dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: Here we present ChimPipe, a modular and easy-to-use method to reliably identify fusion genes and transcription-induced chimeras from paired-end Illumina RNA-seq data. We have also produced realistic simulated datasets for three different read lengths, and enhanced two gold-standard cancer datasets by associating exact junction points to validated gene fusions. Benchmarking ChimPipe together with four other state-of-the-art tools on this data showed ChimPipe to be the top program at identifying exact junction coordinates for both kinds of datasets, and the one showing the best trade-off between sensitivity and precision. Applied to 106 ENCODE human RNA-seq datasets, ChimPipe identified 137 high confidence chimeras connecting the protein coding sequence of their parent genes. In subsequent experiments, three out of four predicted chimeras, two of which recurrently expressed in a large majority of the samples, could be validated.
Cloning and sequencing of the three cases revealed several new chimeric transcript structures, 3 of which with the potential to encode a chimeric protein for which we hypothesized a new role. Applying ChimPipe to human and mouse ENCODE RNA-seq data led to the identification of 131 recurrent chimeras common to both species, and therefore potentially conserved. Conclusions: ChimPipe combines discordant paired-end reads and split-reads to detect any kind of chimeras, including those originating from polymerase read-through, and shows an excellent trade-off between sensitivity and precision. The chimeras found by ChimPipe can be validated in-vitro with high accuracy.
A growing number of bioinformatic methods have been created to detect chimeras amongst such datasets  [31] [32] [33] [34] [35] [36] [37] [38] [39]. These state-of-the art programs usually include 3 steps: (1) mapping and filtering for chimeric reads, (2) chimeric junction detection, and (3) chimera assembly and filtering  [40]. They rely heavily on an underlying mapper to map the reads to the genome (and optionally to the transcriptome), and make use of two kinds of reads for chimera detection: (1) discordant paired-end (PE) reads, i. e. paired-end reads where the two mates map in a way that is not consistent with annotated gene structure, e. g. on different chromosomes, and (2) 'split' reads, i. e. reads that do not map contiguously to the genome but have to be split or fragmented into several blocks (usually two) to map to the genome (Fig.
1 ). In addition, the use of one or two kinds of reads for chimeric junction detection allows one to define 3 classes of approaches: (1) the whole paired-end approach, (2) the direct fragmentation approach, and (3) the paired-end + fragmentation approach  [41]. Benchmarking of these programs has shown a high false positive rate and a poor intersection between their outputs on the same dataset  [42, 43]. On the other hand these programs are usually developed to detect fusion genes in human cancer, and are therefore not always able to detect read-through events and to work on species other than human. In addition, these programs are not always able to predict multiple isoforms per gene pair, and more importantly to provide base pair resolution, preventing their downstream functional validation.
Programs using the paired-end + fragmentation approach usually first find discordant paired-end reads, then make an exon-exon junction database from them, and finally map the yet unmapped reads to this database. They are therefore not able to find split-reads that do not have associated discordant PE reads. Contrary to these programs, ChimPipe finds split-reads and discordant paired-end reads independently, defines chimeric junctions based on the first ones (known to be more sensitive) and uses the second ones as a way to reduce the false positive rate (although their use is not compulsory). The second biggest difference with other tools is the fact that ChimPipe uses mapping tools (GEMtools RNA-seq pipeline and GEM RNA mapper) that guarantee an exhaustive mapping search given the input parameters, which again allows for a higher initial sensitivity.
The ChimPipe method The ChimPipe method is depicted in Fig. 2  and includes 4 consecutive steps: Fig. 1  Two types of RNA-seq reads for chimera detection. This picture shows a chimeric transcript (bottom) made from exons of two genes, A and B, depicted in blue and red respectively (top). This chimeric transcript is supported by two types of reads: a split-read and a discordant paired-end read, that we depict aligned both on the genome (middle-top) and on the transcriptome (middle-bottom). The chimeric junction position on the transcriptome is highlighted by a yellow star both in the split-read and in the chimeric transcript Fig. 2  The ChimPipe method. a RNA-seq reads are first mapped to the genome and transcriptome using the GEMtools RNA-seq pipeline, and the reads that do not map this way are passed to the GEM RNA-mapper to get reads that split map to different chromosomes or strands.
