scholarly journals Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers

2021 ◽  
Vol 17 (10) ◽  
pp. e1009186
Author(s):  
Yao-zhong Zhang ◽  
Seiya Imoto ◽  
Satoru Miyano ◽  
Rui Yamaguchi
Keyword(s):  
Real Data ◽  
Read Depth ◽  
Structural Variants ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Integrate Model ◽  
Refinement Method ◽  
Model Predictions ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution

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.

scholarly journals Enhancing breakpoint resolution with deep segmentation model: a general refinement method for read-depth based structural variant callers

2019 ◽  
Author(s):  
Yao-zhong Zhang ◽  
Seiya Imoto ◽  
Satoru Miyano ◽  
Rui Yamaguchi
Keyword(s):  
Source Code ◽  
Real Data ◽  
Read Depth ◽  
Single Nucleotide ◽  
Structural Variant ◽  
Short Read Sequencing ◽  
Refinement Method ◽  
Size Limitation ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution

AbstractMotivationFor short-read sequencing, read-depth based structural variant (SV) callers are difficult to find single-nucleotide-resolution breakpoints due to the bin-size limitation.ResultsIn this paper, we present RDBKE to enhance the breakpoint resolution of read-depth SV callers using deep segmentation model UNet. We show that UNet can be trained with a small amount of data and applied for breakpoint enhancement both in-sample and cross-sample. On both simulation and real data, RDBKE significantly increases the number of SVs with more precise breakpoints.Availabilitysource code of RDBKE is available athttps://github.com/yaozhong/[email protected]

scholarly journals Sensitive detection of circular DNAs at single-nucleotide resolution using guided realignment of partially aligned reads

2019 ◽  
Author(s):  
Iñigo Prada-Luengo ◽  
Anders Krogh ◽  
Lasse Maretty ◽  
Birgitte Regenberg
Keyword(s):  
Real Data ◽  
Circle Map ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Short Read ◽  
Circular Dna ◽  
Short Read Sequencing ◽  
Circular Dnas ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution

AbstractCircular DNA has recently been identified across different species including human normal and cancerous tissue, but short-read mappers are unable to align many of the reads crossing circle junctions and hence limits their detection from short-read sequencing data. Here, we propose a new method, Circle-Map, that guides the realignment of partially aligned reads using information from discordantly mapped reads. We demonstrate how this approach dramatically increases sensitivity for detection of circular DNA on both simulated and real data while retaining high precision.

scholarly journals Mapping ribonucleotides embedded in genomic DNA to single-nucleotide resolution using Ribose-Map

2020 ◽  
Author(s):  
Alli L. Gombolay ◽  
Francesca Storici
Keyword(s):  
Computing Time ◽  
Wide Distribution ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Hands On ◽  
Nucleotide Resolution ◽  
User Friendly ◽  
Single Nucleotide Resolution

ABSTRACTRibose-Map is a user-friendly, standardized bioinformatics toolkit for the comprehensive analysis of ribonucleotide sequencing experiments. It allows researchers to map the locations of ribonucleotides in DNA to single-nucleotide resolution and identify biological signatures of ribonucleotide incorporation. In addition, it can be applied to data generated using any currently available high-throughput ribonucleotide sequencing technique, thus standardizing the analysis of ribonucleotide sequencing experiments and allowing direct comparisons of results. This protocol describes in detail how to use Ribose-Map to analyze raw ribonucleotide sequencing data, including preparing the reads for analysis, locating the genomic coordinates of ribonucleotides, exploring the genome-wide distribution of ribonucleotides, determining the nucleotide sequence context of ribonucleotides, and identifying hotspots of ribonucleotide incorporation. Ribose-Map does not require background knowledge of ribonucleotide sequencing analysis and assumes only basic command-line skills. The protocol requires less than 3 hr of computing time for most datasets and about 30 min of hands-on time.

BreakID: genomics breakpoints identification to detect gene fusion events using discordant pairs and split reads

2019 ◽  
Vol 35 (16) ◽  
pp. 2859-2861
Author(s):  
Linfang Jin ◽  
Jinhuo Lai ◽  
Yang Zhang ◽  
Ying Fu ◽  
Shuhang Wang ◽  
...  
Keyword(s):  
Gene Fusion ◽  
Source Code ◽  
High Sensitivity ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution ◽  
Fusion Detection ◽  
Better Than

AbstractSummaryHere we developed a tool called Breakpoint Identification (BreakID) to identity fusion events from targeted sequencing data. Taking discordant read pairs and split reads as supporting evidences, BreakID can identify gene fusion breakpoints at single nucleotide resolution. After validation with confirmed fusion events in cancer cell lines, we have proved that BreakID can achieve high sensitivity of 90.63% along with PPV of 100% at sequencing depth of 500× and perform better than other available fusion detection tools. We anticipate that BreakID will have an extensive popularity in the detection and analysis of fusions involved in clinical and research sequencing scenarios.Availability and implementationSource code is freely available at https://github.com/SinOncology/BreakID.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Michael A. Boemo
Keyword(s):  
Dna Replication ◽  
High Throughput ◽  
Single Molecule ◽  
Replication Fork ◽  
Genome Stability ◽  
Brdu Incorporation ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution

Abstract Background Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target for diseases like cancer. In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software. Results This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent. Conclusions This paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair.

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