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Plastic shopping bags are often discarded as litter and can be carried away from roadsides and become tangled on cotton plants in farm fields. This rubbish plastic can end up in the cotton at the gin if not removed before harvest. These bags may not only cause problems in the ginning process but might also become embedded in cotton fibers, reducing the quality and marketable value. Therefore, detecting, locating, and removing the bags before the cotton is harvested is required. Manually detecting and locating these bags in cotton fields is a tedious, time-consuming, and costly process. To solve this, this paper shows the application of YOLOv5 to detect white and brown colored plastic bags tangled at three different heights in cotton plants (bottom, middle, top) using Unmanned Aircraft Systems (UAS)-acquired Red, Green, Blue (RGB) images. It was found that an average white and brown bag could be detected at 92.35% and 77.87% accuracies and a mean average precision (mAP) of 87.68%. Similarly, the trained YOLOv5 model, on average, could detect 94.25% of the top, 49.58% of the middle, and only 5% of the bottom bags. It was also found that both the color of the bags (p < 0.001) and their height on cotton plants (p < 0.0001) had a significant effect on detection accuracy. The findings reported in this paper can help in the autonomous detection of plastic contaminants in cotton fields and potentially speed up the mitigation efforts, thereby reducing the amount of contaminants in cotton gins.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins., preprint

Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, based on a discrete-time spatio-temporal susceptible/exposed/infected/recovered/deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 Covid-19 wave in the state of Texas demonstrate the effectiveness of the proposed model., 17 pages, 5 figures

Background Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty. Results The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution. Conclusion The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression data. Electronic supplementary material The online version of this article (10.1186/s12864-019-5720-3) contains supplementary material, which is available to authorized users.

Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction. A recently proposed method takes into account both model uncertainty and the translational objective, for instance, optimal structural intervention in gene regulatory networks, where the aim is to alter the regulatory logic to maximally reduce the long-run likelihood of being in a cancerous state. The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective. Experimental design involves choosing the experiment that yields the greatest reduction in MOCU. This article introduces finite-horizon dynamic programming for MOCU-based sequential experimental design and compares it with the greedy approach, which selects one experiment at a time without consideration of the full horizon of experiments. A salient aspect of the article is that it demonstrates the advantage of MOCU-based design over the widely used entropy-based design for both greedy and dynamic programming strategies and investigates the effect of model conditions on the comparative performances.

Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range–targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data. The methodology is likelihood-free, using approximate Bayesian computation implemented via a Markov chain Monte Carlo procedure and a kernel-based Optimal Bayesian Classifier. Extensive experimental results demonstrate that the proposed method outperforms classical methods such as linear discriminant analysis and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these.

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we introduce approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method, which are called APF–BKF and APF–BKS, respectively. For joint state and parameter estimation, the APF–BKF is used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the case the unknown parameters are discrete, the proposed ML adaptive filter consists of multiple APF–BKFs running in parallel, in a manner reminiscent of the Multiple Model Adaptive Estimation (MMAE) method in classical linear filtering theory. In the presence of continuous parameters, the proposed ML adaptive filter is based on an efficient particle-based expectation maximization (EM) algorithm for the POBDS model, which is based on a modified Forward Filter Backward Simulation (FFBSi) in combination with the APF–BKS. The performance of the proposed particle-based adaptive filters is assessed through numerical experiments using a POBDS model of the well-known cell cycle gene regulatory network observed through noisy RNA-Seq time series data.

Discrete classification is common in Genomic Signal Processing applications, in particular in classification of discretized gene expression data, and in discrete gene expression prediction and the inference of boolean genomic regulatory networks. Once a discrete classifier is obtained from sample data, its performance must be evaluated through its classification error. In practice, error estimation methods must then be employed to obtain reliable estimates of the classification error based on the available data. Both classifier design and error estimation are complicated, in the case of Genomics, by the prevalence of small-sample data sets in such applications. This paper presents a broad review of the methodology of classification and error estimation for discrete data, in the context of Genomics, focusing on the study of performance in small sample scenarios, as well as asymptotic behavior.

