Changes in growth and maturation parameters of Pacific sardine Sardinops sagax collected off California during a period of stock recovery from 1994 to 2010

Whether fluctuation in density influenced the growth and maturation variables of three aggregated cohorts (fish born during the 1986–1993, 1996–2003 and 2004–2008 periods) of Pacific sardine Sardinops sagax caeruleus collected off the Californian coast from 2004 to 2010 was investigated. Using a von Bertalanffy mixed‐effects model with aggregated cohorts as covariates, estimated growth rate significantly covaried with aggregated cohorts. Growth rate (K) was modelled as a fixed effect and estimated to be 0·264 ± 0·015 (±s.e ). Statistical contrasts among aggregated cohorts showed that the 1996–2003 cohorts had a significantly lower growth rate than the other two aggregated cohorts. The theoretical age at length zero (t₀) and the standard length at infinity (L_S∞) were modelled as random effects, and were estimated to be ?2·885 ± 0·259 (±s.e ) and 273·13 ± 6·533 mm (±s.e ). The relation of ovary‐free mass at length was significantly different among the three aggregated cohorts, with the allometric coefficient estimated to be 2·850 ± 0·013 (±s.e ) for the S. sagax population. The age‐at‐length trajectory of S. sagax born between 1986 and 2008 showed strong density dependence effects on somatic growth rates. In contrast to the density‐dependent nature of growth, the probability to be mature at‐size or at‐age was not significantly affected by aggregated cohort density. The size and the age‐at‐50% maturity were estimated to be 150·92 mm and 0·56 years, respectively. Stock migration, natural fluctuations in biomass and removal of older and larger S. sagax by fishing might have been interplaying factors controlling growth parameters during 1986–2010.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.