Large Number Of DNA Sequences Research Articles

Ultrasensitive detection of exosomes by microchip electrophoresis combining with triple amplification strategies

The analysis of exosomes is significant as they can be used for various pathophysiological processes, especially cancer related intercellular communication. Therefore, a convenient, reliable, and sensitive detection method is urgently needed. Strand displacement amplification (SDA) and catalytic hairpin assembly (CHA) are two kinds of effective isothermal nucleic acid amplification methods. In this article, an efficient quantitative MCE method for detecting human breast cancer cell (MCF-7) exosomes assisted by triple amplification strategies combining cholesterol probe (Chol-probe) with SDA-CHA was first developed. CD63 aptamer was immobilized on the avidin magnetic beads to specifically capture exosomes and then Chol-probe with high affinity was spontaneously inserted into the exosome membrane, which was the first step of amplification strategy to improve detection sensitivity. After magnetic separation, Chol-probe could complement ssDNA and trigger SDA, producing a large number of DNA sequences (Ta) to trigger CHA, achieving SDA-CHA amplification. Under optimal conditions, the detection limit (LOD) for MCF-7 exosomes was as low as 26 particle/μL (S/N = 3). This method provides an effective approach for sensitive and accurate quantification of tumor exosomes, and can be expected to detect exosomes in clinical samples.

SLDMS: A Tool for Calculating the Overlapping Regions of Sequences.

In the field of genome assembly, contig assembly is one of the most important parts. Contig assembly requires the processing of overlapping regions of a large number of DNA sequences and this calculation usually takes a lot of time. The time consumption of contig assembly algorithms is an important indicator to evaluate the degree of algorithm superiority. Existing methods for processing overlapping regions of sequences consume too much in terms of running time. Therefore, we propose a method SLDMS for processing sequence overlapping regions based on suffix array and monotonic stack, which can effectively improve the efficiency of sequence overlapping regions processing. The running time of the SLDMS is much less than that of Canu and Flye in dealing with the sequence overlap interval and in some data with most sequencing errors occur at both the ends of the sequencing data, the running time of the SLDMS is only about one-tenth of the other two methods.

The Reliability of DNA Sequences in Public Databases Belonging to the Most Economically Important Shiitake Culinary-Medicinal Mushroom Lentinus edodes (Agaricomycetes) in Asia.

Large numbers of DNA sequences deposited in the International Nucleotide Sequence Databases (INSD) are erroneously annotated. The erroneous information may lead to misleading conclusions or cause great economic losses to farmers. Lentinus edodes (= Lentinula edodes (Berk.) Pegler) is one of the most important and popular culinary-medicinal mushrooms with a high nutritional value. In this study, experimental and in silico methods were used to correct the sequences annotated as L. edodes in the INSD. A total of 3,426 nucleotide entries were retrieved from public databases, including 140 different types of genetic sequences. Excluding 1,893 genome sequences, the most abundant signatures represented by ITS (258) and IGS1 (259) sequences accounted for 33.23% of the total entries. A total of 3,058 sequences were annotated correctly, 350 were indeterminate, and 18 were annotated erroneously based on the two methods. Correction of sequences will be beneficial for species identification and annotation. Phylogenic analysis based on ITS sequences suggested that L. edodes segregate in four clades in the tree based on ITS sequences. The isolates from China were distributed into two clades. In L. edodes, the intraspecific variation of the ITS2 sequences was much higher than that of the ITS1 sequences. In addition, the genetic diversity of the L. edodes sequences from China was much higher than that of any other regions included in this study. The northwest and southwest regions of China were L. edodes diversity centers.

High‐throughput discovery of functional disordered regions: investigation of transactivation domains

Over 40% of proteins in any eukaryotic genome encode intrinsically disordered regions (IDRs) that do not adopt defined tertiary structures. Certain IDRs perform critical functions, but discovering them is non‐trivial as the biological context determines their function. We present IDR‐Screen, a framework to discover functional IDRs in a high‐throughput manner by simultaneously assaying large numbers of DNA sequences that code for short disordered sequences. Functionality‐conferring patterns in their protein sequence are inferred through statistical learning. Using yeast HSF1 transcription factor‐based assay, we discovered IDRs that function as transactivation domains (TADs) by screening a random sequence library and a designed library consisting of variants of 13 diverse TADs. Using machine learning, we find that segments devoid of positively charged residues but with redundant short sequence patterns of negatively charged and aromatic residues are a generic feature for TAD functionality. We anticipate that investigating defined sequence libraries using IDR‐Screen for specific functions can facilitate discovering novel and functional regions of the disordered proteome as well as understand the impact of natural and disease variants in disordered segments.

