RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions. The comparisons with available experimental data show that our model can successfully predict the 3D structures of RNA pseudoknots from their sequences, and can also make reliable predictions for the stability of RNA pseudoknots with different lengths and sequences over a wide range of monovalent/divalent ion concentrations. Furthermore, we made comprehensive analyses on the unfolding pathway for various RNA pseudoknots in ion solutions. Our analyses for extensive pseudokonts and the wide range of monovalent/divalent ion concentrations verify that the unfolding pathway of RNA pseudoknots is mainly dependent on the relative stability of unfolded intermediate states, and show that the unfolding pathway of RNA pseudoknots can be significantly modulated by their sequences and solution ion conditions. + and Mg2+ in solutions can play an essential role in RNA folding. Although several computational models have been developed to predict 3D structures for RNA pseudoknots to further unveil the mechanisms of their functions, these structure prediction models seldom consider ion conditions departing from the high salt  and temperatures from the room temperature. In this work, we employed our coarse-grained model to predict 3D structures and thermodynamic stability for various RNA pseudoknots in monovalent/divalent ion solutions from their sequences, and made comparisons with extensive experimental data and existing models. In addition, based on our comprehensive analyses for extensive pseudoknots and the wide range of monovalent/divalent ion conditions, we confirmed that the thermally unfolding pathway of RNA pseudoknots is mainly determined by the relative stability of intermediate states, which has been proposed by Thirumalai et al. Our analyses also show that the thermally unfolding pathway of RNA pseudoknots could be apparently modulated by the sequences and ion conditions.RNA pseudoknotted structures and their stability can play important roles in RNA cellular functions such as transcription, splicing and translation. Due to the polyanionic nature of RNAs, metal ions such as Na RNAs can fold into complex three-dimensional 3D) structures to carry out their various biological functions . An RNA D structuThere have been several successful experimental methods to obtain 3D structures of RNAs, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and newly developed cryo-electron microscopy –12. Howe+ and Mg2+) in solutions can play an essential role in RNA folding .To examine the ability of the model on predicting 3D structures of RNA pseudoknots, 17 common pseudoknots (≤ 56nt) which have been determined by experiments as individual molecules were used in our 3D structure prediction. The detailed descriptions of these pseudoknots are listed in Table B in http://www.major.iric.ca/MC-Fold/ and http://rnacomposer.ibch.poznan.pl/, respectively) for predicting tertiary structures of RNAs including pseudoknots with high accuracy , the predicted Tm1 and Tm2 are 49.0°C and 81.9°C, which agree well with the experimental data (50.2°C and 83.0°C) , the mean deviation between Tm’s (Tm1+Tm2) from the Vfold model and the experiments is ~8.4°C [+], the deviation between Tm’s (Tm1+Tm2) from the model of Denesyuk and Thirumalai and the experiment is ~8.2°C [+]. Thus, the present model can be reliable in predicting thermal stability for RNA pseudoknots in monovalent ion solutions, and it is noted that the present model can also provide 3D structures for RNA pseudoknots at different temperatures from the sequences; see To further evaluate the present model, we made the comparisons with the available predictions from the existing models such as the model of Denesyuk and Thirumalai ,58,59 ans ~8.4°C , which is ~8.2°C , a higheTm1’s from the present model with coaxial stacking are generally higher than those without coaxial stacking and appear closer to the experimental values , and calculated the melting temperatures based on the data from the simulations. As shown in +]’s has the similar two transitions in accordance with the available experiments ’s. The sequence and predicted secondary structure of T2 pseudoknot are shown in +] and 1000mM [K+] due to the smaller difference between two transition temperatures Tm1 and Tm2 for T2 pseudoknot [+], Δ = |Tm1-Tm2| ≈ 12°C for T2 pseudoknot and ~24°C for MMTV pseudoknot. In addition, the predicted Tm1 and Tm2 for T2 pseudoknot are also in good accordance with the experimental data with the mean deviation of ~1.1°C over the wide range of [K+]  enhances the stability of RNA pseudoknots as well as the stability of intermediate hairpins, and Tm1 of the transition from folded state to intermediate state is more sensitive to [K+] than Tm2 of the transition from intermediate state to fully unfolded chain. The phenomenon is interesting and is reasonable. Generally, the formation of a pseudoknot structure involves higher charge buildup than the formation of intermediate hairpin states, and hence, the stability of a pseudoknot is more dependent on ions than that of a hairpin [As shown in  hairpin –54.2+ are especially effective in stabilizing RNA tertiary structure [Tm’s predicted by the present model for MMTV and T2 pseudoknots are in accordance with the experiments [2+] with fixed [K+]’s (50mM for MMTV pseudoknot and 100mM for T2 pseudoknot). For MMTV pseudoknot, the mean deviations of Tm1 and Tm2 between predictions and experiments are ~3.9°C and ~2.3°C, respectively, and for T2 pseudoknot, the corresponding mean deviations are ~2.2°C for Tm1 and ~1.5°C for Tm2. This suggests that the present model can nearly make quantitative predictions for the stability of RNA pseudoknots in mixed ion solutions from their sequences, even though the ion effect is involved implicitly in the present model.Previous studies have shown that divalent ions such as Mgtructure ,52–54,79tructure  and the tructure ,69. Hereeriments ,77 over + and Mg2+ on RNA pseudoknot stability is captured by the present model: (i) when [Mg2+] is very low, K+ ions dominate the stability of pseudoknots and the values of Tm including Tm1 and Tm2 are close to those of pure K+ solutions; (ii) the increase of [Mg2+] significantly enhances the stability of RNA pseudoknots against monovalent K+ and such effect would become saturated at very high [Mg2+] due to the strong electrostatic neutralization; see + and Mg2+ and more efficient role of Mg2+ binding ; see 2+ treatment in the present model might slightly underestimate the role of Mg2+ in stabilizing compact pseudoknot structure , at a low temperature , the RNA is completely in folded pseudoknot state with two structural motifs of Stem 1 and Stem 2. As temperature is increased , the fraction of F state decreases gradually, and simultaneously, the fraction of S1 state increases gradually, which indicates that the Stem 2 in the pseudoknot melts first with increasing temperature. When temperature is increased to higher level , the fraction of completely unfolded conformations increases accompanied with the decrease of the fraction of S1 state. Although the S2 state can also be found in this process, the fraction of S2 state relative to all intermediate states is relatively small (~18%). Thus, with the increase of temperature, the dominating unfolding pathway of MMTV pseudoknot is F→S1→U overwhelming the pathway of F→S2→U; see +], there are two comparable unfolding pathways: F→S1→U and F→S2→U, where the population of S1 state is only slightly higher than that of S2 state; see As shown in +] using the Mfold algorithm  increase, i.e., the fraction of S2 state increases from ~0% at 10mM [K+] to ~18.0% at 1M [K+]; see +] stabilizes S2 state more pronouncedly than S1 state since S2 state has a large hairpin than S1 state (excluding dangling tails), i.e., the relative free energy ΔΔG calculated from the salt extension from the TBI model is ~-2.5kcal/mol at 10mM [K+] (at T~45°C) and becomes ~-0.6kcal/mol at 1M [K+] (at T~85°C)  to ~18.5% at 10mM [K+]. This is also attributed to the larger hairpin of S2 state and the corresponding stronger ion effect in structure stabilization, i.e., the relative free energy ΔΔG is ~-0.1kcal/mol at 1M [K+] (at T~75°C) and becomes ~-1.6kcal/mol at 10mM [K+] (at T~40°C) . Mg2+ can play an efficient and special role in stabilizing compact RNA structures , respectively. Solid lines: df/dT, the first derivative of f with respect to temperature from the present model. Dotted lines: the heat capacity Cp from Ref. 78. Symbols: the heat capacity Cp from experiments [+] (c) and 50mM [K+] (d), respectively. Solid lines: df/dT, the first derivative of f with respect to temperature from the present model. Dotted lines: the heat capacity Cp from Ref. 46. Symbols: the heat capacity Cp from experiments ; (b) BWYV pseudoknot at 500mM [K+]. Lines: df/dT, the first derivative of f with the temperature; red: predictions from the model with the coaxial stacking potential; green: predictions from the model without the coaxial stacking potential. Cartoon: the predicted 3D structures of the two pseudoknots at different temperatures. Red/black arrow: predictions from the model with coaxial stacking potential; green arrow: predictions from the model without the coaxial stacking potential.(a) MMTV pseudoknot at 1000mM  and different [Mg2+]’s: (a) 0.1mM [Mg2+], (b) 1mM [Mg2+], and (c) 10mM [Mg2+]. (d-f) T2 pseudoknot at 100mM [K+] and different [Mg2+]’s: (d) 0.1mM [Mg2+], (e) 1mM [Mg2+], and (f) 10mM [Mg2+].(a-c) MMTV pseudoknot at 50mM [K(TIF)Click here for additional data file.


