dataesr/scientific-paragraphs-categorization huggingface dataset:
The first class corresponds to fusion transcripts arising from two different chromosomes, the second class includes parts of two genes belonging to the same chromosome strand. The third and fourth classes involve non-collinear transcription on the same chromosome. This categorization suggests that each class represents distinct, complex synthesis mechanisms. In order to assess Crac's potential to identify new biomarkers, we present data related to acute myeloid leukemia (AML) RNAseq analysis. AML provides a practical cellular model to detect chRNA biomarkers in order to improve classification and patient follow-up in precision medicine  2. Despite the fact that leukemia chRNAs are well characterized, our study highlights new biological cases of chRNA. We identify and validate 17 chRNAs initially detected in 3 AML patients that belong to the 4 classes.
Amendments from Version 1 We have carefully read the manuscript and corrected the typos. We replaced commas with periods in the "AML samples and cells lines" section, and the degree symbols with percentage signs, as required in the "FISH Experiments" section. As requested by the referee, we have added the following statement in the "RNA-seq experiments" section: "The RNAseq was performed using polyA-selection with the TruSeq RNA Lib-Prep Kit (Illumina) adjusted with GATC specific procedure for strand specificity. " We also substituted "MDR" with "MRD" for minimal residual disease. We have also changed the resolution of Figure  5A , increasing the font size.
REVISED NB4-LR2 cell line (provided by CC) were cultured in RPMI 1640 (Invitrogen, USA) containing 10% decomplemented FBS (Dutscher, Brumath, France). For differentiation conditions, NB4 and U937 cells were cultured as previously described  16, 17. The chemical agents used for differentiation were 1µM all-trans retinoic acid (ATRA; Sigma-Aldrich, Gillingham, UK), or 0. 1µM vitamin D (VD) associated with 500pg/ml transforming growth factor beta TGFβ (Promega Corporation, USA) for the NB4 cell line  18. For the U937 cell line, 0. 1µM TTNPB associated with 1µM Targretin (LGD1069) and 0. 1µM • ENCODE publicly available datasets (Supplementary Table  S7 ). RNA-seq analysis and chRNA extraction procedure using Crac and cracTools RNA-seq analyses were performed serially using Crac (V2) and CracTools (V1.
2)  14. Crac is a software for analyzing reads when a reference genome is available, that is completely independent of annotations. It ignores the sequence quality of reads and classifies reads by detecting diverse biological events (mutations, splice junctions, and chRNAs) and sequencing errors from a RNA-seq read collection. In this analysis, we used two Crac versions in succession (V1. 6 and V2) to extract and classify chR-NAs, with the GRCh37/hg19 genome as reference genome. Crac extracts the chimeric reads supporting the chimeric junction (spanning junction) made of a non-collinear arrangement of genomic regions  14. CracTools was then used to aggregate, annotate and filter the chRNA reads and extract the chimeric paired reads (spanning PE) (Figure  S1 ). Reads were annotated according to a GFF file from ENSEMBL Genome Browser (link to GFF file in Data Availability section) by giving priority to location in exons of the annotated genes.
The GFF file was built from ENSEMBL (Ensembl 84 annotations). When reads were located on a non-annotated, transcribed region, the corresponding "NONE" annotation was mentioned. The procedure included classifying chRNA into four categories depending on exon organization, as described in the introduction. This classification resembles the one depicted in Gingeras, 2009  7  and can be summarized as follows: -Class 1, the exons are located on different chromosomes; -Class 2, the exons are collinear but most likely belong to different genes, to be verified through the annotation; -Class 3, the exons are on the same chromosome and same strand, but not in the order in which they are found on DNA; -Class 4, the exons are on the same chromosome but on different strands. For each analyzed chimera, the pipeline provides related information, including a unique read identifier annotated by the pipeline, class, number of spanning junctions and spanning reads:  Candidate fusion transcripts involving adjacent genes within a 3Kb distance region were discarded.
Manual annotation The potential chimeras are listed in Table  S2  with the appropriate features. For each fusion transcript, the Crac software provided a reconstructed sequence comprising, on one hand, the chimeric junction sequence based on the most representative read, and on the other hand, the paired read sequence. The symbol (#) marks the segment of the read that was not sequenced and the (*) symbol marks the junction point (Table  S2  and Supplementary Figure  S1 ). The sequences of reads, both junction and paired, which supported the chimera were mapped (BLAT, UCSC) to the human genome GRCh37/hg19 in order to identify complex biological events (splicing, SNPs, insertions, deletions, repeats, polymorphisms, etc. ). Complementary annotations were identified using the ENSEMBL genome browser to determine exons and spliced variants involved in the transcript.
