History of DNA Sequencing Technologies

2013 ◽  
pp. 3-17
Author(s):  
Lisa D. White
Keyword(s):  
Dna Sequencing ◽  
Sequencing Technologies ◽  
History Of

scholarly journals The History of DNA Sequencing / ISTORIJAT SEKVENCIRANJA DNK

2013 ◽  
Vol 32 (4) ◽  
pp. 301-312 ◽  
Author(s):  
Miodrag Gužvić
Keyword(s):  
Dna Sequencing ◽  
Human Genome ◽  
Genome Project ◽  
Fourth Generation ◽  
Sequencing Technologies ◽  
Current Trends ◽  
Biological Phenomena ◽  
History Of ◽  
Proportional Increase ◽  
The Cost

Summary During the last decade, the cost of DNA sequencing technologies has decreased several orders of magnitude, with the proportional increase of speed and throughput. Human Genome Project took almost 15 years to complete the sequence of the human genome. With the second and third generation technologies, this can be done in the matter of days or hours. This progress and availability of sequencing instruments to virtually every researcher leads to replacing of many techniques with DNA sequencing and opens new venues of research. DNA sequencing is used to investigate basic biological phenomena, and is probably going to be increasingly used in the context of health care (preimplantation diagnostics, oncology, infectious diseases). Current trends are aiming towards the price of 1000$ for sequencing of one human genome. Without any doubt, we can expect improvement of existing and the development of fourth generation technologies in the coming years.

scholarly journals Searching and Indexing Genomic Databases via Kernelization

2014 ◽  
Author(s):  
Travis Gagie ◽  
Simon J Puglisi
Keyword(s):  
Dna Sequencing ◽  
Parameterized Complexity ◽  
Reference Genome ◽  
The Other ◽  
Genomic Databases ◽  
Rapid Advance ◽  
Sequencing Technologies ◽  
History Of

The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper we survey the twenty-year history of this idea and discuss its relation to kernelization in parameterized complexity.

A brief history of (DNA sequencing) time

2007 ◽  
Vol 8 (S1) ◽  
pp. S21-S21
Author(s):  
Elaine R. Mardis
Keyword(s):  
Dna Sequencing ◽  
History Of

scholarly journals From Genetics to Genomics of Epilepsy

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Silvio Garofalo ◽  
Marisa Cornacchione ◽  
Alfonso Di Costanzo
Keyword(s):  
Dna Sequencing ◽  
Dna Microarrays ◽  
Molecular Cytogenetics ◽  
Entire Genome ◽  
Sequencing Technologies ◽  
Genomic Approach ◽  
Molecular Karyotype ◽  
High Throughput Dna Sequencing ◽  
Laboratory Technology ◽  
Near Future

The introduction of DNA microarrays and DNA sequencing technologies in medical genetics and diagnostics has been a challenge that has significantly transformed medical practice and patient management. Because of the great advancements in molecular genetics and the development of simple laboratory technology to identify the mutations in the causative genes, also the diagnostic approach to epilepsy has significantly changed. However, the clinical use of molecular cytogenetics and high-throughput DNA sequencing technologies, which are able to test an entire genome for genetic variants that are associated with the disease, is preparing a further revolution in the near future. Molecular Karyotype and Next-Generation Sequencing have the potential to identify causative genes or loci also in sporadic or non-familial epilepsy cases and may well represent the transition from a genetic to a genomic approach to epilepsy.

Test genetici e consenso informato

2012 ◽  
pp. 68-95
Author(s):  
Marco Seri ◽  
Claudio Graziano ◽  
Daniela Turchetti ◽  
Juri Monducci
Keyword(s):  
Dna Sequencing ◽  
Human Genetics ◽  
Ethical Issues ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Genome Project ◽  
Ethical Challenges ◽  
Sequencing Technologies ◽  
The Human Genome Project

The pace of discovery in the field of human genetics has increased exponentially in the last 30 years. We have witnessed the completion of the Human Genome Project, the identification of hundreds of disease-causing genes, and the dawn of genomic medicine (clinical care based on genomic information). Reduction of DNA sequencing costs, thanks to the so-called "next generation sequencing" technologies, is driving a shift towards the era of "personal genomes", but scientific as well as ethical challenges ahead are countless. We provide an overview on the classification of genetic tests, on informed consent procedures in the context of genetic counseling, and on specific ethical issues raised by the implementation of new DNA sequencing technologies.

scholarly journals Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1419 ◽  
Author(s):  
Jose E. Kroll ◽  
Jihoon Kim ◽  
Lucila Ohno-Machado ◽  
Sandro J. de Souza
Keyword(s):  
Alternative Splicing ◽  
Dna Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Software Suite ◽  
Next Generation Dna Sequencing ◽  
Sequencing Technologies ◽  
Body Map ◽  
Sequencing Platforms

Motivation.Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results.A software suite namedSplicing Expresswas created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills.Splicing Expressperforms automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool namedSplooce. As a final result,Splicing Expresscreates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show thatSplicing Expressis able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Expressis written in Perl and is suitable to run only in UNIX-like systems. More details can be found at:http://www.bioinformatics-brazil.org/splicingexpress.

Improved Compression of DNA Sequencing Data with Cascading Bloom Filters

2018 ◽  
Vol 29 (08) ◽  
pp. 1249-1255
Author(s):  
Kamil Salikhov
Keyword(s):  
Dna Sequencing ◽  
Sequence Data ◽  
Real Data ◽  
Compression Algorithm ◽  
Computational Experiments ◽  
Bloom Filters ◽  
Dna Fragments ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Memory Reduction

Modern DNA sequencing technologies generate prodigious volumes of sequence data consisting of short DNA fragments (reads). Storing and transferring this data is often challenging. With this motivation, several specialized compression methods have been developed. In this paper, we present an improvement of the lossless reference-free compression algorithm, suggested by Rozov et al., based on the technique of cascading Bloom filters. Through computational experiments on real data, we demonstrate that our method results in a significant associated memory reduction in practice.

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