Reduced input RNA (105 copies) required more technical replicates to maintain accuracy, while low RNA inputs (103 copies) suffered from consensus-level errors. Base errors identified at specific motifs identified in all technical replicates were also identified which can be excluded to further increase SNV calling accuracy. These findings indicate that samples with low RNA inputs should be excluded for SNV calling and reinforce the importance of optimising the technical and bioinformatics steps in pipelines that are used to accurately identify sequence variants.High-throughput sequencing such as those provided by Illumina are an efficient way to understand sequence variation within viral populations. However, challenges exist in distinguishing process-introduced error from biological variance, which significantly impacts our ability to identify sub-consensus single-nucleotide variants (SNVs). Here we have taken a systematic approach to evaluate laboratory and bioinformatic pipelines to accurately identify low-frequency SNVs in viral populations. Artificial DNA and RNA “populations” were created by introducing known SNVs at predetermined frequencies into template nucleic acid before being sequenced on an Illumina MiSeq platform. These were used to assess the effects of abundance and starting input material type, technical replicates, read length and quality, short-read aligner, and percentage frequency thresholds on the ability to accurately call variants. Analyses revealed that the abundance and type of input nucleic acid had the greatest impact on the accuracy of SNV calling as measured by a micro-averaged Matthews correlation coefficient score, with DNA and high RNA inputs (10 Advances in high-throughput sequencing (HTS) technologies such as those offered by Illumina allow for the rapid generation of large amounts of deep sequence data. These data can be used to identify sub-consensus single-nucleotide variants (SNVs) essential for understanding viral populations. Indeed, HTS technologies have been used to identify both common and rare SNVs  associatIn order to obtain the requisite high genome coverage depth, polymerase chain reaction (PCR) amplification of the sequencing target is normally required , with reThe bioinformatic pipelines to identify SNVs normally involves the alignment of filtered fastq files against a suitable reference sequence with an alignment software. The resulting Sequence Alignment Map (SAM) and Binary Alignment Map (BAM) files can be used to perform visual controls of the alignment, generate coverage plots, consensus sequences and to identify SNVs. This later point, along with viral haplotype reconstruction analysis can be limited from the accuracy of the dataset. These computational analysis steps employ specific parameters and algorithms that also influence SNV identification . An incrWhile previous studies have simultaneously and systematically investigated the impact of laboratory and bioinformatics protocols on SNV calling and error generation , to the An overview of the laboratory and computational pipelines is shown in 1Kaufbeuren strain of foot-and-mouth disease virus (FMDV) (8218 bp) (GenBank: EU448369) [E. coli competent cells  by a standard heat-shock protocol, with cells being incubated separately overnight at 37 °C on agar plates ). Single colonies were selected and used to inoculate individual 20 mL Luria-Bertani (LB) broths . After incubation for 16 h at 37 °C at 225 rpm, each of the five plasmids were purified using the QIAprep mini kit , as per manufacturer’s instructions, with sanger sequencing used to confirm the presence of the nucleotide substitutions and the Nanodrop used to estimate the DNA yield [The starting template for DNA and RNA populations were derived from five pT7S3 plasmids , each 11,278 bp in length and containing a full-length genome insert of OU448369) . Site-diNA yield .HpAI  as per manufacturer’s instructions. Using the MEGAScript T7 kit (ThemoFisher Scientific), RNA was transcribed from 1 µg of each of the linearised plasmids, with TURBO DNase and the MEGAclear transcription clean-up kit  being used to remove input plasmid DNA (as per manufacturer’s instructions). A qRT-PCR assay, with and without the RT enzyme, demonstrated that the synthesised product was >99.99% RNA [n = 2), 1.11% frequency (n = 2), 0.11% frequency (n = 8) and 0.01% frequency (n = 8).Plasmids were linearised using .99% RNA  Artificig. Following this, supernatant was then transferred into a Phasemaker tube  with an additional 1.25 mL of TRIzol reagent and 300 µL of chloroform being added where after nucleic acid was extracted from the upper aqueous phase, as per the manufacturer’s instructions. Extracted nucleic acid template was confirmed FMDV negative via qRT-PCR [In order to mimic real samples, bovine genomic material was used to dilute the DNA and RNA populations. For this, 25 mg of bovine tongue epithelium tissue , was added to 500 µL of TRIzol reagent  in a 2 mL microcentrifuge tube containing a single 5 mm steel bead . Tubes were placed in the TissueLyser LT instrument  before the epithelium tissue was homogenised at 20 oscillations per second for two minutes. This was followed by a 5 min incubation at 4 °C and centrifugation for 10 min at 8000×  qRT-PCR .High (106 RNA copies/µL), Medium (104 RNA copies/µL) and Low (102 RNA copies/µL)) and two starting inputs for the DNA (High (106 DNA copies/µL) and Low (102 DNA copies/µL)). A total of 10 µL of each of the RNA populations  were used in the Transcriptor High Fidelity cDNA Synthesis Kit  in order to convert the RNA into cDNA. For this reaction, the manufacturer’s instructions were used with the addition of 2 µM of an oligo dT primer  (0.2 ng/µL). Firstly, a 3022 bp product consisting of the leader and capsid encoding regions of FMDV was amplified from each population by using Platinum SuperFi DNA Polymerase , as per the manufacturer’s instructions, with the addition of 3 µL of DNA of each population type and 10 µM of serotype universal FMDV capsid primers  . Results were used to determine a PCR cycle number for each DNA and RNA population input.Aliquots were removed every 2 cycles (from 0 to 40), purified using the Illustra GFX PCR DNA and Gel Band Purification Kit  as per the manufacturer’s instructions and eluted in 50 µL of nuclease-free water, prior to quantification using the QubitFollowing PCR optimisation, all RNA and DNA inputs sets were processed in quadruplicates (with the RT and PCR steps being performed independently). Samples were diluted to 0.2 ng/µL in nuclease-free water prior to library preparation using the Nextera XT DNA sample preparation kit. Final libraries were multiplexed and diluted to 12.5 pM prior to sequencing on an Illumina MiSeq platform using a single 2 × 150 cycle, paired-end sequencing run using a version 2 chemistry MiSeq reagent cartridge. Raw reads were deposited into GenBank under the BioProject accession number PRJNA669475.After sequencing, quality control checks were performed on the raw fastq data using FastQC  (versionTrimmed reads were passed through an in-house bash (bash version 4.1.2(1)) script that iteratively (for each parameter assessed) processed reads through Sickle (version 1.33) , which fUsing an in-house R-script  each alignment file was processed to allocate, for each sequenced position, and all tested frequency thresholds , the basn = 6), triplicate (n = 4) and quadruplicate (n = 1) combinations. For each replicate combination, the classification of each position was based on 100% agreement, i.e., a TP was assigned if all samples in a combination agree. Finally, the TP, TN, FP and FN values across all combinations of replicates over all bases positions were used to calculate a micro-averaged MuMCC, a measure of classification performance, which unlike sensitivity and specificity tests, considers the entire confusion matrix and is able to handle an imbalance of classes , for each base position:The calling of a SNV was dependent on the frequency threshold being applied, with SNVs identified below a tested threshold being considered as a TN, for example, at a frequency threshold of 0.5%, known-SNVs at 0.01% were considered as a TN. This approach was subsequently repeated for all possible duplicate . Following these tests, the aligner, qScore, read length and percentage frequency threshold parameters that produced the highest MuMCC scores were selected in turn for each population type. Areas with higher percentages of error were identified and were used to investigate sequence patterns along the template which could contribute to systematic error.The MuMCC across all sequence positions was used to represent the average performance of a candidate HTS pipeline comprising a combination of experimental and bioinformatic factors  from which the performance assessment was derived. The effect of each laboratory and bioinformatic parameter on MuMCC performance was assessed using ANOVA (R (version: 3.3.1), aov). Tukey’s honest significant different (HSD) test (R (version: 3.3.1), Tukey HSD) was performed post-hoc for parameters identified as having a significant effect  for each population type was established. A total of 18 and 34 cycles was found to be the optimal number for DNA 7 reads were produced, with a mean of 1.26 × 106 reads generated for each sample. Further information regarding the statistics of the MiSeq sequencing run can be found in Following sequencing, a total of 2.52 × 10High sample set, mean coverage ranged from 2.33 × 103  to 7.05 × 104 , while for the DNA Low samples, mean coverage ranged from 3.51 × 103  to 6.63 × 104 .Following sample alignment, BEDTools was used to calculate the average coverage across the amplicon for all parameters tested . For the DNA High samples, mean coverage ranged from 1.60 × 103  to 6.52 × 104 , while for RNA Medium mean coverage ranged from 5.30 × 102  to 6.14 × 104 . Mean coverage across the sequenced amplicon for the RNA Low samples ranged from 3.19 × 103  to 6.63 × 104 .For the RNA Further details on mean sample coverage can be found in To assess which of the tested aligners had the greatest influence on the accuracy of SNV calling, the range of MuMCC scores across all parameters tested  was compared. p value ≤ 0.01). Performing the equivalent comparison for the abundance of material  highlighted significant differences , but significant differences between DNA and RNA populations . An equal MuMCC was observed for all aligners , for the RNA Medium, RNA Low and DNA High starting inputs . Overall, Bowtie2 Global yielded the poorest MuMCC across all population sets tested (Low) produced a significant increase  in accuracy against all aligners with the exception of BWA-MEM.Comparing across aligners, GEM3 produced the highest maximum MuMCC for RNA s tested . Post-hoLow), GEM3 was selected on which all downstream analyses were to be based as it produced the highest MuMCC score for the RNA High input and overall mean MuMCC scores across all other inputs  and the number of technical replicates on the overall accuracy of SNV calling was investigated. High input found that a qScore of q38 for all technical replicates combinations produced the maximum MuMCC scores . While for the RNA Medium input, a maximum MuMCC score of 0.707 was identified when using q35 