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Objectives Diffuse large B-cell lymphoma (DLBCL) is an aggressive non-Hodgkin lymphoma with a heterogenous genetic landscape that can require multiple assays to characterize. We reviewed a 1-step RNA-based assay to determine cell of origin (COO), detect translocations, and identify mutations and to assess the role of the assay in diagnosis. Methods Using a single custom Archer FusionPlex Lymphoma panel, we performed anchored multiplex polymerase chain reaction–based RNA sequencing on 41 cases of de novo DLBCL. Each case was subclassified by COO, and gene fusions and hotspot mutations were identified. The findings were then compared with COO classification by the Hans immunohistochemical algorithm and NanoString technology, cytogenetics, and fluorescence in situ hybridization results. Results Concordant COO classification by the FusionPlex panel and NanoString was observed in 35 of 41 cases (85.3%), with NanoString and Hans concordant in 33 of 41 cases (80.5%) and FusionPlex and Hans concordant in 33 of 41 cases (80.5%). The FusionPlex assay also detected 6 of 11 BCL6 translocations (4 cryptic), 2 of 3 BCL2 translocations, and 2 of 4 MYC translocations. Mutations were detected in lymphoma-related genes in 24 of 41 cases. Conclusion This FusionPlex assay offers a single method for COO classification, mutation detection, and identification of important translocations in DLBCL. Although not replacing traditional testing, it could offer useful data when limited tissue is available

PURPOSE Patients with myelodysplastic syndrome (MDS) are at risk of relapse after allogeneic hematopoietic cell transplantation. The utility of ultra-deep genomic testing to predict and the impact of conditioning intensity to prevent MDS relapse are unknown. METHODS Targeted error-corrected DNA sequencing was performed on preconditioning blood samples from patients with MDS (n = 48) from the Blood and Marrow Transplant Clinical Trials Network 0901 phase III randomized clinical trial, which compared outcomes by allogeneic hematopoietic cell transplantation conditioning intensity in adult patients with < 5% marrow myeloblasts and no leukemic myeloblasts in blood on morphological analysis at the time of pretransplant assessment. Clinical end points (53-month median follow-up) included transplant-related mortality (TRM), relapse, relapse-free survival (RFS), and overall survival (OS). Of the 48 patients examined, 14 experienced TRM, 23 are relapse-free, and 11 relapsed, of which 7 died. RESULTS Using a previously described set of 10 gene regions, 42% of patients (n = 20) had mutations detectable before random assignment to reduced intensity conditioning (RIC) or myeloablative conditioning (MAC). Testing positive was associated with increased rates of relapse (3-year relapse, 40% v 11%; P = .022) and decreased OS (3-year OS, 55% v 79%, P = .045). In those testing positive, relapse rates were higher (3-year relapse, 75% v 17%; P = .003) and RFS was lower (3-year RFS, 13% v 49%; P = .003) in RIC versus MAC arms. Testing additional genes, including those associated with MDS, did not improve prognostication. CONCLUSION This study provides evidence that targeted DNA sequencing in patients with MDS before transplant can identify those with highest post-transplant relapse rates. In those testing positive, random assignment to MAC lowered but did not eliminate relapse risk.

PURPOSE Patients with acute myeloid leukemia (AML) in remission remain at risk for relapse even after allogeneic hematopoietic cell transplantation (alloHCT). AML measurable residual disease (MRD) status before alloHCT has been shown to be prognostic. Whether modulation of the intensity of the alloHCT conditioning regimen in patients with AML who test positive for MRD can prevent relapse and improve survival is unknown. METHODS Ultra-deep, error-corrected sequencing for 13 commonly mutated genes in AML was performed on preconditioning blood from patients treated in a phase III clinical trial that randomly assigned adult patients with myeloid malignancy in morphologic complete remission to myeloablative conditioning (MAC) or reduced-intensity conditioning (RIC). RESULTS No mutations were detected in 32% of MAC and 37% of RIC recipients; these groups had similar survival (3-year overall survival [OS], 56% v 63%; P = .96). In patients with a detectable mutation (next-generation sequencing [NGS] positive), relapse (3-year cumulative incidence, 19% v 67%; P < .001) and survival (3-year OS, 61% v 43%; P = .02) was significantly different between the MAC and RIC arms, respectively. In multivariable analysis for NGS-positive patients, adjusting for disease risk and donor group, RIC was significantly associated with increased relapse (hazard ratio [HR], 6.38; 95% CI, 3.37 to 12.10; P < .001), decreased relapse-free survival (HR, 2.94; 95% CI, 1.84 to 4.69; P < .001), and decreased OS (HR, 1.97; 95% CI, 1.17 to 3.30; P = .01) compared with MAC. Models of AML MRD also showed benefit for MAC over RIC for those who tested positive. CONCLUSION This study provides evidence that MAC rather than RIC in patients with AML with genomic evidence of MRD before alloHCT can result in improved survival.

