Normalized Similarity of RNA Sequences

2005 ◽  
pp. 360-369 ◽  
Author(s):  
Rolf Backofen ◽  
Danny Hermelin ◽  
Gad M. Landau ◽  
Oren Weimann
Keyword(s):  
Rna Sequences

High-resolution nucleic acid sequence mapping via in situ hybridization at the Electron Microscope level

1995 ◽  
Vol 53 ◽  
pp. 752-753
Author(s):  
B.A. Hamkalo ◽  
S. Narayanswami ◽  
A.P. Kausch
Keyword(s):  
Nucleic Acid ◽  
Interphase Nucleus ◽  
Genome Mapping ◽  
Precise Location ◽  
Electron Microscope Level ◽  
Rna Sequences ◽  
Fundamental Interest ◽  
Whole Cells ◽  
Dna And Rna

The availability of nonradioactive methods to label nucleic acids an the resultant rapid and greater sensitivity of detection has catapulted the technique of in situ hybridization to become the method of choice to locate of specific DNA and RNA sequences on chromosomes and in whole cells in cytological preparations in many areas of biology. It is being applied to problems of fundamental interest to basic cell and molecular biologists such as the organization of the interphase nucleus in the context of putative functional domains; it is making major contributions to genome mapping efforts; and it is being applied to the analysis of clinical specimens. Although fluorescence detection of nucleic acid hybrids is routinely used, certain questions require greater resolution. For example, very closely linked sequences may not be separable using fluorescence; the precise location of sequences with respect to chromosome structures may be below the resolution of light microscopy(LM); and the relative positions of sequences on very small chromosomes may not be feasible.

Impact of microRNAs on molecular epidemiology

2011 ◽  
Vol 152 (16) ◽  
pp. 633-641 ◽  
Author(s):  
Katalin Gőcze ◽  
Katalin Gombos ◽  
Gábor Pajkos ◽  
Ingrid Magda ◽  
Ágoston Ember ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Wide Spectrum ◽  
Translation Regulation ◽  
Rna Sequences ◽  
Serum Mirna ◽  
The Past ◽  
Non Coding Rna ◽  
Free Serum

Cancer research concerning short non-coding RNA sequences and functionally linked to RNA interference (RNAi) have reached explosive breakthrough in the past decade. Molecular technology applies microRNA in extremely wide spectrum from molecular tumor prediction, diagnostics, progression monitoring and prevention. Functional analysis of tissue miRNA and cell-free serum miRNA in posttranscription and translation regulation innovated and restructured the knowledge on the field. This review focuses on molecular epidemiology and primary prevention aspects of the small non-coding RNA sequences. Orv. Hetil., 2011, 152, 633–641.

scholarly journals Large-Scale, High-Throughput Validation of Short Hairpin RNA Sequences for RNA Interference

2006 ◽  
Vol 11 (3) ◽  
pp. 236-246 ◽  
Author(s):  
Laurence H. Lamarcq ◽  
Bradley J. Scherer ◽  
Michael L. Phelan ◽  
Nikolai N. Kalnine ◽  
Yen H. Nguyen ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Strong Support ◽  
Gc Content ◽  
Rapid Identification ◽  
Hairpin Rna ◽  
Rna Sequences ◽  
Short Hairpin ◽  
Short Hairpin Rnas ◽  
Interfering Rna

A method for high-throughput cloning and analysis of short hairpin RNAs (shRNAs) is described. Using this approach, 464 shRNAs against 116 different genes were screened for knockdown efficacy, enabling rapid identification of effective shRNAs against 74 genes. Statistical analysis of the effects of various criteria on the activity of the shRNAs confirmed that some of the rules thought to govern small interfering RNA (siRNA) activity also apply to shRNAs. These include moderate GC content, absence of internal hairpins, and asymmetric thermal stability. However, the authors did not find strong support for positionspecific rules. In addition, analysis of the data suggests that not all genes are equally susceptible to RNAinterference (RNAi).

Papovavirus-related RNA sequences in human neurogenic tumours

1982 ◽  
Vol 56 (2) ◽  
pp. 118-122 ◽  
Author(s):  
H. Ibelgaufts ◽  
K. W. Jones
Keyword(s):  
Rna Sequences

scholarly journals PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Jun Meng ◽  
Qiang Kang ◽  
Zheng Chang ◽  
Yushi Luan
Keyword(s):  
Deep Learning ◽  
Noncoding Rna ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Biological Activities ◽  
Support Vector ◽  
Multiple Perspectives ◽  
K Nearest Neighbor ◽  
Rna Sequences ◽  
Deep Learning Model

Abstract Background Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. Results To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). Conclusions PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

Tracking DNA and RNA Sequences at High Resolution

2013 ◽  
pp. 343-366 ◽  
Author(s):  
Dušan Cmarko ◽  
Anna Ligasová ◽  
Karel Koberna
Keyword(s):  
High Resolution ◽  
Rna Sequences ◽  
Dna And Rna

scholarly journals Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ratanond Koonchanok ◽  
Swapna Vidhur Daulatabad ◽  
Quoseena Mir ◽  
Khairi Reda ◽  
Sarath Chandra Janga
Keyword(s):  
Single Molecule ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Direct Sequencing ◽  
Visual Exploration ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Rna Sequences ◽  
Sequencing Technologies ◽  
Signal Features

Abstract Background Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia.

scholarly journals Role of Transposable Elements in Gene Regulation in the Human Genome

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 118
Author(s):  
Arsala Ali ◽  
Kyudong Han ◽  
Ping Liang
Keyword(s):  
Gene Regulation ◽  
Transposable Elements ◽  
Regulatory Sequences ◽  
Rna Sequences ◽  
Expression Of Genes ◽  
Wide Range ◽  
Lineage Specificity ◽  
Regulatory Rnas ◽  
Potential Factors ◽  
Interspersed Repeats

Transposable elements (TEs), also known as mobile elements (MEs), are interspersed repeats that constitute a major fraction of the genomes of higher organisms. As one of their important functional impacts on gene function and genome evolution, TEs participate in regulating the expression of genes nearby and even far away at transcriptional and post-transcriptional levels. There are two known principal ways by which TEs regulate the expression of genes. First, TEs provide cis-regulatory sequences in the genome with their intrinsic regulatory properties for their own expression, making them potential factors for regulating the expression of the host genes. TE-derived cis-regulatory sites are found in promoter and enhancer elements, providing binding sites for a wide range of trans-acting factors. Second, TEs encode for regulatory RNAs with their sequences showed to be present in a substantial fraction of miRNAs and long non-coding RNAs (lncRNAs), indicating the TE origin of these RNAs. Furthermore, TEs sequences were found to be critical for regulatory functions of these RNAs, including binding to the target mRNA. TEs thus provide crucial regulatory roles by being part of cis-regulatory and regulatory RNA sequences. Moreover, both TE-derived cis-regulatory sequences and TE-derived regulatory RNAs have been implicated in providing evolutionary novelty to gene regulation. These TE-derived regulatory mechanisms also tend to function in a tissue-specific fashion. In this review, we aim to comprehensively cover the studies regarding these two aspects of TE-mediated gene regulation, mainly focusing on the mechanisms, contribution of different types of TEs, differential roles among tissue types, and lineage-specificity, based on data mostly in humans.

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