for singlet and q38 for all other technical replicates (MuMCC difference between q35 and q38 for singlets was 0.015). Comparing the distributions for the RNA High input were significantly  higher than the mean MuMCC values of other qScore thresholds tested. This was also observed for the RNA Medium input. For the RNA Low input, the highest MuMCC score was obtained using a qScore of 35 (MuMCC: 0.279), while all other replicate combinations produced MuMCC scores between 0.001 and 0.005.Post-hoc analysis revealed that the MuMCC scores produced using a qScore of 38 across all technical replicate combinations in the RNA High and medium. A qScore of q35 could be selected RNA Low when using singlet replicates only.Based on the above, a qScore of q38 was selected for all replicate combinations of RNA High, the maximum MuMCC score of 1 was identified when a qScore either q30, q35 or q38 was used for singlet and a qScore of q30 was used for duplicate, triplicate or quadruplicate technical replicates. For the DNA Low input, the highest MuMCC score of 0.707 was achieved at q35 for singlet and duplicate and q38 for triplicate and quadruplicate technical replicates. Tukey HSD post-hoc analysis indicated that the use of q38 for singlet and duplicate and q35 for triplicate and quadruplicate technical replicates produce significantly higher  results for the DNA High input compared to other qScore thresholds. For the DNA Low input, a qScore of 35 for all replicate combinations was the most significant . For the DNA Based on the above results, a qScore of q35 was selected for all DNA populations and replicate combinations.Following the choice of aligner and qScore threshold from  the inflAnalysis found that either multiple or no significant read length parameters were identified for all population inputs and replicate numbers. Therefore the choice of read length was based on the shortest length which gave the highest MuMCC scores .High input, a maximum MuMCC score of 0.816 was achieved for singlet (read length of 70–90 bp and 130 bp) and duplicate  replicate combinations. The maximum MuMCC score was found to increase to 0.894 for triplicate and quadruplicate technical replicates when a read length of 130 bp was used. However, minor differences of MuMCC values between the use of a 70 bp and 130 bp read length were identified for triplicates and quadruplicate technical replicates (0.011 and 0.009 MuMCC respectively).Using the RNA Medium input, a maximum MuMCC score of 0.707 was identified regardless of read length and technical replicate input. While for the RNA Low input, only singlet replicates produced a mean MuMCC score above 0.01, with the highest being 0.149 when a read length of 100 and above was used. However, the difference in MuMCC score for RNA Low single replicates was 0.001 between a read length of 70 bp and 100 bp.In contrast, for the RNA Due to the very small MuMCC differences (<0.011) between tested parameters, a read length of 70 bp was selected for all RNA populations and replicate combinations. High input, a maximum MuMCC score of 1 was identified for read lengths of 70 bp, 100 bp, 110 bp and 130 bps. MuMCC scores were found to decrease with the addition of technical replicates, with a maximum score of 0.933 found with duplicate  and 0.913 for triplicate (100 bp) and quadruplicate  replicate combinations respectively. The MuMCC difference between all read lengths tested was found to be <0.01. In contrast, for DNA Low a maximum MuMCC score of 0.707 was identified for all technical replicate combinations regardless of the read length used.For the DNA Based on the above results, a read length of 70 bp was selected for all DNA replicate combinationsUsing the GEM3 aligner and fixing the qScore and read length parameters  allowed for a reduced percentage frequency cut-off to be applied for SNV calling. Further information regarding each frequency threshold can be found within Overall, MuMCC score analysis showed that a greater abundance of starting material and a higher number of technical replicates , suggesting this frequency could be applied to characterise SNVs below 0.2% at the cost of allowing more errors through the analysis process. Within the RNA Medium input, the advantage of increased technical replicates was clear, with a percentage frequency cut-off of 0.8% identified as the most accurate frequency threshold for singlets, while 0.5% was identified for duplicates, 0.3% for triplicates and 0.2% for quadruplicates .For the RNA Low input, only the singlet dataset produced MuMCC scores above 0.01, with the highest MuMCC score obtained from 0.5% frequency cut-off onwards (MuMCC: 0.250). The MuMCC scores for duplicate, triplicate and quadruplicate combinations ranged from −0.003 to 0.007, implying that these results were no better than random. This and the low MuMCC score for singlets demonstrates that the RNA Low input material cannot be used for accurate SNV analysis. For the RNA High input, the maximum MuMCC score obtained for singlet and duplicate replicate combinations was achieved using a frequency cut-off threshold of 0.2% (MuMCC: 1.000 and 0.933 respectively). As with the RNA input, another high MuMCC score was observed at 0.05% for singlet and 0.04% for duplicate combinations (MuMCC: 0.764 and 0.840 respectively), suggesting that this frequency could be applied to characterise SNVs at the cost of allowing more errors through the analysis process. For triplicate and quadruplicate technical replicates, a frequency threshold of 0.04% produced the highest MuMCC scores (MuMCC: 0.891 and 0.913 respectively).With the DNA Low input, a maximum MuMCC score of 0.707 was achieved at a 0.2% frequency regardless of replicate combinations. For the DNA Once the optimised conditions for the HTS SNV calling pipeline had been established  able to most accurately identify true biological variation. Low input, with two out of the four technical replicates having consensus-level error. The increased frequency of errors in the RNA dataset can also be explained by those introduced by the RT step [By investigating different DNA and RNA starting inputs, it was evident that the type and abundance of nucleic acid template impacted SNV call accuracy. As expected, fewer errors were present in the DNA populations, with error decreasing as more starting template was included . As only RT step  and the  RT step . Inefficp value ≤ 0.01) compared to the other tested aligners, with GEM3 producing the highest mean MuMCC score. Because of this, GEM3 was selected as the aligner on which to base all subsequent investigations . The next step was to select the number of replicates required, the read qScore and length that would be passed to GEM3, as well as the frequency cut-off required for SNV calling. A previous study investigating the role of technical and biological replicates for HTS for negating errors caused by the sample preparation and sequencing found that replicates can be used to filter error, increasing the confidence in low-frequency SNVs being called [Low input, with more than one technical replicate resulting in a MuMCC score of almost 0, suggesting that these results were no better than random. A possible explanation for this observation could be due to the low number of starting genomes, with only a small subsection of the RNA population being sequenced in each replicate, reducing the overlap of SNV calls between replicates. Due to the low MuMCC scores, high frequency and consensus level errors present and the fact that the use of technical replicates decrease confidence in the data, it is suggested that sequencing RNA at the same copy number as RNA Low (102 RNA copies/µL) on an Illumina MiSeq platform set using the HTS pipelines in this study be avoided for analysing low-frequency SNVs.We also show that specific algorithms behind short-read alignment programs can influence SNV calling. Five different aligner that each employ different algorithms were tested in order to maximise the accuracy of candidate HTS pipelines. This study found that both GEM3 and BWA-MEM for all samples types produced significantly accurate results , while the MuMCC score was found to decrease with additional DNA High (from 1 to 0.933) and RNA Low replicates (from 0.250 to ~0). However, this study showed that for RNA Medium and DNA High inputs, additional technical replicates allowed for SNVs to be called at lower percentage frequencies (Medium data requiring 4 replicates for this frequency cut-off), with the exception of the DNA High input, where the use of triplicate and quadruplicate combinations allowed for a frequency cut-off as low as 0.04%, which is close to the intrinsic sequencing limit of Illumina chemistry [High and DNA High inputs saw a peak in the MuMCC data at frequency thresholds of 0.06% and below  for the RNA quencies  whist mahemistry . The usend below . This suHigh input dataset resulted in systematic error from a T to a C base at the start of repetitive T base regions, above the chosen 0.2% frequency cut-off for RNA High. As a result, it is recommended that low-frequency T > C based SNVs are treated with caution or removed, The frequencies of predictable error maybe influenced by certain factors, for example: length of homopolymeric region, reagents, sequencing technologies used and individual users of the RT, PCR and sequencing steps. Interestingly, this reproducibility of error within technical replicates is only seen within the RNA High input and, as the RNA input template is reduced, the number of errors identified at similar percentage frequencies in more than one replicate decreases; however, further work is required to investigate this.A study investigating error rates in just Illumina sequencing found that errors were more likely to occur in repetitive regions; however, at the end of the respective region and T > C base change errors were least likely to occur , which s6 copies/µL), coupled with the use of more than one technical replicate can lead to the detection of SNVs ≥0.2% in frequency .In this study, we demonstrated that different nucleic acid types and starting inputs have the greatest impact on the accuracy of calling sub-consensus SNVs. The use of the highest amount of starting template (≥1 × 106 RNA copies/µL) can lead to higher frequencies of predictable error (T to C base error patterns at the start of homopolymeric T base region), which can be used to further exclude error from real variation. We also identified that the use of low RNA inputs (102 RNA copies/µL) led to the presences of high-frequency and consensus-level errors and the use of more than one technical replicate decreased MuMCC scores to ~0, indicating that SNVs called here were no better than random. Our recommendation is that that low RNA inputs should not be used for SNV calling.Furthermore, the use of an RT step on a sample with a high RNA copy number (10By systematically characterising the laboratory and computational input factors, we have established a HTS framework which can be applied to both DNA and RNA viral populations at different inputs to accurately characterise low-frequency SNVs in high coverage Illumina datasets. As our results do not depend on sample origin, this framework can be generally applied to further understand viral population dynamics.


