Abundance Estimation Error Research Articles

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

BackgroundShotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX.ResultsUsing metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene.ConclusionsShotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.

Models for assessing local‐scale co‐abundance of animal species while accounting for differential detectability and varied responses to the environment

AbstractWe developed a new modeling framework to assess how the local abundance of one species influences the local abundance of a potential competitor while explicitly accounting for differential responses to environmental conditions. Our models also incorporate imperfect detection as well as abundance estimation error for both species. As a case study, we applied the model to four pairs of mammal species in Borneo, surveyed by extensive and spatially widespread camera trapping. We detected different responses to elevation gradients within civet, macaque, and muntjac deer species pairs. Muntjac and porcupine species varied in their response to terrain ruggedness, and the two muntjac responded different to river proximity. Bornean endemic species of civet and muntjac were more sensitive than their widespread counterparts to habitat disturbance (selective logging). Local abundance within several species pairs was positively correlated, but this is likely due to the species having similar responses to (unmodeled) environmental conditions or resources rather than representing facilitation. After accounting for environment and correcting for false absences in detection, negative correlations in local abundance appear rare in tropical mammals. Direct competition may be weak in these species, possibly because the ‘ghost of competition past’ or habitat filtering have already driven separation of the species in niche space. The analytical framework presented here could increase basic understanding of how ecological interactions shape patterns of abundance across the landscape for a range of taxa, and also provide a powerful tool for forecasting the impacts of global change.

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

Centralized Collaborative Sparse Unmixing for Hyperspectral Images

Spectral unmixing is very important in hyperspectral image analysis and processing, which aims at identifying the constituent spectra (i.e., endmembers) and estimating their fractional abundances from the mixed pixels. In recent years, sparse unmixing has received considerable interest. However, the acquired hyperspectral images are generally degraded by the noise, making sparse unmixing not faithful enough. To address this issue, this paper proposes a novel framework to couple sparse hyperspectral unmixing and abundance estimation error reduction together. Specifically, with the definition of abundance estimation error, a centralized constraint is incorporated into the collaborative sparse unmixing framework by exploiting the nonlocal redundancy of abundance map. This way we suppress the abundance estimation error, and improve the unmixing accuracy. Meanwhile, the alternating direction method of multipliers is introduced to solve the underlying constrained model. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of our proposed algorithm.

Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea. The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size. We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm. For each independent component we added two constraint conditions: non-negativity and constant sum. We use priority weighting by higher-order statistics, and then the spectral angle match method to overcome the order nondeterminacy. By these steps, endmembers can be extracted and abundance quantified simultaneously. To examine the coverage of a real oil spill and correct our estimate, a simulation experiment and a real experiment were designed using the algorithm described above. The result indicated that, for the simulation data, the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6. We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011. The total oil spill area was 0.224 km2, and the oil spill rate was 22.89%. The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates. It also allows the accurate estimation of the oil spill area.

Computational approaches for isoform detection and estimation: good and bad news.

BackgroundThe main goal of the whole transcriptome analysis is to correctly identify all expressed transcripts within a specific cell/tissue - at a particular stage and condition - to determine their structures and to measure their abundances. RNA-seq data promise to allow identification and quantification of transcriptome at unprecedented level of resolution, accuracy and low cost. Several computational methods have been proposed to achieve such purposes. However, it is still not clear which promises are already met and which challenges are still open and require further methodological developments.ResultsWe carried out a simulation study to assess the performance of 5 widely used tools, such as: CEM, Cufflinks, iReckon, RSEM, and SLIDE. All of them have been used with default parameters. In particular, we considered the effect of the following three different scenarios: the availability of complete annotation, incomplete annotation, and no annotation at all. Moreover, comparisons were carried out using the methods in three different modes of action. In the first mode, the methods were forced to only deal with those isoforms that are present in the annotation; in the second mode, they were allowed to detect novel isoforms using the annotation as guide; in the third mode, they were operating in fully data driven way (although with the support of the alignment on the reference genome). In the latter modality, precision and recall are quite poor. On the contrary, results are better with the support of the annotation, even though it is not complete. Finally, abundance estimation error often shows a very skewed distribution. The performance strongly depends on the true real abundance of the isoforms. Lowly (and sometimes also moderately) expressed isoforms are poorly detected and estimated. In particular, lowly expressed isoforms are identified mainly if they are provided in the original annotation as potential isoforms.ConclusionsBoth detection and quantification of all isoforms from RNA-seq data are still hard problems and they are affected by many factors. Overall, the performance significantly changes since it depends on the modes of action and on the type of available annotation. Results obtained using complete or partial annotation are able to detect most of the expressed isoforms, even though the number of false positives is often high. Fully data driven approaches require more attention, at least for complex eucaryotic genomes. Improvements are desirable especially for isoform quantification and for isoform detection with low abundance.