Tag search approach and gene expression clustering The tag search approach consisted of extracting representative sequences (Tags) 30 nucleotides in length, centered on the chimeric junctions. The latter were then searched in LEUCEGENE and ENCODE publicly available RNA-seq datasets. A FASTA file, listing these tags of interest, designed for each chRNA, was submitted to a specific pipeline (countTags, https://github. com/jaudoux/ countTags) that searched for sequences and their reverse complement with an exact match in the FASTQ files. For each FASTQ file and each tag, the total number of tags (total k-mers) is counted. Final value is given in a delimited table, with a tag count normalized per 5 billion of k-mers. For the gene expression clustering, chRNA were selected as below. Chimeric genes were extracted using cracTools predictions for samples OS110089, OM100011 and OM110223.
Validation of chRNA candidates We performed RNA-seq on samples from 3 AML patients. One presented with a normal karyotype (NK), while the other two presented with an abnormal karyotype (AK), one with an Inv16 and the other with a t (15,17) translocation (sample names 1-3, Supplementary Table  S1 ). The sequences were analyzed using Crac and CracTools. The selected chRNA candidates were tested by qPCR, and sequenced when qPCR displayed a positive signal. Some candidates could not be validated by qPCR due to the difficulty in designing suitable primers. The CBFB-MYH11 and PML-RARA fusion transcripts expressed in the AML-inv16 and AML-t(15;17) samples were identified using both RNA-seq and qPCR analysis, confirming the reliability of RNA-seq and the Crac suite in this type of analysis. We identified 17 chRNAs among 44 candidates; 10/23 from AML-t(15,17), 4/18 from AML-NK, and 3/3 from AML-inv16 (Supplementary Table  S2  and Supplementary Table  S4 ).
Moreover, this lincRNA contributed to the formation of a new chRNA in the case of a translocation between chromosome 13 and 2. New Class 1 PML-RARA variants Four Class 1 junctions involving PML and RARA genes were identified from the analysis of the AML-t(15; 17) OM110223 sample (Supplementary Table  S2 ). Acute promyelocytic leukemia (APL) molecular diagnosis and minimal residual disease (MRD) monitoring are currently based on bcr1, bcr2 or bcr3 fusion transcript detection, depending on the DNA breakpoint 21,  22. PCR and FISH analyses performed during diagnosis showed that patient OM110223's leukemia cells present a short bcr3 transcript which connects exon 3 of the PML gene and exon 9 of the RARA gene (equivalent to the exon 3 of the RARA-001 ENSEMBL transcript). Among the 4 PML-RARA characterized junctions, one of them confirms the short bcr3 variant expression and the "reciprocal" transcript joining the RARA exon 5 and PML exon 4  23.
The primers were designed in the corresponding PML intron free from known transcription, in order to amplify the antisense chRNA. This transcript was only detected in the sequenced sample. The sequencing results of PML-RARA chRNA qPCR amplifications revealed another fusion transcript joining PML exon 3 and RARA exon 10 present in OM110223 leukemia cells, without corresponding spanning junction read. New Class 2, 3 and 4 chRNA Four classes of chimera were identified using the Crac and CracTools pipeline. Associated with specific features and annotations, subcategories could be defined and linked to potential biological mechanisms (Figure  4 ). Among the Class 2 chimera, we observed two distinct categories, depending on the vicinity of the two relevant genes (Figure  4A ). The first category involves a junction between non-adjacent exons separated by thousands of base pairs (SLC16A3-METRNL and UBR5-AZIN1, Figure  4A  and Table  S2 ).
The validated fusion transcript joins the SLC16A3 exon 2 with the METRNL start exon 5 at a distance of 855828bp. As shown from RNA-seq data, the region between the two genes is transcribed (data not shown), which calls into question the hypothesis of an intra-chromosomal deletion. The second subcategory concerns two adjacent genes, fused with a loss of some exons. In the illustrated example of VAMP8-VAMP5, VAMP8-001 transcript exon 4 joined VAMP5-001 transcript exon 2 (Figure  4A ). The subgroup most likely defines a read-through transcript category with an alternative splicing. The Class 3 chimera could reflect three different mechanisms associated with: genomic duplication, polymorphism, or intrinsic transcriptional mechanisms (Figure  4B ). Two cases concern intra-exonic transcripts, with one exon end being present before the start of the same exon.
