A Positive Detecting Code and Its Decoding Algorithm for DNA Library Screening

The study of gene functions requires high-quality DNA libraries. However, a large number of tests and screenings are necessary for compiling such libraries. We describe an algorithm for extracting as much information as possible from pooling experiments for library screening. Collections of clones are called pools, and a pooling experiment is a group test for detecting all positive clones. The probability of positiveness for each clone is estimated according to the outcomes of the pooling experiments. Clones with high chance of positiveness are subjected to confirmatory testing. In this paper, we introduce a new positive clone detecting algorithm, called the Bayesian network pool result decoder (BNPD). The performance of BNPD is compared, by simulation, with that of the Markov chain pool result decoder (MCPD) proposed by Knill et al. in 1996. Moreover, the combinatorial properties of pooling designs suitable for the proposed algorithm are discussed in conjunction with combinatorial designs and dhbox{-}{rm disjunct} matrices. We also show the advantage of utilizing packing designs or BIB designs for the BNPD algorithm.     

Pooled Genomic Indexing (PGI): analysis and design of experiments.

Pooled Genomic Indexing (PGI) is a novel method for physical mapping of clones onto known sequences. PGI is carried out by pooling arrayed clones and generating shotgun sequence reads from the pools. The shotgun sequences are compared to a reference sequence. In the simplest case, clones are placed on an array and are pooled by rows and columns. If a shotgun sequence from a row pool and another shotgun sequence from a column pool match the reference sequence at a close distance, they are both assigned to the clone at the intersection of the two pools. Accordingly, the clone is mapped onto the region of the reference sequence between the two matches. A probabilistic model for PGI is developed, and several pooling designs are described and analyzed, including transversal designs and designs from linear codes. The probabilistic model and the pooling schemes are validated in simulated experiments where 625 rat bacterial artificial chromosome (BAC) clones and 207 mouse BAC clones are mapped onto homologous human sequence.     

A construction of pooling designs with some happy surprises.

The screening of data sets for "positive data objects" is essential to modern technology. A (group) test that indicates whether a positive data object is in a specific subset or pool of the dataset can greatly facilitate the identification of all the positive data objects. A collection of tested pools is called a pooling design. Pooling designs are standard experimental tools in many biotechnical applications. In this paper, we use the (linear) subspace relation coupled with the general concept of a "containment matrix" to construct pooling designs with surprisingly high degrees of error correction (detection.) Error-correcting pooling designs are important to biotechnical applications where error rates often are as high as 15%. What is also surprising is that the rank of the pooling design containment matrix is independent of the number of positive data objects in the dataset.     

The Design of Pooling Experiments for Screening a Clone Map

We consider nonadaptive pooling designs for unique-sequence screening of a 1530-clone map ofAspergillus nidulans.The map has the properties that the clones are, with possibly a few exceptions, ordered and no more than 2 of them cover any point on the genome. We propose two subdesigns of the Steiner systemS(3, 5, 65), one with 65 pools and approximately 118 clones per pool, the other with 54 pools and about 142 clones per pool. Each design allows 1 or 2 positive clones to be detected, even in the presence of substantial experimental error rates. More efficient designs are possible if the overlap information in the map is exploited, if there is no constraint on the number of clones in a pool, and if no error tolerance is required. An information theory lower bound requires at least 12 pools to satisfy these minimal criteria, and an “interleaved binary” design can be constructed on 20 pools, with about 380 clones per pool. However, the designs with more pools have important properties of robustness to various possible errors and general applicability to a wider class of pooling experiments.     

Development of a sample pooling strategy for the serodiagnosis of small ruminant lentiviral infections using the ELITEST-MVV ELISA

To reduce the costs of using the ELITEST-MVV, we explored the possibilities of sample pooling. Straight forward pooling applying the manufacturer's test conditions resulted in a significant loss of sensitivity. This was solved by using lower pre-dilutions of the samples than prescribed. Although an increase of background signal was encountered, discrimination between positive and negative samples was even better at pre-dilutions up to 12.5× as compared to the standard pre-dilution of 100×. This implied that pooling of up to eight samples was feasible. Receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value for the testing of pooled serum samples.

