaction networks provided by Reactome FI with pathways assigned by their functions (blue arrows). D Total numbers (upper panel) and percentage (bottom panel) of SRSF1 regulated AS events, by type (color codes), in overexpressed (upper two rows) and knockdown (bottom two rows) samples compared to their respective control cells.