Introduction Minimal residual disease (MRD) detection in solid tumors describes isolation of circulating tumor DNA (ctDNA) molecules in plasma following definitive treatment of a cancer. Detection of MRD following surgical tumor excision categorizes patients as high risk for disease recurrence. Establishing an MRD approach to treating early-stage NSCLC will facilitate escalation of standard of care (SoC) treatment only in patients destined to relapse from their cancer and overcome challenges associated with conventional adjuvant drug-trial design. Here, we present data from the lung TRACERx study where patients with early-stage NSCLC underwent phylogenetic ctDNA profiling following resection. Methods Patient specific anchored-multiplex PCR (AMP) enrichment panels were generated for 78 lung TRACERx patients who underwent surgery for stage I-III NSCLC; 608 plasma samples were analyzed. Extensive patient-specific cfDNA enrichment panels targeted a median of 196 (range 72 to 482) clonal and subclonal variants detected in primary tumor tissue by multi-region exome sequencing. A novel MRD-caller controlled and estimated background sequencing error to maximize ctDNA detection at low mutant allele frequencies (MAFs). Analytical validation experiments benchmarked assay performance. Results Analytical validation of a 50-variant AMP-MRD assay demonstrated a sensitivity of 89% for mutant DNA at a MAF of 0.008% (with 25ng of DNA input into the assay), specificity was 100% experimentally and 99.9% (95% CI: 99.67 to 99.99%) modelled in-silico. 45 patients suffered relapse of their primary NSCLC; ctDNA was detected at or before clinical relapse in 37 of 45 patients. In these 37 patients the median ctDNA lead-time (time from ctDNA detection to clinical relapse) was 151 days (range 0 to 984 days) and the median time to relapse from surgery was 413 days (range 41 to 1242 days). In 10 of 10 patients who developed second primary cancers during follow-up no ctDNA was detected, reflecting specificity of the MRD assay toward the primary tumor. In 23 patients who remained relapse-free during a median of 1184 days of study follow-up, ctDNA was detected in 1 of 199 time-points analyzed. Analysis of SoC adjuvant surveillance imaging (CT, PET-CT or MRI, 220 encounters) revealed examples of MRD positive patients where SoC radiological surveillance was negative for impending relapse. Through application of large cfDNA enrichment panels targeting up to 483 variants per patient we observed dynamic changes in clonal composition and copy-number status prior to NSCLC relapse, categorized relapse as monoclonal or polyclonal and identified distinct subclonal dynamics during systemic intervention for disease recurrence. Conclusions ctDNA is an adjuvant biomarker capable of both detecting MRD following surgery and defining the clonality of relapsing disease. These data pave the way for clinical trials predicated on escalation of adjuvant standard of care in NSCLC patients who exhibit MRD positive status following surgery. Citation Format: Chris Abbosh, Alexander Frankell, Aaron Garnett, Thomas Harrison, Morgan Weichert, Abel Licon, Selvaraju Veeriah, Bob Daber, Mike Moreau, Adrian Chesh, Kevin Litchfield, Emilia Lim, Daniel Cooke, Clare Puttick, Maise Al Bakir, Fabio Gomes, Akshay Patel, Lizi Manzano, Ariana Huebner, Nicolas Carey, Joan Riley, Paula Roberts, Todd Druley, Jacqui A. Shaw, Nicholas McGranahan, Mariam Jamal-Hanjani, Nicolai Birkbak, Josh Stahl, Charles Swanton, Lung TRACERx consortium. Phylogenetic tracking and minimal residual disease detection using ctDNA in early-stage NSCLC: A lung TRACERx study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT023.

