scholarly journals FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology

2008 ◽  
Vol 24 (15) ◽  
pp. 1729-1730 ◽  
Author(s):  
A. P. Fejes ◽  
G. Robertson ◽  
M. Bilenky ◽  
R. Varhol ◽  
M. Bainbridge ◽  
...  
2021 ◽  
Author(s):  
Yelena Chernyavskaya ◽  
Xiaofei Zhang ◽  
Jinze Liu ◽  
Jessica S. Blackburn

Nanopore sequencing technology has revolutionized the field of genome biology with its ability to generate extra-long reads that can resolve regions of the genome that were previously inaccessible to short-read sequencing platforms. Although long-read sequencing has been used to resolve several vertebrate genomes, a nanopore-based zebrafish assembly has not yet been released. Over 50% of the zebrafish genome consists of difficult to map, highly repetitive, low complexity elements that pose inherent problems for short-read sequencers and assemblers. We used nanopore sequencing to improve upon and resolve the issues plaguing the current zebrafish reference assembly (GRCz11). Our long-read assembly improved the current resolution of the reference genome by identifying 1,697 novel insertions and deletions over 1Kb in length and placing 106 previously unlocalized scaffolds. We also discovered additional sites of retrotransposon integration previously unreported in GRCz11 and observed their expression in adult zebrafish under physiologic conditions, implying they have active mobility in the zebrafish genome and contribute to the ever-changing genomic landscape.


2021 ◽  
Author(s):  
Qiushi Li ◽  
Jeremy S. Morris ◽  
Peter Facchini ◽  
Sam Yeaman

Ephedra sinica is a high-value medicinal plant that produces important phenylpropylamino alkaloids pseudoephedrine and ephedrine. Few genomics resources exist for E. sinica, which has been characterized as a tetraploid with a monoploid genome size of 8.56 Gb. Here we reported a partial genome assembly of E. sinica (12.8 Gb) based on Illumina short-read sequencing technology at low coverage.


2020 ◽  
Author(s):  
Kristaps Bebris ◽  
Inese Polaka

AbstractAdvances in sequencing technology have led to an ever increasing amount of available short read sequencing data. This has, consequently, exacerbated the need for efficient and precise classification tools that can be used in the analysis of this data. As it stands, recent years have shown that massive leaps in performance can be achieved when it comes to approaches that are based in heuristics, and alongside these improvements there has been an ever increasing interest in applying deep learning techniques to revolutionize this classification task. We attempt to gather up these approaches and to evaluate their performance in a reproducible fashion to get a better perspective on the current state of deep learning based methods when it comes to the classification of short read sequencing data.


2009 ◽  
Vol 20 (2) ◽  
pp. 265-272 ◽  
Author(s):  
R. Li ◽  
H. Zhu ◽  
J. Ruan ◽  
W. Qian ◽  
X. Fang ◽  
...  

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190097 ◽  
Author(s):  
Ashley Byrne ◽  
Charles Cole ◽  
Roger Volden ◽  
Christopher Vollmers

Long-read sequencing holds great potential for transcriptome analysis because it offers researchers an affordable method to annotate the transcriptomes of non-model organisms. This, in turn, will greatly benefit future work on less-researched organisms like unicellular eukaryotes that cannot rely on large consortia to generate these transcriptome annotations. However, to realize this potential, several remaining molecular and computational challenges will have to be overcome. In this review, we have outlined the limitations of short-read sequencing technology and how long-read sequencing technology overcomes these limitations. We have also highlighted the unique challenges still present for long-read sequencing technology and provided some suggestions on how to overcome these challenges going forward. This article is part of a discussion meeting issue ‘Single cell ecology’.


2021 ◽  
Vol 139 ◽  
pp. 97-105
Author(s):  
Samuel O. Oyola ◽  
Sonal P. Henson ◽  
Benjamin Nzau ◽  
Elizabeth Kibwana ◽  
Vishvanath Nene

BioTechniques ◽  
2008 ◽  
Vol 45 (1) ◽  
pp. 81-94 ◽  
Author(s):  
Ryan D. Morin ◽  
Matthew Bainbridge ◽  
Anthony Fejes ◽  
Martin Hirst ◽  
Martin Krzywinski ◽  
...  

2020 ◽  
Vol 23 ◽  
pp. 35-40
Author(s):  
Kristaps Bebris ◽  
Inese Polaka

Advances in sequencing technology have led to an ever increasing amount of available short-read sequencing data. This has, consequently, exacerbated the need for efficient and precise classification tools that can be used in the analysis of these data. As it stands, recent years have shown that massive leaps in performance can be achieved when it comes to approaches that are based on heuristics, and apart from these improvements there has been an ever increasing interest in applying deep learning techniques to revolutionize this classification task. We attempt to study these approaches and to evaluate their performance in a reproducible fashion to get a better perspective on the current state of deep learning based methods when it comes to the classification of short-read sequencing data


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