CICERO outperforms commonly used methods, achieving a 95% detection rate for 184 independently validated driver fusions including internal tandem duplications and other non-canonical events in 170 pediatric cancer transcriptomes. Re-analysis of TCGA glioblastoma RNA-seq unveils previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation. Accessible via standard or cloud-based implementation, CICERO enhances driver fusion detection for research and precision oncology. The CICERO source code is available at  Gene fusions resulting from genomic structural variations (SVs), such as translocations, deletions, tandem duplications, and inversions in coding or regulatory regions, can be cancer-initiating events. Diverse types of gene fusions can lead to abnormal function or aberrant transcription of cancer driver genes. For example, activation of kinase and cytokine receptor signaling can be achieved by formation of chimeric transcripts merging exons of two partner genes , internPaired-end short-read sequencing of transcriptomes (termed RNA-seq in the present study) has become a popular approach for fusion detection –13. VariCICERO Is Clipping Extended for RNA Optimization), a fusion gene detection algorithm which takes advantage of the increased next-generation sequencing (NGS) read length of current platforms to assemble RNA-seq reads bearing aberrant mapping signatures. The use of local assembly coupled with additional heuristics implemented to remove transcriptional artifacts enables the detection of diverse types of gene fusions at high sensitivity and accuracy. We show that CICERO is able to achieve high accuracy in analyzing a benchmark data set of 170 pediatric leukemia, solid tumor, and brain tumor transcriptomes and can enhance our ability to detect different types of driver gene fusions beyond the canonical chimeric exon-to-exon fusion transcripts in both pediatric and adult cancers. To further improve accuracy, gene fusions predicted from a single or a cohort of cancer transcriptomes can be curated in FusionEditor, an interactive viewer allowing inspection of protein domains involved in the fusion and evaluation of gene expression status in a fusion-positive sample. The source code is available from https://github.com/stjude/Cicero and a cloud-based implementation enables users to perform rapid RNA-seq fusion analysis via either point-and-click or command-line interfaces at https://platform.stjude.cloud/tools/rapid_rna-seq.To overcome these limitations, we developed CICERO (https://platform.stjude.cloud/tools/rapid_rna-seq) which provides easy access via either an interactive point-and-click interface or the command-line for submitting batch jobs. More importantly, the Cloud pipeline effectively manages the burst of computing required for genome-wide mapping to assess uniqueness of each candidate fusion. This enables completion of the entire workflow on the Cloud, from RNA-seq mapping to fusion detection within hours, even for cases with massive numbers of rearrangements. Predicted gene fusions can then be imported to FusionEditor for manual curation and the curated file can be exported as the final results  to support manual curation. Within each sample, the predicted fusions are listed by quality grade—including high-quality (HQ), low-quality (LQ), or read-through—and annotated with in-frame/truncation status so that a user can prioritize curation of high-confidence calls while retaining the ability to review all predicted fusions , solid tumor (n = 13), and brain tumor (n = 38) ; (2) lowly expressed chimeric fusions (n = 18); (3) non-canonical fusions (n = 36), defined by one of the fusion breakpoints being in a non-coding region and representing mostly enhancer hijacking events; and (4) ITDs (n = 18).The benchmark data set consists of 184 driver gene fusions discovered in 170 samples of leukemia . The detected driver fusions are all classified as high-quality by CICERO; therefore, we compared high-quality predictions by CICERO with calls from STAR-Fusion and Arriba, as these methods predicted relatively few gene fusions. Fusion predictions from ChimeraScan, deFuse, and FusionCatcher were compared with all CICERO calls. For high-quality predictions, the false positive rate of CICERO, STAR-fusion, and Arriba is 78%, 91%, and 82%, respectively Fig. . When coWe ran CICERO followed by manual curation using FusionEditor on RNA-seq data from The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) which included 167 adult GBM patient samples  of DNA, RT-PCR, or capture sequencing. By contrast, an alternative approach attempting to base the truth data set on the “wisdom of the crowd” by using consensus calls reported by multiple methods runs the risk of missing driver fusions detectable only by the minority vote. For example, our study has shown that in most cases, non-canonical fusions resulting in kinase activation are detectable only by FusionCatcher and CICERO  in our benchmark data set.While we used CICERO’s ranking of RNA-seq read-based fusion score in the benchmark analysis Fig. , the heuC-terminal truncation of EGFR is the most prevalent gene fusion discovered in our re-analysis of TCGA GBM RNA-seq. The hotspot breakpoint of the truncation fusions is at the acceptor site of exon 25  after we recognized that the cloud infrastructure is well suited for the scaling up required for genome-wide mapping of each candidate fusion contig. The Rapid RNA-seq platform is able to complete RNA-seq mapping and fusion detection within 2–5 h (~ 100 million reads per RNA-seq sample) even for tumor genomes that underwent massive rearrangements such as chromothripsis. Consequently, Rapid RNA-seq has become our preferred platform for carrying out time-critical fusion detection for our clinical service at St. Jude Children’s Research Hospital. A notable use case of Rapid RNA-seq is to determine the status of kinase fusion for leukemia and lymphoma samples for patients enrolled in the St. Jude Total Therapy Study 17 protocol .Our initial goal for implementing a Cloud-based CICERO pipeline was to broaden the accessibility of this complex workflow by making it accessible via a graphical point-and-click interface. This, coupled with the dynamic visualization features in FusionEditor, allows scientists with no formal training in bioinformatics to perform gene fusion detection followed by expert curation using their biological domain knowledge. The Cloud implementation was renamed “Rapid RNA-seq” on St. Jude Cloud  and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. Notable findings to date include C11orf95-RELA fusions that define supratentorial ependymoma , NTRK fuhttps://github.com/stjude/Cicero. A computational cloud implementation of CICERO is available at https://platform.stjude.cloud/tools/rapid_rna-seq.CICERO enables detection of diverse types of gene fusions in RNA-seq, greatly improving our ability to discover non-canonical fusions and ITDs which are overlooked by existing fusion detection methods. A cloud-based implementation, named Rapid RNA-seq, not only enables broad accessibility of CICERO via a graphical point-and-click interface, it also ensures rapid turn-around time by leveraging the Cloud computing infrastructure, supporting time-critical services. The cloud pipeline is also accessible via a command-line workflow for batch job submission. CICERO is freely available for research use at The input for the fusion detection process is a BAM file generated by mapping of paired-end RNA-seq data using algorithms such as STAR  followedw, as “(read_length-20)/100” to reflect the fact that reads with < 20 bp will generally not be soft-clipped by the aligner:Candidate fusion breakpoints are initially identified by the presence of SCreads, i.e., RNA-seq reads that contain soft-clipped subsequences in their mapping to the reference human genome. To account for mapping ambiguity, SCreads within 3 base pairs of one another are considered a SC cluster, and the position of the cluster is denoted by the position of the SCreads with the longest soft-clipped subsequence. As a SC signature requires at least 20-bp read length, we adjusted the RNA-seq read-count based expression, represented by the variable adjusted_gene_exp, by penalizing short read length with a parameter We use SC_cnt to quantify the total number of SCreads for each SC cluster. An SC cluster is required to meet the following criteria to be considered a candidate fusion breakpoint: (i) SC_cnt ≥ 2 for a genic site or SC_cnt ≥ 5 for an intergenic site; (ii) adjusted_gene_exp ≥ 0.01 for a genic site, to avoid artifacts with very low expression; (iii) the site does not match the highly paralogous regions with excessive mapping artifacts ; and (iv) SCreads transcriptional allelic fraction (SC_TAF), defined as SC_cnt/total mapped reads, exceeds 0.05.For each candidate breakpoint, denoted as bp1, SCreads and their paired mates, along with discordantly mapped read pairs present in the region, are assembled into contigs using CAP3 (with parameters −o 25 −z 2 −h 60 −y 10) . To redu, and will be used as the query for the second BLAT search. If s2 is mapped to multiple locations, bp2 is prioritized for regions with proximity to bp1 , a conservative approach that prioritizes potential local events or library artifacts over gross genomic alterations. Multiple mapping of s2 is not penalized, enabling discovery of fusions in highly repetitive regions. A contig with both bp1 and bp2 located in highly paralogous regions  to determine the validity of candidate fusions and to find the partner breakpoint, denoted bp2. Two rounds of BLAT search are performed. The first round uses the entire contig, and if the full-length contig is mapped to a non-bp1 genomic location, bp1 is discarded as an artifact of paralogous mapping. Otherwise, the portion of the contig not mapped to bp1 is considered to represent the sequence at bp2, denoted as s2Some fusion transcripts caused by a deletion may lack soft-clipped reads, as reads spanning a fusion junction may be mapped as splice junctions by the RNA mapper . One such example can be seen in fusion junction reads for P2RY8-CRLF2, a common oncogenic fusion in leukemia  caused bIf coding exons from two genes are joined by a fusion contig, CICERO performs automated frame-checking by translating the fusion contig and matching the protein sequence to each fusion partner  BLAT is used as an alternative anchoring method, similarly requiring a minimum 10-AA match . When BLAT is used, the fusion contig is masked to the gene B portion to avoid ambiguous anchoring in single-gene internal events. Once the correct coding frame for gene B has been identified, a similar search procedure is followed for gene A, this time seeking upstream into that transcript, to determine whether this frame is compatible with gene A’s coding.To better distinguish bona fide gene fusions from RNA-seq artifacts, we implemented an evidence-based ranking process in CICERO to prioritize fusion candidates during a manual review. The ranking is based on a number of factors: fusion allele frequency, matching length, repetitive mapping, and frame-check results with a quality status determined by matches to known fusion events or artifacts.The following variables are defined to calculate a fusion score:We define the weight of fusion transcript allele frequency (TAF) of soft-clipped reads or splice junction reads (w_TAF) at a candidate locus as follows:The weight of matched contig length (w_Match) at a position:match_length refers to the matching portion of the contig length at the position.p):The score of a fusion at each partner breakpoint (score_p) is the matched length of the blat hits; pi is all the possible blat hits with > 90% matched identity to a fusion contig. A repeat value of 0 represents unique mapping. “area” represents the coverage of fusion junction reads as a sum of the length of all subsequences that can be mapped to the fusion contig.where The fusion score combines the score from the two partners, bp1 and bp2, as follows:“Ort” is set to 2 if the orientation of the fusion is consistent with the transcription orientation of the two partner genes; otherwise, it is set to 1. Frame is set to 2 and 1 for in-frame and out-of-frame fusions, respectively.https://cgap.nci.nih.gov/Chromosomes/Mitelman) and genes with known ITD  were based on literature searches.Predicted fusions with score ≥ 1, the repeat score of bp1 and bp2 < 0.7, and TAF at bp1 and bp2 ≥ 0.01 are retained as candidate fusions which are subsequently categorized as high quality (HQ) if they match known gene fusions or ITDs, read-through (RT), or low quality (LQ) for non-read-through novel events. The final ranking proceeds in the order of HQ, LQ, RT, and within each category individual fusions are ranked by fusion score. The known fusion gene list was compiled from COSMIC , Protein.The benchmark data set was comprised of 170 RNA-seq with 100 bp read length. mRNA-seq and total RNA-seq protocol were used to profile 134  and 36 thttps://tcga-data.nci.nih.gov/docs/publications/gbm_2013/ [The TCGA GBM samples were downloaded from bm_2013/ , which cTo compare the fusion transcript detected by CICERO with those reported by TCGA Research Network, we used data from Table S4 by Brennan et al. As only gene names but not genomic coordinates were listed in Table S4, we considered a fusion detected by both TCGA report and CICERO if its two partner genes were matched.We compared the detectability and ranking of CICERO with the following four widely adopted RNA-Seq fusion detection tools: deFuse , ChimeraThe truth data set used for assessing the false positive (FP) rate of predicted RNA-seq fusions is comprised of fusions that can be validated by somatic DNA structural variations computed from paired tumor-normal WGS data. Among the 170 benchmark samples, 80 have matched tumor-normal whole-genome sequencing data, so we limited our FP analysis to this subset.For each of these 80 cases, we used curated somatic SVs analyzed by our CREST algorithm  as well We performed false positive rate analysis on high-quality CICERO fusions which contain all the detected driver fusions presented in the 80 samples, and all CICERO fusions which include both high- and low-quality predictions. Rearrangements within immunoglobulin  or T cell receptor (TCR) loci were filtered out as these events occurred during normal B cell or T cell development stage. Fusion transcripts resulting from the same DNA structural variation are scored individually; e.g., the 375 true positive fusions predicted by Arriba were considered as 375 events even though they were supported by 185 unique DNA structure variations. We compared the FPs of high-quality predictions with those of STAR-fusion and Arriba as these methods have a comparable total number of predicted gene fusions. Based on a previous report  as well https://platform.stjude.cloud/tools/rapid_rna-seq). The cloud pipeline can accept either unaligned reads in fastq format or a BAM file generated by STAR mapping [An end-to-end pipeline deployable through a graphical point-and-click interface is available on St. Jude Cloud , performance evaluation of benchmark samples by different algorithms (tab 2), sample information about TCGA GBM cohort (tab 3), cancer gene fusions identified in TCGA GBM cohort (tab 4 & 5) and reference files used in CICERO analysis (tab 6).Additional file 3. Additional tables. This file shows the results of false positive rate of different fusion detection methods based on the analysis of 80 cases that have both WGS and RNA-seq.Additional file 4. Review history.