Anaplastic lymphoma kinase (ALK) fusion is a common mechanism underlying pathogenesis of non-small cell lung carcinoma (NSCLC) where these rearrangements represent important diagnostic and therapeutic targets. In this study, we found a new ALK fusion gene, SEC31A-ALK, in lung carcinoma from a 53-year-old Korean man. The conjoined region in the fusion transcript was generated by the fusion of SEC31A exon 21 and ALK exon 20 by genomic rearrangement, which contributed to generation of an intact, in-frame open reading frame. SEC31A-ALK encodes a predicted fusion protein of 1,438 amino acids comprising the WD40 domain of SEC31A at the N-terminus and ALK kinase domain at the C-terminus. Fluorescence in situ hybridization studies suggested that SEC31A-ALK was generated by an unbalanced genomic rearrangement associated with loss of the 3'-end of SEC31A. This is the first report of SEC31A-ALK fusion transcript in clinical NSCLC, which could be a novel diagnostic and therapeutic target for patients with NSCLC.

RNA interference screening using pooled, short hairpin RNA (shRNA) is a powerful, high-throughput tool for determining the biological relevance of genes for a phenotype. Assessing an shRNA pooled screen’s performance is difficult in practice; one can estimate the performance only by using reproducibility as a proxy for power or by employing a large number of validated positive and negative controls. Here, we develop an open-source software tool, the Power Decoder simulator, for generating shRNA pooled screening experiments in silico that can be used to estimate a screen’s statistical power. Using the negative binomial distribution, it models both the relative abundance of multiple shRNAs within a single screening replicate and the biological noise between replicates for each individual shRNA. We demonstrate that this simulator can successfully model the data from an actual laboratory experiment. We then use it to evaluate the effects of biological replicates and sequencing counts on the performance of a pooled screen, without the necessity of gathering additional data. The Power Decoder simulator is written in R and Python and is available for download under the GNU General Public License v3.0.

Introduction: Acute Myeloid Leukemia (AML) is clinically and biologically heterogeneous, requiring the detection of multiple mutations for characterization. For instance, FLT3-ITDs and CEBPA mutations represent important markers in AML, however they are difficult to detect by NGS due to the highly variable nature of ITDs, the high GC content of CEBPA, and the difficulty in mapping repeated sequences to a wild-type reference. Tracking low frequency mutations is also of growing importance. The ability to accurately detect variants at low allele fractions (AFs) using a single test can be used to assess treatment efficacy and potential relapse. Methods: We developed Archer® VariantPlex® myeloid assays based on Anchored Multiplex PCR (AMP™) to detect important mutations in myeloid malignancies. AMP is a target enrichment strategy that uses molecular-barcoded adapters and gene-specific primers for amplification, permitting open-ended capture of DNA fragments from a single end. This approach enables flexible and strand-specific primer design to provide better coverage of ITD-containing and GC-rich regions. We also developed a method to assess SNV sensitivity taking into account both unique coverage depth and noise for single base substitutions. This strategy enables utilization of position-specific detection thresholds and maximizes sensitivity and specificity. We tested this approach using the VariantPlex® Core Myeloid panel, by titrating reference inputs into background normal samples to examine detection of low AF variants. Results: Our assay enables calling of a 30bp FLT3-ITD down to sub-0.05% allele frequencies. Using optimized low AF conditions improves coverage depth, consistency of low AF FLT3-ITD detection, and sensitivity (98.5% of bases are powered to call a true variant at an allele frequency of 3.0% with 1M reads and 200ng of input). We show >1000X unique molecule coverage across the coding region of CEBPA and use this challenging region to visualize the minimum detectable AF (MDAF) at which a variant has a >95% probability of being detected above the noise (95MDAF). Finally, we show consistent single nucleotide variant (SNV), insertion and deletion (indel), and ITD calling at sub-0.5% allele frequencies, and demonstrate the utility of reporting variant-specific MDAFs and normal dataset P-values when analyzing low AF variants. Conclusion: AMP provides NGS-based detection of complex mutation types that are relevant in AML. We demonstrate robust calling of FLT3-ITDs and other variants at low AFs. We also demonstrate full coverage of CEBPA with high depth and low noise such that the 95MDAF predicts confident variant calling at low AFs. This approach is accurate and scalable, enabling simultaneous detection of multiple mutation types across multiple target genes in a single assay. Citation Format: Verity Johnson, Kaitlyn E. Moore, Laura M. Griffin, Aaron Berlin, Abel Licon, Ryan Walters. Accurate detection of low AF variants relevant to AML by Anchored Multiplex PCR and next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1699.