This subcategory is illustrated by two fusion transcripts (METRNL; Figure  4B  and RNF220 data not shown). In the transcript METRNL-001, the 3' part of exon 2 (chr17 (+)/pos 81043015-81043199) is present upstream of the 5' part of exon 2 (chr17 (+)/pos 81042813-81042850). Such transcript could be generated either by transcriptional event or by the transcription of a rearranged allele. Indeed, this subgroup, intragenic chRNA, could highlight the presence of tandem duplication. As an example, we also identified the well-known FLT3 tandem repeat involved in acute myeloid leukemia. This type of tandem duplication is characterized by an overlap in the read's genomic positioning, which can be easily detected using our workflow (Figure  4B ). The last case involves two distant genes. The genomic location of both transcripts is on the same chromosome but at a distance of 102781bp.
Recurrence of chRNA in normal hematopoietic stem cells and AML To extend our analysis, we tested the expression of the validated chRNA in a large cohort of AML samples with different karyotypes (Figure  5A , Table  S1 ) using qPCR. We then classified the chRNA into AML subtypes or tumor-specific chRNA subgroups, taking into account the frequency and tissue-specific expression. We also distinguished the non-tumor chRNA by examining their expression in normal PBMCs. Among the 15 chRNA tested, some of them were widely expressed in all AML subtypes (RNF220-RNF220, METRNL-METRNL and MEA-CTDP1). The frequency and tissue-specific expression did not depend on the chRNA classes. In order to validate our strategy on a large cohort, we analyzed a publicly available dataset of 125 AML and 17 normal CD34+ HSC RNA-seq using a tag search approach (see Materials and methods).
It is worth noticing the involvement of the METRNL gene in two identified chimeras. TRIM28-TRIM28, FLT3-FLT3, PML-RARA (with (3-9) or (5-4) junction) and CBFB-MYH11 tag counts showed high mean expression levels (see Figure  S4 ). FLT3, PML-RARA and CBFB-MYH11 are strong markers in AML, and also useful for prognostic and MRD monitoring. The FLT3 tag identified in OM110223 is not found in other samples, yet the pipeline reveals other FLT3 fusion transcripts, with different sequences in other AMLs (data not shown), indicating several variations at this fusion point. Among the new, TRIM28-TRIM28 chRNA is present at low frequency in normal and abnormal AML karyotype (2/43 AML-NK and 9/82 AML-AK). The high expression level of this chRNA in positive samples (comparable to previously described known markers CBFB-MYH11, FLT3-FLT3 and PML-RARA), suggests it could have a key role in such tumors.
In order to verify whether genes involved in chRNA have an aberrant expression profile  25  that could influence tumorigenesis, we analyzed the impact of gene expression on AML with unsupervised clustering. As described for the read-through chRNA subcategory, we compared RNA-seq data from normal CD34+ HSC and AML-AK subtypes (LEUCEGENE, part3). Figure  6  shows a quantitative analysis obtained with "read-through related" genes of the input cohort. Known AK-AML subgroups could be distinguished from CD34+ HSC by their expression profile. We performed a similar study with the Class 3 tandem duplication subgroup, showing interesting differential profiles with "tandem duplication related" genes, mostly comprised of the newly identified TRIM28, CEBPD and FLT3 (Supplementary Figure  S5 ). (B) Classification of chRNA depends on normal or tumor level expression.
Recent findings have demonstrated the role of read-through chRNA in renal carcinoma and breast cancer  26, 35  , and our data demonstrates that genes involved in these events are differently expressed in AML. More studies are needed to elucidate the physiopathological impact of these chRNAs. The potential of NGS technologies, particularly of RNA-seq, in increasing the capabilities of personalized medicine is clear  2. However, to achieve this, efforts must be made to facilitate the interpretation of complex high throughput data. From ribo-depleted RNAseq data analysis, the characterization of circRNA arising from fusion gene requires the identification of linear junction (lin-J) and circular junction (circ-J) described as spliced and back spliced junction respectively by Guarnerio et al (Oncogenic Role of ; 165 Fusion-circRNAs Derived from Cancer-Associated Chromosomal Translocations, 2016 Cell (2): 289-302 The linear and circular fusion transcripts share the same linear junction.
). The new (3-12) PML-RARA junction we discover corresponds necessarily to a linear junction because of its fusion junction sequence (see figure  at  ). Moreover, in our polyA+ dataset it surely https://bio2m. montp. inserm. fr/papers/AMLchimera2017 corresponds to a linear transcript. It is most probably an alternative splicing product transcribed from the PML-RARA bcr3 translocation since it coexists with the well-known (3-9) PML-RARA bcr3 transcript. However we could not exclude in the biological sample the presence of circRNA emerging from the PML-RARA fusion gene. However polyA RNAseq protocol doesn't enable to reveal them. To answer to the referee remark, we performed complementary experiments and analyzed the Guarnerio dataset ( ) with our pipeline to detect fusion junctions (lin-J BioProjectID: PRJNA315254 and Circ-J) specific to arising from the PML-RARA translocated genes.
We found 3 circRNA PML-RARA and 1 RARA-PML linear fusion junctions but we did not detect the circRNA ones. We performed a tag search approach specific to the PML-RARA circ-junction (F-circ1) described in Fig S1B supplemental information of Guarnerio et al manuscript. We did not find it in their fastQ files. To conclude we were unable to detect PML-RARA cirRNA in Guarnerio J dataset but we confirmed the data recently described by You, X. and Conrad, T. OF with the specific cirRNA Acfs pipeline ( Acfs: accurate circRNA identification and quantification from RNA-Seq data, Scientific Report ). The circRNA search for low abundance will certainly 6,38820; doi: 10. 1038/srep38820; 2016 require more deepness in RNAseq.
Minor Concerns The authors should read the manuscript closely for typos. For example in the AML samples and cells lines section there are commas where there should be periods, in the FISH methods section there are degree signs instead of percent, and two paragraphs before the discussion there is MDR instead of MRD. : We read carefully the manuscript and corrected the typos. Response The authors should include the kits used for ribosomal RNA-depletion/polyA-selection, as well as stranded library preparation. : We added the following comment on page 4: " Response The RNAseq was performed using polyA-selection with the  It would be nice to perform the same RNAseq pipeline analysis on bigger datasets, such as the data from leucegene. 2. It is well known that different software tools behave differently and false positive /negative is a big issue.
The METRNL001 class3 chimera may be a product of backsplicing if the sequencing protocol is not restricted to polyA RNAs We want to thank the referee for taking time to read the manuscript and provide constructive feedback. 1. The first remark concerning LEUCEGENE analysis is relevant as this cohort is a unique dataset of 450 RNA-seq from AML patients and normal hematopoietic cells. This cohort includes diverse types of AML and clinical data with a deepness of 100-300x10 reads/per sample. However, our first aim in the present manuscript was to test our pipeline and to propose a new way to analyze, select and classify chRNA. We thus choose to analyze a small dataset and used the Leucegene cohort only to explore specific expression of the new chRNAs (revealed with our pipeline). This Expression with a tag search approach was based on their recurrence, tumor, subgroup, or patient-specific expression.
The complete leucegene chRNA research represents a big task and constitutes a full-fledged work, we are now performing our RNAseq pipeline on this dataset. In this new task, we will check at a large scale the pipeline performance, in term of memory computing and time consuming, and also validate its capacity to detect all the well-known AML fusion genes. We then should be able to discover new chimeric RNA in AML. 2. We totally agree with the referee and are well aware of potential bias generated with different pipelines. Our group is particularly concerned about software comparison for chimeric RNA detection CRAC was developed in the Lab and compared with other software before we choose it. for this study (Philippe et al, 2013, Genome Biology; an integrated approach to the analysis of genome Biology) and we recently published in September 2017 a benchmark RNA-seq reads method dealing with this topic (Audoux et al, 2017, BMC Bioinformatics; SimBA: A methodology.
