DNA Strings Research Articles

Evolution, Through the Lens of a Physicist

With the following considerations, the author intends to enrich the discussion about chance and the formation of new organisms in biological evolution. As a physicist, he knows that he has already crossed a boundary of disciplines by discussing the occurrence of chance. The natural scientist or biologist leaves the field of natural science to enter the world of ideas, humanities, and metaphysics. A second argument considers the relation between the whole and its parts. Decomposing biological systems to the smallest building blocks (molecules and atoms) does not mean the whole can be constructed from these particles without additional causal agents. This may indicate that the evolutionary process may involve more than changes in a DNA string.

Evolutionary algorithms simulating molecular evolution: a new field proposal.

The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins-the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared with the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein "vocabulary." A major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago or have never evolved (yet). By merging evolutionary algorithms, machine learning, and bioinformatics, we can develop highly customized "designer proteins." We dub the new subfield of computational evolution, which employs evolutionary algorithms with DNA string representations, biologically accurate molecular evolution, and bioinformatics-informed fitness functions, Evolutionary Algorithms Simulating Molecular Evolution.

Multivariate Data Analysis of Multisine Impedimetric Fingerprints in Electroanalysis of Biochemical Compounds

While Electrochemical Impedance Spectroscopy (EIS) is found as one of the most versatile tools of electrochemical characterization, it suffers a major drawback when the process in question is non-stationary. On the other hand, Dynamic Electrochemical Impedance Spectroscopy (DEIS) offers an ultra-fast measurement time. DEIS utilizes an approach where all the elementary components are superimposed as one multi-component perturbation signal, offering impedance spectra collection in seconds. This particular advantage allows for the effective use of DEIS in the monitoring of the adsorption processes in multiple fields, such as (bio)sensing, corrosion science, energy storage, or AFM-coupled impedance mapping.This presentation aims at highlighting the DEIS monitoring utility carried out with changes of an independent variable, such as adsorbate concentration or even under linear polarization sweep. Rapid and accurate determination of adsorption isotherms for green organic corrosion inhibitors or Michaelis-Menten constant for enzyme-based glucose (bio)sensors with DEIS operating in galvanostatic mode and under iDC = 0 were already established. When it comes to impedimetric macromolecular electroanalysis, measuring charge transfer resistance upon analyte adsorption is the most common approach. However, the detection may also base on other factors, such as adsorbed layer capacitance or even disturbance in frequency dispersion of capacitance. Most recently, DEIS under polarization sweep mode allowed us to obtain the unique fingerprint of the electric parameter changes, specific to studied macromolecules (such as DNA strings, viral proteins, RNA polymerase, and enzymes). To effectively use our approach DEIS is combined with multivariate data analysis (principal component analysis with partial least square regression). Multivariate data analysis processes large amounts of generated impedimetric data and defines analyte-sensitive conditions (DC polarization, AC amplitude, frequency range, etc.), which can be done just by using the raw data by the singular-value decomposition method to support the sensing mechanisms. The proposed approach neglects some of the reproducibility issues induced by non-specific adsorption and fouling and reveals the measurement conditions that offer the highest variation of studied parameters upon specific analyte adsorption. The authors acknowledge the financial support of these studies by the National Science Centre (Republic of Poland) under project 2020/37/B/ST7/03262.

Finding motifs using DNA images derived from sparse representations.

Motifs play a crucial role in computational biology, as they provide valuable information about the binding specificity of proteins. However, conventional motif discovery methods typically rely on simple combinatoric or probabilistic approaches, which can be biased by heuristics such as substring-masking for multiple motif discovery. In recent years, deep neural networks have become increasingly popular for motif discovery, as they are capable of capturing complex patterns in data. Nonetheless, inferring motifs from neural networks remains a challenging problem, both from a modeling and computational standpoint, despite the success of these networks in supervised learning tasks. We present a principled representation learning approach based on a hierarchical sparse representation for motif discovery. Our method effectively discovers gapped, long, or overlapping motifs that we show to commonly exist in next-generation sequencing datasets, in addition to the short and enriched primary binding sites. Our model is fully interpretable, fast, and capable of capturing motifs in a large number of DNA strings. A key concept emerged from our approach-enumerating at the image level-effectively overcomes the k-mers paradigm, enabling modest computational resources for capturing the long and varied but conserved patterns, in addition to capturing the primary binding sites. Our method is available as a Julia package under the MIT license at https://github.com/kchu25/MOTIFs.jl, and the results on experimental data can be found at https://zenodo.org/record/7783033.