Four genetic polymorphisms located at the promoter (C-257T) and coding regions of CFH gene (exon 2 G257A, exon 14 A2089G and exon 19 G2881T) were investigated in 121 dengue patients (DENV-3) in order to assess the relationship between allele/haplotypes variants and clinical outcomes. A statistical value was found between the CFH-257T allele (TT/TC genotypes) and reduced susceptibility to severe dengue (SD). Statistical associations indicate that individuals bearing a T allele presented significantly higher protein levels in plasma. The −257T variant is located within a NF-κB binding site, suggesting that this variant might have effect on the ability of the CFH gene to respond to signals via the NF-κB pathway. The G257A allelic variant showed significant protection against severe dengue. When CFH haplotypes effect was considered, the ancestral CG/CG promoter-exon 2 SNP genotype showed significant risk to SD either in a general comparison (ancestral × all variant genotypes), as well as in individual genotypes comparison (ancestral × each variant genotype), where the most prevalent effect was observed in the CG/CG × CA/TG comparison. These findings support the involvement of −257T, 257A allele variants and haplotypes on severe dengue phenotype protection, related with high basal CFH expression.

Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography-mass spectrometry (LC-MS). Our approach relies on a previously proposed model of the LC-MS experiment, as well as on the theory of the optimal Bayesian classifier (OBC). Computation of the OBC requires the combination of a likelihood-free methodology called approximate Bayesian computation (ABC) as well as Markov chain Monte Carlo (MCMC) sampling. Numerical experiments using synthetic LC-MS data based on an actual human proteome indicate that the proposed ABC-MCMC classification rule outperforms classical methods such as support vector machines, linear discriminant analysis, and 3-nearest neighbor classification rules in the case when sample size is small or the number of selected proteins used to classify is large.

Motivation: It is commonly assumed in pattern recognition that cross-validation error estimation is ‘almost unbiased’ as long as the number of folds is not too small. While this is true for random sampling, it is not true with separate sampling, where the populations are independently sampled, which is a common situation in bioinformatics. Results: We demonstrate, via analytical and numerical methods, that classical cross-validation can have strong bias under separate sampling, depending on the difference between the sampling ratios and the true population probabilities. We propose a new separate-sampling cross-validation error estimator, and prove that it satisfies an ‘almost unbiased’ theorem similar to that of random-sampling cross-validation. We present two case studies with previously published data, which show that the results can change drastically if the correct form of cross-validation is used. Availability and implementation: The source code in C++, along with the Supplementary Materials, is available at: http://gsp.tamu.edu/Publications/supplementary/zollanvari13/. Contact: ulisses@ece.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Human leukocyte antigen (HLA) alleles have been correlated with susceptibility or resistance to severe dengue; however, few immunogenetic studies have been performed in Latin American (LA) populations. We have conducted immunogenetic studies of HLA class I and II alleles in a cohort of 187 patients with DENV-3 infection and confirmed clinical diagnosis of either severe dengue, known as dengue hemorrhagic fever (DHF), or the less severe form, dengue fever (DF), in Recife, Pernambuco, Brazil. An association analysis was performed using Fisher’s association test, with odds ratios (ORs) calculated using conditional maximum likelihood estimates. HLA-B*44 (P=0.047, OR = 2.025, 95% CI = 0.97–4.24) was found to be associated with increased susceptibility to DHF in response to DENV-3 infection. In addition, HLA-B*07 (P=0.048, OR = 0.501, one-sided 95% CI = 0–0.99) and HLA-DR*13 (P=0.028, OR = 0.511, one-sided 95% CI = 0–0.91) were found to be associated with resistance to secondary dengue infection by DENV-3. These results suggest that HLA-B*44 supertype alleles and their respective T-cell responses might be involved in susceptibility to severe dengue infections, whereas the HLA-B*07 supertype alleles and DR*13 might be involved in cross-dengue serotype immunity.

The coefficient of determination (CoD) has significant applications in genomics, for example, in the inference of gene regulatory networks. We study several CoD estimators, based upon the resubstitution, leave-one-out, cross-validation, and bootstrap error estimators. We present an exact formulation of performance metrics for the resubstitution and leave-one-out CoD estimators, assuming the discrete histogram rule. Numerical experiments are carried out using a parametric Zipf model, where we compute exact performance metrics of resubstitution and leave-one-out CoD estimators using the previously derived equations, for varying actual CoD, sample size, and bin size. These results are compared to approximate performance metrics of 10-repeated 2-fold cross-validation and 0.632 bootstrap CoD estimators, computed via Monte Carlo sampling. The numerical results lead to a perhaps surprising conclusion: under the Zipf model under consideration, and for moderate and large values of the actual CoD, the resubstitution CoD estimator is the least biased and least variable among all CoD estimators, especially at small number of predictors. We also observed that the leave-one-out and cross-validation CoD estimators tend to perform the worst, whereas the performance of the bootstrap CoD estimator is intermediary, despite its high computational complexity.

