scholarly journals Overview of Genomic Tools for Circular Visualization in next-Generation Genomic Sequencing Era

Author(s):  
Alisha Parveen ◽  
Sukank Khurana ◽  
Abhishek Kumar

After human genome sequencing and rapid changes in genome sequencing methods, we have entered in the era of rapidly accumulating genome-sequencing data. This has poses development of several types of methods for representing results of genome sequencing data. Circular genome visualizations tools are also critical in this area as they provide rapid interpretation and simple visualization of overall data. In the last 15 years, we have seen rapid changes in circular visualization tools after the development of the circos tool with 1–2 tools published per year. Herein we have summarized and revisited all these tools until the third quarter of 2018. 

2019 ◽  
Vol 20 (2) ◽  
pp. 90-99 ◽  
Author(s):  
Alisha Parveen ◽  
Sukant Khurana ◽  
Abhishek Kumar

After human genome sequencing and rapid changes in genome sequencing methods, we have entered into the era of rapidly accumulating genome-sequencing data. This has derived the development of several types of methods for representing results of genome sequencing data. Circular genome visualization tools are also critical in this area as they provide rapid interpretation and simple visualization of overall data. In the last 15 years, we have seen rapid changes in circular visualization tools after the development of the circos tool with 1-2 tools published per year. Herein we have summarized and revisited all these tools until the third quarter of 2018.


PLoS Medicine ◽  
2018 ◽  
Vol 15 (8) ◽  
pp. e1002650
Author(s):  
Muin J. Khoury ◽  
W. Gregory Feero ◽  
David A. Chambers ◽  
Lawrence C. Brody ◽  
Nazneen Aziz ◽  
...  

1995 ◽  
Vol 11 (2) ◽  
pp. 121-125 ◽  
Author(s):  
Richard A. Gibbs

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.


2011 ◽  
pp. 51-84 ◽  
Author(s):  
Richard A. Stein

The 1953 discovery of the DNA double-helical structure by James Watson, Francis Crick, Maurice Wilkins, and Rosalind Franklin, represented one of the most significant advances in the biomedical world (Watson and Crick 1953; Maddox 2003). Almost half a century after this landmark event, in February 2001, the initial draft sequences of the human genome were published (Lander et al., 2001; Venter et al., 2001) and, in April 2003, the International Human Genome Sequencing Consortium reported the completion of the Human Genome Project, a massive international collaborative endeavor that started in 1990 and is thought to represent the most ambitious undertaking in the history of biology (Collins et al., 2003; Thangadurai, 2004; National Human Genome Research Institute). The Human Genome Project provided a plethora of genetic and genomic information that significantly changed our perspectives on biomedical and social sciences. The sequencing of the first human genome was a 13-year, 2.7-billion-dollar effort that relied on the automated Sanger (dideoxy or chain termination) method, which was developed in 1977, around the same time as the Maxam-Gilbert (chemical) sequencing, and subsequently became the most frequently used approach for several decades (Sanger et al., 1975; Maxam & Gilbert, 1977; Sanger et al., 1977). The new generations of DNA sequencing technologies, known as next-generation (second generation) and next-next-generation (third generation) sequencing, which started to be commercialized in 2005, enabled the cost-effective sequencing of large chromosomal regions during progressively shorter time frames, and opened the possibility for new applications, such as the sequencing of single-cell genomes (Service, 2006; Blow, 2008; Morozova and Marra, 2008; Metzker, 2010).


Sign in / Sign up

Export Citation Format

Share Document