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Cluster BE1 Streptomyces bacteriophages belong to the Siphoviridae , with genome sizes over 130 kbp, and they contain direct terminal repeats of approximately 11 kbp. Eight newly isolated closely related cluster BE1 phages contain 43 to 48 tRNAs, one transfer-messenger RNA (tmRNA), and 216 to 236 predicted open reading frames (ORFs), but few of their genes are shared with other phages, including those infecting Streptomyces species.

Background: Somatic mutations are the basis for the generation of potential neoantigens recognized by antitumor T lymphocytes, which may contribute to increase T cell diversity [1]. Next-generation sequencing of expressed T cell receptor (TCR) transcripts allows for quantification and assessing clonality of T cell diversity [2]. Tumors that exhibit a low TCR diversity and potentially having a high clonality have been shown to be lower responders to immunotherapy treatments. Methods: Utilizing the Shannon entropy to arrive at a clonality readout, we sought to gain mechanistic insights and demonstrate a link between clonality and total mutation load. We conducted the analysis using a diverse set of solid tumors and hematological malignancies across three different specimen types (bone marrow (BM), peripheral blood (PB), formalin-fixed paraffin embedded (FFPE tissue)). Specifically, we used the beta TCRSeq NGS and the Comprehensive NGS assays (internally developed) to characterize sample clonality and mutation load respectively. Results: The TCRSeq NGS assay sequences the V(D)J domains of the variable region of the TCR heavy chain (involved in epitope recognition), while the hybrid capture Comprehensive NGS assay detects variants across a 236 gene panel optimized for SNV and INDEL detection. Initial analysis across the different specimen types resulted in average clonalities of BM = 0.255, PB = 0.149, FFPE = 0.092, with average tumor mutation loads of 7.6, 7.6, and 21 respectively. Categorization of all samples by increasing clonality, independent of specimen type, established a clear differentiator with samples harboring a clonality of less than 0.10 to exhibit an average mutation load of 26.33. This was statistically significant from samples harboring clonalities between 0.10 and 0.19, which had an average mutation load of 7.86, and samples with clonalities equal to or greater than 0.20, having an average mutation load of 7.67. Conclusions: Based on two different categorical analyses, lower T cell clonality readouts appear to be associated with solid tumors harboring greater genetic diversity/neoantigen burden than hematological malignancies with higher clonalities. As tumor mutation burden has been correlated with clinical outcomes of varying efficacy, this study provides further insight into the utility of T cell clonality as a “molecular tag” for immune response and resistance in oncological therapeutics. Citation Format: Hua C. Gong, Hua Gong, Andreas Papoutsis, Wen Wu, Christian Laing. Characterization of T cell clonality and tumor mutation burden utilizing the TCRSeq NGS and the comprehensive NGS assays [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 2238.

We present eleven novel features for identifying A-minors in 3D RNA molecules.These features include inter-atomic distances, bond angle and bond types.The features are highly effective when used with machine learning algorithms.This approach will be useful in RNA tertiary motif analysis and applications. RNA tertiary interactions or tertiary motifs are conserved structural patterns formed by pairwise interactions between nucleotides. They include base-pairing, base-stacking, and base-phosphate interactions. A-minor motifs are the most common tertiary interactions in the large ribosomal subunit. The A-minor motif is a nucleotide triple in which minor groove edges of an adenine base are inserted into the minor groove of neighboring helices, leading to interaction with a stabilizing base pair. We propose here novel features for identifying and predicting A-minor motifs in a given three-dimensional RNA molecule. By utilizing the features together with machine learning algorithms including random forests and support vector machines, we show experimentally that our approach is capable of predicting A-minor motifs in the given RNA molecule effectively, demonstrating the usefulness of the proposed approach. The techniques developed from this work will be useful for molecular biologists and biochemists to analyze RNA tertiary motifs, specifically A-minor interactions.

Motif finding in DNA, RNA and proteins plays an important role in life science research. Recent patents concerning motif finding in biomolecular data are recorded in the DNA Patent Database which serves as a resource for policy makers and members of the general public interested in fields like genomics, genetics and biotechnology. In this paper, we present a computational approach to mining for RNA tertiary motifs in genomic sequences. Specifically, we describe a method, named CSminer, and show, as a case study, the application of CSminer to genome-wide search for coaxial helical stackings in RNA 3-way junctions. A coaxial helical stacking occurs in an RNA 3-way junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Experimental results demonstrate the effectiveness of our approach.

