Discovering sequence and structure landscapes in RNA interaction motifs.
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ABSTRACT:
SUBMITTER: Adinolfi M
PROVIDER: S-EPMC6547422 | biostudies-literature | 2019 Jun
REPOSITORIES: biostudies-literature
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Project description:BIOLOGY IS ENCODED IN MOLECULAR SEQUENCES: deciphering this encoding remains a grand scientific challenge. Functional regions of DNA, RNA, and protein sequences often exhibit characteristic but subtle motifs; thus, computational discovery of motifs in sequences is a fundamental and much-studied problem. However, most current algorithms do not allow for insertions or deletions (indels) within motifs, and the few that do have other limitations. We present a method, GLAM2 (Gapped Local Alignment of Motifs), for discovering motifs allowing indels in a fully general manner, and a companion method GLAM2SCAN for searching sequence databases using such motifs. glam2 is a generalization of the gapless Gibbs sampling algorithm. It re-discovers variable-width protein motifs from the PROSITE database significantly more accurately than the alternative methods PRATT and SAM-T2K. Furthermore, it usefully refines protein motifs from the ELM database: in some cases, the refined motifs make orders of magnitude fewer overpredictions than the original ELM regular expressions. GLAM2 performs respectably on the BAliBASE multiple alignment benchmark, and may be superior to leading multiple alignment methods for "motif-like" alignments with N- and C-terminal extensions. Finally, we demonstrate the use of GLAM2 to discover protein kinase substrate motifs and a gapped DNA motif for the LIM-only transcriptional regulatory complex: using GLAM2SCAN, we identify promising targets for the latter. GLAM2 is especially promising for short protein motifs, and it should improve our ability to identify the protein cleavage sites, interaction sites, post-translational modification attachment sites, etc., that underlie much of biology. It may be equally useful for arbitrarily gapped motifs in DNA and RNA, although fewer examples of such motifs are known at present. GLAM2 is public domain software, available for download at http://bioinformatics.org.au/glam2.
| S-EPMC2323616 | biostudies-literature
Project description:Post-transcriptional regulation of gene expression is often accomplished by proteins binding to specific sequence motifs in mRNA molecules, to affect their translation or stability. The motifs are often composed of a combination of sequence and structural constraints such that the overall structure is preserved even though much of the primary sequence is variable. While several methods exist to discover transcriptional regulatory sites in the DNA sequences of coregulated genes, the RNA motif discovery problem is much more difficult because of covariation in the positions. We describe the combined use of two approaches for RNA structure prediction, FOLDALIGN and COVE, that together can discover and model stem-loop RNA motifs in unaligned sequences, such as UTRs from post-transcriptionally coregulated genes. We evaluate the method on two datasets, one a section of rRNA genes with randomly truncated ends so that a global alignment is not possible, and the other a hyper-variable collection of IRE-like elements that were inserted into randomized UTR sequences. In both cases the combined method identified the motifs correctly, and in the rRNA example we show that it is capable of determining the structure, which includes bulge and internal loops as well as a variable length hairpin loop. Those automated results are quantitatively evaluated and found to agree closely with structures contained in curated databases, with correlation coefficients up to 0.9. A basic server, Stem-Loop Align SearcH (SLASH), which will perform stem-loop searches in unaligned RNA sequences, is available at http://www.bioinf.au.dk/slash/.
| S-EPMC55461 | biostudies-literature
Project description:MotivationSystematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process.ResultsTo close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.
| S-EPMC3371842 | biostudies-literature
Project description:Posttranscriptional regulation in eukaryotes requires cis- and trans-acting features and factors including RNA secondary structure and RNA-binding proteins (RBPs). However, a comprehensive view of the structural and RBP interaction landscape of nuclear RNAs has yet to be compiled for any organism. Here, we use our ribonuclease-mediated structure and RBP-binding site mapping approaches to globally profile these features in Arabidopsis seedling nuclei in vivo. We reveal anticorrelated patterns of secondary structure and RBP binding throughout nuclear mRNAs that demarcate sites of alternative splicing and polyadenylation. We also uncover a collection of protein-bound sequence motifs, and identify their structural contexts, co-occurrences in transcripts encoding functionally related proteins, and interactions with putative RBPs. Finally, using these motifs, we find that the chloroplast RBP CP29A also interacts with nuclear mRNAs. In total, we provide a simultaneous view of the RNA secondary structure and RBP interaction landscapes in a eukaryotic nucleus.
| S-EPMC4871124 | biostudies-literature
Project description:MotivationThe motif discovery problem consists of finding recurring patterns of short strings in a set of nucleotide sequences. This classical problem is receiving renewed attention as most early motif discovery methods lack the ability to handle large data of recent genome-wide ChIP studies. New ChIP-tailored methods focus on reducing computation time and pay little regard to the accuracy of motif detection. Unlike such methods, our method focuses on increasing the detection accuracy while maintaining the computation efficiency at an acceptable level. The major advantage of our method is that it can mine diverse multiple motifs undetectable by current methods.ResultsThe repulsive parallel Markov chain Monte Carlo (RPMCMC) algorithm that we propose is a parallel version of the widely used Gibbs motif sampler. RPMCMC is run on parallel interacting motif samplers. A repulsive force is generated when different motifs produced by different samplers near each other. Thus, different samplers explore different motifs. In this way, we can detect much more diverse motifs than conventional methods can. Through application to 228 transcription factor ChIP-seq datasets of the ENCODE project, we show that the RPMCMC algorithm can find many reliable cofactor interacting motifs that existing methods are unable to discover.