In this paper, we construct a one-dimensional convolutional neural network (1DCNN), which directly takes as the input the vibration signal in the mechanical operation process. It can realize intelligent mechanical fault diagnosis and ensure the authenticity of signal samples. Moreover, due to the excellent interpretability of the 1DCNN, we can explain the feature extraction mechanism of convolution and the synergistic work ability of the convolution kernel by analyzing convolution kernels and their output results in the time-domain, frequency-domain. What’s more, we propose a novel network parameter-optimization method by matching the features of the convolution kernel with those of the original signal. A large number of experiments proved that, this optimization method improve the diagnostic accuracy and the operational efficiency greatly. With the increase of system complexity and the increasing variety of data dimensionality, diagnosis systems need better learning and generalization ability, and diagnosis systems based on knowledge and experience alone may find it difficult to meet the needs of modern intelligent fault diagnosis. Moreover, with the deepening of the research, the forms of input signal data become more and more complex and varied, its objectivity and accuracy may be affected if it is still based on previous experience in the feature extraction stage ,2,3. TheThe existing process of intelligent fault diagnosis is shown in The features obtained by the above signal processing methods are usually of high dimensionality or large size, which is not conducive to the subsequent pattern recognition, so many researchers have tried to improve the identifiability of features by using feature transformation methods such as sparse process, data dimension reduction. The most studied methods include Independent Component Correlation Algorithm (ICA) ,12, and The classifier is used to classify the results of feature extraction and transformation, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) are commonly used. For example, Cheng et al. ,15 extraFor simple faults of simple machines, this kind of step-by-step method is not bad. However, as the systems become more and more complex and the dimensionality of the data becomes higher and higher, there will be some new faults. A highly intelligent diagnostic model should have good generalization ability to deal with and identify new faults. Obviously the above methods shown in Therefore, it is very important to obtain an intelligent diagnosis model, which can not only realize the joint optimization of feature extraction and pattern recognition, but also has a good generalization ability. The newly proposed Deep Learning (DL) model provides the possibility to realize such an idea.(1)The first kind is to transform the signals into the frequency domain. To complete the classification, the frequency features are extracted by CNN. For example Janssen et al.  from Ghe(2)The second kind is to use the images of time-series signals as the input of 2DCNN. Wen et al.  used ima(3)The third kind is to convert time-series signals into two-dimensional time-frequency diagrams, and use them as the input of 2DCNN for diagnosis. For example, Guo et al.  used theCNN is such a deep learning structure ,18,19. IThe above three methods are all based on 2DCNN, so many researchers prefer to 2DCNN because of its successful application in the field of image recognition. For the computer, an image is a two-dimensional matrix, while the real-time state signal  during the operation of machine is usually a one-dimensional vector. Therefore, some scholars have tried to construct 1DCNN to realize fault diagnosis. For example, Turker et al.  tested tLike 2DCNN, 1DCNN will also face with the problem of how to determine the network structure and set the parameters. The number of layers, as well as the size and number of convolution kernels will greatly affect the feature extraction results. Focusing on how to optimize the network performance, some scholars have carried out some relevant studies on 2DCNN. For example, Li et al.  proposedIn CNN, the most crucial structure is the convolution layer compose of several convolution kernels. It is worth noting that the frequently used signal processing methods, such as FT and WT, are essentially kernel operations based on the kernel. These methods select the kernels manually, while the kernels of CNN are acquired by adaptive learning. Obviously, for one-dimensional time-series signals, the convolution results of 1DCNN are more interpretable than those of 2DCNN. Therefore, it is necessary to analyze the training results and layer-by-layer transformation results of 1DCNN deeply, which is not only conducive to a better explanation of its diagnostic mechanism, but also helpful to guiding the optimization of network structure and parameters.This paper attempts to construct one-dimensional CNN oriented to fault diagnosis, and uses one-dimensional time-series signals as its input. Through in-depth analysis the convolution (feature extraction) mechanism, it attempt to provide a useful suggestion for how to select the number of convolution kernels, and thus to optimize the network performance. The main contents and research ideas of this paper are as follows: The 1DCNN structure is shown in k represents the convolution kernels, j denotes the number of kernels, M represents the channel number of input b is the bias corresponding to the kernel, f  is the activation function and ∗ is the convolution operator.In the convolution layer, the 1DCNN carries out the convolution operation on the local area of the input signals to generate the corresponding one-dimensional feature maps, and different convolution kernels extract different features from the input signal, respectively. Each convolution kernel detects specific features at all locations on the input feature map, to fulfill the weight-sharing on the same input feature map. This characteristic of local connectivity and weight-sharing effectively reduces the complexity of the network and the number of training parameters. If the L layer is a convolution layer, the formula for the one-dimensional convolution layer is:In the down sampling phase, after the convolution layer, the number of feature maps increases, resulting in the expansion of the data dimensionality, which is not conducive to calculation. Therefore, the average pooling or max pooling method is used to process each feature map at this stage. Average pooling calculates according to the size of the predetermined pooling window, and the maximum pool method selects the maximum parameter within the range of the predetermined window as the output value.l + 1 and its output is given to the fully connected layer, the output of the fully connected layer is:w denotes the weight and b denotes the bias.The fully connected layer of the neuron nodes are connected to all the neuron nodes in the feature map output from the previous layer, and the activation function is the Softmax function. If the final pooling layer is In the process of network training , the paru(t) is the white Gaussian noise series with 10 dB; and The following is a simple time-series signal classification problem to verify the feasibility of the study, four types of signals are generated under the Matlab environment. The signal can be expressed as:In order to ensure the diversity and the otherness between different categories of samples, each type of signal consists of different frequency components. Meanwhile, the frequency components in each sample are subject to uniform distribution within the certain range, while the phase angle is generated randomly between 1 and 100, which can ensure the correctness and diversity of the same type of signals. To increase the complexity and reduce the overfitting phenomenon of the network, the attenuation coefficient and Gaussian white noise are added to the simulation signal. Their time domain and frequency domain diagrams are shown in iN(iM)–iN+1(iM+2) is used to describe the network structure and related parameters of the convolution kernel, where N represents the number of convolution kernels, M represents the size of convolution kernels, and i represents the convolution layer. The learning rate is 1, and the system updates the weights every 10 samples, and the maximum number of iterations is 1000.The network structure consists of an input layer, two convolution layers, two pooling layers, a fully connected layer and an output layer, and each layer is provided with several convolution kernels. In order to simplify the expression, In the experiment, we used those four types of signals, 125 samples for each type of signal . The feature extraction and classification ability of 1DCNN was tested based on the simulation signals, and the results are shown in We can conclude that the 1DCNN has good classification ability to classify those four kinds of signals. The following two preliminary conclusions can obtain by analysis of the training results: the selection of the number and size of convolution kernels in each layer will greatly affect the operation efficiency and training results of the network. As can be seen from The network with the best training effect, whose structure is 3(301)-3(301), is used to extract the convolution kernel of the first convolution layer in different training iteration stages. The signal characteristics of the extracted convolution kernel and its own changing trend are analyzed in this section. The following conclusions can be drawn, with adaptive learning training, the frequency components of the convolution kernel that are the same as the corresponding original signal will be obtained, and it can help to highlight the main features (frequency components) of the input original signal. As the number of iterations increases, the correlation coefficient between the convolution kernels and the input signals is increasing. After the convergence of iterative training, the convolution kernel shows a good correlation with the original signal, which indicates that in the adaptive learning process of the convolution kernel, the feature frequency of the original signal gradually appears in the convolution kernels, and its correlation with the original signal is constantly increasing. In the convolution stage, the more similar the form of the kernel function is to that of the original signal, the better the effect of convolution feature extraction will be.In order to explore the role of convolution kernels further, the output results after the feature extraction (convolution) of the first layer convolution kernel of the original signal are extracted, and the convolution results are analyzed in frequency domain. The analysis results are shown in As can be seen from To comprehend the working mechanism of 1DCNN deeply, a set of convolution kernels in the same layer are analyzed in this section. Since the number of convolution kernels set by the network is 3, that is, there are three convolution kernels convolving the same input simultaneously, and the extracted features of the three convolution kernels will jointly act on the classification and recognition of the signal. Frequency domain analysis of three convolution kernels in the same group as shown in As we can see from the It is worth noting that since only some key feature components need to be extracted to realize the expression and classification, the single convolution kernel will not extract all the frequency components of the input signal as the feature vectors. This intelligent feature extraction mode of the network greatly reduces the redundancy of feature extraction and improves the efficiency of feature extraction and classification.Meanwhile, in order to analyze the relationship between kernels quantitatively, the correlation between the three groups of convolution kernels in As can be seen from these tables, at the end of the training, the correlation between