Introduction: Variant detection in circulating tumor DNA (ctDNA) provides an unprecedented opportunity to study the mutational landscape of cancers with minimal patient invasiveness. Meanwhile, new variants providing information about drug response and disease prognosis are being rapidly discovered. Therefore, to fully harness the power of ctDNA variant detection it is critical to be able to rapidly modify the set of variants targeted in an assay. Here we present a platform for next generation sequencing-based ctDNA assays that allows researchers to readily customize their panel targets. Methods: Anchored Multiplex PCR (AMP™) is a molecular biology technique that allows for near infinite modularity of assay targets while maintaining variant detection sensitivity. In order to ensure assay specificity, AMP utilizes molecular barcoded adapters (MBC) to uniquely label input molecules prior to amplification, enabling post-sequencing error correction. This increases analytical sensitivity by reducing background noise. We also developed bioinformatics methods, which allow the characterization of position-specific error levels. By combining molecular barcode-based error correction and error profile characterization, we can reliably detect low frequency variants with high specificity. Results: We tested the modularity of the AMP ctDNA platform by creating ctDNA panels targeting either 6 kb of cancer variant hotspots or 34 kb of tumor suppressors plus the same 6kb of cancer hotspots (40 kb total target sequence). We then prepared and sequenced libraries using ctDNA-like inputs containing important cancer variants at low allele fractions (AF). We used these data to characterize the noise inherent in AMP library preparation and Illumina sequencing. We then performed a power analysis to measure the lowest AF at which we could detect variants at each target position given the noise level and sequencing coverage. Our results show that we can confidently detect variants with an AF of less than 0.18% in 50% of bases and 0.47% in 95% of bases covered in the 6kb panel. Variant detection sensitivity remained consistent over the 6kb of hotspot targets in the context of the 40kb panel (50% of bases powered to detect 0.17% AF variants and 95% powered to detect 0.45% variants). Across the full 40kb of the larger panel we had statistical power to call 0.23% AF variants at 50% of target bases and to call 0.56% variants at 95% of target bases. These data demonstrate that ctDNA panels can maintain assay performance upon panel modifications. Conclusions: The modular nature of AMP ctDNA panels allows for endless combinations of targeted genes to maximize detection sensitivity in relevant regions while also maintaining assay performance. Our experience with AMP assays indicates that the range in the number of targets that can be included in a panel is quite large, allowing broad implementation of the assays developed on the AMP platform. Citation Format: Matt Egleston, Paula Roberts, Ian Hoskins, Aaron Garnett, Laura Griffin, Abel Licon, Ryan Walters. Development of a Highly Customizable Platform for Variant Detection in Circulating Tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 413.

In this paper, we present a dynamic programming algorithm that runs in polynomial time and allows us to achieve the optimal, non-overlapping segmentation of a long RNA sequence into segments (chunks). The secondary structure of each chunk is predicted independently, then combined with the structures predicted for the other chunks, to generate a complete secondary structure prediction that is thus a combination of local energy minima. The proposed approach not only is more efficient and accurate than other traditionally used methods that are based on global energy minimizations, but it also allows scientists to overcome computing and storage constraints when trying to predict the secondary structure of long RNA sequences.

