Reverse-complement similarity codes for DNA sequences

2002 ◽  
Author(s):  
A.G. D'yachkov ◽  
P.A. Vilenkin ◽  
D.C. Torney ◽  
P.S. White
Keyword(s):  
Dna Sequences ◽  
Reverse Complement

scholarly journals IUPACpal: efficient identification of inverted repeats in IUPAC-encoded DNA sequences

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hayam Alamro ◽  
Mai Alzamel ◽  
Costas S. Iliopoulos ◽  
Solon P. Pissis ◽  
Steven Watts
Keyword(s):  
Genetic Variation ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Inverted Repeats ◽  
Full Potential ◽  
Common Genetic Variation ◽  
Comprehensive Description ◽  
Reverse Complement ◽  
Broad Variation ◽  
Eukaryotic Genomes

Abstract Background An inverted repeat is a DNA sequence followed downstream by its reverse complement, potentially with a gap in the centre. Inverted repeats are found in both prokaryotic and eukaryotic genomes and they have been linked with countless possible functions. Many international consortia provide a comprehensive description of common genetic variation making alternative sequence representations, such as IUPAC encoding, necessary for leveraging the full potential of such broad variation datasets. Results We present IUPACpal, an exact tool for efficient identification of inverted repeats in IUPAC-encoded DNA sequences allowing also for potential mismatches and gaps in the inverted repeats. Conclusion Within the parameters that were tested, our experimental results show that IUPACpal compares favourably to a similar application packaged with EMBOSS. We show that IUPACpal identifies many previously unidentified inverted repeats when compared with EMBOSS, and that this is also performed with orders of magnitude improved speed.

scholarly journals Projection layers improve deep learning models of regulatory DNA function

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 151 ◽  
Author(s):  
Alex Hawkins-Hooker ◽  
Henry Kenlay ◽  
John E. Reid
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
Internal Representation ◽  
Feature Space ◽  
Predictive Performance ◽  
Noncoding Dna ◽  
Reverse Complement ◽  
Regulatory Dna Sequences ◽  
Recurrent Architecture ◽  
Regulatory Dna

With the increasing application of deep learning methods to the modelling of regulatory DNA sequences has come an interest in exploring what types of architecture are best suited to the domain. Networks designed to predict many functional characteristics of noncoding DNA in a multitask framework have to recognise a large number of motifs and as a result benefit from large numbers of convolutional filters in the first layer. The use of large first layers in turn motivates an exploration of strategies for addressing the sparsity of output and possibility for overfitting that result. To this end we propose the use of a dimensionality-reducing linear projection layer after the initial motif-recognising convolutions. In experiments with a reduced version of the DeepSEA dataset we find that inserting this layer in combination with dropout into convolutional and convolutional-recurrent architectures can improve predictive performance across a range of first layer sizes. We further validate our approach by incorporating the projection layer into a new convolutional-recurrent architecture which achieves state of the art performance on the full DeepSEA dataset. Analysis of the learned projection weights shows that the inclusion of this layer simplifies the network’s internal representation of the occurrence of motifs, notably by projecting features representing forward and reverse-complement motifs to similar positions in the lower dimensional feature space output by the layer.

scholarly journals Reverse-Complement Equivariant Networks for DNA Sequences

2021 ◽  
Author(s):  
Vincent Mallet ◽  
Jean-Philippe Vert
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Dna Sequencing ◽  
Dna Sequences ◽  
Double Helix ◽  
Learning Models ◽  
Sequencing Technologies ◽  
Reverse Complement ◽  
Dna Fragment ◽  
Machine Learning Models

As DNA sequencing technologies keep improving in scale and cost, there is a growing need to develop machine learning models to analyze DNA sequences, e.g., to decipher regulatory signals from DNA fragments bound by a particular protein of interest. As a double helix made of two complementary strands, a DNA fragment can be sequenced as two equivalent, so-called reverse complement (RC) sequences of nucleotides. To take into account this inherent symmetry of the data in machine learning models can facilitate learning. In this sense, several authors have recently proposed particular RC-equivariant convolutional neural networks (CNNs). However, it remains unknown whether other RC-equivariant architectures exist, which could potentially increase the set of basic models adapted to DNA sequences for practitioners. Here, we close this gap by characterizing the set of all linear RC-equivariant layers, and show in particular that new architectures exist beyond the ones already explored. We further discuss RC-equivariant pointwise nonlinearities adapted to different architectures, as well as RC-equivariant embeddings of k-mers as an alternative to one-hot encoding of nucleotides. We show experimentally that the new architectures can outperform existing ones.

scholarly journals The importance of Chargaff’s second parity rule for genomic signatures in metagenomics