CRAC tools has and tools for evaluating the performance of RNA-Seq bioinformatic pipelines) been developed in the Lab in response to biologists requests to better characterize, filter and classify predicted fusion transcripts. Moreover, we recently compared the pipeline CRAC and CRACtools with Fusioncatcher in our AML dataset, as it provides interesting and complementary annotation (overlapping databases, protein impact) but the process is time consuming and the filtering step is more stringent, many of the new fusion transcripts we validated are ruled out. Whatever the bioinformatics pipeline used, bias will subsist. In order to improve chRNA discovery, we undertook to couple the bioinformatics approach with biological information and propose a tag 6 we undertook to couple the bioinformatics approach with biological information and propose a tag search approach targeting the chimeric junctions in normal and tumoral large data set.
We demonstrate that this complementary approach works well and will help the biologist to determine the biological relevance of chimeric events. 3. Since we use polyA+ RNA to perform RNAseq, the METRNL001 class3 chimera, which involves a single exon (end of exon 2 METRNL transcribed before the start of the same exon) more probably corresponds to a linear polyA+ transcript involving a short repeat sequence of 5' part of exon 2 as described in Fig4 rather to a circularization of a single exon formed by backsplicing. No competing interests were disclosed.
Acknowledgements Jérôme Audoux is fellowship of the FRM (Appel d'offres urgence pour la bioinformatique, projet  DBI20131228566 ) and  Ronnie Alves are fellowships of the IBC (ANR "Investissement  d'avenir en bioinformatique-projet-IBC); We thank  Philippe Clair  (Engineer,  University of Montpellier, France ) for advice on qPCR (qPHD plateform,  Montpellier GenomiX ) and  Jean-Marc Holder  ( CMO SeqOne ) for text corrections. The authors should read the manuscript closely for typos. For example in the AML samples and cells lines section there are commas where there should be periods, in the FISH methods section there are degree signs instead of percent, and two paragraphs before the discussion there is MDR instead of MRD. The authors should include the kits used for ribosomal RNA-depletion/polyA-selection, as well as stranded library preparation.
This work was supported by the  French ANR IBC  project " Investissement d'avenir en bioinformatique-projet-IBC " and FRM Grant information: " Appel d'offres urgence pour la bioinformatique , projet  DBI20131228566 ". This work was supported by the  France Génomique National  infrastructure, funded as part of the  "Investissements d'Avenir" program  managed by the  Agence Nationale pour la Recherche  ( ANR-10-INBS-09 ). We acknowledge la  Ligue Nationale Contre le Cancer  for financial support ( EL2015. LNCC/JML ) to JML's team, and  Le Cancéropôle Grand Sud-Ouest (GSO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Grant information This work was supported by the  French ANR IBC  project " Investissement d'avenir en bioinformatique-projet-IBC" and FRM "Appel d'offres urgence pour la bioinformatique , projet  DBI20131228566 ". This work was supported by the  France Génomique National  infrastructure, funded as part of the  "Investissements d'Avenir" program  managed by the  Agence Nationale pour la Recherche  ( ANR-10-INBS-09 ). We acknowledge la  Ligue Nationale Contre le Cancer  for financial support ( EL2015. LNCC/JML ) to JML's team, and  Le Cancéropôle Grand Sud-Ouest (GSO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability The Crac and CracTools software is hosted on http://crac. gforge. inria. fr/. Raw data (FASTQ files) for OM100011, OM110223 and OS110089 patients are available under accession number E-MTAB-5767 in the ArrayExpress database at EBI. The publicly available ENCODE datasets (a detailed list is pro- vided in Supplementary Table S7) used in the study are available on https://www. ENCODEproject. org/. The following LEUCEGENE datasets: GSE62190 (82AML-AK), GSE48846 (17 CD34), GSE49642 (43AML-NK) (a detailed list is provided in Supplementary Table S6) are available on https://www. 1. ncbi. nlm. nih. gov/geo/.
, 2011). Moreover, trees have rarely been labelled in the field in previous studies, making new measures on the same trees impossible. TreM types such as woodpecker holes are rare and the detection of their formation requires large tree samples  (Larrieu et al. , 2014a). Huge measurement efforts will be necessary before large databases with repeated observations of TreMs are available. Inferring the rate of TreM formation from cross-sectional data (observation of several individuals representing different development stages) is required because of data availability. However, this poses several challenges. First, we usually do not know the age of trees. It is therefore difficult to relate TreM formation to a time scale. Second, observations are censored: we do not know exactly at what time the TreM formation event has occurred or will occur.