Introduction Acute myeloid leukemia (AML) oncogenesis is thought to require multiple somatic mutations in a "two-hit" process to 1) increase proliferation and 2) prevent maturation of myeloid cells. While FLT3 and KIT mutations are associated with increased proliferation, NPM1, CEBPA and several other mutations can be associated with maturation inhibition. The most common mutations in AML are internal tandem duplications (ITDs) in FLT3, which are detected in more than 20% of pediatric and adult AML cases and are associated with an aggressive phenotype. As FLT3-ITD expressed kinases are sensitive to tyrosine kinase inhibitors, they are of considerable interest for the development of novel AML treatments. Capillary gel electrophoresis can detect ITDs but cannot be easily coupled with assays to detect other mutation types common in AML. Next-generation sequencing (NGS)-based methods enable comprehensive detection of multiple mutation types. However, detection of ITDs by NGS is particularly challenging, in part because of their highly variable nature and the difficulties of mapping repeated sequences to a wild-type reference. Anchored Multiplex PCR (AMP) is a target enrichment strategy for NGS that uses molecular barcoded adaptors and gene-specific primers, permitting open-ended capture of DNA fragments from a single end. We present an approach based on AMP technology and a novel bioinformatics algorithm to detect ITDs in FLT3 as well as other mutation types common in AML. Methods We developed a library preparation assay for NGS to detect FLT3-ITDs as well as other mutations relevant in AML from genomic DNA extracted from clinical samples. We designed AMP probes to cover the commonly mutated juxtamembrane domain and tyrosine kinase domain 1. We further developed a novel de novo sequence assembly algorithm based on over 2000 in silico datasets representing a large range of known ITDs. Results In silico datasets enabled optimization of our sequencing data analysis algorithm, resulting in the detection of over 98% of in silico ITDs with no false positives. The AMP library preparation assay in conjunction with the optimized analysis algorithm enabled sensitive NGS-based detection of ITDs in 16 AML-positive blood samples. These results were consistent with results obtained from standard capillary gel electrophoresis. In addition, point mutations in the TKD of FLT3 and insertions in NPM1 exon 11 were detected in 2/7 and 5/7 FLT3-ITD positive blood samples. Conclusions Our data show that AMP enables accurate NGS-based detection of FLT3-ITDs from clinical DNA samples. As this approach can detect multiple mutation types from a single sample, our AMP library preparation coupled with our NGS analysis algorithm enables simultaneous detection of multiple mutations relevant in AML. Disclosures Van Deusen: ArcherDX, Inc.: Employment. Bessette:ArcherDX, Inc.: Employment. Johnson:ArcherDX, Inc.: Employment. Berlin:ArcherDX, Inc.: Employment. Banos:ArcherDX, Inc.: Employment. Griffin:ArcherDX, Inc.: Employment. Reckase:ArcherDX, Inc.: Employment. Stahl:ArcherDX, Inc.: Employment. Licon:ArcherDX, Inc.: Employment. Kudlow:ArcherDX, Inc.: Employment.

FLT3 internal tandem duplications (ITDs) are found in > 20% of pediatric and adult acute myeloid leukemia (AML) cases and are generally associated with a poor prognosis. Nevertheless, detection of FLT3-ITDs presents a challenge to NGS-based approaches, as many variant callers fail to identify the highly variable repeated sequences associated with FLT3-ITDs. We have developed a novel targeted assay, the Archer™ VariantPlex™ Core AML Panel, that utilizes Anchored Multiplex PCR (AMP™) with probes in multiple locations proximal to exons 13, 14, and 15 of FLT3. These exons encompass the commonly mutated juxtamembrane domain and tyrosine kinase domain 1. This panel yields high-complexity, dual-strand coverage of known FLT3-ITD locations. Because AMP probes, unlike traditional PCR probes, function independently of each other, we are able to produce multiple overlapping “snapshots” of the region of interest, thereby enhancing the ability to confidently identify complex mutation types. The sequenced reads are assembled using a novel de novo assembly algorithm in the Archer Analysis bioinformatics pipeline, in which the resulting consensus is annotated and events that involve FLT3 exons 13 14 or 15 are marked as FLT3 abnormalities. In order to assess Archer Analysis and the VariantPlex Core AML panel, we tested it on >20 patient-derived samples with known FLT3-ITDs. In addition, we generated over two thousand in silico datasets, representing the spectrum of known ITDs, to further test our panel design and analysis algorithm. Each in silico dataset was constructed to simulate reads originating from probes in VariantPlex Core AML Panel. This methodology permitted rigorous optimization of both the VariantPlex Core AML Panel and the analysis algorithm. The VariantPlex Core AML Panel, in conjunction with our novel detection algorithm, showed both exceptional sensitivity and specificity in the detection of FLT3-ITDs. FLT3-ITDs were successfully identified in all patient samples tested, and no false positives were detected. Our in silico datasets showed similarly high sensitivity and specificity. These data indicate that AMP libraries targeting the FLT3-ITD region are ideal for the detection of this complex mutation type, largely because of the overlapping and anchored read structure. Therefore, the Archer VariantPlex Core AML panel, in conjunction with Archer Analysis represents a reliable platform for the detection of FLT3-ITDs, in addition to other mutations commonly found in AML. Citation Format: Marc Bessette, Benjamin Van Deusen, Michael Banos, Laura Johnson, Aaron Berlin, Erik Reckase, Joshua Stahl, Abel Licon, Brian Kudlow. NGS-based detection of FLT3-ITDs with Anchored Multiplex PCR. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3618.

