Diversity Indices as Measures of Functional Annotation Methods in Metagenomics Studies

Handle URI:
http://hdl.handle.net/10754/601401
Title:
Diversity Indices as Measures of Functional Annotation Methods in Metagenomics Studies
Authors:
Jankovic, Boris R.
Abstract:
Applications of high-throughput techniques in metagenomics studies produce massive amounts of data. Fragments of genomic, transcriptomic and proteomic molecules are all found in metagenomics samples. Laborious and meticulous effort in sequencing and functional annotation are then required to, amongst other objectives, reconstruct a taxonomic map of the environment that metagenomics samples were taken from. In addition to computational challenges faced by metagenomics studies, the analysis is further complicated by the presence of contaminants in the samples, potentially resulting in skewed taxonomic analysis. The functional annotation in metagenomics can utilize all available omics data and therefore different methods that are associated with a particular type of data. For example, protein-coding DNA, non-coding RNA or ribosomal RNA data can be used in such an analysis. These methods would have their advantages and disadvantages and the question of comparison among them naturally arises. There are several criteria that can be used when performing such a comparison. Loosely speaking, methods can be evaluated in terms of computational complexity or in terms of the expected biological accuracy. We propose that the concept of diversity that is used in the ecosystems and species diversity studies can be successfully used in evaluating certain aspects of the methods employed in metagenomics studies. We show that when applying the concept of Hill’s diversity, the analysis of variations in the diversity order provides valuable clues into the robustness of methods used in the taxonomical analysis.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Conference/Event name:
KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology
Issue Date:
26-Jan-2016
Type:
Presentation
Appears in Collections:
Computational Bioscience Research Center (CBRC); KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology, January 2016

Full metadata record

DC FieldValue Language
dc.contributor.authorJankovic, Boris R.en
dc.date.accessioned2016-03-16T12:53:24Zen
dc.date.available2016-03-16T12:53:24Zen
dc.date.issued2016-01-26en
dc.identifier.urihttp://hdl.handle.net/10754/601401en
dc.description.abstractApplications of high-throughput techniques in metagenomics studies produce massive amounts of data. Fragments of genomic, transcriptomic and proteomic molecules are all found in metagenomics samples. Laborious and meticulous effort in sequencing and functional annotation are then required to, amongst other objectives, reconstruct a taxonomic map of the environment that metagenomics samples were taken from. In addition to computational challenges faced by metagenomics studies, the analysis is further complicated by the presence of contaminants in the samples, potentially resulting in skewed taxonomic analysis. The functional annotation in metagenomics can utilize all available omics data and therefore different methods that are associated with a particular type of data. For example, protein-coding DNA, non-coding RNA or ribosomal RNA data can be used in such an analysis. These methods would have their advantages and disadvantages and the question of comparison among them naturally arises. There are several criteria that can be used when performing such a comparison. Loosely speaking, methods can be evaluated in terms of computational complexity or in terms of the expected biological accuracy. We propose that the concept of diversity that is used in the ecosystems and species diversity studies can be successfully used in evaluating certain aspects of the methods employed in metagenomics studies. We show that when applying the concept of Hill’s diversity, the analysis of variations in the diversity order provides valuable clues into the robustness of methods used in the taxonomical analysis.en
dc.titleDiversity Indices as Measures of Functional Annotation Methods in Metagenomics Studiesen
dc.typePresentationen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.conference.dateJanuary 25-27, 2016en
dc.conference.nameKAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnologyen
dc.conference.locationKAUST, Thuwal, Saudi Arabiaen
kaust.authorJankovic, Boris R.en
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