| S-EPMC4426842 | biostudies-literature
Project description:Aptamers, short RNA or DNA molecules that bind distinct targets with high affinity and specificity, can be identified using high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX), but scalable analytic tools for understanding sequence-function relationships from diverse HT-SELEX data are not available. Here we present AptaTRACE, a computational approach that leverages the experimental design of the HT-SELEX protocol, RNA secondary structure, and the potential presence of many secondary motifs to identify sequence-structure motifs that show a signature of selection. We apply AptaTRACE to identify nine motifs in C-C chemokine receptor type 7 targeted by aptamers in an in vitro cell-SELEX experiment. We experimentally validate two aptamers whose binding required both sequence and structural features. AptaTRACE can identify low-abundance motifs, and we show through simulations that, because of this, it could lower HT-SELEX cost and time by reducing the number of selection cycles required.
| S-EPMC5042215 | biostudies-literature
Project description:RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM's model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image.
| S-EPMC5737366 | biostudies-literature
Project description:Post-transcriptional regulation in eukaryotes requires cis- and trans-acting features and factors including RNA secondary structure, and RNA-binding proteins (RBPs). However, a comprehensive view of the structural and RBP interaction landscape of RNAs in the nucleus has yet to be compiled for any organism. Here, we use our ribonuclease-mediated structure and RBP binding site mapping approach on Arabidopsis seedling nuclei in vivo to globally profile these features within the nuclear compartment. We reveal opposing patterns of secondary structure and RBP binding levels throughout native messenger RNAs that demarcate alternative splicing and polyadenylation. We also uncover a collection of protein bound sequence motifs, and identify their structural contexts, co-occurrences in transcripts encoding functionally related proteins, and interactions with putative RBPs. Finally, we identify a nuclear role for the chloroplast RBP, CP29A. In total, we provide the first simultaneous view of the RNA secondary structure and RBP interaction landscapes in a eukaryotic nucleus. Protein interaction profile sequencing (PIP-seq) in Arabidopsis seedling nuclei. These are crosslinked with formaldehyde and treated with two RNases (ssRNase and dsRNase) with two replicates
2014-12-16 | E-GEOD-58974 | biostudies-arrayexpress
Project description:BackgroundElevated sequencing error rates are the most predominant obstacle in single-nucleotide polymorphism (SNP) detection, which is a major goal in the bulk of current studies using next-generation sequencing (NGS). Beyond routinely handled generic sources of errors, certain base calling errors relate to specific sequence patterns. Statistically principled ways to associate sequence patterns with base calling errors have not been previously described. Extant approaches either incur decisive losses in power, due to relating errors with individual genomic positions rather than motifs, or do not properly distinguish between motif-induced and sequence-unspecific sources of errors.ResultsHere, for the first time, we describe a statistically rigorous framework for the discovery of motifs that induce sequencing errors. We apply our method to several datasets from Illumina GA IIx, HiSeq 2000, and MiSeq sequencers. We confirm previously known error-causing sequence contexts and report new more specific ones.ConclusionsChecking for error-inducing motifs should be included into SNP calling pipelines to avoid false positives. To facilitate filtering of sets of putative SNPs, we provide tracks of error-prone genomic positions (in BED format).Availabilityhttp://discovering-cse.googlecode.com.
| S-EPMC3622629 | biostudies-literature
Project description:Predicting RNA 3D structure from sequence is a major challenge in biophysics. An important sub-goal is accurately identifying recurrent 3D motifs from RNA internal and hairpin loop sequences extracted from secondary structure (2D) diagrams. We have developed and validated new probabilistic models for 3D motif sequences based on hybrid Stochastic Context-Free Grammars and Markov Random Fields (SCFG/MRF). The SCFG/MRF models are constructed using atomic-resolution RNA 3D structures. To parameterize each model, we use all instances of each motif found in the RNA 3D Motif Atlas and annotations of pairwise nucleotide interactions generated by the FR3D software. Isostericity relations between non-Watson-Crick basepairs are used in scoring sequence variants. SCFG techniques model nested pairs and insertions, while MRF ideas handle crossing interactions and base triples. We use test sets of randomly-generated sequences to set acceptance and rejection thresholds for each motif group and thus control the false positive rate. Validation was carried out by comparing results for four motif groups to RMDetect. The software developed for sequence scoring (JAR3D) is structured to automatically incorporate new motifs as they accumulate in the RNA 3D Motif Atlas when new structures are solved and is available free for download.
| S-EPMC4551918 | biostudies-literature