Modeling protein-DNA binding via high-throughput in vitro technologies.

Protein–DNA binding plays a central role in gene regulation and by that in all processes in the living cell. Novel experimental and computational approaches facilitate better understanding of protein–DNA binding preferences via high-throughput measurement of protein binding to a large number of DNA sequences and inference of binding models from them. Here we review the state of the art in measuring protein–DNA binding in vitro, emphasizing the advantages and limitations of different technologies. In addition, we describe models for representing protein–DNA binding preferences and key computational approaches to learn those from high-throughput data. Using large experimental data sets, we test the performance of different models based on different measuring techniques. We conclude with pertinent open problems.

Function-based identification of mammalian enhancers using site-specific integration.

The accurate and comprehensive identification of functional regulatory sequences in mammalian genomes remains a major challenge. Here we describe Site-specific Integration FACS-sequencing (SIF-seq), an unbiased, medium-throughput functional assay for the discovery of distant-acting enhancers. Pluripotent cell reporter assays, targeted single-copy genomic integration, and flow cytometry are coupled with high-throughput DNA sequencing to enable parallel screening of large numbers of DNA sequences. We demonstrate the utility of this method by functionally interrogating >500 kb of mouse and human sequence for enhancer activity and identifying embryonic stem (ES) cell enhancers at pluripotency loci including NANOG. We also demonstrate the effectiveness of the approach in differentiated cell populations through the identification of cardiac enhancers from cardiomyocytes and neuronal enhancers from neural progenitors. SIF-seq is a powerful and flexible method for the de novo functional identification of mammalian enhancers in a potentially wide variety of cell types.

A PLEA FOR DNA BARCODING OF FORAMINIFERA

DNA barcoding is the molecular identification of species using short, standardized gene sequences. Numerous applications of DNA barcoding in taxonomy, ecology, bioconservation, and biosafety contributed to a spectacular development of this initiative administered by the Consortium for the Barcode of Life (CBOL). Reference databases that assign DNA barcodes to particular morphospecies have been developed for almost all groups of animals, plants, and fungi, as well as some groups of protists (i.e., diatoms, ciliates, amoebae). However, such a database does not exist for foraminifera, despite large numbers of DNA sequences being available. To fill this gap, we initiated the Foram Barcoding (FB) project, whose objective is to create a curated molecular database for modern foraminifera. Each species included in our database is represented by one or several specimens, from which DNA was extracted and sequenced. Species entries include photos of processed specimens, taxonomic references, and DNA barcode sequences. A fragment of the 18S rRNA gene, commonly used in foraminiferal molecular studies, was chosen as the DNA barcode. We believe that the FB project will help resolve at least some misidentification problems that plague foraminiferal taxonomy. We also foresee its further applications in such domains of foraminiferal research as diversity assessment, ecology, biogeography, and biomonitoring. However, we are aware that the impact of the FB database depends mainly on its completeness and accuracy, and thus we appeal to the community of foraminiferologists to support this project by providing material for genetic studies and by contributing their taxonomic expertise in species identification and documentation.

Multiplexed, UVC‐Induced, Sequence‐Dependent DNA Damage Detection

The exposure of DNA to ultraviolet (UV) radiation causes sequence-dependent damage. Thus, there is a need for an analytical technique that can detect damage in large numbers of DNA sequences simultaneously. In this study, we have designed an assay for UVC-induced DNA damage in multiple oligonucleotides simultaneously by using a 96-well plate and a novel automated sample mover. The UVC-induced DNA damage is measured using smart probes, analogs of molecular beacons in which guanosine nucleotides act as the fluorescence quencher. Our results show that the oligonucleotide damage constants obtained with this method are reproducible and similar to those obtained in cuvettes. The calibration curve for poly-dT shows good linearity (R(2) = 0.96), with limits of detection (LOD) and quantification (LOQ) equal to 55 and 183 nm, respectively. The results show that the damage kinetics upon irradiation is sensitive to the different types of photoproducts formed in the different sequences used; i.e. poly-A oligonucleotides containing guanine are damaged at a faster rate than poly-A oligonucleotides containing either thymine or cytosine. Thus, detecting DNA damage in a 96-well plate and quantifying the damage with smart probes are a simple, fast and inexpensive mix-and-read technique for multiplexed, sequence-specific DNA damage detection.

A FAST LOCAL SEARCH ALGORITHM USING HISTOGRAM FEATURES FOR DNA SEQUENCE DATABASE

DNA sequence search is a very important topic in bioinformatics algorithm development. However, this task usually spends much computational time to search on large DNA sequence database. In this paper, we propose an efficient hierarchical DNA sequence search algorithm to improve the search speed while the accuracy is being kept constant. For a given query DNA sequence, firstly, a fast local search algorithm using histogram features is used as a filtering mechanism before scanning the sequences in the database. An overlapping processing is newly added to improve the robustness of the algorithm. A large number of DNA sequences with low similarity will be excluded for latter searching. The Smith-Waterman algorithm is then applied to each remainder sequences. Experimental results using GenBank se- quence data show the proposed algorithm combining histogram information and Smith-Waterman algorithm is more efficient for DNA se- quence search. Keywords- Fast search, DNA sequence, Histogram feature, Smith-Waterman algorithm, Local search.

English

DNA sequence search is a fundamental topic in bioinformatics. The Smith-Waterman algorithm achieved highest accuracy among various sequence alignment tools, but it usually spends much computational time to search on large DNA sequence database. On the contrary, BLAST and FASTA have improved the search speed by using heuristic approaches, by there is a possibility of missing an alignment or giving inaccurate output. This paper presents an efficient hierarchical method to improve the search speed while the accurate is being kept constant. For a given query sequence, firstly, a fast histogram based method is used to scan the sequences in the database. A large number of DNA sequences with low similarity will be excluded for latter searching. The Smith- Waterman algorithm is then applied to each remainder sequences. Experimental results show the proposed method combining histogram information and Smith-Waterman algorithm is a more efficient algorithm for DNA sequence search.

Combinatorial motif analysis of regulatory gene expression in Mafb deficient macrophages

BackgroundDeficiency of the transcription factor MafB, which is normally expressed in macrophages, can underlie cellular dysfunction associated with a range of autoimmune diseases and arteriosclerosis. MafB has important roles in cell differentiation and regulation of target gene expression; however, the mechanisms of this regulation and the identities of other transcription factors with which MafB interacts remain uncertain. Bioinformatics methods provide a valuable approach for elucidating the nature of these interactions with transcriptional regulatory elements from a large number of DNA sequences. In particular, identification of patterns of co-occurrence of regulatory cis-elements (motifs) offers a robust approach.ResultsHere, the directional relationships among several functional motifs were evaluated using the Log-linear Graphical Model (LGM) after extraction and search for evolutionarily conserved motifs. This analysis highlighted GATA-1 motifs and 5’AT-rich half Maf recognition elements (MAREs) in promoter regions of 18 genes that were down-regulated in Mafb deficient macrophages. GATA-1 motifs and MafB motifs could regulate expression of these genes in both a negative and positive manner, respectively. The validity of this conclusion was tested with data from a luciferase assay that used a C1qa promoter construct carrying both the GATA-1 motifs and MAREs. GATA-1 was found to inhibit the activity of the C1qa promoter with the GATA-1 motifs and MafB motifs.ConclusionsThese observations suggest that both the GATA-1 motifs and MafB motifs are important for lineage specific expression of C1qa. In addition, these findings show that analysis of combinations of evolutionarily conserved motifs can be successfully used to identify patterns of gene regulation.

