Month: February 2017


[] [Bioinformatics]

SVPV: A Structural Variant Prediction Viewer for paired-end sequencing datasets

“a tool which presents a visual summary of the most relevant features for SV prediction from WGS data. SV calls from multiple prediction algorithms may be visualised together, along with annotation of population allele frequencies from reference SV datasets. Gene annotations may also be included. The application is capable of running in a GUI mode for visualising SVs one by one, or in batch mode for processing many SVs serially.”


Nanopore Methylation

[PMID:28218898] [Nature Methods]

Detecting DNA cytosine methylation using nanopore sequencing

“By using synthetically methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytosine.”

[PMID:28218897] [Nature Methods]

Mapping DNA methylation with high-throughput nanopore sequencing

“We map three cytosine variants and two adenine variants.”

NGS Errors Due to Damage in DNA Materials

[PMID:28209900] [Science]

DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification

It demonstrates that “many so-called low-frequency genetic variants in large public databases may be due to DNA damage” — DNA material itself, not the calling. “To estimate the extent of damage in public data sets, we determined the GIV scores of individual sequencing runs from the 1000 Genomes Project and a subset of The Cancer Genome Atlas (TCGA) data set. Both data sets showed widespread damage, particularly those leading to an excess of G-to-T variants. Specifically, 41% of the 1000 Genomes Project data sets had a GIVG_T score ≥ 1.5, indicative of damaged samples. Furthermore, 73% of the TCGA sequencing runs showed extensive damage, with a GIVG_T > 2. This indicates that the majority of G-to-T observations are erroneous and establishes damage as a pervasive cause of errors in these data sets.”


[PMID:28205675] [Bioinformatics]

BCFtools/csq: Haplotype-aware variant consequences

“current predictors analyze variants as isolated events, which can lead to incorrect predictions when adjacent variants alter the same codon, or when a frame-shifting indel is followed by a frame-restoring indel. Exploiting known haplotype information when making consequence predictions can resolve these issues.” “BCFtools/csq is a fast program for haplotype-aware consequence calling which can take into account known phase.”