ArticleJournal of computational biology : a journal of computational molecular cell biology2021
Complex Variant Discovery Using Discordant Cluster Normalization.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Complex genomic structural variants (CGSVs) are abnormalities that present with three or more breakpoints, making their discovery a challenge. The majority of existing algorithms for structural variant detection are only designed to find simple structural variants (SSVs) such as deletions and inversions; they fail to find more complex events such as deletion-inversions or deletion-duplications, for example. In this study, we present an algorithm named CleanBreak that employs a clique partitioning graph-based strategy to identify collections of SSV clusters and then subsequently identifies overlapping SSV clusters to examine the search space of possible CGSVs, choosing the one that is most concordant with local read depth. We evaluated CleanBreak's performance on whole genome simulated data and a real data set from the 1000 Genomes Project. We also compared CleanBreak with another algorithm for CGSV discovery. The results demonstrate CleanBreak's utility as an effective method to discover CGSVs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.