b The splitreads from these two mapping steps are then gathered and passed on to the ChimSplice module which derives consensus junctions associated to their expression calculated as the number of staggered split-reads supporting them. The ChimPE module can then associate each chimeric junction found by ChimSplice to their discordant PE reads, splitting them into the ones consistent and the ones inconsistent with the junction. c The ChimFilter module then applies a series of filters to the chimeric junctions obtained until this point in order to discard false positives, leading to d a set of reliable chimeric junctions to which it associates several pieces of information such as a category (readthrough, intrachromosomal, inverted, interstand, or interchromosomal), and the supporting evidence in terms of number of staggered split-reads and number of consistent PE reads, among others (i) Exhaustive paired-end and split read mapping with GEM.
The paired-end reads are initially mapped in 3 ways with the GEMtools RNA-seq pipeline (http:// gemtools. github. io/docs/rna_pipeline. html): to the genome, to the transcriptome and de novo. Firstly, the reads are mapped to the genome with GEM  [44] , allowing up to 4% mismatches and indels. Secondly, the reads are mapped to the transcriptome with the same mapping parameters, the transcriptome being composed of all biologically valid combinations of exons within each gene (therefore also including annotated splice junctions). This transcriptome is built from the gene annotation and allows mapping of reads spanning exon to exon junctions that would not match to the reference genome due to the presence of introns. Thirdly, the reads are split-mapped to the genome with the GEM RNA mapper (http://algorithms.
Finally, each chimeric junction is associated to the number of consistent and inconsistent PE reads, which can be used in the downstream ChimFilter filtering module to filter out artefactual chimeras. (iv) ChimFilter. Chimeric junction candidates are filtered to produce a final set of more reliable chimeras. Firstly, based on the principle that false positives due to read misalignment would not be supported by both sources of evidence, ChimPipe requires a candidate chimera to be supported by both split-reads and consistent PE reads. Two different support based filtering schemes are applied depending on whether the chimeric junction involves annotated or novel splice sites. By default, chimeric junctions with annotated splice sites must be supported by at least one consistent PE read, one split-read and three total (consistent PE + split) reads, while those with novel splice sites have to be supported by at least three consistent PE reads, three split-reads and six total reads.
It has to be noted that ChimPipe can also start from already aligned reads (bam file) provided they include evidence of intra-chromosomal chimeric junctions, and that ChimPipe does not only output chimeric junctions but also a standard bam file (from step (i) of the pipeline) that can be used for more standard RNA-seq analyses such as differential gene expression or transcript reconstruction. Finally ChimPipe has been designed to require minimal information about the PE RNA-seq dataset on which it is run, since it guesses the Illumina quality offset, the strandedness, and the mate configuration in case of directional data. Note that ChimPipe's documentation includes a tutorial and an example.
We developed ChimSim, a program to simulate chimeric transcripts from a gene annotation, a genome, and numbers of read-through, intra-chromosomal, inverted, interstrand and interchromosomal chimeric transcripts to create from the gene annotation (see Additional data section and Additional file 1: Supplementary methods). Using ChimSim on the the Gencode v19 protein-coding genes  [46]  and the hg19 genome, we generated a simulated dataset of 250 chimeric transcripts homogeneously distributed in the 5 chimera classes (50 from each class) (Additional file 2). Knowing that about 60% of transcripts from protein coding (pc) and long non-coding RNA (lncRNA) genes are usually expressed in a given condition  [47] , we sampled 60% of transcripts from Gencode v19 pc and lncRNA gene transcripts, totalling 101,961 transcripts (Additional file 2).
Knowing that when a chimera is expressed, its parent genes are often also expressed  [10] , we added the parent transcripts of the 250 chimeras to the sampled transcripts, totalling 102,149 non chimeric transcripts (Additional file 2). The 102,399 transcripts resulting from the union of the 250 chimeric transcripts and the 102,149 non-chimeric transcripts, were then passed on to the art_illumina program of the ART suite (version 2. 3. 7, [48] ), to simulate Illumina non directional paired-end RNA-seq reads of 3 different lengths: 50bp, 76bp and 101bp, called PE50, PE76 and PE101 respectively. Several parameters were used in addition to read length and pairedendness, to make our simulated chimera data closer to real RNA-seq data, including insert size mean and standard deviation, read coverage and sequencing quality profile (see Additional file 1: Supplementary methods for details).
The sequencing quality profile was learnt from real Illumina PE data of the same read length using the art_profiler_illumina program of the ART suite (version 2. 3. 7, Additional file 2 and Additional file 1: Supplementary methods). Using these parameters, ART generated 32. 3, 21. 1 and 15. 7 million PE reads for the PE50, PE76 and PE101 respectively (Additional file 2 and Additional file 1: Supplementary methods). The benchmark was done for each read length separately. For real data with experimentally validated chimeras, we used two previously published datasets: the leukemia/melanoma cancer study from Berger et al. (  [25] , that we call the Berger set), and the breast cancer study from Edgren et al. (  [45] , that we call the Edgren set). The Berger set was composed of the K562 chronic myelogenous leukemia cell line associated to two different insert size ranges, 300-400 bp and 400-600 bp, of the 501Mel melanoma cell line and of 5 melanoma patient-derived short-term cultures, and came with 14 RT-PCR validated fusion genes (Table  1 ).
The Edgren set was composed of 4 breast cancer cell lines (of which two were associated to two different median insert sizes, 100bp and 200bp), and came with 27 RT-PCR validated fusion genes. For the Edgren set we used an additional 13 fusion genes that were found and RT-PCR-validated by a re-analysis of the Edgren et al. data by Kangaspeska et al. [49] , totalling 40 fusion genes (Table  1 ). The benchmark was done for each library separately, but is provided for the pool of libraries of each dataset, for clarity reasons. Since the chimeras specifically targeted by the Berger and the Edgren studies were only fusion genes, the read-through events were removed from all programs' predictions before running the benchmark. Since we wanted to do the evaluation both at the gene pair level and at the junction level, and since an RT-PCR validated fusion gene is merely a gene pair together with the cDNA sequence corresponding to its junction, we used the blat program  [50]  to align the cDNA sequences to the hg19 human genome, and further manually curated these alignments to obtain the exact chimeric junction coordinates for each fusion gene (see Additional file 1: Supplementary methods).
This procedure resulted in 16 and 42 chimeric junctions for the Berger and Edgren sets respectively, indicating the presence of two different isoforms for one gene pair in each set (Additional file 1: Table  S2 ). The chimera detection programs that we chose to benchmark together with ChimPipe (version 0. 9. 3) were the following: • FusionMap (version 8. 0. 2. 32,  [33] ) • PRADA (version 1. 2, [38]) • Chimerascan (version 0. 4. 5, [34]) • TopHatFusion (version 2. 0. 12, [32] ). We chose these programs because their method was published and for one of the following three reasons: (1) they were shown to have good results in several independent studies (for example FusionMap and Chimerascan) or (2) they were used in studies associated with gold-standard chimera RNA-seq datasets (for example PRADA in  [25]  and Chimerascan in  [24] ) or (3) they were extensively used by the community (for example TopHatFusion).
At the gene pair level the top program on the simulated data is Chimerascan followed by ChimPipe, FusionMap, PRADA and finally TopHatFusion, with a generally quite high Pr for all programs but a Sn above 0. 75 only for Chimerascan and ChimPipe. For real data, Chimerascan is still the top program in terms of Sn followed by ChimPipe, however its number of predicted gene pairs is 1 to 2 orders of magnitude higher than the one of ChimPipe. The trend for Sn on real data is similar to the one of simulated data, but the Edgren gene pairs seem to be easier to predict than the Berger gene pairs, with a higher Sn of the programs for the former. Note that PRADA is a program that also has a good compromise between Sn and number of predicted gene pairs on real data. At the junction level, ChimPipe achieves the best results on both the simulated and the real data with a Sn around 0.