Caenorhabditis elegans, many with essential functions . Not surprisingly, microtubules and MAPs are required during early Caenorhabditis elegans embryogenesis when cell divisions predominate . Temporally-controlled microtubule disruption by either drugs or transgenic constructs can bypass these early requirements and have shown that microtubules are also necessary for proper epidermal morphogenesis during mid-embryonic development . However, it is still unclear what complement of MAPs function during this stage of development to pattern the overall microtubule network and control dynamic instability.Microtubules are critical to a number of vital cellular processes, including cell division, intracellular transport, cell movement, and cell structure. Microtubule-associated proteins (MAPs) facilitate these various microtubule functions by regulating dynamic instability, crosslinking, and trafficking, among others (Alfaro-Aco and Petry 2015). There are over 60 conserved MAPs in C. elegans UNC-33 is an ortholog to collapsin response mediator proteins (CRMPs), a family thought to regulate the dynamic instability of microtubules . Mutants of unc-33 exhibit defects in axonal outgrowth and guidance, resulting in paralysis and locomotor defects . The dendrites of sensory neurons in these animals have far more microtubules than in wildtype and they are often larger in diameter with other structural defects . In addition to these neuronal effects, homozygous mutant animals of the classic allele, unc-33(e204), also appear shorter and stouter than wildtype animals. While some Dumpy animals are caused by mutations in genes important for cuticle development (Kusch and Edgar 1986), others are caused by mutations in genes important for embryonic epidermal morphogenesis, such as sma-1 , let-502 , and lin-26 (Ferguson and Horvitz 1985).Two other C. elegans gene products, DHP-1 and DHP-2, are almost 70% identical in sequence with UNC-33 and are also evolutionarily related to vertebrate CRMPs . In situ hybridization showed dhp-1 mRNA in hypodermal cells from late gastrula to at least 2-fold body elongation, and a GFP-tagged version of DHP-1 was also expressed in the larval hypodermis . There are currently no associated phenotypes to dhp-1,so we hypothesized that UNC-33 and DHP-1 may be functionally redundant in the epidermis during C. elegans embryogenesis.unc-33(e204) homozygous animals as a sensitized background and disrupted dhp-1 expression by feeding RNA interference (RNAi). Because we were specifically interested in potential embryonic effects, we scored the number of embryos that failed to hatch and took this as a percentage of the total progeny (unc-33(e204) mutants. As expected for a negative control, the empty feeding vector (L4440) fed to either wildtype or unc-33(e204) homozygous animals showed a low level of lethality, 3.4% and 9.3% on average, respectively. These data were not significantly different from each other in a pairwise comparison (p=0.872). In wildtype animals, the positive control, hmp-2(RNAi), resulted in 17.9% of embryos that failed to hatch but this was not found to be significantly different to the L4440 control (p=0.068). It is important to note, however, that less than 1% of hatched hmp-2(RNAi) animals were normal larva; the vast majority were Hmp with severe body morphology defects. Surprisingly, unc-33(e204);hmp-2(RNAi) resulted in 65.3% of embryos that failed to hatch, a significant increase compared to both the negative control in the same strain and hmp-2(RNAi) in wildtype (p < 0.001 for both). In both wildtype and unc-33(e204) homozygous animals, knockdown of dhp-1 did not significantly alter the percent of embryos that failed to hatch .To determine if any functional redundancy exists between UNC-33 and DHP-1, we used  progeny . When anunc-33(e204) is considered the reference allele, it is also a weak loss-of-function  and may still provide enough UNC-33 activity to obscure any redundancy with DHP-1 that might be revealed by dhp-1 knockdown. The unexpected interaction between hmp-2 and unc-33 is intriguing, however, given that our hypothesis focused on the role of UNC-33 and DHP-1 in the embryonic epidermis. Knockdown of hmp-2/β-catenin by feeding RNAi was chosen as a positive control because of the reproducible embryonic and larval phenotypes in wildtype. The embryonic lethality and body morphology defects from hmp-2(RNAi) are due to the essential role of HMP-2/β-catenin at adherens junctions during epidermal morphogenesis . HMP-2/β-catenin binds the intracellular tail of the transmembrane HMR-1/cadherin, strengthening cell-cell adhesion by simultaneously binding to the actin-binding protein HMP-1/α-catenin . During epidermal morphogenesis, a subset of epidermal cells migrates from the dorsal surface towards the ventral midline of the embryo. Once ventral enclosure is completed, actomyosin contractions in the epidermis shorten the cells along the dorsoventral axis while lengthening occurs along the anteroposterior axis, elongating the body of the animal 4-fold by the time of hatching (Chisholm and Hardin 2005). F-actin fails to properly localize to adherens junctions when there is a loss of HMP-2/ β-catenin or HMP-1/α-catenin, producing shorter animals with dorsal humps when the process of body elongation fails . The phenotypes we observed in wildtype after hmp-2(RNAi) by feeding are relatively weak as RNAi by injection, or animals that are homozygous null, results in almost complete embryonic lethality . Several studies have implicated microtubules in the regulation of cadherin-based adhesions , including one in C. elegans that showed disrupting microtubules at the 1.5/1.7-fold stage of elongation also perturbed junctional E-cadherin appearance and turnover . The greater percentage of embryos that failed to hatch after hmp-2 knockdown in unc-33(e204) compared to wildtype might further support a role for microtubules at adherens junctions and indicate a novel role for UNC-33 in regulating microtubule dynamics during epidermal morphogenesis. Future experimentation using advanced microscopy and additional genetic analyses will be needed to determine how and when this particular genetic interaction impacts embryogenesis.Our hypothesis regarding UNC-33 and DHP-1 was unfortunately not supported by this experiment. While C. elegans strains were cultured using standard protocols (Brenner 1974). Strains used include N2 [wildtype Bristol] and CB204 [unc-33(e204) IV]. Bacterial clones for feeding RNAi were obtained from the Ahringer library (Kamath and Ahringer 2003). Each gene insert was verified by Sanger sequencing through Eurofins Genomics using the following vector primers: GTCAGTGAGCGAGGAAGCAAC and CTCTTCGCTATTACGCCAGCTG. For knockdown by feeding RNAi, bacterial cultures were plated on NGM plates supplemented with 25 μg/mL carbenicillin, 10 μg/mL tetracycline, and 1 mM IPTG, and incubated for 3 days at room temperature to induce double-stranded RNA. L4 worms at 20 °C were fed bacteria for 48 hours and then singled to individual plates. After laying eggs for approximately 18 hours, the adults were removed and all progeny were counted via a dissecting microscope. After an additional 24 hours, the remaining number of eggs were counted. The violin plot, basic summary statistics, two-way ANOVA Type III, and the post-hoc Tukey analysis were all completed using R.


Since the emergence of the SARS-CoV-2 pandemic in December 2019 about 500,000 people died within the first 6 months. The virus itself, as well as the related political decisions, intensified an increasing feeling of fear in billions of people worldwide. However, while some people remained unperturbed, others experienced panic over the current situation. In order to investigate individual differences in the perceptions, emotions and behaviors in response to the Coronavirus pandemic, an online survey was conducted between 6th and 27th of March 2020. Participants included 7309 individuals from 96 countries, who provided information on socio-demographics, personality, political orientation and general life satisfaction. To determine the specificity of fear of Coronavirus, we also investigated fear related to two other current political issues: the refugee and the climate crises. Overall, in parallel with the escalation of the pandemic, fear of Coronavirus increased significantly over the 22-day period, with the strongest predictors being the personality variable neuroticism, as well as education, sex and being an at-risk person. A detailed longitudinal analysis of the largest sample, Germany, revealed that political orientation was also an important predictor of fear of Coronavirus. Specifically, conservatives were more afraid of Coronavirus than liberals. However, as the perceived threat of the virus increased, the influence of political orientation disappeared, whereas personality remained a stable predictor. The pattern of results regarding the perceived threat of the refugee and climate crises painted a different picture: political orientation was by far the best predictor, more important even than personality. Conservatives were more worried about the refugees, and liberals about climate change. Cross-cultural analyses showed pronounced differences between countries, dependent on the crisis. Nonetheless, the importance of personality for the prediction of fear of Coronavirus remained stable over time and across the world within the investigated 22-day period. The Coronavirus  causes the respiratory disease Covid-19 and represents the greatest health threat faced by mankind in decades, causing a steep increase in worldwide morbidity and mortality and eliciting widespread fear. Since its emergence in December 2019 in Wuhan, China, the virus spread rapidly around the globe and costed hundreds of thousands of lives within the first 6 months. The public health systems in some countries were on the verge of collapse, while the infection rate continued to rise. It was increasingly apparent that the threat of Coronavirus had been underestimated. Initially, the prevailing opinion was that the risk of Coronavirus is comparable to that of the common influenza virus. As the public realized the implications of the lack of available treatments for Covid-19, and that younger people without any pre-existing conditions can die from the disease, associated levels of fear and the perceived threat of the Coronavirus subjectively increased. The consequences of this were manifold. To curb the rapid infection rate, many governments took strong measures, such as shutting down wide areas and imposing restrictions on movement and freedom of assembly. For most people, social life had been reduced to a minimum. Shops were closed, companies halted production and services, millions of people were working from home, had been reduced to part-time work or lost their jobs. The media reported widespread instances of “panic buying,” meaning people were purchasing and hoarding groceries in vast quantities, particularly toilet paper, soap, disinfectant, and food. Many people were afraid of leaving the house. The pandemic also highlighted aspects of moral behavior, e.g., many people had volunteered to help others, either directly in the healthcare system or in their neighborhoods, by supporting elderly and at-risk neighbors. In contrast, some people continued to deny any potential danger and disobeyed political restrictions. The Corona crisis revealed all extremes of human behavior from panic to irresponsible ignorance, and from egoism to selfless altruism. What are the reasons for this variation in human behavior? Is the fear of Coronavirus specific, or are the people who panic in response to the virus also afraid of other perceived societal threats?To address these questions, we launched an online survey, assessing personality and perceptions of social threat. This survey was initially only available in Germany, however, after 1 week, we created an English language version, which was available internationally. From a theoretical perspective, personality variables are the most promising starting point to address questions about individual differences in behavior, because personality is defined as the predisposition to respond to a certain class of stimuli with a certain class of behaviors, and these stimulus-response configurations are stable over time . TherefoFear and anxiety belong to the basic set of emotions common to all ethnicities and cultures and to non-human mammals . ConsequAll personality theories have at least one dimension representing the predisposition of sensitivity to negative stimuli, and thus a vulnerability for anxiety disorders. Neuroticism is arguably the best-known example of such traits. Neurotic individuals are anxious, moody, tense, tend to worry and are often depressed . NeurotiIn addition to personality and political attitudes, socio-demographic variables are of interest. Are there gender- or age- related differences in fear of Coronavirus, or does education level influence how people cope with the pandemic? It is well established that women tend to be more anxious in general, relative to men , but is The aim of our study is to explain individual differences in the fear of Coronavirus, considering both changes in fear levels over time (in a between-subjects design) and comparisons between different countries. In addition, we want to investigate the specificity of fear of Coronavirus by comparing it with two other current political issues; the refugee and the climate crises.An online study was run between 6th and 27th of March 2020, aiming to identify personality traits and socio-demographic predictors of individual differences in fear of Coronavirus. During the first 6 days, a longer (25 min) version of the study was run, restricted to a German speaking sample. We subsequently translated the study to English, shortened it to 15 min, and made it available internationally. We amended the original German survey to correspond with this new English-language version. The reason for shortening the survey was to increase participation. The original version contained a longer, more nuanced personality measure. Therefore, analyses of the cross-cultural data started on day 7, when the international survey was launched. Participant recruitment was carried out via social media, such as Twitter and Facebook. Participation was completely anonymous and was not incentivized. Participants provided informed consent before beginning the study, which was conducted in accordance with the ethical declaration of Helsinki . Upon coMage = 33.23, SD = 11.78, range: 18–89; females = 5611, males = 1661, other = 37). The cross-cultural analyses included the following 13 countries and group of countries: Germany (N = 3469), Denmark (N = 662), Great Britain (N = 387), Eastern Europe , United States (N = 282), Netherlands (N = 251), Italy (N = 225), former Yugoslavia , France (N = 192), Ireland (N = 158), Australia and New Zealand (N = 164), Austria (N = 118), and Sweden (N = 94). The grouping of nations was based on cultural, geographical and historical similarities.In total, 7309 participants from 96 different countries completed the survey  or pre-existing illness. Furthermore, we included questions regarding general life satisfaction  and political orientation . Personality was assessed using the 10-item short version of the Big Five Inventory (BFI) . The behF-value probability < 0.05, exclusion criteria: F > 0.10) to obtain the most parsimonious model. Pairwise deletion of variables was applied. In the interpretation of results, only predictors explaining ≥5% of incremental variance were considered. In addition, Pearson correlations were calculated to test for associations between political orientation and the three perceived threats . In the cross-cultural analyses, ANCOVA models were calculated for each of the three fears, using each country/group of countries as a between-subjects factor and age and level of education as covariates. For post hoc comparisons between groups (countries) simple contrasts were calculated.To test the effects of the independent variables time (days of study) and sex on the dependent variable “fear of Coronavirus,” we conducted a two-factorial analysis of variance (ANOVA). Multivariate linear regression models were calculated to identify the best predictors for the dependent variables . Predictors  were entered stepwise  = 41.61, p ≤ 0.00001, eta2 = 0.108) and sex  = 71.36, p ≤ 0.00001, eta2 = 0.010). Perceived fear of Coronavirus increased over time and was significantly higher among women compared to men   = 141.62, p ≤ 0.00001). High neuroticism, having a higher level of education, being female, and being an at-risk person  emerged as the strongest predictors of higher levels of fear of Coronavirus.Multiple linear regression analysis revealed that the four variables neuroticism (β = 0.197), education (β = 0.164), sex (β = 0.085), and being an at-risk person (β = 0.079) could significantly explain variance in fear of Coronavirus  = 14.69, p ≤ 0.00001, eta2 = 0.083) and an influence of sex, with women being more afraid than men  = 25.75, p ≤ 0.00001, eta2 = 0.007). Confining the analysis to the German sample  = 62.77, p ≤ 0.00001), two predictors significant for the total sample, neuroticism (β = 0.230) and education (β = 0.076), were replicated. However, the predictive ability of sex and being an at-risk person was replaced by political orientation (β = 0.075) for this sample. Right-leaning political orientation was associated with greater fear of Coronavirus. Although sex did not emerge as a significant predictor for the German sample, significantly higher levels of fear were observed for women relative to men (see above).The German sample also revealed an increase in fear of Coronavirus over time  = 44.73, p ≤ 0.00001) revealed three predictors; neuroticism (β = 0.234), political orientation (β = 0.113), and being considered high-risk (β = 0.094). Higher neuroticism, right-ward political orientation, and belonging to the at-risk group predicted greater fear of Coronavirus during the initial days of the study.Due to the fact that data collection in the German sample 415 started 6 days before the international survey was launched, we could consider the effect of time on the predictors of our dependent variable. As the most pronounced increase in fear of Coronavirus was observed between days 7 and 8, we recalculated the regression models in two different time periods: days 1–7 (period I) and days 8–22 (period II). For period I, the model  = 41.45, p ≤ 0.00001); neuroticism (β = 0.247), education (β = 0.105), and sex (β = 0.088). Besides higher neuroticism, having a higher level of education and being female were the best predictors of heightened fear during the later period of the study.Three predictors emerged for period II  = 132.04, p ≤ 0.00001) showed that political orientation (β = 0.317) was the strongest predictor, followed by education (β = −0.108), life satisfaction (β = −0.070), age (β = 0.107), agreeableness (β = −0.079), and anxiety . People with a more conservative political orientation, a lower level of education, lower general life satisfaction, of older age, low agreeableness and high anxiety have higher levels of fear about the threat posed by refugees.A different picture emerged for results pertaining to perceived threat from the refugee crisis. The multiple regression model for the total sample  = 104.59, p ≤ 0.00001). However, political orientation again emerged as the strongest predictor (β = −0.169), followed by anxiety , sex (β = 0.092), education (β = 0.092), age (β = −0.071), and neuroticism (β = 0.069). For this model, a liberal political orientation, high anxiety and neuroticism, high education, being female and young are associated with greater fear of climate change.The climate change model explained substantially less variance than did the fear of refugees model  = 21.26, p ≤ 0.00001, eta2 = 0.053). As can be seen from post hoc contrasts. The country reporting the greatest fear of Coronavirus, Ireland, had significantly higher scores in comparison to all other countries, except the United States.Results of the cross-cultural data  showed significant differences between countries  = 11.12, p ≤ 0.00001, eta2 = 0.045). States of Eastern Europe and states of the former Yugoslavia reported fear scores that were significantly higher than in all other countries  = 3.94, p ≤ 0.00001, eta2 = 0.010). France, Italy, and the United States reported higher than average fear, while those