Epigenetic Regulation of Driver Genes in Testicular Tumorigenesis

In testicular germ cell tumor type II (TGCT), a seminoma subtype expresses an induced pluripotent stem cell (iPSC) panel with four upregulated genes, OCT4/POU5F1, SOX17, KLF4, and MYC, and embryonal carcinoma (EC) has four upregulated genes, OCT4/POU5F1, SOX2, LIN28, and NANOG. The EC panel can reprogram cells into iPSC, and both iPSC and EC can differentiate into teratoma. This review summarizes the literature on epigenetic regulation of the genes. Epigenetic mechanisms, such as methylations of cytosines on the DNA string and methylations and acetylations of histone 3 lysines, regulate expression of these driver genes between the TGCT subtypes. In TGCT, the driver genes contribute to well-known clinical characteristics and the driver genes are also important for aggressive subtypes of many other malignancies. In conclusion, epigenetic regulation of the driver genes are important for TGCT and for oncology in general.

ISMatch: A real-time hardware accelerator for inexact string matching of DNA sequences on FPGA

Since DNA strings suffer from variations like mutation, noisy sampling, and transmission, instead of searching for the exact match, the inexact string matching (ISM) of DNA sequences is preferred. Due to the large amount of data and massive data-dependency, the ISM algorithm is not suitable for being implemented into a general-purpose hardware. Towards this, we propose ISMatch, a novel specialized hardware architecture for computing the ISM in a fast and energy-efficient way. Our implementation on a Xilinx Ultrascale+ FPGA shows up to 70× and 2.2× clock cycles reduction compared to the ARM-based and the HLS implementations, respectively.

A Chaotic Image Encryption Scheme Based on Genetic Central Dogma and KMP Method

In this paper, an image cryptosystem based on genetic central dogma (GCD), Knuth–Morria–Pratt (KMP) algorithm and a chaotic system is developed. The KMP algorithm is firstly used to bind DNA strings to obtain the next array, which participates in the design of the chaotic initial condition, and then the secure chaotic sequences are produced by employing the sliding idea in pattern string matching. In the present procedure, a DNA-level two-way pixel’s shuffle is achieved by a shared stack push operation and it is adopted in the permutation module for the purpose of accelerating the overall pixel’s shuffle. Subsequently, the pixel values are substituted by simulating the process of protein synthesis in GCD, in which the DNA replication and RNA replication form the basis of the DNA-triploid mutation and new RNA mutation rules, respectively. Experimental simulations and extensive cryptanalysis fully vindicate that superior security effects in addition to satisfactory low time complexity can be simultaneously obtained by the proposed confusion-substitution scheme.

Bio-Inspired Multi-Level Hybrid Crypto System

Research in information Security is very much essential as there is an increase in data breach. DNA cryptography is one of the invention in the field of data security which provides hope for an unbreakable algorithm. This paper presents a multi-level hybrid crypto system based on DNA cryptography and DNA steganography. The proposed method uses a DNA sequence from publicly available DNA data base as a onetime pad. Initially plaintext is converted into DNA form of cipher text and then using central dogma of molecular biology concept DNA strings are converted into amino acid codes which represents the protein form of data. The crux of the algorithm lies on the one-time pad used for encryption process. The proposed system combines the advantages of DNA cryptography, DNA steganography and one time pad. Security analysis shows that proposed method is strong enough to resist brute force attacks as well as cryptanalytic attacks.

Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage

DNA has evolved as a cutting-edge medium for digital information storage due to its extremely high density and durable preservation to accommodate the data explosion. However, the strings of DNA are prone to errors during the hybridization process. In addition, DNA synthesis and sequences come with a cost that depends on the number of nucleotides present. An efficient model to store a large amount of data in a small number of nucleotides is essential, and it must control the hybridization errors among the base pairs. In this paper, a novel computational model is presented to design large DNA libraries of oligonucleotides. It is established by integrating a neural network (NN) with combinatorial biological constraints, including constant GC-content and satisfying Hamming distance and reverse-complement constraints. We develop a simple and efficient implementation of NNs to produce the optimal DNA codes, which opens the door to applying neural networks for DNA-based data storage. Further, the combinatorial bio-constraints are introduced to improve the lower bounds and to avoid the occurrence of errors in the DNA codes. Our goal is to compute large DNA codes in shorter sequences, which should avoid non-specific hybridization errors by satisfying the bio-constrained coding. The proposed model yields a significant improvement in the DNA library by explicitly constructing larger codes than the prior published codes.

Progress toward Plug-and-Play Polymer Strings for Optical Tweezers Experiments: Concatenation of DNA Using Streptavidin Linkers.

Streptavidin is a tetrameric protein that is renowned for its strong binding to biotin. The robustness and strength of this noncovalent coupling has led to multitudinous applications of the pairing. Within the streptavidin tetramer, each protein monomer has the potential to specifically bind one biotin-bearing moiety. Herein, by separating various streptavidin species that have had differing numbers of their four potential binding sites blocked, several different types of “linking hub” were obtained, each with a different valency. The identification of these species and the study of the plugging process used to block sites during their preparation were carried out using capillary electrophoresis. Subsequently, a specific species, namely, a trans-divalent linker, in which the two open biotin-binding pockets are approximately opposite one another, was used to concatenate two ∼5 kb pieces of biotin-terminated double-stranded DNA. Following the incubation of this DNA with the prepared linker, a fraction of ∼10 kb strings was identified using gel electrophoresis. Finally, these concatenated DNA strings were stretched in an optical tweezer experiment, demonstrating the potential of the methodology for coupling and extending molecules for use in single-molecule biophysical experiments.

Counting (Watson-Crick) palindromes in Watson-Crick conjugates

A DNA string is a Watson-Crick (WK-)palindrome when the complement of its reverse is equal to itself. The Watson-Crick mapping θ is an involution that is also an antimorphism. In this paper, we study a generalization of conjugates of a word that incorporate the notion of WK-involution θ called θ-conjugates or WK-conjugates. We consider some general properties of the set Cθ(w), i.e., the set of θ-conjugates of a word w. We characterize words with the same set of θ-conjugates and also provide words where the set of conjugates of a word is equal to the set of θ-conjugates of the word. We study the distribution of palindromes and Watson-Crick palindromes, also known as θ-palindromes, among both the set of conjugates and θ-conjugates of a word w. We also find the structure of words that have at least one (WK-)palindrome in Cθ(w).

Synchrotron-Based Fourier-Transform Infrared Micro-Spectroscopy (SR-FTIRM) Fingerprint of the Small Anionic Molecule Cobaltabis(dicarbollide) Uptake in Glioma Stem Cells.