Application of ensemble classification rules in genomics and proteomics has become increasingly common. However, the problem of error estimation for these classification rules, particularly for bagging under the small-sample settings prevalent in genomics and proteomics, is not well understood. Breiman proposed the "out-of-bag" method for estimating statistics of bagged classifiers, which was subsequently applied by other authors to estimate the classification error. In this paper, we give an explicit definition of the out-of-bag estimator that is intended to remove estimator bias, by formulating carefully how the error count is normalized. We also report the results of an extensive simulation study of bagging of common classification rules, including LDA, 3NN, and CART, applied on both synthetic and real patient data, corresponding to the use of common error estimators such as resubstitution, leave-one-out, cross-validation, basic bootstrap, bootstrap 632, bootstrap 632 plus, bolstering, semi-bolstering, in addition to the out-of-bag estimator. The results from the numerical experiments indicated that the performance of the out-of-bag estimator is very similar to that of leave-one-out; in particular, the out-of-bag estimator is slightly pessimistically biased. The performance of the other estimators is consistent with their performance with the corresponding single classifiers, as reported in other studies.

Background The complement system, a key component that links the innate and adaptive immune responses, has three pathways: the classical, lectin, and alternative pathways. In the present study, we have analyzed the levels of various complement components in blood samples from dengue fever (DF) and dengue hemorrhagic fever (DHF) patients and found that the level of complement activation is associated with disease severity. Methods and Results Patients with DHF had lower levels of complement factor 3 (C3; p = 0.002) and increased levels of C3a, C4a and C5a (p

Background Dengue virus infection causes a wide spectrum of illness, ranging from sub-clinical to severe disease. Severe dengue is associated with sequential viral infections. A strict definition of primary versus secondary dengue infections requires a combination of several tests performed at different stages of the disease, which is not practical. Methods and Findings We developed a simple method to classify dengue infections as primary or secondary based on the levels of dengue-specific IgG. A group of 109 dengue infection patients were classified as having primary or secondary dengue infection on the basis of a strict combination of results from assays of antigen-specific IgM and IgG, isolation of virus and detection of the viral genome by PCR tests performed on multiple samples, collected from each patient over a period of 30 days. The dengue-specific IgG levels of all samples from 59 of the patients were analyzed by linear discriminant analysis (LDA), and one- and two-dimensional classifiers were designed. The one-dimensional classifier was estimated by bolstered resubstitution error estimation to have 75.1% sensitivity and 92.5% specificity. The two-dimensional classifier was designed by taking also into consideration the number of days after the onset of symptoms, with an estimated sensitivity and specificity of 91.64% and 92.46%. The performance of the two-dimensional classifier was validated using an independent test set of standard samples from the remaining 50 patients. The classifications of the independent set of samples determined by the two-dimensional classifiers were further validated by comparing with two other dengue classification methods: hemagglutination inhibition (HI) assay and an in-house anti-dengue IgG-capture ELISA method. The decisions made with the two-dimensional classifier were in 100% accordance with the HI assay and 96% with the in-house ELISA. Conclusions Once acute dengue infection has been determined, a 2-D classifier based on common dengue virus IgG kits can reliably distinguish primary and secondary dengue infections. Software for calculation and validation of the 2-D classifier is made available for download.

The Eighth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS’2011) was held in College Station, Texas on April 1-2, 2011. The Conference General Chair was Ulisses Braga-Neto, the MCBIOS President for the 2010-2011 term, from Texas A&M University. There were nearly 200 registrants and 140 abstracts were submitted, divided into 48 oral presentation abstracts and 92 poster session abstracts. In addition, participants attended talks by very distinguished Keynote speakers. Joan W. Bennett, from Rutgers University and Member of the National Academy of Science, presented the talk “Chromosomal Composition and Computational Competence;” Donald Geman, from The Johns Hopkins University and co-inventor of the Gibbs Sampler and Random Forest Classifiers, lectured on “Measuring Network Regulation and Differential Expression by Rank Conservation;” while John Weinstein, Chair of the Department of Bioinformatics and Computational Biology at the University of Texas M.D. Anderson Cancer Center, talked on “Personalizing Cancer Medicine in the Era of Next-Generation Sequencing: Omics and Informatics.” The conference also benefited from invited dinner and lunch speakers, who gave informal, highly informative and entertaining talks. Edward R. Dougherty, from Texas A&M University and Director of Computational Biology at the Translational Genomics Institute, asked the audience “Is Biological Science Delightful?” whereas Ernesto Marques, from the Center for Vaccine Research at the University of Pittsburgh, gave the talk “Activation of the Complement System in Dengue Infection: Opportunities for Computational Modeling.” Participants also had the opportunity to attend hands-on workshops on NCBI tools, presented by Peter Cooper from NCBI/NIH, and on protein evolution, presented by Hugh Nicholas and Troy Wymore, from the Pittsburgh Supercomputing Center. The winners of conference awards were: Best Oral Presentations (students): First place: Suzanne Matthews, Texas A&M University Second place (3-way tie): Lori Dalton, Texas A&M University Second place: Shelton Griffith, Oak Ridge National Lab Second place: Winston Haynes, Hendrix College Best Oral Presentations (Post-Doctoral fellows): Yan Li, NCTR Fan Zhang, IUPUI Best Poster (Computation): First place: Tianchuan Du, Southern University Second place: Christopher Cathey, Jackson State University Third place: Ralph Crosby, Texas A&M University Best Poster (Biology): First place: Awantika Singh, UALR/UAMS Second place: Mohammed Shahrokh Esfahani, Texas A&M University Third place: Fang-Han Hsu, Texas A&M University MCBIOS Outstanding Service Award: Dr. Jonathan D. Wren, Oklahoma Medical Research Foundation

