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Ambivalent covariance models.

ABSTRACT: Evolutionary variations let us define a set of similar nucleic acid sequences as a family if these different molecules execute a common function. Capturing their sequence variation by using e. g. position specific scoring matrices significantly improves sensitivity of detection tools. Members of a functional (non-coding) RNA family are affected by these variations not only on the sequence, but also on the structural level. For example, some transfer-RNAs exhibit a fifth helix in addition to the typical cloverleaf structure. Current covariance models - the unrivaled homology search approach for structured RNA - do not benefit from structural variation within a family, but rather penalize it. This leads to artificial subdivision of families and loss of information in the RFAM database.We propose an extension to the fundamental architecture of covariance models to allow for several, compatible consensus structures. The resulting models are called ambivalent covariance models. Evaluation on several RFAM families shows that coalescence of structural variation within a family by using ambivalent consensus models is superior to subdividing the family into multiple classical covariance models.A prototype and source code is available at http://bibiserv.cebitec.uni-bielefeld.de/acms.

SUBMITTER: Janssen S

PROVIDER: S-EPMC4504443 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Ambivalent covariance models.

Janssen Stefan S Giegerich Robert R

BMC bioinformatics 20150528

<h4>Background</h4>Evolutionary variations let us define a set of similar nucleic acid sequences as a family if these different molecules execute a common function. Capturing their sequence variation by using e. g. position specific scoring matrices significantly improves sensitivity of detection tools. Members of a functional (non-coding) RNA family are affected by these variations not only on the sequence, but also on the structural level. For example, some transfer-RNAs exhibit a fifth helix ...[more]

PMID: 26017195

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Project description:The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016-2017, 2017-2018, 2018-2019, and 2019-2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance-covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling.

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Ambivalent covariance models.

Publications

Ambivalent covariance models.

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