of Germany and Austria were below average . Given the overlap in neural circuitry and neurochemistry between fear and anxiety, the strong intercorrelation between these constructs (r = 0.596) is unsurprising . March 13th marked the closure of all schools and kindergartens in Germany, which made the severity of the situation much more evident to the general public. When the two time periods, i.e., pre- and post-school closures, are contrasted, the personality dimension of neuroticism remained important in the German sample. However, the influence of other predictors, e.g., political orientation and being an at-risk person appeared to be transient. Political orientation, in particular, seemed to be less important the more severe the crisis became. During the early stages of the pandemic when infection rates were low, people who identified with more conservative ideologies reported greater fear of the virus than did voters who favor more liberal parties. This supports well-documented findings from the literature of higher disgust sensitivity and fear of contamination and infection among conservatives e.g., . In liner = 0.366; period II: r = 0.333), as well as less fear over climate change . These results were consistent across both time intervals. However, the associations between political orientation and fear of the Coronavirus  was only apparent during period I.Political orientation was also of interest with regards to the refugee and climate crises, since it was the strongest predictor of both issues. Data from the German sample suggested an association between a more conservative political orientation and greater perceived threat from refugees (period I: r = 0.309), while liberalism was linked to fear of climate change (r = −0.201). In the total sample, political orientation was not associated with fear of Coronavirus. Two limitations should be noted in this respect; first, data are not available for days 1–6 for the international sample, i.e., before the intensification of the crisis, e.g., through closing of schools etc.; second, our study did not set out to explicitly examine changes in political orientation, thus no causal conclusions can be made. It seems logical to assume, however, that the influence of political orientation on fear of climate change, fear of refugees or any other potential crisis would also disappear, once the public perception of these crises became life threatening. In this respect, the present findings raise important implications for the messaging around climate change and refugee aid.The role of political orientation as a predictor of perceived threat was particularly interesting in the present study. Previous research has established links between conservatism and negative attitudes toward refugees  as well The present results also highlighted differences in how each of the three threats were perceived internationally. Eastern European states and states belonging to former Yugoslavia were more afraid of refugees; Italians and the French were more worried about climate change. Interestingly, in Germany, Austria and Sweden, the perceived threat of Coronavirus was lowest. One possible explanation for this finding is that these countries have good healthcare systems and that Coronavirus lethality rates were quite low there compared to other countries over the investigated 22-day period. Most importantly, the comparison of 767 the predictors of the three threats showed that the personality 768 trait neuroticism best predicted fear of Coronavirus, whereas 769 political orientation played a dominant role in predicting fear of 770 refugees and climate change.r = 0.102) and did not believe that information was being deliberately withheld by authorities (r = −0.068). Our data also indicates that the perceived objectivity of media reportage is associated with fear of the virus. People who believed that the media downplays the severity of the crisis (r = 0.288) or deliberately withheld information (r = 0.219) were more afraid.Lower levels of education predicted greater fear of refugees in the total sample. The mass media presents us with infinite information on political crises, encompassing both evidence-based facts and recommendations, as well as many “fake news” stories, e.g., that the climate change is a great swindle. A higher level of education helps people to filter information and to prevent panic . In the It has to be pointed out that the present study has some limitations that deserve discussion. First, cross-cultural studies have always the problem that not all country-specific differences can be controlled for. However, in the present study the cross-cultural data included only days 7–22 and day 7 is March 12th, the day after the WHO has declared the Coronavirus a global pandemic. This means that despite differences in infection rates across countries, the virus was present as a threat in the population worldwide. There are also differences in the salience of the refugee crisis across countries. But this salience is largely dependent on the subjective perception rather on the objective threat. For example, in Europe the number of refugees in a country is not correlated with the extent of xenophobia. In order to minimize a possible bias, we excluded participants who stated that their country is not a target or transit country for refugees. Another methodological shortcoming refers to the single item measure of the three dependent variables . Although multiple item scales are preferable  we refrained from adding additional items because we did not find that other items capture additional aspects that were not at least implicitly included in our global item.In conclusion, this study aimed to provide insight into how personality, demographic factors  and political attitudes influence the perception of threat caused by the Coronavirus. The data indicate that the personality variable neuroticism, related to negative emotionality, predicted higher perceived threat from Coronavirus. Neuroticism outweighed the contribution of other important factors, including political orientation, gender and education level. These data raise practical points, which governments need to consider to decrease the public’s fear of Coronavirus, including a push for clear messaging around the virus, stronger quality control among media outlets to promote objectivity and reduce the prevalence of “fake news” stories, and increased promotion of – and support for – mental health organizations, which have a valuable role to play in helping the public to manage anxiety at this time.https://osf.io/b5yxc/?view_only=6ad94a8fed194bda845f29778a27ab03.The dataset presented in the study can be found at Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.JVL: conceptualization, project administration, programming, writing, statistical analyses, and data curation. JIL: conceptualization, programming, and data curation. SH: programming and data curation. ÉD: review and editing and consulting. TG: conceptualization and review and editing. MR: conceptualization, supervision, writing, and statistical analyses. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Izydor Fajersztajn-Krzemicki Fig.  was bornIn the same year, Izydor Fajersztajn-Krzemicki travelled around Europe and deepened his knowledge of medicine. He worked at the clinics directed by Richard von Krafft-Ebing in Vienna and Adolf von Strümpell in Erlangen . In 1894In 1898, Fajersztajn-Krzemicki became the head of the Department of Nervous Illnesses at the Public Policlinic, and soon afterwards the director of all policlinics . He heldFajersztajn-Krzemicki was not only a neurology practitioner, consummate clinician and diagnostician, but also an inquisitive scientist. The first experiments he conducted during his studies at Warsaw University , under tHowever, Fajersztajn-Krzemicki has gone down in the history of neurology thanks to his later studies. He was a pioneer of the histopathology of the nervous system. In 1901, he was the first to describe the silver staining method of visualization of nerve fibers in an article in German entitled “Ein neues Silberimprägnationsverfahren als Mittel zur Färbung der Axencylinder” (A new silver impregnation process as a means of coloring the axons), which was published in the journal “Neurologisches Zentralblatt” . Soon afFajersztajn-Krzemicki was a modest person and he did not fight for the name of discoverer of this method. As mentioned by another Polish neurologist, Kazimierz Orzechowski, Fajersztajn-Krzemicki was content with the letter from the distinguished neuroscientist Ludwig Edinger 1855–1918) [55–1918 , in whicFajersztajn-Krzemicki was one of the first scientists to use Röntgen radiation to photograph the structures located in the brain, as well as constructing (according to his invention) a macrotome to do cross sections of this organ , 3. HoweIn the 1930s, Izydor Fajersztajn-Krzemicki practised ophthalmology. He examined visual fields using an instrument called a “campimeter” and developed his own formula to study the blind spot [Due to the achievements of Fajersztajn-Krzemicki, after his death, the Polish neurologist Jakub Rothfeld wrote “Polish neurology lost one of the bravest worker. Krzemicki was a comprehensive mind with a wide range of interests in the area of medicine, physics, biology and mathematics. As a scientist he was precise, critical and demanding, especially with himself” .


C. subternata Vogel. populations.This study has three broad aims: to (a) develop genus-specific primers for High Resolution Melt analysis (HRM) of members of Cyclopia Vent., (b) test the haplotype discrimination of HRM compared to Sanger sequencing, and (c) provide an example of using HRM to detect novel haplotype variation in wild The Cape Floristic Region (CFR), located along the southern Cape of South Africa.C. subternata populations, and phylogeographic patterns of C. subternata were explored.Polymorphic loci were detected through a screening process of sequencing 12 non-coding chloroplast DNA segments across 14 Cyclopia species. Twelve genus-specific primer combinations were designed around variable cpDNA loci, four of which failed to amplify under PCR; the eight remaining were applied to test the specificity, sensitivity and accuracy of HRM. The three top performing HRM Primer combinations were then applied to detect novel haplotypes in wild C. subternata populations with high accuracy (96%). Sensitivity below 100%  was resolved through sequence confirmation of each cluster resulting in a final accuracy of 100%. Phylogeographic analyses revealed that wild C. subternata populations tend to exhibit phylogeographic structuring across mountain ranges (accounting for 73.8% of genetic variation base on an AMOVA), and genetic differentiation between populations increases with distance .We present a framework for applying HRM to non-model systems. HRM accuracy varied across the PCR products screened using the genus-specific primers developed, ranging between 56 and 100%. The nucleotide variation failing to produce distinct melt curves is discussed. The top three performing regions, having 100% specificity , were able to detect novel haplotypes in wild After screening for regions with high HRM clustering specificity—akin to the screening process associated with most PCR based markers—the technology was found to be a high throughput tool for detecting genetic variation in non-model plants. Describing intra-population genetic diversity across a species range requires access to sufficiently variable genetic markers that can be applied to large sample sets in an efficient and cost effective manner. The lack of widely transferable marker systems with these qualities has impeded phylogeographic work in the past, especially in developing countries that harbour much of the planet’s biodiversity . High ReThe HRM process is briefly described here. The inclusion of a DNA saturating fluorescent dye during PCR produces double stranded DNA molecules with dye bound to each base pair. As such, the presence of double stranded PCR product is measured by its fluorescence. As the PCR products are heated the double stranded DNA molecules dissociate, or melt, releasing the dye, resulting in a decrease in detected fluorescence. The rate at which a DNA fragment melts is dependent on the binding chemistry of the nucleotide sequence of the complementary strands under analysis. Therefore, by plotting the decrease in fluorescence against the steady rate of temperature increase, a melt curve determined by the DNA template under analysis is produced. The resultant melt curve differences (curve shape and melt peak (Tm)) are potentially indicative of sequence variation among PCR products.The genotyping and mutation scanning abilities of HRM have been tested using well described systems in the past, including: artificially generated SNPs  and lociXiphias gladius Bloch, Xiphiidae) populations. Alnus glutinosa (L.) Gaertn., Betulaceae), screening 154 accessions across 23 populations. These studies, and most others applying HRM to non-model organisms  develops a set of genus-specific primers for the HRM analysis of non-coding cpDNA loci to test: (b) the haplotype discrimination sensitivity, specificity, and accuracy of HRM, and (c) the potential application of HRM for haplotype detection in wild Cyclopia populations, focusing here on C. subternata. This study demonstrates that (when optimized) HRM is a fast, accurate, and cost effective tool for haplotype detection in non-model organisms, successfully describing the geographic structuring of genetic diversity in wild C. subternata populations.Here we test the application of HRM for non-model taxa, Cyclopia Vent., which is endemic to the Cape Floristic Region (CFR) and consists of 23 described species; two of which are considered extinct  and various others ranging from critically endangered to vulnerable , the Eastern Cape Department of Economic Development, Environmental Affairs and Tourism , and the Eastern Cape Parks and Tourism Agency (Permit number: RA_0185).To maximise the amount of genetic variation detected and the transferability of the primers designed across the genus, 14 species , it has been suggested that shorter PCR products are likely to produce more pronounced melt curve differences than larger products with the same nucleotide variation . Univershttps://www.ncbi.nlm.nih.gov/genbank/).Developing HRM primers requires prior knowledge of the nucleotide variation of regions across samples. The means of acquiring such data is dependent on the resources available to the researcher and the availability of existing sequence data for the study organisms. Thus template data could range from Next Generation Sequencing derived genomic data to the application of HRM to existing microsatellite markers, or existing data available from international nucleotide sequence databases such as GenBank  exhibited low levels of differentiation amongst species . The PHRED base-calling program (Sequences were assembled using CondonCode Aligner  program  was used program  and visuPrimer design was guided by two constraining factors: (1) sequences had to contain conservative regions with a high GC content that could form the primer binding template, and (2) these regions had to flank polymorphic sites. Wherever possible, internal HRM primers were designed in a way that would split a region into neighboring loci, as suggested by http://www.ncbi.nlm.nih.gov/tools/primer-blast/). The sub-family Faboideae was used as the reference taxon to check for primer specificity searched against the NCBI Reference Sequence representative genomes (http://www.ncbi.nlm.nih.gov/refseq/); PCR product size was limited to between 50 and 550 bp  was quantified using a NanoDrop 2000c spectrophotometer  and 5 ng/L DNA dilutions were made for HRM analysis. High Resolution Melt analysis was conducted for all primer pairs developed, with 16 replicates amplified per sample (haplotype). However, only replicates that produced sufficient PCR products, as determined from PCR amplification curves (see examples in All reactions (PCR amplification and subsequent HRM) took place in a 96 well plate CFX Connect  in 10 µL reaction setups, consisting of 4 µL genomic DNA (5 ng/µL), 1 µL each primer (10 mM) and 5 µL Precision Melt Supermix containing hot-start iTaqTM DNA polymerase, dNTPs, MgCl2, EvaGreen dye .Polymerase Chain Reaction amplification and melt conditions were as per manufacturer’s specifications  and the C. subternata populations.Following the descriptions of C. subternata populations.Only three regions  were found to have an HRM clustering specificity of 100% . Thus thThe same approach as atpI-atpH intergenic spacer and ndhA intron, respectively. Haplotype discrimination by HRM was calculated using the C. subternata samples sequenced for haplotype confirmation, following the same formula as before.The cpDNA regions that were used to design the primers used for HRM haplotype detection were amplified and sequenced (following the same protocols as before) to confirm the haplotype identity of HRM clusters. The loci amplified by MLT S1–MLT S2 and MLT S3–MLT S4 are adjacent to one another and by sequencing the full atpI-atpH intergenic spacer, the sequence identity of both loci could be confirmed with reduced sequencing and alignment effort. Moreover, the position of the loci amplified by the HRM primers occurred near the center of their respective parent regions and unidirectional sequencing using the reverse primers of Cyclopia specific HRM primers’. All wild C. subternata samples that underwent HRM analysis were then assigned the haplotype identity of the HRM cluster they belonged using a custom R script written by A.J.P (provided as File S1 which includes example files for running the script). The cpDNA regions under investigation (atpI-atpH intergenic spacer and ndhA intron) are maternally inherited in tandem and not subject to recombination (The haplotypes detected via HRM clustering and confirmed by sequencing (described above) were assembled following the same procedure described under ‘Developing bination , and wer[v1.2.1]  network  inset co[v1.2.1] . Two C. e mapped  in QGIS [v3.2.2] .ade4 [v1.7] library  and one targetting ndhA intron (MLT U1–U2), were selected for haplotype discovery in wild C. subternata populations.Nucleotide differences between haplotypes failing to produce distinct melt curves, and thus undifferentiated by HRM clustering, are summarized in C. subternata populations revealed no variation in the cpDNA haplotypes amplified by the MLT S3–MLT S4 primer combination, confirmed by sequencing, and the locus was subsequently excluded from further analyses. Five distinct haplotypes were verified by sequencing a subset of samples  from each HRM cluster for the remaining two primer combinations.High Resolution Melt curve analysis of accessions from wild C. subternata populations. However, haplotype richness was overestimated by HRM (sensitivity of 87.6% and 95.5% for MLT S1–MLT S2 and MLT U1–MLT U2 respectively), both cpDNA regions had accuracies of 96%. However, as these additional clusters were sequenced for haplotype confirmation, samples were assigned the true identity of haplotypes resolving any potential issues of low sensitivity.Of the 142 samples less than 29% were required to be sequenced for haplotype confirmation. Both loci were found to have 100% specificity, i.e., HRM successfully discriminated among all haplotypes detected in wild atpI-atpH intergenic spacer (MLT S1–MLT S2) and 344 bp from the ndhA intron (MLT U1–MLT U2), with a GC content of 29%. An additional 310 base pairs (bp) were amplified by MLT S3–MLT S4, revealing no nucleotide variation. The dataset contained five polymorphic sites; four transversions, one transition, and a seven bp indel , with significance increasing when tested against log transformed geographic distance . The AMOVA revealed significant (p < 0.05) structuring across mountain ranges, accounting for 73.8% of genetic variation  or multiple SNPs differentiating them. However, seven haplotypes differing by multiple SNPs did not produce distinct melt curves , suggestThe primer design choices in this study were largely based on the suggestions that nucleotide variation in shorter DNA strands will have a more pronounced impact on melt curve shape and intensity. This appears to have not been the case and larger PCR products performed as well, if not better, than smaller regions, as detected elsewhere . Future Cyclopia populations with no cases of different haplotypes occurring in the same cluster (specificity = 100%).High Resolution Melt analysis using the two best performing primer pairs that amplified variable regions proved to be a highly accurate (96% for both regions screened) means of detecting haplotypes variation in wild C. subternata samples were screened across the two cpDNA regions in two days, with immediate insights into the underlying levels of genetic variation (based on HRM clusterings). This rapid data production comes at a minimal cost per sample, which in this study amounted to $ 11.09 including all PCR amplification and sequencing for the phylogeographic analysis of C. subternata. A costing analysis based on quotes obtained in 2017, for a broader Cyclopia research project that employed Anchored Hybrid Enrichment  that have been harvested and traded as C. subternata. We suggested that additional work be done to describe the C. plicata and C. subternata populations in this area to confirm potential sympatry between these two morphologically and ecologically similar species (C. subternata populations (sampled from the Tsitsikamma and Outeniqua mountains) screened and the Agricultural Research Council’s (ARC) genebank accessions  may reduce population divergence through rare long distance dispersal events. Seed, in contrast, is dispersed locally by ants  unique haplotypes occur within populations, and (b) these unique haplotypes are spatially structured. These patterns of genetic diversity need to be acknowledged in the management of this economically important species, with seed and seedling not translocated outside of the mountain range that they were sourced from.The geographic distribution of C. subternata, all haplotypes were differentiated. While the framework described herein provides a clear guideline on generating the markers required for applying HRM to non-model systems, some analytical adjustments may be required based on the HRM platform available to the lab in question. The high throughput of HRM offers the molecular ecologist the opportunity to increase intrapopulation sample numbers without increasing project costs, while the automated clustering provides real time insights into the underlying levels of genetic variation. Furthermore, this technology may be particularly well suited to the study of conserved and slow mutating nuclear regions and the chloroplast genome of plants , and GC content.All Click here for additional data file.10.7717/peerj.9187/supp-3Table S3MN930746–MN930776 and MN930777– MN930802 for the atpI-atpH intergenic spacer and ndhA intron respectively.The genbank accession numbers for the sequenced samples are Click here for additional data file.10.7717/peerj.9187/supp-4Table S4Click here for additional data file.10.7717/peerj.9187/supp-5Fig. S5Click here for additional data file.10.7717/peerj.9187/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.9187/supp-7File S1Click here for additional data file.


Drosophila blanks/lump (CG10630) as a facilitator of RNAi, yet its molecular function has remained unknown. The protein carries two dsRNA binding domains (dsRBD) and blanks mutant males have a spermatogenesis defect. We demonstrate that blanks selectively boosts RNAi triggered by dsRNA of nuclear origin. Blanks binds dsRNA via its second dsRBD in vitro, shuttles between nucleus and cytoplasm and the abundance of siRNAs arising at many sites of convergent transcription is reduced in blanks mutants. Since features of nascent RNAs - such as introns and transcription beyond the polyA site – contribute to the small RNA pool, we propose that Blanks binds dsRNA formed by cognate nascent RNAs in the nucleus and fosters its export to the cytoplasm for dicing. We refer to the resulting small RNAs as blanks exported siRNAs (bepsiRNAs). While bepsiRNAs were fully dependent on RNA binding to the second dsRBD of blanks in transgenic flies, male fertility was not. This is consistent with a previous report that linked fertility to the first dsRBD of Blanks. The role of blanks in spermatogenesis appears thus unrelated to its role in dsRNA export.RNA interference targets aberrant transcripts with cognate small interfering RNAs, which derive from double-stranded RNA precursors. Several functional screens have identified  Drosophila, these can arise via RNA-directed RNA polymerase activity during replication of certain RNA viruses in the cytoplasm (The formation of double-stranded RNA (dsRNA) requires the presence of two RNA strands with complementary sequence. In ytoplasm ,2 or by ytoplasm . Furtherytoplasm  and geneytoplasm . The Dicytoplasm ,17. Manyytoplasm  may enteDrosophila cells  pathway starting from dsRNA ,22–23. Fla cells . Previoug factor  and indig factor ,33, but blanks-dependent endo-siRNA loci at sites of convergent transcription, which we refer to as blanks-exported siRNAs or bepsiRNAs.Here, we demonstrate that Blanks only stimulates silencing triggered by dsRNA that is generated in the nucleus. Blanks does not associate with Dcr-2 and it is not required for Ago2-loading of small RNAs. However, Blanks binds dsRNA, shuttles between nucleus and cytoplasm and likely facilitates the export of dsRNA for cytoplasmic dicing. We present a series of strongly R-mtnDEpromoter-Flag3 cassette. Then, the cell population was diluted and cultured in the presence of 50% conditioned medium to derive single-cell clones. Finally, we used PCR with primers spanning the insertion site to verify the absence of non-modified loci. Prolonged culturing without CuSO4 in the culture medium results in nearly complete inactivity of the edited locus .For the knock-down of cas9 in Figure Cells were harvested and washed twice in PBS before lysing them in 150 mM KAc pH 7.4, 30 mM HEPES pH 7.4, 5 mM MgAc, 1 mM DTT, 15% glycerol, 1% tergitol and 1 tablet of protease