Many missing-value (MV) imputation methods have been developed for microarray data, but only a few studies have investigated the relationship between MV imputation and classification accuracy. Furthermore, these studies are problematic in fundamental steps such as MV generation and classifier error estimation. In this work, we carry out a model-based study that addresses some of the issues in previous studies. Six popular imputation algorithms, two feature selection methods, and three classification rules are considered. The results suggest that it is beneficial to apply MV imputation when the noise level is high, variance is small, or gene-cluster correlation is strong, under small to moderate MV rates. In these cases, if data quality metrics are available, then it may be helpful to consider the data point with poor quality as missing and apply one of the most robust imputation algorithms to estimate the true signal based on the available high-quality data points. However, at large MV rates, we conclude that imputation methods are not recommended. Regarding the MV rate, our results indicate the presence of a peaking phenomenon: performance of imputation methods actually improves initially as the MV rate increases, but after an optimum point, performance quickly deteriorates with increasing MV rates.

The recent development of high-throughput molecular genetics technologies has brought a major impact to bioinformatics and systems biology. These technologies have made possible the measurement of the expression profiles of genes and proteins in a highly parallel and integrated fashion. The examination of the huge amounts of genomic and proteomic data holds the promise for understanding the complex interactions between genes and proteins, the functional processes of a cell, and the impact of various factors on a cell, and ultimately, for enabling the design of new technologies for intelligent management of diseases. This special issue focuses on modeling and processing of data arising in bioinformatics, genomics, and proteomics using signal processing methods. The importance of signal processing techniques is due to their important role in extracting, processing, and interpreting the information contained in genomic and proteomic data. It is our hope that signal processing methods will lead to new advances and insights in uncovering the structure, functioning and evolution of biological systems. The special issue consists of nine papers that span a wide range of problems and applications in bioinformatics, genomics, and proteomics such as design of compressive sensing microarrays, analysis of missing values in microarray data, and effect of imputation techniques on post genomic inference methods, RNA sequence alignment, detection of periodicity in genomic sequences and gene expression profiles, clustering and classification of gene and protein expression data, and intervention in probabilistic Boolean networks. Next, we will briefly introduce the papers reported in this special issue. W. Dai et al. analyze how to design a microarray that it is fit for compressive sensing and that captures also the biochemistry of probe-target DNA hybridization. Algorithms and design results are reported for determining probe sequences that satisfy the binding requirements and for evaluating the target concentrations. M. S. B. Sehgal et al. address the general problem of improving post genomic knowledge discovery procedures such as the selection of the most significant genes and inference of gene regulatory networks using missing microarray data imputation techniques. It is shown that instead of neglecting missing data, recycling microarray data via robust imputation techniques can yield substantial performance improvements in the subsequent post genomic discovery procedures. B.-J. Yoon developed a novel efficient and robust approach for fast and accurate structural alignment of RNAs, including pseudoknots. The proposed method turns out to accelerate the dynamic programming algorithm for family-specific models such as profile-csHMMs and CMs, and to be robust to small parameter changes that are present in the model used to predict the constraint. The paper by J. Epps explains in detail the origins of ambiguity in period estimation for symbolic sequences, and proposes a novel hybrid autocorrelation-IPDFT technique for periodicity characterization of sequences. W. Zhao et al. developed a novel algorithm for identification of genes involved in cyclic processes by combining gene expression analysis and prior knowledge. The proposed cyclic-genes detection algorithm is validated on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, and shown to represent a valuable technique for unveiling pathways related to cyclic processes. T. J. Hestilow and Y. Huang propose a novel method for gene clustering using the shape information of gene expression profiles. The shape information which is represented in terms of normalized and time-scaled forward first-order differences is then exploited by a variational Bayes clustering approach and a non-Bayesian (Silhouette) cluster statistic, and shown to yield promising results in clustering time-series microarray data. The paper by W. Zhao et al. proposes a new clustering approach to combine the traditional clustering methods with power spectral analysis of time series gene expression measurements. Simulation results confirm that the proposed clustering approach provides superior performance relative to hierarchical, K-means, and self-organizing maps, and yields additional information about temporal regulated genetic processes, for example, cell-cycle. T. T. Vu and U. M. Braga-Neto address the important problem of assessing the effectiveness of bagging in the classification of small-sample genomic and proteomic data sets. Representative experimental results are presented and discussed. Finally, the paper by B. Faryabi et al. studies the effects on intervention performance in the context of probabilistic Boolean networks due to a reduction in the values of the model parameters.