The effect was an overall improvement of the programs' performances (except ChimPipe) but did not change the overall message above. Since some programs have a quite different behaviour at the gene pair and at the junction level, we also computed for each program and each evaluation set, the average and standard deviation of the distance between the predicted and the true junction in case the gene pair was correctly predicted (Fig. 4  and Additional file 1: Table  S6 ). It showed that ChimPipe, FusionMap and PRADA almost always provide the exact junction coordinates on simulated data and the Berger real set, while this is not the case for Chimerascan and TopHatFusion, with a worse behaviour for the latter on the simulated data and for the former on the Berger set. One can note that for simulated data, the distance between Chimerascan predicted and true junction tends to increase with read length (Additional file 1: Figure  S4 ).
Although real data does not allow to compute precision or false positive rate, we expect the number of programs predicting a given chimera to be correlated to the likelihood of this chimera to be a TP. We computed the For simulated data the provided measures are sensitivity (in red), precision (in blue), and F1score (in green), while for the two real datasets (Berger in red and Edgren in blue), the only provided measures are sensitivity (bars) and the total number of predictions (at the top of each bar). Here we show the results on PE76 simulated data, for the 250 simulated chimeric junctions (i. e. including read-through events). For the benchmark on real data, read-through events, i. e. junctions with a length smaller than 100kb when on the same chromosome, same strand and expected genomic order, were removed from the output of each program before the evaluation intersection between the gene pairs predicted by each program on each of the two real sets (Berger and Edgren) (Fig.
5 ), and saw that PRADA, ChimPipe and FusionMap predicted fewer unique gene pairs, while TopHatFusion and Chimerascan predicted many unique gene pairs, consistent with the previous benchmark results (Fig. 3 ). We also confirmed that a gene pair predicted by at least 2 programs was more likely to be real since 26% (respectively 65%) of the gene pairs predicted by 2 programs on the Berger (resp. Edgren) set are TP (i. e. validated by RT-PCR), while only 0% (respectively 1%) of the ones predicted by 1 program are TP. Regarding implementation, while some programs require a single step to predict chimeras (apart from the genome and/or transcriptome indexing), which is the case for ChimPipe, FusionMap and Chimerascan, some other programs require many different successive steps to obtain them, making the whole process more cumbersome.
Detection and validation of novel chimeras In order to survey the human chimera landscape more extensively, ChimPipe was run on 106 ENCODE CSHL PE RNA-seq experiments from 15 human cell lines, 3 RNA fractions (polyadenylated, non-polyadenylated, total) and 6 cell compartments (whole cell, cytosol, nucleus, chromatin, nucleolus, nucleoplasm) (  [47]  and Additional file 1: Table  S7 ). At stringent settings (10 supporting staggered split-reads and 5 discordant paired-end reads in at least one experiment), we found a total of 1195 chimeric junctions over all experiments. Of these, 525 had each of their two ends falling in a unique different protein coding gene, and 142 were either expressed recurrently (at least 1 supporting read in at least 11 out of the 15 cell lines) or very highly and specifically (at least 100 total reads in a single cell line).
Therefore, we assessed the protein-coding potential of the 3 validated chimeric junctions. For each chimeric junction, we reconstructed the theoretical chimeric transcript structures by combining the RefSeq reference mRNAs for the 5' and 3' parent genes compatible with the junction and searched for Open Reading Frames (ORFs) in the six possible translational frames with the NCBI ORF Finder (http://www. ncbi. nlm. nih. gov/ gorf/gorf. html). One case out of the 3 (UBA2-WTIP), which has already been reported  [20] , maintained the frame of the two parent genes, UBA2 and WTIP, while the other two, PICALM-SYTL2 and RPL38-TTYH2, did not. Interestingly, this chimera is recurrently expressed in 72 out of the 106 experiments, which include the 15 cell lines, the 3 RNA fractions and 5 out of the 6 cell compartments (cell, cytosol, nucleus, nucleoplasm and chromatin) (Table  3  and Additional file 3).
Common recurrent chimeras between human and mouse In order to find common, and therefore potentially evolutionary conserved, chimeras between human and mouse, we applied ChimPipe to human and mouse ENCODE RNA-seq data  [56]. These data consist of 2 bioreplicates of 18 human cell lines and 30 mouse tissues, from which long polyadenylated RNA was extracted and deeply sequenced (at least 100 million PE reads). We applied ChimPipe to each bioreplicate separately, and asked each reported junction to be supported by at least one split-read and one discordant PE read (see Additional file 1: Supplementary methods). We then gathered all reported junctions within each species, and found a total of 9339 chimeric junctions in human and 6447 chimeric junctions in mouse. In order to discard chimeras derived from genomic rearrangements, we also required the chimeras to be recurrent, i.