A model for cost-benefit analysis of pooling was applied which combines the economics of the technical performance of the modified assay and other additional cost factors connected with pooling such as hands-on time for composing the pools, expected seroprevalence in the test population, sample tracing and testing the individual samples of positive pools.

We concluded that pooling of samples was only feasible for monitoring SRLV-free accredited flocks because of their very low prevalence of infection. A pool consisting of five samples turned out to be the economical optimum although technically pool sizes of 10 samples were permitted.     

Nonadaptive algorithms for threshold group testing with inhibitors and error-tolerance

A group test gives a positive (negative) outcome if it contains at least u (at most l) positive items, and an arbitrary outcome if the number of positive items is between thresholds l and u. This problem introduced by Damaschke is called threshold group testing. It is a generalization of classical group testing. Chen and Fu extended this problem to the error-tolerant version and first proposed efficient nonadaptive algorithms. In this article, we extend threshold group testing to the k-inhibitors model in which a test has a positive outcome if it contains at least u positives and at most k-1 inhibitors. By using (d + k - l, u; 2e + 1]-disjunct matrix we provide nonadaptive algorithms for the threshold group testing model with k-inhibitors and at most e-erroneous outcomes. The decoding complexity is O(n(u+k) log n) for fixed parameters (d, u, l, k, e).     

Considerations for Group Testing: A Practical Approach for the Clinical Laboratory

Group testing, also known as pooled sample testing, was first proposed by Robert Dorfman in 1943. While sample pooling has been widely practiced in blood-banking, it is traditionally seen as anathema for clinical laboratories. However, the ongoing COVID-19 pandemic has re-ignited interest for group testing among clinical laboratories to mitigate supply shortages. We propose five criteria to assess the suitability of an analyte for pooled sample testing in general and outline a practical approach that a clinical laboratory may use to implement pooled testing for SARS-CoV-2 PCR testing. The five criteria we propose are: (1) the analyte concentrations in the diseased persons should be at least one order of magnitude (10 times) higher than in healthy persons; (2) sample dilution should not overly reduce clinical sensitivity; (3) the current prevalence must be sufficiently low for the number of samples pooled for the specific protocol; (4) there is no requirement for a fast turnaround time; and (5) there is an imperative need for resource rationing to maximise public health outcomes. The five key steps we suggest for a successful implementation are: (1) determination of when pooling takes place (pre-pre analytical, pre-analytical, analytical); (2) validation of the pooling protocol; (3) ensuring an adequate infrastructure and archival system; (4) configuration of the laboratory information system; and (5) staff training. While pool testing is not a panacea to overcome reagent shortage, it may allow broader access to testing but at the cost of reduction in sensitivity and increased turnaround time.     

A new pooling strategy for high-throughput screening: the Shifted Transversal Design

Background  

In binary high-throughput screening projects where the goal is the identification of low-frequency events, beyond the obvious issue of efficiency, false positives and false negatives are a major concern. Pooling constitutes a natural solution: it reduces the number of tests, while providing critical duplication of the individual experiments, thereby correcting for experimental noise. The main difficulty consists in designing the pools in a manner that is both efficient and robust: few pools should be necessary to correct the errors and identify the positives, yet the experiment should not be too vulnerable to biological shakiness. For example, some information should still be obtained even if there are slightly more positives or errors than expected. This is known as the group testing problem, or pooling problem.     

Prevalence of major depression one year after predictive testing for Huntington's disease

Psychiatric hospitalizations, completed suicides, and suicide attempts are rare after predictive testing for Huntington's disease (HD). Case studies have shown that major depression can be a consequence of being tested, although no studies have shown how common this is. The present study evaluated the prevalence of major depression during the first year after disclosure. We conducted retrospective data and chart reviews of 153 persons (50 testing positive, 103 testing negative) evaluated every 3 months for depression. There was no significant baseline difference in the percentage of "positives" and "negatives" who had pre-testing major depressive episodes (14% vs. 12%, respectively). A senior psychiatrist reviewed data from the Schedule for Affective Disorders and Schizophrenia-Change Version, from the Beck Depression Inventory, and from clinical notes for every follow-up contact completed. The 1-year prevalence of major depression among positives was 6.0%, compared to 3.0% among negatives (p = 0.30), and an estimated 3% population prevalence. One-year prevalence of clinically significant depressive symptoms, whether or not major depression was diagnosed, was 20.0% in positives and 12.6% in negatives (p = 0.17). Although not statistically significant, depressive symptoms and major depression occurred more frequently among those who tested positive. Despite some evidence to the contrary, including our own studies, a positive predictive test for HD is not psychologically benign. Clinical testing programs should assess patients for depressive symptoms after testing, and patients with clinically significant complaints should be referred to a mental health professional.     