The anionic cobaltabis (dicarbollide) [3,3′-Co(1,2-C2B9H11)2]−, [o-COSAN]−, is the most studied icosahedral metallacarborane. The sodium salts of [o-COSAN]− could be an ideal candidate for the anti-cancer treatment Boron Neutron Capture Therapy (BNCT) as it possesses the ability to readily cross biological membranes thereby producing cell cycle arrest in cancer cells. BNCT is a cancer therapy based on the potential of 10B atoms to produce α particles that cross tissues in which the 10B is accumulated without damaging the surrounding healthy tissues, after being irradiated with low energy thermal neutrons. Since Na[o-COSAN] displays a strong and characteristic ν(B-H) frequency in the infrared range 2.600–2.500 cm−1, we studied the uptake of Na[o-COSAN] followed by its interaction with biomolecules and its cellular biodistribution in two different glioma initiating cells (GICs), mesenchymal and proneural respectively, by using Synchrotron Radiation-Fourier Transform Infrared (FTIR) micro-spectroscopy (SR-FTIRM) facilities at the MIRAS Beamline of ALBA synchrotron light source. The spectroscopic data analysis from the bands in the regions of DNA, proteins, and lipids permitted to suggest that after its cellular uptake, Na[o-COSAN] strongly interacts with DNA strings, modifies proteins secondary structure and also leads to lipid saturation. The mapping suggests the nuclear localization of [o-COSAN]−, which according to reported Monte Carlo simulations may result in a more efficient cell-killing effect compared to that in a uniform distribution within the entire cell. In conclusion, we show pieces of evidence that at low doses, [o-COSAN]− translocates GIC cells’ membranes and it alters the physiology of the cells, suggesting that Na[o-COSAN] is a promising agent to BNCT for glioblastoma cells.

Procedurally Generated Artworks Based on Multiple Sequence Alignment of Orthologous Gene Copies

Abstract In this article, the author presents a novel approach to the procedural generation of artwork series based on multiple sequence alignment of orthologous gene copies. In the strategy developed, nucleotides present in a string of DNA (A, G, C, T) were each assigned to an existing artwork. New visual compositions were then created by collaging columns of pixels from each of the existing four artworks according to the arrangement of nucleotides after orthologous genes were aligned. The resulting outcome was a distinctive set of artworks in which visual differences were governed by nucleotide divergence at genes of interest due to evolutionary processes.

Sensitivity of cohesin\u2013chromatin association to high-salt treatment corroborates non-topological mode of loop extrusion

Cohesin is a key organizer of chromatin folding in eukaryotic cells. The two main activities of this ring-shaped protein complex are the maintenance of sister chromatid cohesion and the establishment of long-range DNA–DNA interactions through the process of loop extrusion. Although the basic principles of both cohesion and loop extrusion have been described, we still do not understand several crucial mechanistic details. One of such unresolved issues is the question of whether a cohesin ring topologically embraces DNA string(s) during loop extrusion. Here, we show that cohesin complexes residing on CTCF-occupied genomic sites in mammalian cells do not interact with DNA topologically. We assessed the stability of cohesin-dependent loops and cohesin association with chromatin in high-ionic-strength conditions in G1-synchronized HeLa cells. We found that increased salt concentration completely displaces cohesin from those genomic regions that correspond to CTCF-defined loop anchors. Unsurprisingly, CTCF-anchored cohesin loops also dissipate in these conditions. Because topologically engaged cohesin is considered to be salt resistant, our data corroborate a non-topological model of loop extrusion. We also propose a model of cohesin activity throughout the interphase, which essentially equates the termination of non-topological loop extrusion with topological loading of cohesin. This theoretical framework enables a parsimonious explanation of various seemingly contradictory experimental findings.

Spatial coherence of light inside three-dimensional media

Speckle is maybe the most fundamental interference effect of light in disordered media, giving rise to fascinating physical phenomena and cutting edge applications. While speckle formed outside a sample is easily measured and analysed, true bulk speckle, as formed inside random media, is difficult to investigate directly due to the obvious issue of physical access. Furthermore, its proper theoretical description poses enormous challenges. Here we report on the first direct measurements of spatially resolved intensity correlations of light inside a disordered medium, using embedded DNA strings decorated with emitters separated by a controlled nanometric distance. Our method provides in situ access to fundamental properties of bulk speckles as their size and polarization degrees of freedom, both of which are found to deviate significantly from theoretical predictions. The deviations are explained, by comparison with rigorous numerical calculations, in terms of correlations among polarization components and non-universal near-field contributions at the nanoscale.

