Modeling sequencing errors by combining Hidden Markov models.

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Effort, reward and self-reported mental health: a simulation study on negative affectivity bias.

Background : In the present article, we propose an alternative method for dealing with negative affectivity (NA) biases in research, while investigating the association between a deleterious psychosocial environment at work and poor mental health. First, we investigated how strong NA must be to cause an observed correlation between the independent and dependent variables. Second, we subjectively assessed whether NA can have a large enough impact on a large enough number of subjects to invalidate the observed correlations between dependent and independent variables.Methods : We simulated 10,000 populations of 300 subjects each, using the marginal distribution of workers in an actual population that had answered the Siegrist's questionnaire on effort and reward imbalance (ERI) and the General Health Questionnaire (GHQ).Results : The results of the present study suggested that simulated NA has a minimal effect on the mean scores for effort and reward. However, the correlations between the effort and reward imbalance (ERI) ratio and the GHQ score might be important, even in simulated populations with a limited NA.Conclusions : When investigating the relationship between the ERI ratio and the GHQ score, we suggest the following rules for the interpretation of the results: correlations with an explained variance of 5% and below should be considered with caution; correlations with an explained variance between 5% and 10% may result from NA, although this effect does not seem likely; and correlations with an explained variance of 10% and above are not likely to be the result of NA biases. [Authors]

Male mutation bias and possible long-term effects of human activities.

The ability of a population to adapt to changing environments depends critically on the amount and kind of genetic variability it possesses. Mutations are an important source of new genetic variability and may lead to new adaptations, especially if the population size is large. Mutation rates are extremely variable between and within species, and males usually have higher mutation rates as a result of elevated rates of male germ cell division. This male bias affects the overall mutation rate. We examined the factors that influence male mutation bias, and focused on the effects of classical life-history parameters, such as the average age at reproduction and elevated rates of sperm production in response to sexual selection and sperm competition. We argue that human-induced changes in age at reproduction or in sexual selection will affect male mutation biases and hence overall mutation rates. Depending on the effective population size, these changes are likely to influence the long-term persistence of a population.

Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.

Correcting Interdevice Bias of Horizontal White-to-White and Sulcus-to-Sulcus Measures Used for Implantable Collamer Lens Sizing.

PURPOSE: To assess the agreement and repeatability of horizontal white-to-white (WTW) and horizontal sulcus-to-sulcus (STS) diameter measurements and use these data in combination with available literature to correct for interdevice bias in preoperative implantable collamer lens (ICL) size selection. DESIGN: Interinstrument reliability and bias assessment study. METHODS: A total of 107 eyes from 56 patients assessed for ICL implantation at our institution were included in the study. This was a consecutive series of all patients with suitable available data. The agreement and bias between WTW (measured with the Pentacam and BioGraph devices) and STS (measured with the HiScan device) were estimated. RESULTS: The mean spherical equivalent was -8.93 ± 5.69 diopters. The BioGraph measures of WTW were wider than those taken with the Pentacam (bias = 0.26 mm, P < .01), and both horizontal WTW measures were wider than the horizontal STS measures (bias >0.91 mm, P < .01). The repeatability (Sr) of STS measured with the HiScan was 0.39 mm, which was significantly reduced (Sr = 0.15 mm) when the average of 2 measures was used. Agreement between the horizontal WTW measures and horizontal STS estimates when bias was accounted for was г = 0.54 with the Pentacam and г = 0.64 with the BioGraph. CONCLUSIONS: Large interdevice bias was observed for WTW and STS measures. STS measures demonstrated poor repeatability, but the average of repeated measures significantly improved repeatability. In order to conform to the US Food and Drug Administration's accepted guidelines for ICL sizing, clinicians should be aware of and account for the inconsistencies between devices.