Replication,Communication, and the Population Dynamics of Scientific Discovery

Many published research results are false (Ioannidis, 2005), and controversy continues over the roles of replication and publication policy in improving the reliability of research. Addressing these problems is frustrated by the lack of a formal framework that jointly represents hypothesis formation, replication, publication bias, and variation in research quality. We develop a mathematical model of scientific discovery that combines all of these elements. This model provides both a dynamic model of research as well as a formal framework for reasoning about the normative structure of science. We show that replication may serve as a ratchet that gradually separates true hypotheses from false, but the same factors that make initial findings unreliable also make replications unreliable. The most important factors in improving the reliability of research are the rate of false positives and the base rate of true hypotheses, and we offer suggestions for addressing each. Our results also bring clarity to verbal debates about the communication of research. Surprisingly, publication bias is not always an obstacle, but instead may have positive impacts—suppression of negative novel findings is often beneficial. We also find that communication of negative replications may aid true discovery even when attempts to replicate have diminished power. The model speaks constructively to ongoing debates about the design and conduct of science, focusing analysis and discussion on precise, internally consistent models, as well as highlighting the importance of population dynamics.     

More Powerful Likelihood Ratio Tests for Isotonic Binomial Proportions

Binomial group testing involves pooling individuals into groups and observing a binary response on each group. Results from the group tests can then be used to draw inference about population proportions. Its use as an experimental design has received much attention in recent years, especially in public‐health screening experiments and vector‐transfer designs in plant pathology. We investigate the benefits of group testing in situations wherein one desires to test whether or not probabilities are increasingly ordered across the levels of an observed qualitative covariate, i.e., across strata of a population or among treatment levels. We use a known likelihood ratio test for individual testing, but extend its use to group‐testing situations to show the increases in power conferred by using group testing when operating in this constrained parameter space. We apply our methods to data from an HIV study involving male subjects classified as intraveneous drug users.     

C911: A Bench-Level Control for Sequence Specific siRNA Off-Target Effects

Small interfering RNAs (siRNAs) have become a ubiquitous experimental tool for down-regulating mRNAs. Unfortunately, off-target effects are a significant source of false positives in siRNA experiments and an effective control for them has not previously been identified. We introduce two methods of mismatched siRNA design for negative controls based on changing bases in the middle of the siRNA to their complement bases. To test these controls, a test set of 20 highly active siRNAs (10 true positives and 10 false positives) was identified from a genome-wide screen performed in a cell-line expressing a simple, constitutively expressed luciferase reporter. Three controls were then synthesized for each of these 20 siRNAs, the first two using the proposed mismatch design methods and the third being a simple random permutation of the sequence (scrambled siRNA). When tested in the original assay, the scrambled siRNAs showed significantly reduced activity in comparison to the original siRNAs, regardless of whether they had been identified as true or false positives, indicating that they have little utility as experimental controls. In contrast, one of the proposed mismatch design methods, dubbed C911 because bases 9 through 11 of the siRNA are replaced with their complement, was able to completely distinguish between the two groups. False positives due to off-target effects maintained most of their activity when the C911 mismatch control was tested, whereas true positives whose phenotype was due to on-target effects lost most or all of their activity when the C911 mismatch was tested. The ability of control siRNAs to distinguish between true and false positives, if widely adopted, could reduce erroneous results being reported in the literature and save research dollars spent on expensive follow-up experiments.     