2017 ◽  
Author(s):  
Fabio Gori ◽  
Dimitrios Mavroeidis ◽  
Mike SM Jetten ◽  
Elena Marchiori
Keyword(s):  
Dna Sequences ◽  
Dimensional Space ◽  
Mapping Function ◽  
Metagenomic Data ◽  
Base Calling ◽  
Parity Rule ◽  
Genomic Signatures ◽  
Alignment Free ◽  
Source Organism ◽  
Reverse Complement

AbstractAn important problem in metagenomic data analysis is to identify the source organism, or at least taxon, of each sequence. Most methods tackle this problem in two steps by using an alignment-free approach: first the DNA sequences are represented as points of a real n-dimensional space via a mapping function then either clustering or classification algorithms are applied. Those mapping functions require to be genomic signatures: the dissimilarity between the mapped points must reflect the degree of phylogenetic similarity of the source species. Designing good signatures for metagenomics can be challenging due to the special characteristics of metagenomic sequences; most of the existing signatures were not designed accordingly and they were tested only on error-free sequences sampled from a few dozens of species.In this work we analyze comparatively the goodness of existing and novel signatures based on tetranu-cleotide frequencies via statistical models and computational experiments; we also study how they are affected by the generalized Chargaff’s second parity rule (GCSPR), which states that in a given sequence longer than 50kbp, inverse oligonucleotides are approximately equally frequent. We analyze 38 million sequences of 150 bp-1,000 bp with 1% base-calling error, sampled from 1,284 microbes. Our models indicate that GCSPR reduces strand-dependence of signatures, that is, their values are less affected by the source strand; GCSPR is further exploited by some signatures to reduce the intra-species dispersion. Two novel signatures stand out both in the models and in the experiments: the combination signature and the operation signature. The former achieves strand-independence without grouping oligonucleotides; this could be valuable for alignment-free sequence comparison methods when distinguishing inverse oligonucleotides matters. Operation signature sums the frequencies of reverse, complement, and inverse tetranucleotides; having 72 features it reduces the computational intensity of the analysis.

scholarly journals Projection layers improve deep learning models of regulatory DNA function

2018 ◽  
Author(s):  
Alex Hawkins-Hooker ◽  
Henry Kenlay ◽  
John Reid
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
Internal Representation ◽  
Feature Space ◽  
Predictive Performance ◽  
Noncoding Dna ◽  
Reverse Complement ◽  
Regulatory Dna Sequences ◽  
Recurrent Architecture ◽  
Regulatory Dna

AbstractWith the increasing application of deep learning methods to the modelling of regulatory DNA sequences has come an interest in exploring what types of architecture are best suited to the domain. Networks designed to predict many functional characteristics of noncoding DNA in a multitask framework have to recognise a large number of motifs and as a result benefit from large numbers of convolutional filters in the first layer. The use of large first layers in turn motivates an exploration of strategies for addressing the sparsity of output and possibility for overfitting that result. To this end we propose the use of a dimensionality-reducing linear projection layer after the initial motif-recognising convolutions. In experiments with a reduced version of the DeepSEA dataset we find that inserting this layer in combination with dropout into convolutional and convolutional-recurrent architectures can improve predictive performance across a range of first layer sizes. We further validate our approach by incorporating the projection layer into a new convolutional-recurrent architecture which achieves state of the art performance on the full DeepSEA dataset. Analysis of the learned projection weights shows that the inclusion of this layer simplifies the network’s internal representation of the occurrence of motifs, notably by projecting features representing forward and reverse-complement motifs to similar positions in the lower dimensional feature space output by the layer.

Transcription factor/DNA interactions visualized by electron spectroscopic imaging

1992 ◽  
Vol 50 (1) ◽  
pp. 450-451
Author(s):  
David P. Bazett-Jones ◽  
Mark L. Brown
Keyword(s):  
Transcription Factor ◽  
Dna Sequences ◽  
Transcription Initiation ◽  
Molecular Mechanisms ◽  
Phosphorus Content ◽  
Spectroscopic Imaging ◽  
Electron Spectroscopic Imaging ◽  
Dna Backbone ◽  
Two Parameters ◽  
High Level

A multisubunit RNA polymerase enzyme is ultimately responsible for transcription initiation and elongation of RNA, but recognition of the proper start site by the enzyme is regulated by general, temporal and gene-specific trans-factors interacting at promoter and enhancer DNA sequences. To understand the molecular mechanisms which precisely regulate the transcription initiation event, it is crucial to elucidate the structure of the transcription factor/DNA complexes involved. Electron spectroscopic imaging (ESI) provides the opportunity to visualize individual DNA molecules. Enhancement of DNA contrast with ESI is accomplished by imaging with electrons that have interacted with inner shell electrons of phosphorus in the DNA backbone. Phosphorus detection at this intermediately high level of resolution (≈lnm) permits selective imaging of the DNA, to determine whether the protein factors compact, bend or wrap the DNA. Simultaneously, mass analysis and phosphorus content can be measured quantitatively, using adjacent DNA or tobacco mosaic virus (TMV) as mass and phosphorus standards. These two parameters provide stoichiometric information relating the ratios of protein:DNA content.

