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Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDH mut ) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies., Methods: Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10 K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model., Results: While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200 K cells) and bulk TCGA glioma data (n = 711 samples)., Conclusion: Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems tested (N = 9) in the clinical validation cohort for local recurrence (5-year area under the curve (AUC) 0.81) and overall survival (5-year AUC 0.80). The increase in AUC compared with the standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval 0.07 to 0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% confidence interval 0.37 to 0.78, P = 0.0001) and suggested postoperative management could be refined for 29.8% of patients. In sum, our results identify a targeted gene expression biomarker that improves discrimination of meningioma outcomes, including prediction of postoperative radiotherapy responses., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Comprehensive investigation of CD8+ T cells in acute myeloid leukemia (AML) is essential for developing immunotherapeutic strategies beyond immune checkpoint blockade. Herein, we performed single-cell RNA profiling of CD8+ T cells from 3 healthy bone marrow donors and 23 newly diagnosed (NewlyDx) and 8 relapsed/refractory (RelRef) patients with AML. Cells coexpressing canonical exhaustion markers formed a cluster constituting <1% of all CD8+ T cells. We identified two effector CD8+ T-cell subsets characterized by distinct cytokine and metabolic profiles that were differentially enriched in NewlyDx and RelRef patients. We refined a 25-gene CD8-derived signature correlating with therapy resistance, including genes associated with activation, chemoresistance, and terminal differentiation. Pseudotemporal trajectory analysis supported enrichment of a terminally differentiated state in CD8+ T cells with high CD8-derived signature expression at relapse or refractory disease. Higher expression of the 25-gene CD8 AML signature correlated with poorer outcomes in previously untreated patients with AML, suggesting that the bona fide state of CD8+ T cells and their degree of differentiation are clinically relevant. Immune clonotype tracking revealed more phenotypic transitions in CD8 clonotypes in NewlyDx than in RelRef patients. Furthermore, CD8+ T cells from RelRef patients had a higher degree of clonal hyperexpansion associated with terminal differentiation and higher CD8-derived signature expression. Clonotype-derived antigen prediction revealed that most previously unreported clonotypes were patient-specific, suggesting significant heterogeneity in AML immunogenicity. Thus, immunologic reconstitution in AML is likely to be most successful at earlier disease stages when CD8+ T cells are less differentiated and have greater capacity for clonotype transitions., (©2023 American Association for Cancer Research.)

Introduction: Meningiomas are the most common primary intracranial tumor. Recently, various genetic classification systems for meningioma have been described. We sought to identify clinical drivers of different molecular changes in meningioma. As such, clinical and genomic consequences of smoking in patients with meningiomas remain unexplored., Methods: 88 tumor samples were analyzed in this study. Whole exome sequencing (WES) was used to assess somatic mutation burden. RNA sequencing data was used to identify differentially expressed genes (DEG) and genes sets (GSEA)., Results: Fifty-seven patients had no history of smoking, twenty-two were past smokers, and nine were current smokers. The clinical data showed no major differences in natural history across smoking status. WES revealed absence of AKT1 mutation rate in current or past smokers compared to non-smokers (p = 0.046). Current smokers had increased mutation rate in NOTCH2 compared to past and never smokers (p < 0.05). Mutational signature from current and past smokers showed disrupted DNA mismatch repair (cosine-similarity = 0.759 and 0.783). DEG analysis revealed the xenobiotic metabolic genes UGT2A1 and UGT2A2 were both significantly downregulated in current smokers compared to past (Log2FC = - 3.97, padj = 0.0347 and Log2FC = - 4.18, padj = 0.0304) and never smokers (Log2FC = - 3.86, padj = 0.0235 and Log2FC = - 4.20, padj = 0.0149). GSEA analysis of current smokers showed downregulation of xenobiotic metabolism and enrichment for G2M checkpoint, E2F targets, and mitotic spindle compared to past and never smokers (FDR < 25% each)., Conclusion: In this study, we conducted a comparative analysis of meningioma patients based on their smoking history, examining both their clinical trajectories and molecular changes. Meningiomas from current smokers were more likely to harbor NOTCH2 mutations, and AKT1 mutations were absent in current or past smokers. Moreover, both current and past smokers exhibited a mutational signature associated with DNA mismatch repair. Meningiomas from current smokers demonstrate downregulation of xenobiotic metabolic enzymes UGT2A1 and UGT2A2, which are downregulated in other smoking related cancers. Furthermore, current smokers exhibited downregulation xenobiotic metabolic gene sets, as well as enrichment in gene sets related to mitotic spindle, E2F targets, and G2M checkpoint, which are hallmark pathways involved in cell division and DNA replication control. In aggregate, our results demonstrate novel alterations in meningioma molecular biology in response to systemic carcinogens., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype-phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li-Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel., Results: Here we assess the accuracy of vicinity-based HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the vicinity-based HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that vicinity-based HMMs can accurately impute common and uncommon variants., Conclusions: Our results indicate that locality-based imputation models can be effectively used for genotype imputation. The parameter settings that we identified can be used in future methods and vicinity-based HMMs can be used for re-structuring and parallelizing new imputation methods. The source code for the vicinity-based HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer ., (© 2022. The Author(s).)

