2004
DOI: 10.1273/cbij.4.133
|View full text |Cite
|
Sign up to set email alerts
|

Evaluation of the color-coding method for searching tandem repeats in prokaryotic genomes

Abstract: By using the color-coding (CC) method, which is based on visual inspection by eyes, tandem repeats (TRs) were searched in the Yersinia pestis, Deinococcus radiodurans and Haemophilus influenzae genomes by three independent inspectors, and the detected TRs were compared to investigate the individual variations among inspectors in detecting TRs. We also compared the CC method with Tandem Repeats Finder (TRF) that is one of the algorithmic methods for searching TRs, in the detection ability of TRs, demonstrating … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2009
2009
2024
2024

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 21 publications
(16 reference statements)
0
1
0
Order By: Relevance
“…We further annotated the coverage depth (normalized to genome-wide average coverage) for each microsatellite and its flanking regions in a prior single-cell sequencing dataset amplified by Primary Template-directed Amplification (PTA, Bioskryb) (Gonzalez-Pena et al 2021) in anticipation of potential future design of panels for profiling single cell genomes that have undergone initial non-uniform single-cell genome amplification. We also added annotations for replication timing based on 4D nucleome data in GM12878 lymphoblastoid cells as well as Repli-chip data of neural progenitor cells (Mizuta et al 2004; Mousavi et al 2019). Finally, we annotated estimated mutation rates calculated by a linear regression model we created with the ‘lm’ function in R (with default settings) trained on mutation rate data of autosomal intergenic microsatellite loci from a prior study (Gymrek et al 2017); the model specifically utilized the ‘uninterrupted length’ and ‘motif size’ annotations to predict mutation rate, because these best explained the variance in the test dataset (Gymrek et al 2017).…”
Section: Methodsmentioning
confidence: 99%
“…We further annotated the coverage depth (normalized to genome-wide average coverage) for each microsatellite and its flanking regions in a prior single-cell sequencing dataset amplified by Primary Template-directed Amplification (PTA, Bioskryb) (Gonzalez-Pena et al 2021) in anticipation of potential future design of panels for profiling single cell genomes that have undergone initial non-uniform single-cell genome amplification. We also added annotations for replication timing based on 4D nucleome data in GM12878 lymphoblastoid cells as well as Repli-chip data of neural progenitor cells (Mizuta et al 2004; Mousavi et al 2019). Finally, we annotated estimated mutation rates calculated by a linear regression model we created with the ‘lm’ function in R (with default settings) trained on mutation rate data of autosomal intergenic microsatellite loci from a prior study (Gymrek et al 2017); the model specifically utilized the ‘uninterrupted length’ and ‘motif size’ annotations to predict mutation rate, because these best explained the variance in the test dataset (Gymrek et al 2017).…”
Section: Methodsmentioning
confidence: 99%