The use of an adaptive acoustic-survey design to estimate the abundance of highly skewed fish populations

Abstract Harbitz, A., Ona, E., and Pennington, M. 2009. The use of an adaptive acoustic-survey design to estimate the abundance of highly skewed fish populations. – ICES Journal of Marine Science, 66: 1349–1354. The uncertainty (relative root-mean-square error) of abundance estimates based on a simple and easily implemented adaptive design for an acoustic survey is examined. The study is limited to surveys with parallel transects and independent observations with extremely skewed distributions. The adaptive approach defines a stratum for each conventional observation and how to take additional observations in that stratum when the acoustic density exceeds a predetermined threshold. The cost (sailing distance) of each added observation is about three times that of a conventional observation. This method was demonstrated using high-resolution transect data from a herring (Clupea harengus) acoustic survey conducted in 2006 off the north coast of Norway. The primary sampling unit for this winter survey was 5 nautical miles, and the usual distance between transects was 20 nautical miles. The results indicate that an adaptive survey design would substantially reduce the root-mean-square error of the abundance estimates compared with that of the conventional survey design.

The relationship between sampling intensity and sampling error—empirical results from acoustic surveys in Polish vendace lakes

The relationship between sampling effort and the variance of hydroacoustic estimates of fish abundance was studied on the basis of 17 surveys in four lakes. These differed in their morphometric and trophic characteristics, but all contained fish communities dominated by vendace ( Coregonus albula L.). Regression between the degree of coverage and the coefficient of variation was produced. The sampling error of abundance estimates was less than 10% at coverage higher than 2 for all the observations. Seasonal changes in fish spatial distribution did not affect the variability significantly. The zigzag survey design gave less variable results than regular parallel transects but the difference was not statistically significant.

Towards an optimal sampling strategy for Alexandrium catenella (Dinophyceae) benthic resting cysts

The study proposes methodological developments to optimize sampling strategy of resting cysts of Alexandrium catenella to estimate their abundance with a predefined error. This work also aims to provide information on spatial distribution of resting cysts in sediments. The distribution mode of A. catenella resting cysts related to the abundance variability was studied through sediment cores sampling on four different spatial scales and using Ludox CLX gradient density method. The quantification method underestimates by a factor of 2 the resting cysts abundance in one gram of sediment. Application of Taylor's power law allowed us to define a compromise between sampling effort and abundance estimation error. In the case of A. catenella resting cysts from Thau lagoon, the optimal sampling strategy consists of sampling 10 stations on a surface of 2 km 2 for a given coefficient of variability (C) of 15%, sampling 3 sediment cores at each station ( C = 30%) and counting only one replicate by core ( C = 18%). Results related to the application of Taylor's power law are closely dependent on resting cyst density and aggregation in a given sediment. In our area, A. catenella resting cysts are mainly observed in the upper 3 cm of sediment. Horizontally, their heterogeneity is lower on 10 cm 2 surface and tends to stabilize itself beyond a surface of 10 m 2. Each author has to carry out this pre-sampling effort for his own resting cysts-forming species, in his own area, in order to increase accuracy of resting cyst mapping.