Subpopulations of mature murine thymocytes: properties of CD4-CD8+ and CD4+CD8- thymocytes lacking the heat-stable antigen

The heat-stable antigen (HSA), recognized by the monoclonal antibodies M1/69, B2A2, and J11d, is low or absent on the surface of most murine peripheral T cells but present on all but 3% of thymocytes. The CD4-CD8+ and CD4+CD8- or "single positive" thymic populations may be divided into further subgroups based on surface HSA expression. One group, CD4-CD8+ and expressing very high levels of HSA (HSA++), is an immature, T cell antigen receptor (TcR) negative, outer cortical blast cell. However, a further subdivision of CD4-CD8+ and CD4+CD8- single positives may be made, into those negative to low for HSA (HSA-) and those expressing moderate amounts of HSA (HSA+). The proportion of HSA- single positives is low in the thymus of young mice, whereas the proportion of HSA+ single positives is similar to that of the adult. Both the HSA- and the HSA+ subsets of single positive thymocytes from adult mice are CD3+ and express the normal peripheral T cell incidence of V beta 8 determinants on the TcR. On stimulation with concanavalin A in limit-dilution culture both HSA- and HSA+ subsets of single positive thymocytes give a high frequency of proliferating clones, and the clones from both HSA- and HSA+ subsets of CD4-CD8+ thymocytes are cytotoxic. Thus both HSA- and HSA+ single positive thymocytes are functionally mature. The HSA- subsets of single positive thymocytes differ from the HSA+ subsets in being slightly larger in size, in expressing higher levels of MEL-14, in binding more peanut agglutinin, and in including a proportion of cells expressing high levels of the Pgp-1 glycoprotein. It is suggested that HSA- CD4-CD8+ and HSA- CD4+CD8- thymocytes are more mature than their HSA+ counterparts, and might represent a previously activated or "memory" thymic subpopulation.     

Group testing to annihilate pairs applied to DNA cross-hybridization elimination using SYBR Green I.

A group testing (or pooling) method for DNA strands that identifies at least one strand in a pair of cross-hybridized oligonucleotides is given. This pooling method can be extended to any population of objects where certain pairs together produce an observable function or signal. Pairs of objects may work together to produce an undesirable result or a detrimental function. If just a single element of such a pair is identified and eliminated, then the undesirable function of that pair is destroyed. In particular, the ability to ensure that a set DNA probes do not yield undesired cross-hybridizations is important when these probes and/or their complements are used in the production of a hybridization signal that is intended to convey information. Here we report a "proof of principle" method, similar to those used to screen DNA libraries, that screens pools of probes for unwanted cross-hybridization events and identifies the offending probes. In the reported experiment, a cross-hybridized duplex in a pool of probes is detected by using the fluorescent dye SYBR Green I. This dye is known to produce greater fluorescence when bound to duplex DNA as opposed to single-stranded DNA. The method described here is sensitive, fast, and simple.     

Biases induced by pooling samples in microarray experiments

MOTIVATION: If there is insufficient RNA from the tissues under investigation from one organism, then it is common practice to pool RNA. An important question is to determine whether pooling introduces biases, which can lead to inaccurate results. In this article, we describe two biases related to pooling, from a theoretical as well as a practical point of view. RESULTS: We model and quantify the respective parts of the pooling bias due to the log transform as well as the bias due to biological averaging of the samples. We also evaluate the impact of the bias on the statistical differential analysis of Affymetrix data.     

An inhibitor typing scheme applicable to Lancefield group E streptococci

A previously described inhibitor typing scheme for hemolytic streptococci has been utilized to test 12 well-characterized strains of group E streptococci. These strains were differentiated into five inhibitor production (P) types and four inhibitor sensitivity (S) types: seven different strain "fingerprints" (combinations of P-type and S-type) being demonstrated. Inhibitor production by group E streptococci was increased under conditions of anaerobic incubation. The inhibitor fingerprints were stable on repeated testing of strains and it is suggested that the scheme is of value both for the labelling of serologically untypable strains and for subdividing strains belonging to existing serotypes.     

