Deoxyribonucleic Acid Sequence Data Research Articles

Genome sequencing using machine learning with a special focus on tuberculosis

Machine learning is becoming increasingly prevalent. However, in the discipline of Bioinformatics and Computational Biology, it is not a popular use case. Machine learning techniques are used in only a few technologies. The majority of the tools are built using deterministic techniques and algorithms. Deoxyribonucleic acid (DNA) is a biological macromolecule composed up of deoxyribonucleic acid. Its main function is to store data. Due to breakthroughs in sequencing technology, DNA sequence data is presently rising at an exponential pace, ushering the study of DNA sequences into the big data age. Machine learning is also a powerful tool for massive processing it learns on its own from large volumes of data. We've talked about machine learning techniques and how they can be used to improve genome sequencing accuracy. In our review we have also discussed about genome sequence for Mycobacterium Tuberculosis. Tuberculosis is because of the bacteria, Tuberculosis caused by Mycobacterium tuberculosis. TB is considered one of the leading the reasons for dying all over the world. MDR-TB is a form of germs that cause tuberculosis that is not susceptible to anti-TB medications such as isoniazid (INH) and rifampin (RMP).

DESCRIPTION AND PHYLOGENETIC AFFINITIES OF A NEW SPECIES OF NEOPSILOTREMA (DIGENEA: PSILOSTOMIDAE) FROM LESSER SCAUP, AYTHYA AFFINIS (ANSERIFORMES: ANATIDAE).

Neopsilotrema is a small genus of psilostomid digeneans parasitic in the intestine of birds in the Palearctic and Nearctic. At present, the genus includes 4 species: Neopsilotrema lisitsynae from the Palearctic and Neopsilotrema affine, Neopsilotrema lakotae, and Neopsilotrema marilae from the Nearctic. Herein, we describe a new species, Neopsilotrema itascae n. sp., from lesser scaup Aythya affinis collected in Minnesota. The species can be distinguished from congeners on the basis of the ventral sucker:oral sucker width ratio, body width:length ratio, and cirrus sac size, along with other characters. We generated new 28S ribosomal deoxyribonucleic acid (DNA) and NADH dehydrogenase (ND1) mitochondrial DNA sequence data of a variety of psilostomids from the Palearctic and Nearctic along with sequences of the ribosomal internal transcribed spacer (ITS) region (ITS1 + 5.8S + ITS2) from 3 Neopsilotrema species. The molecular phylogenetic affinities of a variety of psilostomid taxa were studied using 28S sequence data. The 28S sequences of psilostomids demonstrated 1-7.9% intergeneric divergence, whereas the sequences of ND1 had 17.7-34.1% intergeneric divergence. The interspecific divergence among members of Neopsilotrema was somewhat lower (0.2-0.5% in 28S; 0.3-0.4% in ITS; 12-15.7% in ND1). Our comparison of DNA sequences along with morphologic study suggests Holarctic distribution of N. lisitsynae.

Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA

Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining in recent years. Finally, we summarize the content of the review and look into the future of some research directions for the next step.

A lossless DNA data hiding approach for data authenticity in mobile cloud based healthcare systems

We present a lossless Deoxyribonucleic Acid (DNA) sequence hiding method that can be used for ensuring authenticity of DNA sequence in the context of Mobile Cloud based healthcare systems. Hiding data within DNA sequence results in permanent information loss in DNA sequence. Therefore, providing DNA sequence authenticity using data hiding is challenging. Moreover, existing works on DNA data hiding require a reference DNA sequence data to retrieve hidden data. Hence, current methods are not blind approaches and inappropriate for ensuring authenticity of DNA sequence in the Mobile Cloud. The proposed method hides authentication data within DNA sequence, extracts authentication data, and reconstructs the DNA sequence without any loss of information. From there, our proposed approach guarantees DNA sequence authenticity and integrity in Mobile Cloud based healthcare systems. We present a security analysis of our method to show that the method is secured. We conduct several experiments to demonstrate the performance of our proposed method.

