scholarly journals AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision

2011 ◽  
Vol 27 (5) ◽  
pp. 595-603 ◽  
Author(s):  
Alexej Abyzov ◽  
Mark Gerstein
Author(s):  
Quang Tran ◽  
Alexej Abyzov

Abstract Summary Defining the precise location of structural variations (SVs) at single-nucleotide breakpoint resolution is a challenging problem due to large gaps in alignment. Previously, Alignment with Gap Excision (AGE) enabled us to define breakpoints of SVs at single-nucleotide resolution; however, AGE requires a vast amount of memory when aligning a pair of long sequences. To address this, we developed a memory-efficient implementation—LongAGE—based on the classical Hirschberg algorithm. We demonstrate an application of LongAGE for resolving breakpoints of SVs embedded into segmental duplications on Pacific Biosciences (PacBio) reads that can be longer than 10 kb. Furthermore, we observed different breakpoints for a deletion and a duplication in the same locus, providing direct evidence that such multi-allelic copy number variants (mCNVs) arise from two or more independent ancestral mutations. Availability and implementation LongAGE is implemented in C++ and available on Github at https://github.com/Coaxecva/LongAGE. Supplementary information Supplementary data are available at Bioinformatics online.


2021 ◽  
Vol 17 (10) ◽  
pp. e1009186
Author(s):  
Yao-zhong Zhang ◽  
Seiya Imoto ◽  
Satoru Miyano ◽  
Rui Yamaguchi

Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data and the bin-based calculation. In this paper, we propose to use the deep segmentation model UNet to learn base-wise RD patterns surrounding breakpoints of known SVs. We integrate model predictions with an RD-based SV caller to enhance breakpoints in single-nucleotide resolution. We show that UNet can be trained with a small amount of data and can be applied both in-sample and cross-sample. An enhancement pipeline named RDBKE significantly increases the number of SVs with more precise breakpoints on simulated and real data. The source code of RDBKE is freely available at https://github.com/yaozhong/deepIntraSV.


FEBS Letters ◽  
1988 ◽  
Vol 234 (2) ◽  
pp. 295-299 ◽  
Author(s):  
M. Vojtíšková ◽  
S. Mirkin ◽  
V. Lyamichev ◽  
O. Voloshin ◽  
M. Frank-Kamenetskii ◽  
...  

Nanoscale ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 538-547 ◽  
Author(s):  
Hyungbeen Lee ◽  
Sang Won Lee ◽  
Gyudo Lee ◽  
Wonseok Lee ◽  
Kihwan Nam ◽  
...  

Here, we demonstrate a powerful method to discriminate DNA mismatches at single-nucleotide resolution from 0 to 5 mismatches (χ0 to χ5) using Kelvin probe force microscopy (KPFM).


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