the same set of convolution kernels is very small, that is, each convolution kernel extracts part of the input signal features, which are independent of each other, and multiple features jointly complete the feature extraction of the input signal. The same set of convolution kernels cooperates on the recognition and classification of signals when facing the classification problem, realizing the feature sparsity and reducing the redundancy between features.Above all, in the 1DCNN, since the convolution kernel is equivalent to the frequency domain filter, the same set of convolution kernels will represent the original signal from different perspectives, they can extract the features of the original signal, and fuse these features to realize the abstract expression of the original signal. Then, when we need to set the convolution kernel parameter, the number of the original signal frequency component and the number of convolution kernel could be corresponding, so that each convolution kernel (filter) can correspond to express some typical frequency component. In this way, each feature component of the original signal can express fully, the insufficient or redundant feature expression in the feature extraction process will be reduced, and the classification recognition rate and network computing speed will be improved.In the actual diagnosis process, because the mechanical operation will be affected by many factors, its vibration signal is often non-stationary, and contains a large amount of noise. In order to verify the relationship between 1DCNN performance and signal frequency components and the number of network convolution kernels presented in this paper, validation experiments were carried out on the bearing state signal of the public data set from Case Western Reserve University (CWRU). The object of this test is the drive-end bearing, which is a SKF6205 deep groove ball bearing. The single-point damage of the bearing was caused by EDM, and the sampling frequency is 12 kHz. There are four states of bearings: normal, rolling element damage, inner ring damage, outer ring damage, with a damage diameter of 0.007 inches. n is the number of sampling points of vibration signals, N is the number of segments.Since the bearing vibration signal is one-dimensional, considering that the main information of the signal contains the impact generated in different states, we introduce a sliding segmentation method as shown in There are about 100,000 sampling points for each type of the bearings state signals. The first 80,000 points are used as the training samples. The sample length is 1024 and the overlapping length is 226. Therefore, the number of training samples is 400. The remaining points used as the test samples. In order to test the ability of robust and generalization, the overlapping sampling method was not used on 100 test samples.Before training, the original signal was analyzed preliminarily, the time-domain signals of four kinds of state signals and their FFT results are shown in The convolution result is the feature extraction result. It is not only the output of the convolution layer, but also the input of the subsequent layer. It represents the features extracted by the convolution kernel adaptively learned from training samples. By visualizing the convolution results and analyzing the frequency components, the function of each convolution kernel in real-time fault diagnosis could be revealed. In addition, it is also clear that which features in the signal are selected by the convolution kernel as the classification “standard” to represent the original signal. This may also provide new ideas for some artificial feature extraction methods. The FFT of the convolution results are shown in In Convolution kernel plays a key role in feature extraction. The number and size of convolution kernel will greatly influence the effect of feature extraction. Therefore, in this section, the convolution kernel of the first layer of the network was analyzed. The cooperative classification of multiple convolution of the same input signal was analyzed, and the relationship between the number of convolution kernels and the feature extraction effect was obtained. The FFT results of the convolution kernel are shown in As shown in the Through the above analysis, the correctness and feasibility of the study was proved, and it provided a new idea for one-dimensional network optimization. In order to fully express each feature component of the original signal and avoid the over-fitting phenomenon caused by feature extraction redundancy, when setting the network parameters, the number of convolution kernels must correspond to the number of main frequency components of the input signals. According to the In order to verify the effectiveness of the proposed method, a network of other structures was constructed and compared with the optimized network. The learning rate is 1, and the batch-size is 10, and the maximum iteration is both 2000. The comparison results are shown in As can be seen from the In the 1DCNN, the convolution kernel acts as the frequency domain filter. Its signal feature form will be similar to the input signals after convolution with the input signals, and the main frequency components of input signals will be extracted. Moreover, the same set of convolution kernels extract the features of input signals from different perspectives, and the extraction results act on the classification together, and the collaborative optimization of signal classification is achieved. Through the study of its convolutional mechanism, it explains why 1DCNN has such a good feature extraction ability.The network parameters (the number of convolution kernels) can be optimized by analyzing the frequency domain characteristics of the input signals. The optimization method consists of two steps: firstly, preliminary analysis the original signal by signal processing methods , and counting the number of the typical frequency components. Secondly, to express the characteristics of the input signals and improve the network efficiency greatly, and save computing resources, the number of convolution kernels should correspond to the number of typical frequency components of the input signal as far as possible.In this paper, a fault diagnosis method based on 1DCNN is proposed. This method does not need to do any artificial signal processing on the input signals, and effectively avoids the loss of useful information. By visualizing the output results of convolution and the convolution kernels, this paper explore the feature extraction mechanism of convolution kernel, and the main conclusions can be summarized as follows:


For TRδ, a total of 300 clonal rearrangements were detected in 158 of 183 samples (86%). Beside clonal Vδ2-Dδ3, Dδ2-Dδ3, and Vδ2-Jα we identified a novel group of recurrent Dδ-Jα rearrangements, comprising Dδ2 or Dδ3 segments fused predominantly to Jα29. For IGH-JH, 329 clonal rearrangements were identified in 172 of 183 samples (94%) including novel types of V(D)J joining. Oligoclonality was found in ~1/3 (n = 57/183) of ALL samples. Genomic breakpoints were identified in 71 BCP-ALL. A distinct MRD high-risk subgroup of IGH-V(D)J-germline ALL revealed frequent deletions of IKZF1 (n = 7/11) and the presence of genomic fusions (n = 10/11). Quantitative measurement using genomic fusion breakpoints achieved equivalent results compared to conventional V(D)J-based MRD testing and could be advantageous upon persistence of a leukemic clone. Taken together, selective gc-HTS expands the spectrum of suitable MRD targets and allows for the identification of genomic fusions relevant to risk and treatment stratification in childhood ALL.B-cell precursor (BCP) ALL carry a variety of classical V(D)J rearrangements as well as genomic fusions and translocations. Here, we assessed the value of genomic capture high-throughput sequencing (gc-HTS) in BCP ALL ( The treatment response measured by sensitive MRD techniques at the end of induction therapy (EOI) has been shown to be one of the strongest parameters for risk stratification. Initially, Southern blot and semiquantitative PCR-based methodologies were applied which demonstrated frequent clonal rearrangements of IGH and TR-δ segments in BCP-ALL3. Current technological approaches to the measurement of MRD comprise quantitative real-time PCR or multicolor flow cytometry allowing for reproducible quantification of residual leukemic cells with a sensitivity of one leukemic cell among 10,000 normal cells (1 × 10−4)5. In order to trace leukemic cells in a clinical setting, the rapid identification of suitable markers is essential for their subsequent quantification. A PCR-based screening method is currently implemented as the standard procedure for the detection of patient-specific V(D)J rearrangements6. With the establishment of recent, PCR-based high-throughput sequencing techniques (HTS) the analytical process of identification and characterization of patient-specific V(D)J rearrangements largely relies on bioinformatics12. As an alternative to PCR-based amplification of target sequences, HTS of directly captured genomic fragments has recently been employed for the identification of rearrangements14. The advantage of the latter approach is the potential identification of unknown fusion sequences, i.e., rare or unusual V(D)J rearrangements or chromosomal translocations not confined to IG or TR genes which could be used for MRD diagnostics and/or targeted therapy of actionable fusion proteins, such as ABL or JAK2 class fusions16.Precise monitoring of minimal residual disease (MRD) during the first weeks of treatment supports decisions on escalation or de-escalation of therapy in patients with acute lymphoblastic leukemia (ALL)DUX4-IGH and EPOR-IGH.Here we show that genomic capture high-throughput sequencing (gc-HTS) identified a novel type of recurrent clonal V(D)J TRδ Dδ-Jα rearrangements as well as diverse genomic fusion breakpoints which can be used as sensitive and specific markers in clinical diagnostics of MRD for risk-adapted treatment stratification and targeted intervention. ALL samples devoid of detectable IGH-V(D)J rearrangements were associated with high levels of MRD at EOI and often featured genomic fusion events, such as www.clinicaltrials.gov: GPOH-COALL08-09 EU-21076/NCT01228331) except for BCR-ABL1 positive ALL patients who were treated according to the international EsPhALL trial 17. Patient samples were obtained after written informed consent of the patients’ parents or legal guardians and with approval by institutional ethics boards .Patients involved in this study were enrolled in COALL03-07 and COALL08-09 multicenter trials (n = 183) were diagnosed as B-precursor ALL devoid of KMT2A and ETV6-RUNX1 rearrangements including a subgroup of n = 19 BCP-ALL carrying a known BCR-ABL rearrangement as a positive control for precise breakpoint characterization. For identification of IKZF1 and P2RY8-CRLF2 (PAR1) deletions multiplex ligation-dependent probe amplification ) was used. MRD-negative or weakly positive ALL (minimum quantitative threshold < 1 × 10−4) exhibiting genetic low-risk features were excluded from this study except n = 7 subsequently relapsing ALL (n = 3 MRD-negative and n = 4 weakly positive cases). Selected ALL samples were grouped into three different quantitative categories based on their EOI MRD levels  as indicated in Fig. All gc-HTS analysed cases n = 83 were dTRδ or IGH-JH rearrangements at diagnosis of ALL in comparison to a standard PCR-based approach. In parallel, we analyzed gene-fusion events targeting cytokine receptors or kinases as observed in Ph-like ALL16. Methodological details and the open source software Segemehl for detection of gene fusions have been previously described18. Supplemental Table 4. Sequence data have been submitted to the European Nucleotide Archive (ENA) and they are publicly available at: http:/www.ebi.ac.uk/ena/data/view/PRJEB35051. Sample