One-fifth of meningiomas classified as benign by World Health Organization (WHO) histopathological grading will behave malignantly. To better diagnose these tumors, several groups turned to DNA methylation, whereas we combined RNA-sequencing (RNA-seq) and cytogenetics. Both approaches were more accurate than histopathology in identifying aggressive tumors, but whether they revealed similar tumor types was unclear. We therefore performed unbiased DNA methylation, RNA-seq, and cytogenetic profiling on 110 primary meningiomas WHO grade I and II). Each technique distinguished the same three groups (two benign and one malignant) as our previous molecular classification; integrating these methods into one classifier further improved accuracy. Computational modeling revealed strong correlations between transcription and cytogenetic changes, particularly loss of chromosome 1p, in malignant tumors. Applying our classifier to data from previous studies also resolved certain anomalies entailed by grouping tumors by WHO grade. Accurate classification will therefore elucidate meningioma biology as well as improve diagnosis and prognosis.

Background: Meningiomas are the most common intracranial neoplasms. Although genomic analysis has helped elucidate differences in survival, there is evidence that racial disparities may influence outcomes. African Americans have a higher incidence of meningiomas and poorer survival outcomes. The etiology of these disparities remains unclear, but may include a combination of pathophysiology and other factors., Objective: To determine factors that contribute to different clinical outcomes in racial populations., Methods: We retrospectively reviewed 305 patients who underwent resection for meningiomas at a single tertiary care facility. We used descriptive statistics and univariate, multivariable, and Kaplan-Meier analyses to study clinical, radiographical, and histopathological differences., Results: Minority patients were more likely to present through the emergency department than an outpatient clinic (P < .0001). They were more likely to present with more advanced clinical symptoms with lower Karnofsky Performance scores, more frequently had peritumoral edema (P = .0031), and experienced longer postoperative stays in the hospital (P = .0053), and African-American patients had higher hospitalization costs (P = .046) and were more likely to be publicly insured. Extent of resection was an independent predictor of recurrence freedom (P = .039). Presentation in clinic setting trended toward an association with recurrence-free survival (P = .055). We observed no significant difference in gross total resection rates, postoperative recurrence, or recurrence-free survival., Conclusion: Minority patients are more likely to present with severe symptoms, require longer perioperative hospitalization, and generate higher hospitalization costs. This may be due to socioeconomic factors that affect access to health care. Targeting barriers to access, especially to subspecialty care, may facilitate more appropriate and timely diagnosis, thereby improving patient care and outcomes., (Copyright © Congress of Neurological Surgeons 2021. All rights reserved.)

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Meningiomas are the most common primary intracranial tumor. Recent next generation sequencing analyses have elaborated the molecular drivers of this disease. We aimed to identify and characterize novel fusion genes in meningiomas., Methods: We performed a secondary analysis of our RNA sequencing data of 145 primary meningioma from 140 patients to detect fusion genes. Semi-quantitative rt-PCR was performed to confirm transcription of the fusion genes in the original tumors. Whole exome sequencing was performed to identify copy number variations within each tumor sample. Comparative RNA seq analysis was performed to assess the clonality of the fusion constructs within the tumor., Results: We detected six fusion events (NOTCH3-SETBP1, NF2-SPATA13, SLC6A3-AGBL3, PHF19-FOXP2 in two patients, and ITPK1-FBP2) in five out of 145 tumor samples. All but one event (NF2-SPATA13) led to extremely short reading frames, making these events de facto null alleles. Three of the five patients had a history of childhood radiation. Four out of six fusion events were detected in expression type C tumors, which represent the most aggressive meningioma. We validated the presence of the RNA transcripts in the tumor tissue by semi-quantitative RT PCR. All but the two PHF19-FOXP2 fusions demonstrated high degrees of clonality., Conclusions: Fusion genes occur infrequently in meningiomas and are more likely to be found in tumors with greater degree of genomic instability (expression type C) or in patients with history of cranial irradiation.