Abstract 3414: Determining microsatellite instability (MSI) status of colorectal cancers through next-generation sequencing (NGS)

Abstract Introduction: The MSI status of a tumor is often a marker of deoxyribonucleic acid (DNA) mismatch repair deficiency. Recent clinical trials have shown MSI-High (MSI-H) tumors are more likely to respond to checkpoint inhibitor immunotherapy. The United States Food and Drug Administration recently granted approval for a checkpoint inhibitor in metastatic solid tumor that demonstrates high microsatellite instability. Here we show the accuracy of determining the MSI status of colorectal cancers (CRC) using either TruSight™ Tumor 170 (TST170), a NGS-based 170 gene panel for solid tumor profiling, or whole exome sequencing (WES). Further, we report the accuracy of identifying the MSI status through sequencing of tumor-only samples without the subject-matched normal DNA. Experimental Method: The MSI status of 52 CRC tumors (51 formalin-fixed, paraffin embedded and 1 fresh-frozen sample) were evaluated using a microsatellite instability (MSI) assay (Promega), which is a detection method that uses capillary electrophoresis to analyze PCR-products. For comparison to WES, the tumors and subject-matched normal DNA libraries were prepared and enriched using TruSight™ Oncology reagents (Illumina). Targeted NGS libraries were prepared and enriched for the TST170-gene panel using the TST170 library kit (Illumina). NGS libraries were sequenced with the HiSeq™ 2000, HiSeq™ 2500, or NextSeq™ 550 instruments (Illumina). MSI scores of the 52 tumors were calculated through a novel internally developed bioinformatics algorithm. Data Summary: From the 52 subjects assessed through Promega's MSI detection kit, 28 and 24 were found to be MSI-H and microsatellite stable (MSS), respectively. These data were used as the benchmark for the MSI status of these subjects. Both the NGS-based tumor-normal data from WES and TST170 achieved a 100% concordance with PCR-based MSI detection. In addition, when only the sequencing data of the tumor samples were used without the subject-matched normal DNA, the Illumina MSI algorithm could identify the MSI status of the 52 subjects with 98% concordance with Promega's MSI detection kit. Conclusion: Collectively, this data indicates that NGS can be utilized for determining tumor MSI status using WES or the TST170 panel. In addition, a bioinformatics algorithm was developed that successfully categorized tumors according to their MSI status without requiring subject-matched normal DNA sequence data. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Alex So, Shile Zhang, Shannon Kaplan, Joyee Yao, Phillip Le, Christine Glidewell-Kenney, Kristina Kruglyak, Jenny Jie Chen, Ali Kuraishy, Ina Deras, Karen Gutekunst. Determining microsatellite instability (MSI) status of colorectal cancers through next-generation sequencing (NGS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3414.

DIAGRAM FILOGENIK HASIL SEKUENS BASA DNA MENGGUNAKAN PROGRAM MEGA-7 (MOLECULAR EVOLUTIONARY GENETICS ANALYSIS)

Sequencing techniques Deoxyribonucleic Acid (DNA) at this time is a method to identify the genetic basis of living beings, including to determine the accurate genetic relationship of several DNA sequence data. Kinship clearly and easily can be expressed in the form of a phylogenetic tree diagram (Phylogenetic Tree). This diagram represents kinship obtained from the elektroferogram process of electrophoresis sequencing of DNA bases. From some sequences experimental results using sequence analysis application program gradually, starting from installing, editing, and alignment so as to obtain the analysis and phylogenetic branching diagram view. This study uses a sequence analysis program Mega-7 (Molecular Evolutionary Genetics Analysis) to obtain a phylogenetic diagram for high accuracy and relatively easy to operate. This program can translate image data into nucleotide bases that shows the kinship of the distance the nearest branch with a standard sequence. The major advantage of this program is that it can do some analysis in a single program.