identifier are available upon request.Diagnostic samples were analyzed by gc-HTS to detect clonal In this study we sought to explore the potential of gc-HTS to concurrently identify clonal rearrangements and genomic breakpoints for the sensitive and specific detection of minimal residual disease in B-cell precursor ALL Fig. .TRδ gene locus on chromosome 14p12 using 196 capture probes that cover a 42 kb region between Vδ2 and Vδ3 , the majority of which involved Vδ2 joined to Dδ3 or to Jα as shown in Table n = 39) or Dδ3 (n = 24) in 53 of 183 samples (29%), with Jα29 being the most common single TRJα fragment (n = 25). The frequency of these rearrangements substantially exceeded the previously reported prevalence in gene-specific PCR-based analyses21. To revalidate NGS-derived TRδ rearrangements by Sanger sequencing, amplicons were generated by a PCR strategy positioning forward primers in intronic Dδ2 and Dδ3 regions combined with a Jα29 specific reverse primer J-joining was identified in 172 of the 183 samples (94%) with 329 clonal sequences including complete V(D)J-rearrangements as well as incomplete DJ-joining. Hence, gc-HTS identified n = 11 ALL samples truly devoid of IGH-V(D)J rearrangements (Table IGH-V(D)J rearrangements were identified in n = 19 cases (10%).In BCP-ALL, clonal nce Fig. . IGH-V(DTR/IG V(D)J rearrangements we explored the potential of gc-HTS to identify the genomic breakpoints of leukemia-specific gene fusions as diagnostic targets. As a positive control, we first examined breakpoint-flanking sequences in a subgroup of BCR-ABL1 positive ALL (n = 19) by gc-HTS. ABL1-directed capturing and subsequent paired-end sequencing identified the genomic breakpoint in 17 of 19 samples at bp resolution indicating a high sensitivity and specificity of our analytical approach. As a next step TRδ, IGH, kinase and cytokine receptor genes as outlined in Supplemental Table n = 183 BCP-ALL we identified n = 71 non-V(D)J gene-fusion events and deciphered their genomic fusion breakpoints. These mutations recurrently involved ABL1 , CRLF2 , DUX4 (n = 10 DUX4-IGH), EPOR (n = 6 EPOR-IGH), PDGFRB (n = 5 EBF1-PDGFRB), JAK2  and less frequently mostly transcription-factor associated genes  and a C-terminal transactivation domain (TAD)24. We mapped the genomic breakpoint to a region 3859 bp upstream of the ATG start codon, which was fused to IGH-JH6 suggesting an aberrant expression driven by the IGH-enhancer  in two ALL samples from patients with Down syndrome (DS)26. Breakpoints were located in different introns of the SPIDR gene displaying 53 kb and 7.8 kb distance to the 3’-end of CEBPD at primary diagnosis (UPN23 and UPN24). The latter patient developed relapsing disease 10 years after the initial diagnosis, albeit displaying a different breakpoint 257 kb downstream of the 3’-end of CEBPD indicative of a different sub-clone or potentially an independent secondary leukemia. Furthermore, in UPN27 a genomic fusion of IGH-JH4 into the MCCC2 gene comprising 17 coding exons on chromosome 5q13 was found with the breakpoint located in intron 16. As a consequence, MCCC2 exon 17 and a part of intron 16 were fused to the IGH-JH4 region suggesting that the translocation event is non-functional or that an alternative gene further downstream is involved. Finally, in UPN29 we identified a genomic breakpoint 8994 bp 5’ of the first exon of ELK2AP, a processed pseudogene on chromosome 14q3228. The functional consequences of the ELK2AP-JH1 fusion that was likely caused by an inversion affecting the IGH locus are unknown29. The exact genome coordinates of all novel genomic breakpoints identified here were confirmed by PCR and Sanger sequencing  that involves c-MYC as well as IGH-VH336. The second rearrangement involved the TRδ-Dδ2 segment fused to a region immediately 5’ of the start codon of ACIN1 located on chromosome 14q11 indicative of a non-functional break on the reverse strand , which is associated with translocations affecting the IGH locus on chromosome 14q3225.With regard to TRδ, we identified two previously unrecognized rearrangements into the TRδ Fig. . Split-rRCSD1-ABL1, FOXP1-ABL1, BCR-JAK2, PAX5-JAK2, and ETV6-JAK238. We identified a novel breakpoint in CSF1R intron 11 fused to a yet undefined intergenic region on chromosome 5. This genomic fusion contains a random seven-nucleotide insertion that was not discernible in previously analyzed ALL cases indicating a specific breakpoint with yet unknown functional relevance. The reverse breakpoint was located in intron 10 fused to intron 4 of the CCNJL gene  carried genomic rearrangements compared to 17 and 26% fusion events in ALL exhibiting a moderate or high MRD burden, respectively  that led to hematopoietic stem cell transplantation in first remission. Another DUX4rearr ALL (UPN5) with an MRD burden of 9 × 10−3 at EOI developed two relapses, finally exhibiting a phenotypical switch to the myeloid lineage but stable clonal V(D)J-rearrangements used as molecular MRD markers40. ALL harboring DUX4-rearrangements have previously been described as a prognostically favorable ALL-subgroup often containing concomitant intragenic ERGdel or a deregulation of ERG by expression of an alternatively spliced, dominant-negative ERG isoform 31. In our DUX4rearr ALL cohort, ERGdel were detected in 2/10 cases by MLPA analysis, whereas ERGalt was identified in 4 of 5 available DUX4rearr ALL samples by isoform-specific RT-PCR in accordance with previously reported data31.A high burden of MRD after induction therapy (EOI) is generally associated with a poor prognosisn = 7) relapsing later on with weakly positive or negative MRD at EOI analyzed in this study, a single case harbored an EPOR-IGH rearrangement (UPN11). Intriguingly, re-analysis of MRD in this case utilizing the EPOR-IGH breakpoint and a clonal Vδ2-Jα22 rearrangement as additional markers identified by gc-HTS would have resulted in an assignment to a higher risk group and more intensified treatment  indicates a leukemic transformation at a very immature, early stage of B-cell development9. Germline IGH-V(D)J ALL were associated with poor treatment response, frequent IKZF1 deletions and genomic rearrangements such as DUX4-IGH, EPOR-IGH, BCR-ABL1, FOXP1-ABL1, EBF1-PDGFR-ß, or aCRLF2-IGH (Table A germline V(D)J constitution at the −4. These features render TRδ markers more suitable than other targets such as TRγ due to a low or absent background amplification.As outlined above we identified several novel TRδ-Jα clonal rearrangements which we sought to evaluate as optional markers for MRD diagnostics. Moreover, we established individual, patient-specific RQ-PCR assays based on genomic breakpoint sequences of non-V(D)J rearrangements. In regard to novel TRδ Dδ2- and Dδ3-specific rearrangements, we designed two RQ-PCR assays based on their germline sequences to allow for a Jα-independent quantification Fig. . In compELK2AP-JH1) compared to routinely assessed TRß and a concomitant loss of two TRγ markers. Targeting the IGH locus in this sample, gc-HTS identified two unusual IGH-JH associated fusions not detectable by a PCR-based approach, both of which were discernible at relapse. Firstly, an IGH-JH6 segment was rearranged into an intronic region between two IGH-VH segments (7365 bp 5’ of VH7*34). Secondly, a somatic fusion event of the ETS-family pseudogene ELK2AP with an IGH-JH1 segment was deciphered at primary diagnosis and at relapse.In line with this notion, gc-HTS identified three additional MRD markers in a relapsed ALL J rearrangements including their respective breakpoints were evaluated as diagnostic targets for MRD analysis. In contrast to the BCR-ABL1-positive ALL-subgroup which clearly exhibited breakpoint-specific signals upon remission likely originating from a rearrangement persisting in the hematopoietic stem cell population, non-V(D)J rearrangements in general revealed MRD kinetics that were very similar to conventional IG/TR-V(D)J rearrangements  lack leukemia-specific conventional genomic V(D)J rearrangements at TR/IG loci or unambiguous cell surface epitopes preventing a reliable and sensitive measurement of MRD42. In addition, RQ-PCR and flow cytometry are limited in the sensitive detection of subclonal disease, which is often driven by non-V(D)J rearrangements or gene fusions not detected by conventional PCR-based MRD analyses. In line with this notion, it is generally appealing to implement predisposing or causative genetic lesions as diagnostic targets for the measurement of MRD as established for various MLL-gene fusions in infant ALL or, as recently published, for ETV6-RUNX1 breakpoints45. High-throughput genome, exome and transcriptome sequencing has recently unfolded a variety of previously unrecognized genetic subtypes of B-cell precursor acute lymphoblastic leukemia46. Here, we present gc-HTS as an analytical approach to the combined identification of clonality and individual genomic breakpoints in B-cell precursor ALL. In accordance with PCR-based analyses, we identified classical V(D)J rearrangements at IGH and TRδ gene loci in 95% and 84% of ALL samples, respectively6. As an advancement, our PCR-independent approach allows for the detection of V(D)J recombinations and non-V(D)J rearrangements not discernible by PCR using pre-selected primer sets. Hence, gc-HTS substantially expands the spectrum of individual markers per ALL sample including distinct genomic aberrations, the latter of which are less prone to clonal evolution and early loss of specificity. In contrast, secondary mutational events or targets with an oligoclonal appearance such as PAR1- or ERG- deletions are less suitable for a reliable and robust MRD analysis.The analysis of MRD by real-time quantitative genomic PCR or multicolor flow cytometry has been firmly established as an integral part of clinical diagnostics of acute lymphoblastic leukemia47. In a recently published study, paired analysis of diagnostic/relapse ALL samples by PCR-based HTS revealed that initial (“major”) clones with a frequency >5% or an absolute number of reads (ARC) >10,000 showed the highest stability at relapse48. gc-HTS does not achieve a read-depth comparable with PCR-based HTS but our preliminary data indicate a detection limit of genomic capturing for rearrangements in heterogeneous cell populations with a blast fraction of ~20%, which is sufficient for most diagnostic settings. To date, clinically relevant RQ-PCR-based MRD testing is confined to initially selected major clones. Currently, most NGS-based approaches use a nested PCR workflow for clonality detection, a strategy that requires rigorous precautions under strict GLP conditions47. By contrast, gc-HTS affixes sample-specific barcodes to the genomic fragments during the initial steps of library preparation, minimizing the risk of false-positive target assignment and contamination. In effect, the lower read-depth of gc-HTS might be compensated by the direct sequencing of enriched genomic fragments that enables us to detect rearrangements not detectable by PCR-based methods.In comparison, PCR-based HTS has a greater capability to detect minor V(D)J clones at diagnosisIGH were detected the function of which has yet to be defined. In line with earlier observations, we identified a subgroup of children with ALL displaying a germline IGH that was associated with a poor response to induction treatment and accordingly high levels of MRD9. Strikingly, this IGH germline ALL group exhibited a high frequency of IKZF1 deletions and non-V(D)J rearrangements including CRLF2-IGH, EPOR-IGH, and EBF1-PDGFRß that can be exploited as leukemia-specific targets in clinical diagnostics of MRD. Early identification of this high-risk subgroup is a prerequisite for treatment stratification or timely intervention. Unexpectedly, the IGH germline ALL-subgroup also included DUX4-rearranged ALL lacking concurrent ERG-deletions, but some expressed the dominant-negative ERGalt isoform49. In