RNA junctions are important structural elements that form when three or more helices come together in space in the tertiary structures of RNA molecules. Determining their structural configuration is important for predicting RNA 3D structure. We introduce a computational method to predict, at the secondary structure level, the coaxial helical stacking arrangement in junctions, as well as classify the junction topology. Our approach uses a data mining approach known as random forests, which relies on a set of decision trees trained using length, sequence and other variables specified for any given junction. The resulting protocol predicts coaxial stacking within three- and four-way junctions with an accuracy of 81% and 77%, respectively; the accuracy increases to 83% and 87%, respectively, when knowledge from the junction family type is included. Coaxial stacking predictions for the five to ten-way junctions are less accurate (60%) due to sparse data available for training. Additionally, our application predicts the junction family with an accuracy of 85% for three-way junctions and 74% for four-way junctions. Comparisons with other methods, as well applications to unsolved RNAs, are also presented. The web server Junction-Explorer to predict junction topologies is freely available at: http://bioinformatics.njit.edu/junction.

RNA junctions are secondary structure elements formed when three or more helices come together. They are present in diverse RNA molecules with various fundamental functions in the cell. To better understand the intricate architecture of three-dimensional RNAs, we analyze currently solved 3D RNA junctions in terms of basepair interactions and three-dimensional configurations. First, we study basepair interaction diagrams for solved RNA junctions with five to ten helices and discuss common features. Second, we compare these higher-order junctions to those containing three or four helices and identify global motif patterns such as coaxial-stacking and parallel and perpendicular helical configurations. These analyses show that higher order junctions organize their helical components in parallel and helical configurations similar to lower order junctions. Their sub-junctions also resemble local helical configurations found in three and four-way junctions, and are stabilized by similar long-range interaction preferences such as A-minor interactions. Furthermore, loop regions within junctions are high in adenine but low in cytosine. And, in agreement with previous studies, we suggest that coaxial stacking between helices likely forms when the common single stranded loop is small in size; however, other factors such as stacking interactions involving non-canonical basepairs and proteins can greatly determine or disrupt coaxial stacking. Finally, we introduce the ribo-base interactions: when combined with the along-groove packing motif, these ribo-base interactions form novel motifs involved in perpendicular helix-helix interactions. Overall, these analyses suggest recurrent tertiary motifs that stabilize junction architecture, pack helices, and help form helical configurations that occur as sub-elements of larger junction networks. The frequent occurrence of similar helical motifs suggest Nature’s finite and perhaps limited repertoire of RNA helical conformation preferences. More generally, studies of RNA junctions and tertiary building blocks can ultimately help in the difficult task of RNA 3D structure prediction.

RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/., Comment: 45 pages, 5 figures

Mycobacteriophages – viruses of mycobacterial hosts – are genetically diverse but morphologically are all classified in the Caudovirales with double-stranded DNA and tails. We describe here a group of five closely related mycobacteriophages – Corndog, Catdawg, Dylan, Firecracker, and YungJamal – designated as Cluster O with long flexible tails but with unusual prolate capsids. Proteomic analysis of phage Corndog particles, Catdawg particles, and Corndog-infected cells confirms expression of half of the predicted gene products and indicates a non-canonical mechanism for translation of the Corndog tape measure protein. Bioinformatic analysis identifies 8–9 strongly predicted SigA promoters and all five Cluster O genomes contain more than 30 copies of a 17 bp repeat sequence with dyad symmetry located throughout the genomes. Comparison of the Cluster O phages provides insights into phage genome evolution including the processes of gene flux by horizontal genetic exchange.

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires RNA tertiary structure knowledge. Although modeling approaches for the study of RNA structures and dynamics lag behind efforts in protein folding, much progress has been achieved in the past two years. Here, we review recent advances in RNA folding algorithms, RNA tertiary motif discovery, applications of graph theory approaches to RNA structure and function, and in silico generation of RNA sequence pools for aptamer design. Advances within each area can be combined to impact many problems in RNA structure and function.

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires knowledge of their tertiary structures. Though computational RNA folding approaches exist, they often require manual manipulation and expert intuition; predicting global long-range tertiary contacts remains challenging. Here we develop a computational approach and associated program module (RNAJAG) to predict helical arrangements/topologies in RNA junctions. Our method has two components: junction topology prediction and graph modeling. First, junction topologies are determined by a data mining approach from a given secondary structure of the target RNAs; second, the predicted topology is used to construct a tree graph consistent with geometric preferences analyzed from solved RNAs. The predicted graphs, which model the helical arrangements of RNA junctions for a large set of 200 junctions using a cross validation procedure, yield fairly good representations compared to the helical configurations in native RNAs, and can be further used to develop all-atom models as we show for two examples. Because junctions are among the most complex structural elements in RNA, this work advances folding structure prediction methods of large RNAs. The RNAJAG module is available to academic users upon request.