Limits...
Exploration and retrieval of whole-metagenome sequencing samples.

Seth S, Välimäki N, Kaski S, Honkela A - Bioinformatics (2014)

Bottom Line: Over the recent years, the field of whole-metagenome shotgun sequencing has witnessed significant growth owing to the high-throughput sequencing technologies that allow sequencing genomic samples cheaper, faster and with better coverage than before.We apply a distributed string mining framework to efficiently extract all informative sequence k-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples.We evaluate the performance of the proposed approach on two human gut metagenome datasets as well as human microbiome project metagenomic samples.

View Article: PubMed Central - PubMed

Affiliation: Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland, Genome-Scale Biology Program and Department of Medical Genetics, University of Helsinki, Helsinki, Finland, and Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.

Show MeSH

Related in: MedlinePlus

Given a set of metagenomic samples, our objective is to be able to retrieve relevant samples to a query sample. For this, we need to extract relevant features and evaluate a pairwise similarity (or dissimilarity) measure. The samples are then ranked in the order of increasing dissimilarity from the query
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4230234&req=5

btu340-F1: Given a set of metagenomic samples, our objective is to be able to retrieve relevant samples to a query sample. For this, we need to extract relevant features and evaluate a pairwise similarity (or dissimilarity) measure. The samples are then ranked in the order of increasing dissimilarity from the query

Mentions: Our objective is to extract and select suitable features for representing WMS sequencing samples and to form a pairwise dissimilarity measure for a collection of such samples. Given this dissimilarity, one can query with a sample and retrieve other samples that are similar to it (Fig. 1). The measure needs to be reasonably rapidly computable, yet captures relevant differences between the samples, and does all this with as little prior biological knowledge and annotations as possible, as detailed quantitative prior knowledge is typically not yet available for metagenomics.Fig. 1.


Exploration and retrieval of whole-metagenome sequencing samples.

Seth S, Välimäki N, Kaski S, Honkela A - Bioinformatics (2014)

Given a set of metagenomic samples, our objective is to be able to retrieve relevant samples to a query sample. For this, we need to extract relevant features and evaluate a pairwise similarity (or dissimilarity) measure. The samples are then ranked in the order of increasing dissimilarity from the query
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230234&req=5

btu340-F1: Given a set of metagenomic samples, our objective is to be able to retrieve relevant samples to a query sample. For this, we need to extract relevant features and evaluate a pairwise similarity (or dissimilarity) measure. The samples are then ranked in the order of increasing dissimilarity from the query
Mentions: Our objective is to extract and select suitable features for representing WMS sequencing samples and to form a pairwise dissimilarity measure for a collection of such samples. Given this dissimilarity, one can query with a sample and retrieve other samples that are similar to it (Fig. 1). The measure needs to be reasonably rapidly computable, yet captures relevant differences between the samples, and does all this with as little prior biological knowledge and annotations as possible, as detailed quantitative prior knowledge is typically not yet available for metagenomics.Fig. 1.

Bottom Line: Over the recent years, the field of whole-metagenome shotgun sequencing has witnessed significant growth owing to the high-throughput sequencing technologies that allow sequencing genomic samples cheaper, faster and with better coverage than before.We apply a distributed string mining framework to efficiently extract all informative sequence k-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples.We evaluate the performance of the proposed approach on two human gut metagenome datasets as well as human microbiome project metagenomic samples.

View Article: PubMed Central - PubMed

Affiliation: Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland, Genome-Scale Biology Program and Department of Medical Genetics, University of Helsinki, Helsinki, Finland, and Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.

Show MeSH
Related in: MedlinePlus