Genome size in natural and synthetic autopolyploids and in a natural segmental allopolyploid of several Triticeae species

Nuclear DNA amount (1C) was determined by flow cytometry in the autotetraploid cytotype of Hordeum bulbosum, in the cytologically diploidized autotetraploid cytotypes of Elymus elongatus, Hordeum murinum subsp. murinum and Hordeum murinum subsp. leporinum, in Hordeum marinum subsp. gussoneanum, in their progenitor diploid cytotypes, and in a newly synthesized autotetraploid line of E. elongatus. Several lines collected from different regions of the distribution area of every taxon, each represented by a number of plants, were analyzed in each taxon. The intracytotype variation in nuclear DNA amount of every diploid and autotetraploid cytotype was very small, indicating that no significant changes have occurred in DNA amount either after speciation or after autopolyploid formation. The autotetraploid cytotypes of H. bulbosum and the cytologically diploidized H. marinum subsp. gussoneanum had the expected additive amount of their diploid cytotypes. On the other hand, the cytologically diploidized autotetraploid cytotypes of E. elongatus and H. murinum subsp. murinum and H. murinum subsp. leporinum had considerably less nuclear DNA (10%-23%) than the expected additive value. Also, the newly synthesized autotetraploid line of E. elongatus showed similar reduction in DNA as its natural counterpart, indicating that the reduction in genome size occurred in the natural cytotype during autopolyploidization. It is suggested that the diploid-like meiotic behavior of these cytologically dipolidized autotetraploids is caused by the instantaneous elimination of a large number of DNA sequences, different sequences from different homologous pairs, leading to differentiation of the constituent genomes. The eliminated sequences are likely to include those that participate in homologous recognition and initiation of meiotic pairing. A gene system determining exclusive bivalent pairing by utilizing the differentiation between the two groups of homologues has been presumably superimposed on the DNA reduction process.

Effects of CpG Methylation on Recognition of DNA by the Tumour Suppressor p53

Methylation of DNA is one of the mechanisms controlling the expression landscape of the genome. Its pattern is altered in cancer and often results in the hypermethylation of the promoter regions and abnormal expression of tumour suppressor genes. Methylation of CpG dinucleotides located in the binding sites of transcription factors may contribute to the development of cancers by preventing their binding or altering their specificity. We studied the effects of CpG methylation on DNA recognition by the tumour suppressor p53, a transcription factor involved in the response to carcinogenic stress. p53 recognises a large number of DNA sequences, many of which contain CpG dinucleotides. We systematically substituted a CpG dinucleotide at each position in the consensus p53 DNA binding sequence and identified substitutions tolerated by p53. We compared the binding affinities of methylated versus non-methylated sequences by fluorescence anisotropy titration. We found that binding of p53 was not affected by cytosine methylation in a majority of cases. However, for a few sequences containing multiple CpG dinucleotides, such as sites in the RB and Met genes, methylation resulted in a four- to sixfold increase in binding of p53. This approach can be used to quantify the effects of CpG methylation on the DNA recognition by other DNA-binding proteins.

Shear acoustic wave biosensor for detecting DNA intrinsic viscosity and conformation: A study with QCM-D

Direct biosensors are devices operating by monitoring the amount of surface-bound analyte. In this work a new approach is presented where a label-free acoustic biosensor, based on a QCM-D device, and solution viscosity theory, are used to study DNA intrinsic viscosity. The latter is quantitatively related to the DNA conformation and specifically the molecule's shape and size, in a manner that is independent of the amount of bound DNA mass. It is shown that acoustic measurements can clearly distinguish between ds-DNA of same shape (straight rod) but various sizes (from 20 to 198 bp (base pairs)) and same mass and size (90 bp) but various shapes (“straight”, “bent”, “triangle”). These results are discussed in the broader context of “coil” and sphere-like molecules detected on surfaces. A mathematical formula is presented relating the length of straight, surface-protruding DNA to the acoustic ratio Δ D/Δ f. The development of real-time rapid techniques for the characterization of DNA intrinsic curvature as well as DNA conformational changes upon interaction with proteins is of significance to analytical biotechnology due to the large number of DNA sequences and potential DNA bending proteins involved in genome analysis and drug screening.

Protein–DNA recognition: Breaking the combinatorial barrier

We have developed a computational methodology that aims at predicting the binding sites of proteins on DNA. The crucial point of the methodology is to enable the protein binding energies to be calculated for very large numbers of DNA sequences (>10 80) in a reasonable amount of computer time. In contrast to an earlier approach, we take into account both DNA and protein flexibility during the calculations. The speed-up comes from dividing the optimization into sub-problems involving overlapping DNA fragments, several nucleotide pairs in length, with associated protein fragments. Each optimization provides energies that can be added together to reconstitute the total binding energy of the protein for any chosen base sequence. We describe this new methodology and carry out some preliminary tests on biologically relevant proteins and protein complexes.