The Fox genes of Branchiostoma floridae

The Fox genes are united by encoding a fork head domain, a deoxyribonucleic acid (DNA)-binding domain of the winged-helix type that marks these genes as encoding transcription factors. Vertebrate Fox genes are classified into 23 subclasses named from FoxA to FoxS. We have surveyed the genome of the amphioxus Branchiostoma floridae, identifying 32 distinct Fox genes representing 21 of these 23 subclasses. The missing subclasses, FoxR and FoxS, are specific to vertebrates, and in addition, B. floridae has one further group, FoxAB, that is not found in vertebrates. Hence, we conclude B. floridae has maintained a high level of Fox gene diversity. Expressed sequence tag and complementary DNA sequence data support the expression of 23 genes. Several linkages between B. floridae Fox genes were noted, including some that have evolved relatively recently via tandem duplication in the amphioxus lineage and others that are more ancient.

Inferences from DNA Data: Population Histories, Evolutionary Processes and Forensic Match Probabilities

SummaryWe develop a flexible class of Metropolis–Hastings algorithms for drawing inferences about population histories and mutation rates from deoxyribonucleic acid (DNA) sequence data. Match probabilities for use in forensic identification are also obtained, which is particularly useful for mitochondrial DNA profiles. Our data augmentation approach, in which the ancestral DNA data are inferred at each node of the genealogical tree, simplifies likelihood calculations and permits a wide class of mutation models to be employed, so that many different types of DNA sequence data can be analysed within our framework. Moreover, simpler likelihood calculations imply greater freedom for generating tree proposals, so that algorithms with good mixing properties can be implemented. We incorporate the effects of demography by means of simple mechanisms for changes in population size and structure, and we estimate the corresponding demographic parameters, but we do not here allow for the effects of either recombination or selection. We illustrate our methods by application to four human DNA data sets, consisting of DNA sequences, short tandem repeat loci, single-nucleotide polymorphism sites and insertion sites. Two of the data sets are drawn from the male-specific Y-chromosome, one from maternally inherited mitochondrial DNA and one from the β-globin locus on chromosome 11.

Statistical Analysis of Deoxyribonucleic Acid Sequence Data--A Review

SUMMARY A review is made of new statistical problems presented by the recent rapid growth in availability of deoxyribonucleic acid (DNA) sequence information. These problems include the analysis of DNA sequence composition, the detection of sequence homologies, the identification of sequence control elements and the design of genetic probes. The advent of rapid and accurate techniques for the sequencing of deoxyribonucleic acid (DNA) (Sanger, 1981) has created an upheaval in genetics which in only a few years has had a major effect on wide sectors of biology, medicine and agriculture. Whereas classical Mendelian genetics dealt with genes for the most part as discrete, particulate units of inheritance, the availability of the actual molecular composition of genes, as heralded by the discovery of the chemical structure of DNA in 1953, has made it possible to examine the process of evolution and the working of the genetic machinery in much greater detail. Statistics has traditionally had a close association with genetics, an association which has worked to the benefit of both subject areas. From the early involvement last century of the biometrical school of heredity which brought the development of such basic and familiar techniques as correlation and regression, to the steady growth of mathematical and statistical genetics following rediscovery of Mendel's laws in 1900, the study of the scientific basis of inheritance has provided both the stimulus for methodological research and also a rich and important field of statistical application (see, for example, Wright (1968)). In this review we make a survey of some of the new problems presented to statisticians by the fast growing accumulation of DNA sequence data (see also Weir (1983, 1988)). These problems can be broadly grouped into three types. First, sequence data offer information of an exceptionally precise and detailed character with which to refine established methods of statistical genetics, for example, in the analysis of variation within and between populations and in the inference of phylogenetic relationships. Secondly, an entirely new class of problems arises from the frequent requirement of molecular biologists to compare one sequence with one or more others to determine to what extent there may be similarities between them which could indicate either an evolutionary or a functional relationship. Thirdly,