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Find video protocols related to scientific articles indexed in Pubmed.
A comparison study of succinct data structures for use in GWAS.
BMC Bioinformatics
PUBLISHED: 06-25-2013
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In recent years genetic data analysis has seen a rapid increase in the scale of data to be analyzed. Schadt et al (NRG 11:647-657, 2010) offered that with data sets approaching the petabyte scale, data related challenges such as formatting, management, and transfer are increasingly important topics which need to be addressed. The use of succinct data structures is one method of reducing physical size of a data set without the use of expensive compression techniques. In this work, we consider the use of 2- and 3-bit encoding schemes for genotype data. We compare the computational performance of allele or genotype counting algorithms utilizing genotype data encoded in both schemes.
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Lampe1: an ENU-germline mutation causing spontaneous hepatosteatosis identified through targeted exon-enrichment and next-generation sequencing.
PLoS ONE
PUBLISHED: 02-22-2011
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Using a small scale ENU mutagenesis approach we identified a recessive germline mutant, designated Lampe1 that exhibited growth retardation and spontaneous hepatosteatosis. Low resolution mapping based on 20 intercrossed Lampe1 mice revealed linkage to a ?14 Mb interval on the distal site of chromosome 11 containing a total of 285 genes. Exons and 50 bp flanking sequences within the critical region were enriched with sequence capture microarrays and subsequently analyzed by next-generation sequencing. Using this approach 98.1 percent of the targeted DNA was covered with a depth of 10 or more reads per nucleotide and 3 homozygote mutations were identified. Two mutations represented intronic nucleotide changes whereas one mutation affected a splice donor site in intron 11-12 of Palmitoyl Acetyl-coenzyme A oxygenase-1 (Acox1), causing skipping of exon 12. Phenotyping of Acox1(Lampe1) mutants revealed a progression from hepatosteatosis to steatohepatitis, and ultimately hepatocellular carcinoma. The current approach provides a highly efficient and affordable method to identify causative mutations induced by ENU mutagenesis and animal models relevant to human pathology.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.