The Warburg effect is a tumor-related phenomenon that could potentially be targeted therapeutically. Here, we showed that glioblastoma (GBM) cultures and patients' tumors harbored super-enhancers in several genes related to the Warburg effect. By conducting a transcriptome analysis followed by ChIP-Seq coupled with a comprehensive metabolite analysis in GBM models, we found that FDA-approved global (panobinostat, vorinostat) and selective (romidepsin) histone deacetylase (HDAC) inhibitors elicited metabolic reprogramming in concert with disruption of several Warburg effect-related super-enhancers. Extracellular flux and carbon-tracing analyses revealed that HDAC inhibitors blunted glycolysis in a c-Myc-dependent manner and lowered ATP levels. This resulted in the engagement of oxidative phosphorylation (OXPHOS) driven by elevated fatty acid oxidation (FAO), rendering GBM cells dependent on these pathways. Mechanistically, interference with HDAC1/-2 elicited a suppression of c-Myc protein levels and a concomitant increase in 2 transcriptional drivers of oxidative metabolism, PGC1α and PPARD, suggesting an inverse relationship. Rescue and ChIP experiments indicated that c-Myc bound to the promoter regions of PGC1α and PPARD to counteract their upregulation driven by HDAC1/-2 inhibition. Finally, we demonstrated that combination treatment with HDAC and FAO inhibitors extended animal survival in patient-derived xenograft model systems in vivo more potently than single treatments in the absence of toxicity.

Background: Meningiomas constitute one-third of all primary brain tumors. Although typically benign, about 20% of these tumors recur despite surgery and radiation, and may ultimately prove fatal. There are currently no effective chemotherapies for meningioma. We, therefore, set out to develop patient-derived orthotopic xenograft (PDOX) mouse models of human meningioma using tumor., Method: Of nine patients, four had World Health Organization (WHO) grade I tumors, five had WHO grade II tumors, and in this second group two patients also had recurrent (WHO grade III) meningioma. We also classified the tumors according to our recently developed molecular classification system (Types A, B, and C, with C being the most aggressive). We transplanted all 11 surgical samples into the skull base of immunodeficient (SCID) mice. Only the primary and recurrent tumor cells from one patient-both molecular Type C, despite being WHO grades II and III, respectively-led to the formation of meningioma in the resulting mouse models. We characterized the xenografts by histopathology and RNA-seq and compared them with the original tumors. We performed an in vitro drug screen using 60 anti-cancer drugs followed by in vivo validation., Results: The PDOX models established from the primary and recurrent tumors from patient K29 (K29P-PDOX and K29R-PDOX, respectively) replicated the histopathology and key gene expression profiles of the original samples. Although these xenografts could not be subtransplanted, the cryopreserved primary tumor cells were able to reliably generate PDOX tumors. Drug screening in K29P and K29R tumor cell lines revealed eight compounds that were active on both tumors, including three histone deacetylase (HDAC) inhibitors. We tested the HDAC inhibitor Panobinostat in K29R-PDOX mice, and it significantly prolonged mouse survival ( p < 0.05) by inducing histone H3 acetylation and apoptosis., Conclusion: Meningiomas are not very amenable to PDOX modeling, for reasons that remain unclear. Yet at least some of the most malignant tumors can be modeled, and cryopreserved primary tumor cells can create large panels of tumors that can be used for preclinical drug testing.

RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNVs). Here, we present CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER integrates the multiscale smoothing of expression signal and allelic shift signals for CNV calling. The allelic shift signal measures the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER employs an efficient methodology for the generation of a genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it for correction of CNVs calls. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.

Background: The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented., Results: TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold.TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a significance threshold are shown to be more accurate for TurboFold than for alternative methods that estimate base pairing probabilities. TurboFold-MEA, which uses base pairing probabilities from TurboFold in a maximum expected accuracy algorithm for secondary structure prediction, has accuracy comparable to the best performing secondary structure prediction methods. The computational and memory requirements for TurboFold are modest and, in terms of sequence length and number of sequences, scale much more favorably than joint alignment and folding algorithms., Conclusions: TurboFold is an iterative probabilistic method for predicting secondary structures for multiple RNA sequences that efficiently and accurately combines the information from the comparative analysis between sequences with the thermodynamic folding model. Unlike most other multi-sequence structure prediction methods, TurboFold does not enforce strict commonality of structures and is therefore useful for predicting structures for homologous sequences that have diverged significantly. TurboFold can be downloaded as part of the RNAstructure package at http://rna.urmc.rochester.edu.