contrast to the previously reported favorable outcome of DUX4rearr-ALL we observed a remarkably poor treatment response of DUX4rearr ALL mostly leading to hematopoietic stem cell transplantation in first or second remission32. In line with this notion, Zaliova and colleagues have recently described a DUX4 overexpressing subgroup that showed a poor molecular response to therapy50. Larger prospective studies are needed to determine the prognostic impact of DUX4rearr/ERGalt in ALL.Beyond conventional rearrangements, previously unrecognized IG and recurrent TRδ rearrangements were deciphered, which increase the spectrum of suitable targets for clinical diagnostics of MRD. By identification of the Dδ-Jα subgroup additional targets are available for MRD testing. Nevertheless, based on these results TRδ-related oligoclonality or ongoing rearrangement patterns should be handled with caution as they can potentially result in divergent MRD values in follow-up samples. Importantly, novel previously unrecognized fusion partner genes of 52. Using gc-HTS in the BCR-ABL1-positive ALL group we observed rare failures in the identification of genomic breakpoints that were potentially due to AT-rich- or repetitive sequences at probe hybridization sites or gaps in probe design targeting intron 1 of ABL1. Improved probe design with greater coverage at these sites may eliminate such drawbacks in the future.From a technological perspective, gc-HTS might advance the clinical diagnostics of MRD by precise identification of genomic breakpoints and non-captured genomic partner genes such as RCSD1 or FOXP1, which is necessary for treatment stratification for instance of ABL-class (genomic) fusion-carrying ALLIn summary, the PCR-independent genomic approach presented here constitutes a very sensitive and robust method that enables laboratories to detect a broad spectrum of markers relevant for MRD diagnostics and also for targeted therapeutic intervention for instance in Ph-like ALL. A bespoke selection of genomic targets, a flexible workflow and the requirement for only very small amounts of diagnostic material should make this method widely applicable.Supplemental material


Alternative RNA processing plays an essential role in shaping cell identity and connectivity in the central nervous system. This is believed to involve differential regulation of RNA processing in various cell types. However, in vivo study of cell type-specific post-transcriptional regulation has been a challenge. Here, we describe a sensitive and stringent method combining genetics and CLIP (crosslinking and immunoprecipitation) to globally identify regulatory interactions between NOVA and RNA in the mouse spinal cord motoneurons.We developed a means of undertaking motoneuron-specific CLIP to explore motoneuron-specific protein–RNA interactions relative to studies of the whole spinal cord in mouse. This allowed us to pinpoint differential RNA regulation specific to motoneurons, revealing a major role for NOVA in regulating cytoskeleton interactions in motoneurons. In particular, NOVA specifically promotes the palmitoylated isoform of the cytoskeleton protein Septin 8 in motoneurons, which enhances dendritic arborization.Our study demonstrates that cell type-specific RNA regulation is important for fine tuning motoneuron physiology and highlights the value of defining RNA processing regulation at single cell type resolution.The online version of this article (10.1186/s13059-018-1493-2) contains supplementary material, which is available to authorized users. A thorough understanding of the complexities of the mammalian central nervous system (CNS) requires detailed knowledge of its cellular components at the molecular level. RNA regulation has a central role in establishing cell identity and function across the numerous cell types in the CNS –7. TradiRecent technological breakthroughs using RiboTag and BAC-TRAP mouse lines have allowed for translational profiling at single cell type resolution –13. ThesRNA processing is regulated by RNA-binding proteins. Two of the best-studied are NOVA1 and NOVA2, neuron-specific KH-type RNA-binding proteins that bind to YCAY motifs and regulate alternative splicing and polyadenylation –16. UsinIndeed, NOVA proteins are essential for the function of multiple neuronal cell types –24. In pNova1 and Nova2, the two mammalian Nova paralogs, encode proteins harboring three nearly identical KH-type RNA binding domains. Nova genes are widely expressed among various neuronal cell types in the spinal cord, including the MNs. For NOVA2, two isoforms arise from alternative translational start sites, which do not shown detectable RNA binding differences . Our strategy to study MN-specific NOVA regulation is twofold—to express AcGFP (Aequorea coerulescens green fluorescent protein)-tagged NOVA specifically in MNs, followed by CLIP using antibodies against the AcGFP epitope tag.2 = 0.94), with tetramers that may overlap YCAY by three or more nucleotides  showing the highest enrichment at both NOVA2 and AcGFP-NOVA2 peaks  promoter. The AcGFP-Nova2 cassette was inserted into a bacterial artificial chromosome (BAC) harboring the Chat promoter through homologous recombination Chat:GFP-Nova2 transgenic mice were born at expected Mendelian ratios and were phenotypically indistinguishable from wild-type littermates throughout their lifespan (data not shown). Expression of the GFP-NOVA2 fusion protein was confirmed in the spinal cords of both Chat:GFP-Nova2 lines, but not the wild-type controls Fig. . To asse32P-labeling of bound RNA. The presence of labeled GFP-NOVA2–RNA complexes was dependent on both UV crosslinking and the expression from the Chat:GFP-Nova2 transgene, further demonstrating the specificity of GFP-NOVA2 CLIP under these conditions , generating 17,353,049 unique WSC NOVA CLIP reads  were identified for MN NOVA CLIP, which harbor CLIP reads originating from at least four of the eight biological replicates (biologic complexity (BC) ≥ 4 out of 8; Additional file 2 = 0.97), as were WSC CLIP reads , and 97 out of the 104 (93%) lamina I–VIII enriched transcripts had lower NOVA binding in MN . Taken together, we conclude that HITS-CLIP in Chat:GFP-Nova2 lines captured MN-specific transcriptome-wide NOVA–RNA interactions.We further systematically examined transcripts with well-defined anatomic expression patterns in spinal cord. Spinal cord gray matter exhibits a pattern of lamination consisting of ten laminal layers, with large alpha-MN pools located in lamina IX . Based oWe were particularly interested in NOVA binding sites along a given gene that were disproportionally over- or under-represented in MNs compared to WSC  by analyzing neighborhood tetramer distributions. We discovered a marked overrepresentation of polypyrimidine (YYYY) tetramers around intronic NOVA binding sites over-represented in MN Fig. , as wellhttp://zhanglab.c2b2.columbia.edu/index.php/Quantas).Since NOVA plays an important role in regulating neuronal transcript splicing, we tested whether MN-specific NOVA binding correlates with MN-specific alternative splicing. We took advantage of a high quality MN RNA-seq dataset, where spinal MNs in 3-month-old wild-type mice were collected by laser capture microdissection (LCM) and subjected to RNA-seq . For theNova targets are differentially spliced between MNs and WSC  suggests that Nova contributes in an important way to MN-specific splicing patterns.Around 30% (1620 out of 5418) of MN-expressed alternative exons showed biologically consistent differential splicing in MNs compared to WSC . The increased utilization of E3a in MNs positively correlated with a dramatically enhanced NOVA binding site in MN 121 nt downstream of E3a  mouse brain RNA-seq dataset .–51.Sept8 dataset , and ideSept8 encodes a family member of the septin proteins, which are multi-functional components of the cytoskeleton [Sept8 harbors two alternative terminal exons, exon 10a and 10b , while splice acceptor 3 is preferentially bypassed  readthrough and inclusion of the downstream exon 10b. A minigene was constructed using the genomic region of the 3′ end of exon 10a and the 5′ part of intron 10  site in MNs led to higher exon 10b usage observed in MNs.To test whether direct NOVA association with the YCAY sites is necessary for the observed regulation, we generated two mutant minigenes by disrupting YCAY sites while preserving the GC content Fig. . Mutant Alternative usage of exons 10a and 10b confers the C-terminal variation between SEPT8 protein isoforms X5 and X1, respectively. SEPT8 undergoes palmitoylation in vivo , which iSept8 exon 10b encodes a potential filopodia-inducing motif (FIM), characterized by two adjacent palmitoylated cysteines  and nearby basic residues   in primary mouse hippocampal neurons which express SEPT8-X1 . Meanwhile, total dendritic length showed a 65% reduction (p = 9.2 × 10− 11), and sholl analysis revealed a similar 66% reduction in critical value in neurons with SEPT8-X1 KD . Even more strikingly, SEPT8-X1 KD led to an almost complete absence of dendritic spines—dendrites became smooth with a few focal swellings at distal ends . These abnormalities were partially rescued by co-expression of the shRNA-resistant SEPT8-X1, but not SEPT8-X5 or the palmitoylation-deficient SEPT8-X1-mut . On the other hand, to our surprise, NOVA2 knockdown did not significantly affect dendritic spine density  which efficiently depleted 76% of the endogenous NOVA2 in N2A cells  . Here weMNs are among the largest neurons in the central nervous system. Their distinct morphology, characterized by a long axon and intricate dendritic arbor, renders traditional cell purifications based on enzymatic tissue digestion and cell purification methods particularly unsatisfactory for understanding the molecular biology of the whole neuron, especially given abundant evidence for RNA localization within the dendritic arbor. The cell type-specific CLIP strategy developed here allows robust and quantitative identification of NOVA binding sites in MNs in vivo, in both the cell bodies and processes. This generated a transcriptome-wide NOVA–RNA interaction atlas in MNs with NOVA binding sites in over 4000 genes. NOVA binding in MNs reflects MN transcriptome signatures, which further demonstrated the cell type specificity of our assay.p < 2.2 × 10− 16; Additional file The cell type-specific CLIP strategy developed here allowed delineation of a subset of MN NOVA targets from WSC. These MN NOVA targets are enriched in genes encoding synaptic functions to a similar extent compared to WSC NOVA targets, yet they are especially enriched in genes encoding cytoskeleton interacting proteins. Enrichment of MN versus WSC CLIP signals in this subset of genes is significant even when adjusted to MN/WSC transcript level differences , while inhibiting the FIM-encoding exon in the other four . It is possible that changes of various NOVA targets upon NOVA KD mitigate the overall effect on dendritic spine density.While SEPT8-X1 promotes dendritic spine formation, we were not able to detect changes in dendritic spine density in NOVA2 KD neurons. This may be related to insufficient effects from NOVA2 KD, or from contributing indirect factors, such as NOVA promotion of protein isoforms with antagonistic effects on spine formation. For example, based on in silico palmitoylation site prediction , we idenNova family members were paralyzed and failed to cluster acetyl-choline receptors at the neuromuscular junctions [Nova-knockout MNs were engineered to constitutively express the Nova-regulated Z+ alternatively spliced isoform of agrin, but surprisingly remained paralyzed. Phrenic nerve stimulation revealed that the axonal synaptic machinery for conducting action potentials and synaptic vesicle release from the axon was functional, leading to the conclusion that dysregulation of additional MN NOVA RNA targets contribute to a proximal physiologic defect in Nova-KO