Conclusions We have presented ChimPipe, a novel method for the accurate detection of chimeras from PE RNA-seq data, based on the independent use of discordant PE reads and split-reads. In addition to fusion genes and trans-splicing events, ChimPipe is able to detect read-through events, which is now recognized as the most prevalent class of real chimeras in both normal and tumour tissues  [20, 29, 30]. ChimPipe is general enough to be able to work on any eukaryotic species with a genome and an annotation available. This allows to study chimera evolution but also to investigate the impact of chimeras on individuals from species on which we have more control than human (for example livestock). ChimPipe can also predict several isoforms per gene pair and the exact chimeric junction coordinates, which are essential for chimeric transcript reconstruction and downstream biological validation.
ChimPipe is easy to run since it only requires a genome, a gene annotation and a pair of RNA-seq fastq files (once the indexing of the genome and transcriptome have been done), and guesses many other things such as the directionality, the mate configuration and the Illumina offset quality. For advanced users, many parameters, such as expression threshold or parent gene sequence similarity threshold, can be tuned. ChimPipe provides both a complete and a filtered set of chimeric junctions, with additional information about them, such as chimera category, expression support and the list of reads supporting the junction (Additional file 1: Table  S1 ). In addition to chimeric junctions, ChimPipe provides a standard bam file obtained from the GEMtools RNA pipeline (step (i) of Fig. 2 ), that can be used for downstream analyses such as differential gene expression or transcript reconstruction.
Benchmarking of ChimPipe together with four state-ofthe art chimera detection tools on both simulated and real data, showed ChimPipe to have a very good precision (close to 1), and to be the second most sensitive program (Sn of ≈ 0. 8), therefore showing a very good balance between sensitivity and precision. Additionally ChimPipe's performances on simulated and real data are comparable, and not much impacted by read length. ChimPipe's performances are also similar at the gene pair and at the junction level, which is not the case for all programs since they tend to predict gene pairs better than junctions (see Fig. 3 ). It has to be noted that ChimPipe needs non negligible computer resources to achieve these results, since it requires ≈ 30 Gb of RAM and half a day to run with 4 threads, on the PE76 simulated data (21 million PE reads).
The application of ChimPipe to 106 ENCODE PE RNAseq samples allowed the detection of 137 highly reliable chimeras, of which 4 were chosen for RT-PCR validation, and of which 3 were indeed validated and further cloned and sequenced. The UBA2-WTIP chimera additionally preserved the frame of the 2 parent genes UBA2 and WTIP, and was therefore completely sequenced, leading to 3 completely novel transcript structures. If translated these 3 novel transcripts would lead to a chimeric protein with the ThiF and the UAE-Ubf domains from the UBA2 protein and with the 3 LIM domains from the WTIP protein. We hypothesize that this protein may induce the SUMOylation machinery to post-translationally modify and regulate novel targets, due to the interaction of its LIM-domains with novel proteins. The application of ChimPipe to 36 human and 60 mouse ENCODE PE RNA-seq experiments also allowed the identification of 131 recurrent chimeras common to both species, and therefore potentially conserved.
Although their large majority connect adjacent genes and should originate from read-through events, some cases are also distant or located on different chromosomes. Tens of them are detected in more than 10 samples. Despite these advantages, ChimPipe could be improved in at least 2 aspects: (1) it could provide all the chimeric transcripts compatible with the chimeric junction (module for which we already have a tested code) as additional information, (2) it could be made more robust by being reimplemented in a pipeline specific language such as nextflow (http://www. nextflow. io/). Finally it has to be noted that our contribution goes beyond the ChimPipe program, since we provide two additional programs: (1) a chimera simulator program, called ChimSim (https://github. com/Chimeratools/ChimSim), and (2) a chimera benchmark program, called ChimBench (https://github.