Adding Matrix Control: Insertion-Deletion Systems with Substitutions III

Insertion-deletion systems have been introduced as a formalism to model operations that find their counterparts in ideas of bio-computing, more specifically, when using DNA or RNA strings and biological mechanisms that work on these strings. So-called matrix control has been introduced to insertion-deletion systems in order to enable writing short program fragments. We discuss substitutions as a further type of operation, added to matrix insertion-deletion systems. For such systems, we additionally discuss the effect of appearance checking. This way, we obtain new characterizations of the family of context-sensitive and the family of recursively enumerable languages. Not much context is needed for systems with appearance checking to reach computational completeness. This also suggests that bio-computers may run rather traditionally written programs, as our simulations also show how Turing machines, like any other computational device, can be simulated by certain matrix insertion-deletion-substitution systems.

Molecular Imaging: PARP-1 and Beyond.

The genetic code to life is balanced on a string of DNA that is under constant metabolic and physical stress from environmental forces. Nearly all diseases have a genetic component caused by or resulting in DNA damage that alters biology to drive pathogenesis. Recent advancements in DNA repair biology have led to the development of imaging tools that target DNA damage response and repair proteins. PET has been used for early detection of oncogenic processes and monitoring of tumor response to chemotherapeutics that target the DNA repair machinery. In the field of precision medicine, imaging tools provide a unique opportunity for patient stratification by directly measuring drug target expression or monitoring therapy to identify early responders. This overview discusses the state of the art on molecular imaging of DNA damage and repair from the past 5 years, with an emphasis on poly[adenosine diphosphate ribose]polymerase-1 as an imaging target and predictive biomarker of response to therapy.

On conflict free DNA codes

DNA storage has emerged as an important area of research. The reliability of DNA storage system depends on designing the DNA strings (called DNA codes) that are sufficiently dissimilar. In this work, we introduce DNA codes that satisfy a special constraint. Each codeword of the DNA code has a specific property that any two consecutive sub-strings of the DNA codeword will not be the same (a generalization of homo-polymers constraint). This is in addition to the usual constraints such as Hamming, reverse, reverse-complement and $GC$-content. We believe that the new constraint will help further in reducing the errors during reading and writing data into the synthetic DNA strings. We also present a construction (based on a variant of stochastic local search algorithm) to calculate the size of the DNA codes with all the above constraints, which improves the lower bounds from the existing literature, for some specific cases. Moreover, a recursive isometric map between binary vectors and DNA strings is proposed. Using the map and the well known binary codes we obtain few classes of DNA codes with all the constraints including the property that the constructed DNA codewords are free from the hairpin-like secondary structures.

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

BackgroundNucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data.ResultsIn this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification.ConclusionsResults computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time.

Fuzzy String Matching Procedure

Background: There are different methodologies for DNA comparison based on two string algorithms, which are dependent on crisp logical principles, where there is no room for verbal (linguistic) uncertainty. These are successfully applicable procedures in DNA bioinformatics researches even by taking into consideration probabilistic random variability components based on the probability distribution functions of various types. Objective: The main purpose of this paper is to review first briefly all available DNA string matching methodologies that are based on crisp logic and then to suggest a new method based on the fuzzy logic rules and application. Methods: There are different methodologies for DNA comparison based on two string algorithms, which are dependent on crisp logical principles, where there is no room for verbal (linguistic) uncertainty. These are successfully applicable procedures in DNA bioinformatics researchers even by taking into consideration probabilistic random variability components based on the probability distribution functions of various types. Results: Fuzzy number representation of each gene implies some sort of uncertainty or unhealthiness in some or all the genes. Their better identifications can be achieved on the basis of fuzzy numbers with different membership degrees, which imply the unhealthiness or healthiness of the genes and their collective behaviors. Conclusion: After the development of fuzzy number representation of the text string coupled with crisp pattern string their relationships are searched at different shift operations, and hence, the possibility of defaulters are identified in the text string with a certain degree of membership.