MNs. Here, MN-specific CLIP reveals a major role of NOVA in regulating the MN cytoskeleton, including promoting dendritic complexity. Dendrites are the main information-receiving sites of neurons. Spinal MNs, as the gateway controllers of the CNS motor outputs, have elaborate dendritic structures to meet the highly complex demand of precisely coordinating muscle contractions spatially and temporally [We have previously shown that NOVA proteins play an essential role in MN physiology . MNs in unctions . Successmporally , 64. Earmporally , 66, sugGlra2) splicing [Nova target selection beyond the specificity determined by sequence and structural constraints.When we compared the NOVA–RNA interactions in MNs and WSC, we uncovered over 2000 sites transcriptome-wide that are differentially bound by NOVA in MNs compared to WSC. In this way cell-specific CLIP offers the possibility of revealing cell type-specific regulatory phenomena that are otherwise obscured from analysis of whole tissues. What underlies this unique NOVA binding profile in MNs? It has been shown that combinatorial control is integral in RNA processing regulation. Cellular RNA-binding protein networks play a pivotal role in determining target selection and binding dynamics of a given RNA-binding protein . For NOVsplicing . Interessplicing  suggestsNova [Camk2a-neurons in the forebrain [Cell type-specific CLIP is in general applicable to any cell type and a great variety of RNA-binding proteins. The current study is neuron-specific due to the nature of Nova , 69, butNova , and Camorebrain . And as orebrain , RNA-ediorebrain , and epiorebrain , expandiorebrain . CombiniWe examined whether GFP-NOVA2 expression in our BAC-transgenic MNs could have affected our observations relative to unperturbed neurons. The BAC-transgenic system has intrinsic expression biases and might, for example, have skewed the MN NOVA-PTBP ratios, impacting the observed NOVA binding increases around pyrimidine-rich regions. However, the high concordance between NOVA binding changes in our transgenic MNs and differential splicing in MNs without exogenous NOVA strongly argues for the physiological relevance of our transgenic model. A more elegant approach of studying cell type-specific RNA–protein interactions would be to generate conditionally epitope-tagged knock-in lines followed by the introduction of a cell type-specific Cre recombinase expression. Epitope-tagged protein would be expressed in the desired cell type from its endogenous promoter, thus preserving protein stoichiometry; this strategy has already shown promise with the ubiquitous RNA-binding protein PABPC1 .Caution should be taken when comparing cell type-specific RNA binding profiles across different cell types or tissues. One issue is that differences in CLIP library complexities between comparison groups may lead to unequal statistical power in identifying over- versus under-represented binding sites. In our case, although we pooled spinal cords from multiple transgenic mice per biological replicate and performed twice as many biological replicates as the WSC CLIP, we were only able to generate less than one-eighth of CLIP reads from MN-specific CLIP compared to WSC CLIP. This is likely due to low abundance of MN-specific GFP-NOVA2 compared to the WSC NOVA pool Fig. . We hypoAnother challenge of analyzing cell type-specific CLIP data involves obtaining the matching cell type-specific transcriptomes, which is essential for teasing out functionally important protein–RNA interactions. A variety of technologies, such as fluorescence-activated cell sorting, LCM, and TRAP-seq can be explored for this purpose. We utilized a published RNA-seq dataset on laser captured MNs for the MN-specific transcriptome. Our transcriptome reference for WSC NOVA CLIP, on the other hand, is less than ideal. Since NOVA expression is restricted in neurons, the ideal dataset would be neuron-specific transcriptome profiling generated using technologies such as TRAP-seq or cTag PAPERCLIP.Cell type-specific CLIP may be relevant for the study of many neurological diseases, such as ALS and ataxias, where defined cell types are pathologically affected during the entire course or the initial stages of disease progression. Interplay between the affected cells and their cellular context plays an important role in disease progression –79. Our Here we demonstrate the feasibility and physiological relevance of delineating neuronal cell type-specific RNA regulation. Through cell type-specific epitope tagging of the RNA-binding protein NOVA2, we generated a MN-specific NOVA–RNA interaction map using CLIP. Cell type-specific CLIP revealed a major role of NOVA in regulating cytoskeleton interactions in MNs, including promoting the palmitoylated isoform of a cytoskeleton protein, Septin 8, which enhances dendritic arbor complexity. MN-specific NOVA binding predicts MN-specific alternative RNA processing, further supporting the idea that cell type-specific RNA regulation contributes to cell identity. Our study highlights a non-incremental gain of knowledge moving from whole tissue- to single cell type-based RNA regulation analysis. As our strategy for cell type-specific CLIP is highly adaptable, we envision wide application of this method in a variety CNS cell types as well as disease models.The following antibodies were used in CLIP experiments: anti-GFP mouse monoclonal antibodies 19F7 and 19C8 , anti-NOVA serum from a paraneoplastic opsoclonus-myoclonus ataxia (POMA) patient. The following antibodies and dilutions were used for immunoblotting: anti-NOVA patient serum (1:2000), mouse anti-GAPDH monoclonal antibody 6C5 , rabbit anti-RBFOX3 (1:500) , rabbit NIH3T3 and COS-1 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum, 100 U/mL penicillin, and 100 μg/mL streptomycin. NIH3T3 and COS-1 cells were transfected with plasmid constructs using lipofectamine 2000 (Invitrogen), according to the manufacturer’s instructions.Mouse hippocampal neurons were isolated from 18-day-old CD1 mouse embryos and cultured in 24-well plates according to established protocols. For shRNA knockdown and rescue experiments, 0.4 μg of shRNA vector and 0.6 μg of protein expressing vector or control were transfected at DIV 10 using 3 μL of NeuroMag following the manufacturer’s protocol. Transfected neurons were fixed at DIV 12 for immunofluorescence staining and confocal imaging (see below).Nova2 coding sequence was cloned into a pLD53.SC2 plasmid. Subsequently, a DNA fragment homologous to Chat 5′ UTR region was PCR amplified using primers GCCAGGCATCTGAGAGGC and CCTAGCGATTCTTAATCCAGAGTAGCAGAGCTG and inserted into the plasmid through blunt end ligation at AgeI site. The sequence of the resulting plasmid pLD53.SC2-AcGFP-Nova2 was confirmed by Sanger sequencing and deposited to the Addgene database. Recombinant BAC was generated using RP23-246B12 and pSC2-GFP-Nova2 as described previously [In brief, AcGFP-fused eviously  and micrParaformaldehyde-fixed spinal cords were transversely sectioned at 14 μm thickness and kept at − 80 °C. Before immunofluorescence staining, sections were rehydrated in PBS for 10 min, followed by a 1-h block in blocking buffer containing 100 mM Tris-HCl, pH 7.5, 150 mM NaCl, 0.2% Triton X-100, 5% horse serum. Primary antibodies diluted in blocking buffer were subsequently added to the sections followed by overnight incubation at room temperature. Secondary antibody hybridization and TO-PRO-3 staining were performed using Alexa Fluor conjugated antibodies diluted in 100 mM Tris-HCl, pH 7.5, 150 mM NaCl, 0.2% Triton X-100 with 1 μM TO-PRO-3. Confocal images were taken using an inverted LSM 510 laser scanning confocal microscope (Zeiss).For immunofluorescence staining on hippocampal neurons, cells were fixed in 4% paraformaldehyde at room temperature for 10 minutes, permeabilized with 0.1% Triton-X 100 in PBS at room temperature for 15 minutes, and blocked in PBS containing 0.1% Tween-20 and 10% donkey serum for 1 h at room temperature. The primary antibodies, diluted in PBS containing 1% BSA and 0.1% Tween-20, were added to the cells and incubated overnight at room temperature. Alexa Fluor conjugated secondary antibodies (Jackson Immunoresearch) were used at 1:500 dilution to detect either the tag epitope or proteins of interest. Confocal images were taken using an inverted LSM 880 NLO laser scanning confocal microscope (Zeiss).2 three times at 0 °C. Crosslinked tissues were collected by centrifugation at 4000 g for 2 minutes. HITS-CLIP experiments were performed as previously described [Spinal cords were hydraulically extruded in Hank’s Balanced Salt Solution (HBSS) and triturated seven times with a 21½ gauge needle in 10 mL HBSS. Immediately after trituration, tissue suspensions were crosslinked 400 mJ/cmescribed , 26 withhttp://www.novocraft.com) without iterative trimming using parameters –t 85 -l 25 -s 0 -o Native -r None. Following mapping, pentamer frequencies immediately downstream of CLIP reads were ranked. CLIP reads immediately upstream of the top 20 pentamers with zero or one mismatch to GTGTC were removed. We have recently discovered that under our reverse transcription (RT) conditions, the 3′ end of the RT primer could prime reverse transcription by hybridizing to GUGUC or similar pentamers in the CLIPed RNA, leading to their preferential amplification . Bioinformatically removing such reads circumvents this technical caveat.Bioinformatic processing and mapping of CLIP NGS reads were performed similarly as previously described with slight modifications , 82, 83.NOVA binding peaks were defined as previously described using gene regions compiled from refseq, UCSC known genes, and ESTs plus their downstream 10 kb as transcription units , 27, 80.P values of differential NOVA binding on specific sites were calculated using Fisher’s exact test on the following 2 × 2 matrix:Gene-wise CLIP read enrichment between WSC and MNs was evaluated using the Bioconductor edgeR package with tagwise dispersion model . P valuewhere m and n stand for the number of MN and WSC CLIP reads in a given JP, respectively, while M and N stand for the total number of intronic/exonic MN and WSC CLIP reads within all intronic/exonic JPs in the corresponding transcript, respectively. Coverage is defined as the smallest number among the 2 × 2 matrix. Benjamini-Hochberg multiple-testing correction was performed using all intronic or exonic JPs with a minimum coverage of 10.WT transgenic mice in SJL/B6 mixed background. For each replicate, poly(A) selected RNA from spinal cord was used to prepare RNA-seq library using an Illumina TruSeq RNA sample prep kit. We generated 100-nt paired-end reads using an Illumina Genome Analyzer II. In order to compare WSC and MN RNA-seq, we trimmed our WSC reads to 74 nt to match the read length of the MN RNA-seq dataset by Bandyopadhyay et al. (GSE38820). Both WSC and MN datasets were mapped to the mouse (mm9) genome and exon junctions using OLego with default parameters  [p values of differential alternative splicing, and the FDR was estimated using the Benjamini-Hochberg method. Alternative exon splicing was analyzed as described [We performed WSC RNA-seq on two biological replicates of 3-month-old SOD1er read) . Only reer read) . Fisher’escribed . Alternaescribed  were conGO analysis was performed using GOrilla by running unranked lists of target and background genes . For bacFor motif enrichment around differential MN intronic NOVA peaks, tetramer occurrences 100 nt around the center of each peak were counted and compared to those 100 nt around all intronic or exonic NOVA peaks using a hypergeometric test.Sept8 minigene, the C57BL/6 genomic region amplified with primers TACGACTCACTATAGGGCGAATTCGGATCCGCATGAATTCTGACCCCTGTGA and CAATAAACAAGTTCTGCTTTAATAAGATCTCCGTAACCTGGCTACCAGTGA was cloned into BamHI and BglII digested pSG5 vector using HIFI DNA assembly kit (New England Biolabs). Mutant minigenes were constructed by assembling the PCR