Introduction: Next-generation sequencing (NGS) has become a critical diagnostic assay to identify pathologic SNVs, CNVs, and gene rearrangements. However, it has not been routinely used to assess expression levels of target genes that could indicate patient populations responsive to therapeutics. This is largely due to the absence of reliable bioinformatics tools for the assessment of expression levels within RNA assays. We have developed a novel within-sample distribution-based method that assesses the relative extremity of expression for individual genes. Our predominate focus has been on assessing the expression of NTRK1, NTRK2, NTRK3, ROS1, and ALK, however this method can be readily applied to other genes and NGS platforms. Methods: Within the kinase domain, the expression of a targeted region is represented by the number of unique deduplicated reads for NGS studies and normalized probe expression values (based on spike-in controls) for NanoString studies. A Poisson distribution is used to represent primer expression with the parameters estimated via maximum likelihood. The interquartile range (IQR) of the entirety of the sample's read counts is calculated and only those that do not exceed the third quartile bound by more than 150% of the IQR are considered during parameter estimation. A probability is then assigned to each of the primers based on their read counts. A geometric mean of the individual primer probabilities represents the probability value for the entire gene. Expression values are reported as -log10 (p-value) and cutoffs of 6 and 1 were used to call significant expression for NGS and NanoString platforms, respectively. Results: The NGS-based approach correctly identified significant expression in all gene rearrangement positive cell lines (n = 11). In cell lines not harboring gene rearrangements, the NGS and NanoString platforms showed 100% concordance in calling significant expression (n = 12). In a gene rearrangement negative FFPE cohort, concordance between NGS and NanoString platforms was 97%-99% for target genes (n = 102). ROS1 and ALK were most commonly found to be significantly expressed in FFPE samples (5% and 2%). Conclusions: We have developed a statistical-based approach to detecting expression levels for RNA-based NGS assays. This can be applied to cohort studies to not only identify clinical samples that may benefit from targeted kinase inhibitor therapeutics but also correlate with predicted outcome of disease. Citation Format: Robert Shoemaker, Aaron Berlin, Amy Diliberto, Heather Ely, Marissa Chen, Danielle Murphy, Jason Christiansen, Vince Reuter, Abel Licon. A novel, statistical-based method to determine RNA expression by next-generation sequencing in clinical FFPE samples. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5274.

As ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation, their secondary structures have been the focus of many recent studies. Despite the computing power of supercomputers, computationally predicting secondary structures with thermodynamic methods is still not feasible when the RNA molecules have long nucleotide sequences and include complex motifs such as pseudoknots. This paper presents RNAVLab (RNA Virtual Laboratory), a virtual laboratory for studying RNA secondary structures including pseudoknots that allows scientists to address this challenge. Two important case studies show the versatility and functionalities of RNAVLab. The first study quantifies its capability to rebuild longer secondary structures from motifs found in systematically sampled nucleotide segments. The extensive sampling and predictions are made feasible in a short turnaround time because of the grid technology used. The second study shows how RNAVLab allows scientists to study the viral RNA genome replication mechanisms used by members of the virus family Nodaviridae.

Despite the computing power of emerging technologies, predicting long RNA secondary structures with thermodynamics-based methods is still infeasible, especially if the structures include complex motifs such as pseudoknots. This paper presents preliminary results on rebuilding RNA secondary structures by an extensive and systematic sampling of nucleotide chunks. The rebuilding approach merges the significant motifs found in the secondary structures of the single chunks. The extensive sampling and prediction of nucleotide chunks are supported by grid technology as part of the RNAVLab functionality. Significant motifs are identified in the chunk secondary structures and merged in a single structure based on their recurrences and other statistical insights. A critical analysis of the strengths, weaknesses, and future developments of our method is presented.

Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. An RNA molecule is a linear polymer which folds back on itself to form a three dimensional (3D) functional structure. In this paper we briefly address mathematical problems associated with the grid computing approach to RNA structure prediction. In particular, we introduce models to partition a large RNA molecule into smaller segments to be assigned to different computers on the grid. Based on these models, we formulate a sampling strategy to select RNA segments for computational prediction to maximize prediction consistency. This strategy is under construction as part of RNAVLab, our unified environment for computational RNA structure analysis, i.e., prediction, alignment, comparison, and classification. A first prototype of RNAVLab is presented and used to investigate the possible association of secondary structure types with RNA functions by analyzing secondary structures for a family of nodavirus genomes.