inhibitor per 10 ml . Lysis was further facilitated using a Bioruptor . The lysate was cleared by centrifugation before it was incubated with Protein G Dynabeads (Invitrogen) that had been coated with 1 μl of anti-FLAG M2 (Sigma) per 10 μl beads. The beads were washed twice with 150 mM KAc pH 7.4, 30 mmM HEPES pH 7.4, 5 mM MgAc, 0.1% tergitol and twice with 150 mM KAc pH 7.4, 30 mmM HEPES pH 7.4, 5 mM MgAc. The beads were boiled in SDS loading dye for analysis on a SDS-PAGE and subsequent Western Blot performed as previously described . AntibodDetails on our immunoprecipitations for mass spectrometry are given in the supplementary information.The β-elimination and deep sequencing library preparation were performed as previously described  with theDrosophila genome annotation file  with linux command line tools , then created a list of overlapping genes with opposite orientation extended by 300 nt on the 3′-end using bedtools window , retaining only the non-matching reads. This dataset was then mapped to the Drosophila genome (version 6.02) with no mismatch allowed, only reporting reads that map uniquely. The overlap of this analysis with the regions of convergent transcription (see above) was determined by applying bedtools intersect with the –c option. We normalized differences in sequencing depth by calculating the ppm values relative to all genome matching reads in the filtered dataset.The sequencing libraries were first size-selected to 21-mers and then filtered by mapping to the Drosophila genome matching reads was used to normalize for differences in sequencing depth between the libraries in all cases. The sequencing data has been deposited at the European Nucleotide Archive (ENA) under accession number PRJEB32123.The analysis of the bepsiRNAs in the testes samples was performed by first selecting only 21 nt long reads and removing the transposon-matching reads as described above. The remaining reads were mapped to all extended gene regions  using bowtie. The total number of 6 cells/ml and treated either with 200 μM Importazol (Sigma) or an equal volume of DMSO. The localization of the GFP fusion proteins was quantified 16 h later by visual inspection with a fluorescence microscope; images were acquired on a Zeiss LSM710 confocal microscope. A live cell DNA-counterstain was performed by adding 1 μl Hoechst 33342 (10 μg/ml) to 10 μl resuspended cells.The Blanks-GFP or H2Av-GFP cells were diluted to 2 × 10The RNA preparation of the 23 nt long double-stranded siRNA forming 21 bp and a 2 nt 3′ overhang on each end and the RNA-binding assay was performed as previously described .attB site by oligonucleotide annealing and cloning the product into the NdeI site. The blasticidin resistance from plasmid pMH3 , a 2 aa insertion of KE after K104, three single aa exchanges  and two silent point mutations . In the remainder of this manuscript, the numbers of nucleotides and amino acids refer to the corresponding position in the reference sequence. The FLAG-NLS sequence and the mutations in the dsRBD2 were introduced via the PCR primers into the inserts of the corresponding plasmids.The plasmid backbone for the expression of the blanks variant transgenes in flies was pKF63  that wasmid pMH3  was exci5′ wildtype’ and either the ‘3′ wt-dsRBD2’ or ‘3′ mut-dsRBD2’ primer. The primers used for the PCR were ‘5′ wildtype’ and ‘3′ wt-dsRBD2’. The PCR products were cloned into the pGEX-6P-1 backbone using BamHI and NotI.The inserts for the recombinant expression vectors were created by PCR from the vectors created above with the ‘All primer sequences are available in supplementary Table S1 (note the separate tabs in the Excel file).w[1118] stock and the resulting red-eyed flies (from the transgene) were used for crosses to generate the homozygous transgene flies.For generation of the transgenic lines the plasmids with a mini-white marker were injected into embryos and inserted into an attp2 site via the ϕC31 integrase method . The inj1 w*; +; Mi[MIC]blanksMI10901y) were crossed with males from the homozygous blanks transgene lines. F1 virgins were crossed with males from the balancer stock yw; +; D/TM3,Sb. Through meiotic recombination both the blanks mutant and the blanks transgene can end up on the same chromosome. Flies with red eyes were backcrossed with the balancer stock to isolate the chromosome carrying the transgene with the mini-white marker. Brothers and sisters from the backcross were used to generate a homozygous stock and the presence of the MI10901blanks mutant was confirmed by PCR.For the generation of the rescue line, mutant virgins (yw virgins at 25°C (aged for two days after hatching) and those were then allowed to lay eggs for 4 days. Pupae were counted 9–10 days after that.One male of the fly line whose fertility was assessed was crossed with two About 150 testes were hand-dissected for each RNA isolation and library preparation. The dissected testes were crushed in 500 μl TRIzol reagent (Ambion) using a pestle. RNA was then isolated according to the manufacturer's protocol and directly used for library preparation.600 = 0.1 in 1 l LB with ampicillin and chloramphenicol that was supplemented with 0.5% glucose. The culture was grown at 25°C until OD600 = 0.6 and then induced with 1 mM IPTG for 2 h. Cells were harvested, washed once in PBS and frozen in 8 ml lysis buffer . After thawing lysis was facilitated using a Bioruptor . The lysate was cleared by centrifugation and by passing it through a syringe filter before loading on a GSTrap HP column . The column was washed with 10 ml lysis buffer, 5 ml high salt buffer  and again with 10 ml lysis buffer. Elution was done in 5 ml 50 mM Tris pH 8.0, 1,5 M, 5 mM DTT and 20 mM reduced glutathione. The elution fractions containing the protein were pooled and incubated with 2 U of PreScission protease  over night at 4°C to cleave off the GST-tag. The cleaved protein was diluted in 3 volumes of 50 mM HEPES pH 7.0 and loaded on a HiTrap SP HP ion exchange column . The column was washed with 10 ml 50 mM HEPES, pH 7.0 and eluted in 50 mM HEPES pH 7.0 with a gradient from 0 to 1000 mM NaCl. Fractions containing the protein were pooled and diluted in 3 volumes of 100 mM KAc pH 7.4, 10 mM HEPES pH 7.4, 2 mM MgAc, 5 mM DTT and then concentrated using Amicon Ultra Centrifugal Filters with a 10 kDa cutoff.The expression plasmids were transformed in BL21 (DE3) pLysS cells. The expression culture was inoculated at ODblanks as a stimulating factor , omission of copper sulfate from the culture medium leads to transcriptional shutdown of the blanks locus modified at all alleles  of cells that either expressed blanks or not. Four days later, we determined the remaining Cas9 protein levels as a function of Blanks expression by western blotting  and then restricted the alignments for uniquely matching 21-mer reads.The identification in several screens suggested that s Figure  and Dcr-s Figure  to assesFor further analysis, we compiled a list of sites with the potential to be convergently transcribed. The annotated mRNA 3′-ends generally correspond to the mapped poly-adenylation site(s). Yet, the transcribing RNA polymerase continues a certain stretch downstream until it is either stripped off the DNA by Xrn2   or termicis-NAT loci) do produce bona fide siRNAs, albeit at a low level. When the small RNAs from our blanks shut-down cell line were analyzed, we saw that a subset of these regions generated fewer siRNAs in the absence of blanks . Finally, the siRNA generating part of the convergent overlap is neither limited to the 3′-region nor to exons; rather, the dsRNA seems to form at the level of the nascent transcript. For example, the transcript of the NitFhit gene is fully contained within the first intron of the Gk1 locus but runs in the opposite orientation . They arise via bi-directional transcription through a process that is still mechanistically unclear. We thus wondered whether blanks was required for the biogenesis of TE-targeting siRNAs in cultured S2-cells. To sum up the reads in our libraries for each TE sequence, we mapped the full libraries to the TE consensus sequence set. Between libraries, the read counts were normalized to the number of uniquely matching, non-TE reads from our previous analysis. A scatter plot of the shut-down cell lines vs. the parental cells revealed, as expected, that the absence of Dcr-2 severely reduced the abundance of TE-targeting siRNAs  appear to be already present in the wild-type sequence of the first dsRBD instance in Blanks (V165N166 instead of KK at the corresponding position). We quantified binding to a 23 nt synthetic RNA ligand (21 bp and a 2 nt 3′-overhang on each end) by fluorescence anisotropy as previously described , consistent with the affinity of an individual rather than tandem dsRBDs for siRNA . This effect strength of an introduced point mutation is comparable to observations with other dsRBP proteins  as well as NLS-Blanks with either a functional or a mutant dsRBD2. Consistent with the S2-cell data  from ell data , our anaf Figure . The redblanks mutant . For a (semi)-quantitative description, we therefore only retained those loci that gave rise to at least 10 reads in each of our libraries. If Blanks acts on dsRNA, then the resulting siRNAs should show roughly equal amounts of sense- and antisense-matching reads. When we plotted the change between wild-type and blanks mutant for each locus as a function of the corresponding sense/antisense ratio, we saw that the genes with a roughly equal sense and antisense representation preferentially showed a dependence on blanks . In the wildtype library, siRNA-levels in genic regions were overall higher than in the t Figure , though  in vivo .blanks-dependent siRNAs mapped preferentially to the exonic regions . We then ranked the loci by the wt/mutant ratio and selected all loci that had a ratio ≥5  is a phenomenon where aberrant RNA is detected, converted to double-stranded RNA (if necessary) and processed into siRNAs, which then target the aberrant RNA for degradation. In addition to cytoplasmic dsRNA, e.g. from a replicating virus, aberrant nuclear transcripts can also be funneled into a siRNA response. For example, siRNAs repress endogenous retroviruses (ERV) and transposable elements in somatic and germ line cells of osophila . While tosophila , the fat nucleus  but the  nucleus ; nuclear nucleus . The exp nucleus . In thisblanks/lump/CG10630 has previously been identified as a small RNA silencing factor  generates siRNAs at much higher levels from the region of overlap that is upstream of the polyA-site in each gene, arguing that the dsRNA precursor is formed between processed mRNAs. These siRNAs depend on dcr-2 but not on blanks. Perhaps in these cases, the mature mRNA molecules form dsRNA between their complementary regions only once they are in the cytoplasm and thus have no need for nuclear export facilitation of dsRNA. Loading of the bepsiRNAs as well as transposon-targeting siRNAs into Ago2 was not impaired when Blanks was absent. Accordingly, the role of Blanks must be upstream of dsRNA processing by Dcr-2.Our small RNA sequencing from a genome-edited cell line with inducible s Figure . The siRs Figure , thus gientified  natural blanks mutant flies .An alternative hypothesis for the molecular role of  network . We did blanks for their export, we propose that Blanks predominantly facilitates export of long dsRNAs generated in the nucleus. This role is reminiscent of the mammalian dsRBD proteins NF90/ILF3, Staufen as well as ADAR1 and their dsRNA-dependent association with Exportin-5 or Transportin-1  duplex RNA structure and a single-stranded 3′-overhang . Since tportin-1 . A previ factors . We noteblanks mutant phenotype is male infertility caused by a genetically and biochemically distinct phenomenon. The nascent transcripts that continue beyond the polyA-site are short-lived; in contrast, Ago2-loaded siRNAs are stable and the bepsiRNAs can thus help us to describe the ephemeral transcription beyond annotated 3′-ends. Yet, while such ‘fossils’ of non-coding transcription may be informative for us, they probably present no advantage to the organism.Which biological role could blanks-dependent siRNAs play? Although we identified a substantial number of loci that generate blanks-dependent siRNAs, their abundance at each site is low and we do not expect a biologically relevant effect on the abundance of the corresponding mRNAs. Furthermore, the major blanks and its paralog CG12493 are genes that recently emerged. They show less conservation than e.g. loqs . Since the bepsiRNAs we describe are very similar between testes and S2 cells yet CG12493 is only present in the testes sample, any functional redundancy between the two proteins does not seem to include bepsiRNA biogenesis.The Drosophila Blanks and mammalian NF90 is restricted to the two dsRBDs and a basic stretch preceding the first dsRBD. Of note, the first dsRBD instance in NF90 also contributes only little to dsRNA binding  with the accession number PRJEB32123. The proteomic data is available at ProteomeXchange with the identifier PXD013792.gkaa072_Supplemental_FilesClick here for additional data file.