amplified vector backbone  with synthetic gBlocks (IDT DNA) carrying the desired mutations. Minigene (1 μg) was co-transfected into COS-1 cells with 1 μg of control vector or NOVA/RBFOX3 expression vector. Cells were harvested 48 h post-transfection for immunoblotting or RT-qPCR following standard protocols. Primers CTGACCCCGCATATGTTCCTGTGT and CAAGCTGGCTATCCTGGGCCTCTT were used to amplify an exon 10a region, while primers GTCTGCGATGGTTTTGCAGAGGTG and GGCCACAGGAAATGGAGATGTGAG were used for an intron 10 amplicon. Three independent experiments were performed for statistical comparison.To construct the We seeded 250,000 COS-1 cells per well in six-well plates and transfected them with 2 μg of constructs expressing FLAG-HA-tagged SEPT8 variants 18–24 h later. Acyl-RAC assay was performed using the CAPUREome S-Palmitoylated Protein Kit (Badrilla) according to the manufacturer’s protocol. Treated protein lysates were subsequently immunoblotted with rabbit anti-HA antibody.Maximum projected confocal images were produced using ImageJ using only linear adjustments. Dendrites were semi-automatically traced using the Simple Neurite Tracer plugin. Critical value and number of dendritic branching points were deduced by using the “sholl analysis” and “analyze skeleton” plugins. Fifteen to 17 neurons were analyzed per group.Additional file 1:Supplemental material and methods. (DOCX 115 kb)Additional file 2:Figure S1. GFP-Nova2 retains Nova2 RNA binding preference. Figure S2. HITS-CLIP on BAC transgenic spinal cords and WSC. Figure S3 Data analysis method. Figure S4. Bioinformatic analyses of disproportionally different NOVA binding sites in MNs. Figure S5. Additional cases where MN-specific NOVA binding correlates with MN-specific alternative splicing. Figure S6. NOVA regulates Sept8 ALE usage. Figure S7. Illustration of palmitoylation detection assay; SEPT8 expression in hippocampal cultures; SEPT8-X1 and NOVA2 knockdown in cell lines. Figure S8. NOVA binding on genes encoding cytoskeleton interacting proteins are over-represented in MNs. (PDF 30346 kb)Additional file 3:a Sequencing and mapping statistics of MN and WSC NOVA CLIP. b Sequencing and mapping statistics of MN and WSC NOVA CLIP. c mm9 coordinates and genomic annotation of WSC NOVA CLIP peaks. d List of known NOVA targets harboring WSC NOVA CLIP peaks. e List of known NOVA targets harboring MN NOVA CLIP peaks. f GO enrichment of known MN versus WSC NOVA targets. g Transcripts with enriched NOVA CLIP signals in MNs compared to WSC. h Transcripts with depleted NOVA CLIP signals in MNs compared to WSC. (XLSX 13044 kb)Additional file 4:a Differential analysis results for all intronic joint peaks. b Differential analysis results for all intronic joint peaks. (XLSX 14058 kb)Additional file 5:a Transcript regions differentially spliced between MNs and WSC. b For the nine exons with over- or under-represented MN NOVA binding sites in regulatory “hotspots”, correlation between observed and predicted dI between MNs and WSC. (XLSX 365 kb)Additional file 6:a List of alternative last exons differentially utilized in MNs vs WSC. b List of alternative last exons differentially utilized in wild-type vs Nova2 knockout mouse brain. (XLSX 69 kb)Additional file 7:List of RNA linkers and DNA primers used in CLIP. (XLSX 70 kb)Additional file 8:Review history for this manuscript. (DOCX 74 kb)


GATA2, RUNX1, TET2, SRSF2, IDH2, PTPN11, WT1, NPM1 and CEBPA showed significant AIs. While the effect size was small in general, GATA2 exhibited the largest allelic imbalance. By pooling heterogeneous data from three independent AML cohorts with paired DNA and RNA sequencing (N = 253), we could validate the preferential transcription of GATA2-mutated alleles. Differential expression analysis of the genes with significant AI showed no significant differential gene and isoform expression for the mutated genes, between mutated and wild-type patients. In conclusion, our analyses identified AI in nine out of eleven recurrently mutated genes. AI might be a common phenomenon in AML which potentially contributes to leukaemogenesis.The patho-mechanism of somatic driver mutations in cancer usually involves transcription, but the proportion of mutations and wild-type alleles transcribed from DNA to RNA is largely unknown. We systematically compared the variant allele frequencies of recurrently mutated genes in DNA and RNA sequencing data of 246 acute myeloid leukaemia (AML) patients. We observed that 95% of all detected variants were transcribed while the rest were not detectable in RNA sequencing with a minimum read-depth cut-off (10x). Our analysis focusing on 11 genes harbouring recurring mutations demonstrated allelic imbalance (AI) in most patients.  The extent to which such mutations are transcribed into RNA is largely unknown. One of the main reasons for this lack of knowledge is due to the intrinsic complexity of transcriptome sequence data, which makes it difficult to implement variant calling procedures2. Thus, variant identification from RNA sequences (RNA-Seq) is considered inferior to that from DNA sequences (DNA-Seq). Recent developments in computational algorithms address these issues and established splice-aware alignment of transcriptome sequences in an effective manner4. The choice of the aligner and variant caller has a major influence on variant detection7. In addition, finding insertions and deletions (INDELs) in RNA-Seq is still one of the major challenges due to the complexity of RNA splicing8. The RNA variants can be compared with the variants from DNA to determine the reliability of RNA-Seq analysis pipelines for variant discovery9.Genomic alterations in cancer are heterogeneous and complex but mainly thought to disturb protein function or gene expression10. In contrast, another group observed 99% concordance of somatic mutations detected between DNA- and RNA-Seq in an analysis of mouse tumour cell lines11. They also examined the allelic imbalance (AI) and concluded that mutated and wildtype alleles were expressed equally irrespective of their mutation status. Few other studies have looked at the AI of somatic mutations between DNA and RNA13. Rhee and colleagues analysed the AI of somatic mutations in the cancer genome atlas (TCGA) cohort from five human solid tumour types and found differences in allele-specific expression among splice site mutations, nonsense SNVs and frameshift INDELs12. TCGA reported allelic biases in the expression of mutations in DNMT3A, RUNX1, TET2, TP53, WT1 and PHF6 between paired DNA- and RNA-Seq data in acute myeloid leukaemia (AML) samples13. Despite the limitation of this analysis due to low mutation counts in the cohort, AI could be explained by copy number changes, loss of heterozygosity or hemizygosity in the case of PHF6. However, the higher expression of the mutant alleles could not be explained sufficiently in all other cases13. Celton et al. studied the expression levels of GATA2 among normal karyotype AML samples and observed the existence of allele-specific expression in samples with low GATA2 expression and further demonstrated an increased DNA methylation in the lower expressed allele14.O’Brien and colleagues compared whole exome sequencing (WES) and RNA-Seq data from 27 lung cancer pairs of tumour and matched normal samples and found only 14% overlap among single nucleotide variants (SNVs) detectedAlthough the phenomenon of AI was observed in different cancer types, there is no systematic analysis or validation of such imbalances for recurrently mutated genes in AML. In our study, we examined the correlation between DNA and RNA Variant Allele Frequencies (VAFs) of recurrently mutated genes in 499 AML patients to determine AI. In contrast to previous analyses, we were able to compare high coverage DNA and transcriptome sequences in a large and homogenously sampled patient cohort and validate our findings in independent data sets. We identified a subgroup of genes that showed AI which potentially contributes to the pathogenic effect of these mutations.15. Out of those, only 11 genes met our filtering criteria and were examined further  was used as the discovery cohort. We focused on 36 genes which were recurrently mutated in more than 1% of AML patients16. The RNA-Seq variants were binned based on their read depth , DNA-exclusive variants (not detected in RNA with minimum read depth of 4x) and RNA-exclusive variants (not detected in DNA with minimum read depth cut-off of 30x). The variants were called in the recurrently mutated regions in AML as defined in previous studies  and DNA-exclusive (122) variants, in the remaining 2,606 variants. Of the variants detected in DNA-Seq, 95.4% were also found in RNA-Seq (transcribed variants). Our observations based on genotype information alone showed that 92.3% of all filtered variants display no observable changes in VAFs between DNA and RNA sequences Fig. . The obsPTPN11, whereas considerable imbalances towards mutant transcript abundance was observed for GATA2, RUNX1, TET2, SRSF2 and IDH2  remained stable between DNA and RNA in all patients. The effect of mutation type on the AI was also observed  for SNVs and INDELs to determine the weighted allelic imbalance (WAI). The model on SNVs showed a substantial imbalance towards wild-type transcript abundance for DH2 Fig. . On the ved Fig. . Non-synGATA2 mutations showed the highest allele-specific mutant transcript abundance in our cohort, but GATA2 mutations are rare in AML. To validate our results, we pooled GATA2 mutated samples along with samples harbouring mutations in 8 other genes of interest from external data sets with paired DNA- and RNA-Seq data . The WAI analysis was modified to account for the differences in cohorts (methods).GATA2, suggesting consistent preferential transcript abundance  in the discovery cohort. The rest of the genes showed no significant AI.We were able to validate the significant shift of AI towards mutant allelic abundance in RNA for nce Fig. . DiffereWe extended our investigation to patients without recurrent mutations in the genes of interest , to determine if they also show allele-specific transcript abundance in AML. All common SNPs from the AMLCG cohort were extracted and filtered using the criteria we previously established (Supplementary). We then extracted all dbSNP annotated variants  and performed our WAI analysis to compare the minor allele frequencies (MAFs) of the common variants  due to the vast differences in sequence coverage. We addressed this issue by transforming the VAFs of both sequences into expected and observed mutant allele reads ensuring their comparability.One of the major advantages of quantifying imbalances of transcript abundance in a uniform cohort lies in the reduction of ascertainment bias, which in turn improves the validity of the results. Studies comparing WES to transcriptome sequencing defined AI using the allelic fraction difference (RNA VAF minus DNA VAF)9. However, RNA-Seq still remains unsuitable for variant discovery due to the large number of false positive variant calls . To control for this, it is essential to select an ideal read depth cut-off. We approached this issue by optimizing the parameters for variant calling in DNA-Seq and using less stringent parameters for RNA-Seq to avoid the loss of true positive variants. We then visualized the concordance rate of homozygous and heterozygous variants with respect to incremental RNA read depths assuming similar proportions of the called variants. Indeed, the proportions of SNVs converged at 10x and remained stable at higher read depths, showing that 10x read depth is a reliable cut-off for RNA-Seq SNV calling, which is in agreement with the cut-off defined by Ley et al. for TCGA13. Similarly, Quinn et al. showed 89% specificity in calling SNPs at 10x cut-off19. Although a cut-off of 10x for RNA-Seq seems to be sufficient for variant calling, there is a potential bias to be addressed in the case of gene mutations