NDLI: Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses

Content Provider	PubMed Central
Author	Zaneveld, Jesse R. R. Thurber, Rebecca L. V.
Copyright Year	2014
Abstract	Complex symbioses between animal or plant hosts and their associated microbiotas can involve thousands of species and millions of genes. Because of the number of interacting partners, it is often impractical to study all organisms or genes in these host-microbe symbioses individually. Yet new phylogenetic predictive methods can use the wealth of accumulated data on diverse model organisms to make inferences into the properties of less well-studied species and gene families. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail. These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. We consider these phylogenetically informed predictive techniques from disparate fields as examples of a general class of algorithms for Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting organismal traits in a variety of contexts, including the study of complex host-microbe symbioses.
Related Links	http://dx.doi.org/10.3389/fmicb.2014.00431
Starting Page	431
File Format	PDF
ISSN	1664302X
e-ISSN	1664302X
Journal	Frontiers in Microbiology
Volume Number	5
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2014-08-01
Access Restriction	Open
Rights Holder	Frontiers Media S.A.
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Microbiology Microbiology (medical)

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Algorithms of Ancestral Gene Length Reconstruction

Corrigendum: Ancestral Sequence Reconstructions of MotB Are Proton-Motile and Require MotA for Motility

Evolution and Diversity of Biosynthetic Gene Clusters in Fusarium

Understanding Microbial Multi-Species Symbioses

Act together—implications of symbioses in aquatic ciliates

Analysis of GEMMs helps unravel genetic heterogeneity in colorectal cancer

Polyextremotolerant black fungi: oligotrophism, adaptive potential, and a link to lichen symbioses

Multiple adaptations to polar and alpine environments within cyanobacteria: a phylogenomic and Bayesian approach

Genomic reconstruction of the history of extant populations of India reveals five distinct ancestral components and a complex structure

Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses

Similar Documents

Algorithms of Ancestral Gene Length Reconstruction

Corrigendum: Ancestral Sequence Reconstructions of MotB Are Proton-Motile and Require MotA for Motility

Evolution and Diversity of Biosynthetic Gene Clusters in Fusarium

Understanding Microbial Multi-Species Symbioses

Act together—implications of symbioses in aquatic ciliates

Analysis of GEMMs helps unravel genetic heterogeneity in colorectal cancer

Polyextremotolerant black fungi: oligotrophism, adaptive potential, and a link to lichen symbioses

Multiple adaptations to polar and alpine environments within cyanobacteria: a phylogenomic and Bayesian approach

Genomic reconstruction of the history of extant populations of India reveals five distinct ancestral components and a complex structure

Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses