Estimating and Correcting for Bias in Population Assessments of Sooty Shearwaters

Abstract: We investigated the precision and accuracy of an infrared burrowscope for detecting sooty shearwater (Pufffinus griseus) chicks at 13 plots from 3 islands in southern New Zealand in 2003. We partially excavated burrows systems to reveal the entire burrow contents after 2 teams of observers had prospected all burrow entrances. Accuracy was similar between islands and observer teams at approximately 85%. The majority of the inaccuracy stemmed from failure to detect some chicks. Logistic regression modeling identified 4 burrow characteristics occurring between the entrance and the nest-site that influenced detection of burrow occupants. Detection was lower at nest-sites further from burrow entrances, in burrows with a high rate of burrow division, and in burrows with a high level of curvature. There was a positive relationship between the interaction of rate of division and curvature and detection of chicks. Distance from the burrow entrance was the only parameter that could be reliably used as a predictor of detection rate, so a reduced model containing only this variable was constructed to correct for burrowscope bias. The correction factor performed well on The Snares and Bench Island where predicted bias was very similar to observed levels (within 5%), but bias was overestimated on Putauhinu by up to 19.1%. Consistent bias, lack of damage to burrows from excavation, and the successful application of a correction factor all indicate the value of further testing burrowscope accuracy on other burrow-nesting seabird species.     

NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads

Background

The popularity of new sequencing technologies has led to an explosion of possible applications, including new approaches in biodiversity studies. However each of these sequencing technologies suffers from sequencing errors originating from different factors. For 16S rRNA metagenomics studies, the 454 pyrosequencing technology is one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation). Moreover, retaining a higher portion of the sequencing data by preserving as much of the read length as possible while maintaining the error rate within an acceptable range, will have important consequences at the level of taxonomic precision.

Results

The new error correction algorithm proposed in this work - NoDe (Noise Detector) - is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units.

Conclusions

NoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0520-5) contains supplementary material, which is available to authorized users.     

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

Background

RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.

Results

We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site’s coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.

Conclusions

These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1456-x) contains supplementary material, which is available to authorized users.     

Increased SFHR gene correction efficiency with sense single-stranded DNA

BACKGROUND: The correction of a mutated gene by the small fragment homologous replacement (SFHR) method is a highly attractive approach for gene therapy. However, the current SFHR method with a heat-denatured double-stranded PCR fragment yielded a low correction efficiency. METHODS: Single-stranded (ss) DNA fragments were prepared from ss phagemid DNA and tested in a gene correction assay with an inactivated Hyg-EGFP fusion gene, as a model target. RESULTS: A 606-nt sense, ss DNA fragment dramatically (12-fold) improved the gene correction efficiency, although the antisense strand showed only minimal correction efficiency. CONCLUSIONS: These results suggest that the use of a sense, single-stranded DNA fragment is useful in the SFHR method for the correction of mutated genes.     

Validity of hand-to-foot measurement of bioimpedance: standing compared with lying position

Objective: To assess the reliability of the standing measurement of hand‐to‐foot bioimpedance compared with measurements made in the lying position. Research Methods and Procedures: In 205 volunteers 6 to 89 years of age, 111 males and 94 females from six ethnic groups, effects of posture, time, and age on hand‐to‐foot resistance were studied over a range of body size. The effect of time in a position on resistance was also recorded in a small subset (n = 10), and repeat measurements over 3 days at the same time of the day were recorded in another subset (n = 12). Results: Lying impedance was consistently higher than standing, with the relationship (resistance lying/resistance standing) for the children (5 to 14 years) being 1.031, progressing to a ratio of 1.016 in those >60 years. The time spent static in either position did change resistance measurements—a decrease of up to 9 Ω (mean 5 Ω, 1.0%) over 10 minutes of standing and an increase of up to 7 Ω (mean 3 Ω, 0.7%) with lying. Discussion: In the field, measurements of hand‐to‐foot bioimpedance can be made in the standing position, and, with appropriate adjustment, previously validated recumbent equations can be used. Given that errors in the measurement of height and weight also affect the reliability of the derivation of body fat from bioelectrical conductance, the errors that may arise from a more practical standing measurement rather than lying are minimal.     

Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk

In case-control studies of inherited diseases, participating subjects (probands) are often interviewed to collect detailed data about disease history and age-at-onset information in their family members. Genotype data are typically collected from the probands, but not from their relatives. In this article, we introduce an approach that combines case-control analysis of data on the probands with kin-cohort analysis of disease history data on relatives. Assuming a marginally specified multivariate survival model for joint risk of disease among family members, we describe methods for estimating relative risk, cumulative risk, and residual familial aggregation. We also describe a variation of the methodology that can be used for kin-cohort analysis of the family history data from a sample of genotyped cases only. We perform simulation studies to assess performance of the proposed methodologies with correct and mis-specified models for familial aggregation. We illustrate the proposed methodologies by estimating the risk of breast cancer from BRCA1/2 mutations using data from the Washington Ashkenazi Study.     

Recursive construction of perfect DNA molecules from imperfect oligonucleotides

Making faultless complex objects from potentially faulty building blocks is a fundamental challenge in computer engineering, nanotechnology and synthetic biology. Here, we show for the first time how recursion can be used to address this challenge and demonstrate a recursive procedure that constructs error‐free DNA molecules and their libraries from error‐prone oligonucleotides. Divide and Conquer (D&C), the quintessential recursive problem‐solving technique, is applied in silico to divide the target DNA sequence into overlapping oligonucleotides short enough to be synthesized directly, albeit with errors; error‐prone oligonucleotides are recursively combined in vitro, forming error‐prone DNA molecules; error‐free fragments of these molecules are then identified, extracted and used as new, typically longer and more accurate, inputs to another iteration of the recursive construction procedure; the entire process repeats until an error‐free target molecule is formed. Our recursive construction procedure surpasses existing methods for de novo DNA synthesis in speed, precision, amenability to automation, ease of combining synthetic and natural DNA fragments, and ability to construct designer DNA libraries. It thus provides a novel and robust foundation for the design and construction of synthetic biological molecules and organisms.     

Chemical gene synthesis: strategies, softwares, error corrections, and applications

Chemical synthesis of DNA sequences provides a powerful tool for modifying genes and for studying gene structure, expression and function. Modified genes and consequently protein/enzymes can bridge genomics and proteomics research or facilitate commercial applications of gene and protein technologies. In this review, we will summarize various strategies, designing softwares and error correction methods for chemical gene synthesis, particularly for the synthesis and assembly of long DNA molecules based on polymerase cycling assembly. Also, we will briefly discuss some of the major applications of chemical synthesis of DNA sequences in basic research and applied areas.     

Split-test Bonferroni correction for QEEG statistical maps

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer’s disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.     

Thermal unfolding studies show the disease causing F508del mutation in CFTR thermodynamically destabilizes nucleotide-binding domain 1

Misfolding and degradation of CFTR is the cause of disease in patients with the most prevalent CFTR mutation, an in-frame deletion of phenylalanine (F508del), located in the first nucleotide-binding domain of human CFTR (hNBD1). Studies of (F508del)CFTR cellular folding suggest that both intra- and inter-domain folding is impaired. (F508del)CFTR is a temperature-sensitive mutant, that is, lowering growth temperature, improves both export, and plasma membrane residence times. Yet, paradoxically, F508del does not alter the fold of isolated hNBD1 nor did it seem to perturb its unfolding transition in previous isothermal chemical denaturation studies. We therefore studied the in vitro thermal unfolding of matched hNBD1 constructs ±F508del to shed light on the defective folding mechanism and the basis for the thermal instability of (F508del)CFTR. Using primarily differential scanning calorimetry (DSC) and circular dichroism, we show for all hNBD1 pairs studied, that F508del lowers the unfolding transition temperature (T_m) by 6–7°C and that unfolding occurs via a kinetically-controlled, irreversible transition in isolated monomers. A thermal unfolding mechanism is derived from nonlinear least squares fitting of comprehensive DSC data sets. All data are consistent with a simple three-state thermal unfolding mechanism for hNBD1 ± F508del: N(±MgATP) ⇄ I_T(±MgATP) → A_T → (A_T)_n. The equilibrium unfolding to intermediate, I_T, is followed by the rate-determining, irreversible formation of a partially folded, aggregation-prone, monomeric state, A_T, for which aggregation to (A_T)_n and further unfolding occur with no detectable heat change. Fitted parameters indicate that F508del thermodynamically destabilizes the native state, N, and accelerates the formation of A_T.