which are often sub-clonal. As an example in the case of PTPN11 mutations, VAF in DNA-Seq is usually low (median «50%) and thus a 10x read depth cut-off might not be ideal to confidently call the mutations or observe lack of transcription in RNA-Seq15. In contrast to SNVs, we were not able to define a reliable cut-off for INDELs due to their lower distribution per read depth in RNA-seq.A major drawback of using RNA-Seq for variant calling is the inherent low coverage in regions of interest, when compared to targeted DNA-Seq. Nevertheless, in accordance with previous publications we show that it is possible to validate the majority of the genomic variants using RNA-Seq (95.4%)GATA2 exhibiting the largest difference. Such preferential mutant allele transcript abundance has been observed before in low GATA2 expressing specimens of normal karyotype AML14. In the same study, the involvement of epigenetic mechanisms in allele-specific transcript abundance was demonstrated as well14. A similar observation of mono-allelic expression of the mutant allele of GATA2 was made by Al Seraihi et al.20. The down-regulation of GATA2 expression was shown to be a decisive step in the progression of leukaemia by transcriptional analysis in mouse models21. Ley et al. showed preferential allelic transcript abundance of RUNX1 and TET2 and preferential transcript abundance of the wild-type allele of WT1 in an analysis of the TCGA cohort, which is consistent with our analysis13.We observed preferential transcript abundance in nine genes Fig.  that werSome of the results can potentially be explained by the difference in the half-life of the RNA transcript of mutated and wild-type alleles, resulting from differential transcript stability. The phenomenon of uniparental disomy, copy number alterations or genomic imprinting might also be responsible for some AIs. Regardless of the mechanism, our results show small but significant imbalance in the transcription towards certain alleles. This does not seem to be random since it only occurs in genes affected by recurrent alterations. The WAI analysis based on SNPs showed no AI, in AML patients who did not harbour recurrent mutations in the genes. This observation implies an association of the presence of mutations and AI in these recurrently mutated genes.GATA2 mutations in our pooled validation cohort mainly due to their larger effect size when compared to other genes. The differences in the preferential allelic transcript abundance of mutant versus wild-type alleles among the primary and validation cohorts in NPM1 might be due to its smaller effect size in the primary cohort. Minor technical differences such as library preparations might have also prevented us to validate such small effect sizes in NPM1 and other genes.Irrespective of the sequencing techniques and cohorts we studied, we were able to independently validate the effect for SRSF2.While we were able to show an effect of recurrent mutations on allele-specific transcript abundance in AML, we did not detect differential expression of transcript isoforms between mutated and non-mutated patients. Specifically, the recurrent mutations observed in the differentially expressed transcripts isoforms were also present in transcripts that showed no relevant differential expression between the two groups. This observation is not compatible with the simplistic assumption that differential expression seen in patients with mutated and non-mutated genes can be solely attributed to its mutation status. Thus, it remains unclear which additional factors contribute to the observed AI. Nevertheless, a differential isoform transcript expression in mutated and non-mutated patients can be detected in three genes harbouring recurrent mutations and may imply a reduced expression of mutant alleles or may be the effect of counteracting mechanisms in the case of preferential wild type allelic abundance observed in et al.’s analysis, that demonstrated a tendency for negative allelic fraction differences12. Furthermore, we were unable to validate the negative allelic fraction difference among stop-gain SNVs observed by Rhee et al.12. Our results regarding stop-gain SNVs was also not compatible with known biological mechanisms such as nonsense-mediated RNA decay22. This might be due to the differences in sequencing techniques used in the studies. Rhee et al. compared RNA-Seq with WES, whereas we used targeted DNA-Seq12. Another explanation could lie in the different tumour types analysed and completely different genes included in each study. Rhee et al. included five different tumour types  to determine the AI among the somatic mutations (AML was not included)12, thus suggesting a varying allele-specific expression between different genes and tumour entities. We tried to address this by including gene and mutation type interactions in our regression model but were unable to proceed further due to the few numbers of mutations.Our analysis on mutation types showed that frameshift INDELs have an increased mutant allele abundance which contradicts Rhee 23.The impact of AI on oncogenesis is unclear and may vary between different variants and diseases but it is tempting to speculate that the changes in expression enhance the impact of the underlying gene alteration (e.g. increasing the effect of a gain of function mutation). At the moment, sufficient data is missing to determine the incidence of this phenomenon. Functional analyses are a technically demanding challenge that can only be partially addressed by current routinely applied molecular methods. Potentially, more sophisticated tools to regulate gene expression output levels in mammalian cells will be able to address this question in the futureIn summary, we demonstrated the existence of allele-specific transcript abundance among some of the recurrently mutated genes under study in AML. We suggest that the preferential transcription of wild-type or mutant alleles could be a common and under-appreciated phenomenon in AML and further research will be required to determine the potential effect of allele-specific transcript abundance in AML pathogenesis.16. Our validation cohorts include patients from DKTK (n = 40), TCGA (n = 116) and HELSINKI (n = 97)26. We only included patients having both DNA and matched RNA sequencing as well. Also, we restricted to 36 genes which were recurrently mutated in more than 1% of the AML patients by Metzeler et al.15. A summarized flow diagram with inclusion and exclusion of samples is shown in Fig. Our primary cohort consist of German AML Co-operative Group (AMLCG) study participants, sampled at initial diagnosis from 1999 and 2008 trials (n = 246). Details regarding the treatment protocols and patient selection were published previously16. The samples were sequenced paired-end (2 × 250 bp) on an IlluminaMiSeq instrument . Additionally, Whole Transcriptome Sequencing (Lexogen SENSE mRNA-Seq kit V2) was performed using a paired-end (2 × 100 bp), strand-specific, poly(A)-selected protocol16. Downstream analyses of both sequencing procedures included adapter clipping and quality trimming and was followed by sequence alignment. DNA and RNA sequences were mapped to the reference genome (hg19), using the BWA-MEM and the STAR aligner respectively27. In the case of RNA-Seq, sequence duplicates were removed after the alignment procedure. After processing the aligned sequences, SNVs were called using VarScan2, while INDELs were called using VarDict29. A detailed descriptions of both pipelines can be found in the supplementary methods and in Supplementary Fig. 25. The sequence variants from both DNA and RNA were called using a variant calling pipeline similar to the one used in our primary cohort. Since raw sequencing files (fastq) were not accessible for TCGA and HELSINKI cohorts, the alignment files (bam) were integrated directly into the variant calling. The main difference in the variant calling procedure between the primary and the validation cohorts was the minimum read depth cut-off for DNA-Seq (10x when compared with our primary cohort 30x). This difference is due the differences in the sequencing methods.A total of 246 samples (AMLCG) underwent DNA sequencing using a custom amplicon-based targeted enrichment assay  of 68 genes, which are recurrently mutated in AMLSeveral filtering criteria, including read depth, strand bias, mapping and base quality biases, position bias etc. along with custom filtering definitions (Supplementary), were applied to find the optimum balance between eliminating false positives variants and retaining true positives  including the observed and the expected RNA variant read depth in sequence fragments were used to determine the weighted allelic imbalance (WAI) of the mutations. We used a bootstrap approach (which does not rely on Gaussian distribution) to infer the statistic p-value and confidence intervals30. According to these models, an estimation of significant difference  between the observed and expected RNA variant depth among variants in a gene indicate the presence of WAI in the respective gene. We defined the expected RNA variant read depth as follows:Recurrent mutations identified in our previous analysis, were selected from our dataset and their VAFs were compared between DNA- and RNA-SeqAnd the following linear regression modelsFor every gene in the primary cohort:For every mutation type in the primary cohort:For every gene in the validation cohort:Model (1) grouped the mutations for each gene separately and was adjusted for mutation types such as synonymous and non-synonymous SNVs, stop/gain SNVs, frameshift and non-frameshift insertions, deletions and substitutions, in order to determine the possible effect of mutations on the difference of VAF between DNA and RNA. Each mutation pair (DNA and RNA) was considered as individual entity in the regression model even in the case of patients with multiple mutations on the same gene. Model (2) grouped the mutations by mutation type and was adjusted for gene as well. The regression models were applied on SNVs and INDELs separately. We also applied Model (1) on common SNPs in patients without recurrent mutations on the respective genes to determine the existence of allele-specific expression in general, irrespective of the mutational status. Model (3), modified from Model (1), was used to calculate the WAI in the validation cohort.31. The quantified read counts with less than one count per million in five samples were filtered out and the rest were normalized (TMM) using edgeR (v3.20.9)32. They were then grouped based on the recurrent mutations of each gene with substantial WAI and the differential expression of those genes and transcripts were analysed using limma (v3.34.1) with sample-specific quality weight adjustments in the experiment design 33. The fold changes were calculated and were adjusted for multiple testing.The AI and allele-specific expression of recurrent mutations were further investigated by differential expression of genes and transcripts. The transcript quantification and aggregated gene quantification of our cohort was carried out using Salmon (v0.9.1)34. All statistical analyses were performed using R (v3.4.3) and were adjusted for multiple testing using Benjamini & Hochberg procedure36. We considered an adjusted p-value cut-off of 0.05 as significant.All the processing of DNA- and RNA-Seq were carried out on an in-house Galaxy platform (v15.10.2)Study protocols were approved by the institutional review boards of the participating centers. All study protocols were in accordance with the Declaration of Helsinki, the ethical standards of the responsible committee on human experimentation  and were approved by the institutional review boards of the participating centers. All patients provided written informed consent for inclusion on the clinical trial and in the genetic analyses.Supplementary: Allelic Imbalance of Recurrently Mutated Genes in Acute Myeloid Leukaemia