Putting numbers on the network connections

DNA-protein interactions are fundamental to many biological processes, including the regulation of gene expression. Determining the binding affinities of transcription factors (TFs) to different DNA sequences allows the quantitative modeling of transcriptional regulatory networks and has been a significant technical challenge in molecular biology for many years. A recent paper by Maerkl and Quake1 demonstrated the use of microfluidic technology for the analysis of DNA-protein interactions. An array of short DNA sequences was spotted onto a glass slide, which was then covered with a microfluidic device allowing each spot to be within a chamber into which the flow of materials was controlled by valves. By trapping the DNA-protein complexes on the surface and measuring their concentrations microscopically, they could determine the binding affinity to a large number of DNA sequences that were varied systematically. They studied four TFs from the basic helix-loop-helix family of proteins, all of which bind to E-box sites with the consensus CAnnTG (where "n" can be any base), and showed that variations in affinity for different sites allows each TF to regulate different genes.

Betaine, Dimethyl Sulfoxide, and 7-Deaza-dGTP, a Powerful Mixture for Amplification of GC-Rich DNA Sequences

Currently, polymerase chain reaction is the most used technique in many laboratories for either diagnostic or molecular biology purposes. Despite the large number of DNA sequences that can be easily analyzed, some GC-rich sequences are refractory to amplification due to the formation of secondary intramolecular structures. To overcome this problem, several molecules have been described to improve polymerization. Here we show that a combination of three additives--betaine, dimethyl sulfoxide, and 7-deaza-dGTP--was essential to achieve amplification of DNA sequences of three disease genes showing a GC content ranging from 67 to 79%.

Metagenomics Offers a Big-Picture View of the Diversity and Distribution of Marine Viruses

Metagenomics Offers a Big-Picture View of the Diversity and Distribution of Marine Viruses

Gene expression profiling of adipose tissue:: individual, depot-dependent, and sex-dependent variabilities

The availability of large numbers of DNA sequences and the development of technologies to analyze the function of these sequences have allowed the simultaneous study of different interactive molecular pathways involved in development and metabolism. One of these technologies uses microarrays to analyze the expression of large numbers of genes simultaneously. The power of this technology has been demonstrated by the discovery of the developmental and cold-regulated appearance of brown adipose tissue (BAT) within the mammary gland.1 Only recently has DNA microarray technology been applied to gene expression profiling of adipose tissue. Adipose tissue is an active player in the regulation of energy homeostasis. It consists of functionally heterogeneous and specialized tissues that can store and mobilize lipids and is able to release a vast number of bioactive molecules, called adipocytokines.2 Adipose tissue metabolism and thus gene expression are greatly influenced by endogenous factors such as sex, hormones, and individual genetic makeup and by exogenous factors, of which dietary and nutrition factors are the most important. We provide an overview of the currently available data on gene expression profiling in adipose tissue, with a special focus on individual, depot-dependent, and gender-dependent variability. Further, we emphasize the need to investigate adipose tissue gene expression on an individual basis because such an investigation should help to relate individual physiologic and nutrition response profiles to gene expression profiles. This link can lead to the identification of previously unknown genetic pathways and help to establish risk profiles for individuals.

A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays.

There is considerable scientific interest in knowing the probability that a site-specific transcription factor will bind to a given DNA sequence. Microarray methods provide an effective means for assessing the binding affinities of a large number of DNA sequences as demonstrated by Bulyk et al. (2001, Proceedings of the National Academy of Sciences, USA 98, 7158-7163) in their study of the DNA-binding specificities of Zif268 zinc fingers using microarray technology. In a follow-up investigation, Bulyk, Johnson, and Church (2002, Nucleic Acid Research 30, 1255-1261) studied the interdependence of nucleotides on the binding affinities of transcription proteins. Our article is motivated by this pair of studies. We present a general statistical methodology for analyzing microarray intensity measurements reflecting DNA-protein interactions. The log probability of a protein binding to a DNA sequence on an array is modeled using a linear ANOVA model. This model is convenient because it employs familiar statistical concepts and procedures and also because it is effective for investigating the probability structure of the binding mechanism.