DNA sequence mapping in interphase and metaphase chromosomes by fluorescence in situ hybridization

1992 ◽  
Vol 50 (1) ◽  
pp. 496-497
Author(s):  
Barbara Trask ◽  
Susan Allen ◽  
Anne Bergmann ◽  
Mari Christensen ◽  
Anne Fertitta ◽  
...  
Keyword(s):  
In Situ Hybridization ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Dual Band ◽  
Nick Translation ◽  
Metaphase Chromosomes ◽  
Band Pass ◽  
Texas Red ◽  
Fluorescent Spot

Using fluorescence in situ hybridization (FISH), the positions of DNA sequences can be discretely marked with a fluorescent spot. The efficiency of marking DNA sequences of the size cloned in cosmids is 90-95%, and the fluorescent spots produced after FISH are ≈0.3 μm in diameter. Sites of two sequences can be distinguished using two-color FISH. Different reporter molecules, such as biotin or digoxigenin, are incorporated into DNA sequence probes by nick translation. These reporter molecules are labeled after hybridization with different fluorochromes, e.g., FITC and Texas Red. The development of dual band pass filters (Chromatechnology) allows these fluorochromes to be photographed simultaneously without registration shift.

Three-Dimensional Structure of Cloned T3 Connector Protein at 1.6nm Resolution

1990 ◽  
Vol 48 (1) ◽  
pp. 282-283
Author(s):  
José L. Carrascosa ◽  
José M. Valpuesta ◽  
Hisao Fujisawa
Keyword(s):  
Dna Sequences ◽  
Expression Vector ◽  
Three Dimensional ◽  
Deae Cellulose ◽  
Dna Packaging ◽  
Dimensional Structure ◽  
Two Dimensional ◽  
Freezing And Thawing ◽  
Three Dimensional Structure ◽  
Dimensional Reconstruction

The head to tail connector of bacteriophages plays a fundamental role in the assembly of viral heads and DNA packaging. In spite of the absence of sequence homology, the structure of connectors from different viruses (T4, Ø29, T3, P22, etc) share common morphological features, that are most clearly revealed in their three-dimensional structure. We have studied the three-dimensional reconstruction of the connector protein from phage T3 (gp 8) from tilted view of two dimensional crystals obtained from this protein after cloning and purification.DNA sequences including gene 8 from phage T3 were cloned, into Bam Hl-Eco Rl sites down stream of lambda promotor PL, in the expression vector pNT45 under the control of cI857. E R204 (pNT89) cells were incubated at 42°C for 2h, harvested and resuspended in 20 mM Tris HC1 (pH 7.4), 7mM 2 mercaptoethanol, ImM EDTA. The cells were lysed by freezing and thawing in the presence of lysozyme (lmg/ml) and ligthly sonicated. The low speed supernatant was precipitated by ammonium sulfate (60% saturated) and dissolved in the original buffer to be subjected to gel nitration through Sepharose 6B, followed by phosphocellulose colum (Pll) and DEAE cellulose colum (DE52). Purified gp8 appeared at 0.3M NaCl and formed crystals when its concentration increased above 1.5 mg/ml.

DNA methylation in satellite repeats disorders

2019 ◽  
Vol 63 (6) ◽  
pp. 757-771 ◽  
Author(s):  
Claire Francastel ◽  
Frédérique Magdinier
Keyword(s):  
Dna Methylation ◽  
Human Genome ◽  
Repetitive Dna ◽  
Dna Sequences ◽  
Satellite Repeats ◽  
Tremendous Progress ◽  
Genes Encoding ◽  
Dna Elements ◽  
Near Future

Abstract Despite the tremendous progress made in recent years in assembling the human genome, tandemly repeated DNA elements remain poorly characterized. These sequences account for the vast majority of methylated sites in the human genome and their methylated state is necessary for this repetitive DNA to function properly and to maintain genome integrity. Furthermore, recent advances highlight the emerging role of these sequences in regulating the functions of the human genome and its variability during evolution, among individuals, or in disease susceptibility. In addition, a number of inherited rare diseases are directly linked to the alteration of some of these repetitive DNA sequences, either through changes in the organization or size of the tandem repeat arrays or through mutations in genes encoding chromatin modifiers involved in the epigenetic regulation of these elements. Although largely overlooked so far in the functional annotation of the human genome, satellite elements play key roles in its architectural and topological organization. This includes functions as boundary elements delimitating functional domains or assembly of repressive nuclear compartments, with local or distal impact on gene expression. Thus, the consideration of satellite repeats organization and their associated epigenetic landmarks, including DNA methylation (DNAme), will become unavoidable in the near future to fully decipher human phenotypes and associated diseases.

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