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Background RNA-Seq is the recently developed high-throughput sequencing technology for profiling the entire transcriptome in any organism. It has several major advantages over current hybridization-based approach such as microarrays. However, the cost per sample by RNA-Seq is still prohibitive for most laboratories. With continued improvement in sequence output, it would be cost-effective if multiple samples are multiplexed and sequenced in a single lane with sufficient transcriptome coverage. The objective of this analysis is to evaluate what sequencing depth might be sufficient to interrogate gene expression profiling in the chicken by RNA-Seq. Results Two cDNA libraries from chicken lungs were sequenced initially, and 4.9 million (M) and 1.6 M (60 bp) reads were generated, respectively. With significant improvements in sequencing technology, two technical replicate cDNA libraries were re-sequenced. Totals of 29.6 M and 28.7 M (75 bp) reads were obtained with the two samples. More than 90% of annotated genes were detected in the data sets with 28.7-29.6 M reads, while only 68% of genes were detected in the data set with 1.6 M reads. The correlation coefficients of gene expression between technical replicates within the same sample were 0.9458 and 0.8442. To evaluate the appropriate depth needed for mRNA profiling, a random sampling method was used to generate different number of reads from each sample. There was a significant increase in correlation coefficients from a sequencing depth of 1.6 M to 10 M for all genes except highly abundant genes. No significant improvement was observed from the depth of 10 M to 20 M (75 bp) reads. Conclusion The analysis from the current study demonstrated that 30 M (75 bp) reads is sufficient to detect all annotated genes in chicken lungs. Ten million (75 bp) reads could detect about 80% of annotated chicken genes, and RNA-Seq at this depth can serve as a replacement of microarray technology. Furthermore, the depth of sequencing had a significant impact on measuring gene expression of low abundant genes. Finally, the combination of experimental and simulation approaches is a powerful approach to address the relationship between the depth of sequencing and transcriptome coverage.

Background Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides. Results We present BPDA, a Bayesian approach for peptide detection in data produced by MS instruments with high enough resolution to baseline-resolve isotopic peaks, such as MALDI-TOF and LC-MS. We model the spectra as a mixture of candidate peptide signals, and the model is parameterized by MS physical properties. BPDA is based on a rigorous statistical framework and avoids problems, such as voting and ad-hoc thresholding, generally encountered in algorithms based on template matching. It systematically evaluates all possible combinations of possible peptide candidates to interpret a given spectrum, and iteratively finds the best fitting peptide signal in order to minimize the mean squared error of the inferred spectrum to the observed spectrum. In contrast to previous detection methods, BPDA performs deisotoping and deconvolution of mass spectra simultaneously, which enables better identification of weak peptide signals and produces higher sensitivities and more robust results. Unlike template-matching algorithms, BPDA can handle complex data where features overlap. Our experimental results indicate that BPDA performs well on simulated data and real MS data sets, for various resolutions and signal to noise ratios, and compares very favorably with commonly used commercial and open-source software, such as flexAnalysis, OpenMS, and Decon2LS, according to sensitivity and detection accuracy. Conclusion Unlike previous detection methods, which only employ isotopic distributions and work at each single charge state alone, BPDA takes into account the charge state distribution as well, thus lending information to better identify weak peptide signals and produce more robust results. The proposed approach is based on a rigorous statistical framework, which avoids problems generally encountered in algorithms based on template matching. Our experiments indicate that BPDA performs well on both simulated data and real data, and compares very favorably with commonly used commercial and open-source software. The BPDA software can be downloaded from http://gsp.tamu.edu/Publications/supplementary/sun10a/bpda.