A novel method is presented for predicting the common secondary structures and alignment of two homologous RNA sequences by sampling the 'structural alignment' space, i.e. the joint space of their alignments and common secondary structures. The structural alignment space is sampled according to a pseudo-Boltzmann distribution based on a pseudo-free energy change that combines base pairing probabilities from a thermodynamic model and alignment probabilities from a hidden Markov model. By virtue of the implicit comparative analysis between the two sequences, the method offers an improvement over single sequence sampling of the Boltzmann ensemble. A cluster analysis shows that the samples obtained from joint sampling of the structural alignment space cluster more closely than samples generated by the single sequence method. On average, the representative (centroid) structure and alignment of the most populated cluster in the sample of structures and alignments generated by joint sampling are more accurate than single sequence sampling and alignment based on sequence alone, respectively. The 'best' centroid structure that is closest to the known structure among all the centroids is, on average, more accurate than structure predictions of other methods. Additionally, cluster analysis identifies, on average, a few clusters, whose centroids can be presented as alternative candidates. The source code for the proposed method can be downloaded at http://rna.urmc.rochester.edu.

A novel method is presented for joint prediction of alignment and common secondary structures of two RNA sequences. The joint consideration of common secondary structures and alignment is accomplished by structural alignment over a search space defined by the newly introduced motif called matched helical regions. The matched helical region formulation generalizes previously employed constraints for structural alignment and thereby better accommodates the structural variability within RNA families. A probabilistic model based on pseudo free energies obtained from precomputed base pairing and alignment probabilities is utilized for scoring structural alignments. Maximum a posteriori (MAP) common secondary structures, sequence alignment and joint posterior probabilities of base pairing are obtained from the model via a dynamic programming algorithm called PARTS. The advantage of the more general structural alignment of PARTS is seen in secondary structure predictions for the RNase P family. For this family, the PARTS MAP predictions of secondary structures and alignment perform significantly better than prior methods that utilize a more restrictive structural alignment model. For the tRNA and 5S rRNA families, the richer structural alignment model of PARTS does not offer a benefit and the method therefore performs comparably with existing alternatives. For all RNA families studied, the posterior probability estimates obtained from PARTS offer an improvement over posterior probability estimates from a single sequence prediction. When considering the base pairings predicted over a threshold value of confidence, the combination of sensitivity and positive predictive value is superior for PARTS than for the single sequence prediction. PARTS source code is available for download under the GNU public license at http://rna.urmc.rochester.edu.

Background: Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting the alignments and/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the purpose of establishing alignment constraints based on nucleotide alignment and insertion posterior probabilities. Using a hidden Markov model, posterior probabilities of alignment and insertion are computed for all possible pairings of nucleotide positions from the two sequences. These alignment and insertion posterior probabilities are additively combined to obtain probabilities of co-incidence for nucleotide position pairs. A suitable alignment constraint is obtained by thresholding the co-incidence probabilities. The constraint is integrated with Dynalign, a free energy minimization algorithm for joint alignment and secondary structure prediction. The resulting method is benchmarked against the previous version of Dynalign and against other programs for pairwise RNA structure prediction., Results: The proposed technique eliminates manual parameter selection in Dynalign and provides significant computational time savings in comparison to prior constraints in Dynalign while simultaneously providing a small improvement in the structural prediction accuracy. Savings are also realized in memory. In experiments over a 5S RNA dataset with average sequence length of approximately 120 nucleotides, the method reduces computation by a factor of 2. The method performs favorably in comparison to other programs for pairwise RNA structure prediction: yielding better accuracy, on average, and requiring significantly lesser computational resources., Conclusion: Probabilistic analysis can be utilized in order to automate the determination of alignment constraints for pairwise RNA structure prediction methods in a principled fashion. These constraints can reduce the computational and memory requirements of these methods while maintaining or improving their accuracy of structural prediction. This extends the practical reach of these methods to longer length sequences. The revised Dynalign code is freely available for download.