Regression models for disease prevalence with diagnostic tests on pools of serum samples

Whether the aim is to diagnose individuals or estimate prevalence, many epidemiological studies have demonstrated the successful use of tests on pooled sera. These tests detect whether at least one sample in the pool is positive. Although originally designed to reduce diagnostic costs, testing pools also lowers false positive and negative rates in low prevalence settings and yields more precise prevalence estimates. Current methods are aimed at estimating the average population risk from diagnostic tests on pools. In this article, we extend the original class of risk estimators to adjust for covariates recorded on individual pool members. Maximum likelihood theory provides a flexible estimation method that handles different covariate values in the pool, different pool sizes, and errors in test results. In special cases, software for generalized linear models can be used. Pool design has a strong impact on precision and cost efficiency, with covariate-homogeneous pools carrying the largest amount of information. We perform joint pool and sample size calculations using information from individual contributors to the pool and show that a good design can severely reduce cost and yet increase precision. The methods are illustrated using data from a Kenyan surveillance study of HIV. Compared to individual testing, age-homogeneous, optimal-sized pools of average size seven reduce cost to 44% of the original price with virtually no loss in precision.     

Genomic mapping by anchoring random clones: a mathematical analysis.

A complete physical map of the DNA of an organism, consisting of overlapping clones spanning the genome, is an extremely useful tool for genomic analysis. Various methods for the construction of such physical maps are available. One approach is to assemble the physical map by "fingerprinting" a large number of random clones and inferring overlap between clones with sufficiently similar fingerprints. E.S. Lander and M.S. Waterman (1988, Genomics 2:231-239) have recently provided a mathematical analysis of such physical mapping schemes, useful for planning such a project. Another approach is to assemble the physical map by "anchoring" a large number of random clones--that is, by taking random short regions called anchors and identifying the clones containing each anchor. Here, we provide a mathematical analysis of such a physical mapping scheme.     

Theoretical analysis of library screening using a N-dimensional pooling strategy.

A solution to the problem of library screening is analysed. We examine how to retrieve those clones that are positive for a single copy landmark from a whole library while performing only a minimum number of laboratory tests: the clones are arranged on a matrix (i.e in 2 dimensions) and pooled according to the rows and columns. A fingerprint is determined for each pool and an analysis allows selection of a list containing all the positive clones, plus a few false positives. These false positives are eliminated by using another (or several other) matrix which has to be reconfigured in a way as different as possible from the previous one. We examine the use of cubes (3 dimensions) or hypercubes of any dimension instead of matrices and analyse how to reconfigure them in order to eliminate the false positives as efficiently as possible. The advantage of the method proposed is the low number of tests required and the low number of pools that require to be prepared [only 258 pools and 282 tests (258 + 24 verifications) are needed to screen the 72,000 clones of the CEPH YAC library (1) with a sequence-tagged site]. Furthermore, this method allows easy and systematic screenings and can be applied to a large physical mapping project, which will lead to an interesting map with a low, precisely known, rate of error: when fingerprinting a 150 Mb chromosome with the CEPH YAC library and 1750 sequence-tagged sites, 903,000 tests would be necessary to obtain about 20 contigs of an average length of 6.7 Mb, while only about one false positive would be expected in the resultant map. Finally, STSs can be ordered by dividing a clone library into sublibraries (corresponding to groups of microplates for example) and testing each STS on pooled clones from each sublibrary. This allows to dedicate to each STSs a fingerprint that consists in the list of the positive pools. In many cases these fingerprints will be enough to order the STSs. Indeed if large YACs (greater than 1 Mb) can be obtained, the combined screening of DNA families and YAC DNA pools would allow an integrated construction of both genetic and physical maps of the human genome, that will also reduce the optimal number of meioses needed for a 1 centimorgan linkage map.     

Integration of animal linkage and BAC contig maps using overgo hybridization

The alignment of genome linkage maps, defined primarily by segregation of sequence-tagged site (STS) markers, with BAC contig physical maps and full genome sequences requires high throughput mechanisms to identify BAC clones that contain specific STS. A powerful technique for this purpose is multi-dimensional hybridization of "overgo" probes. The probes are chosen from available STS sequence data by selecting unique probe sequences that have a common melting temperature. We have hybridized sets of 216 overgo probes in subset pools of 36 overgos at a time to filter-spotted chicken BAC clone arrays. A four-dimensional pooling strategy, including one degree of redundancy, has been employed. This requires 24 hybridizations to completely assign BACs for all 216 probes. Results to date are consistent with about a 10% failure rate in overgo probe design and a 15-20% false negative detection rate within a group of 216 markers. Three complete rounds of overgo hybridization, each to sets of about 39,000 BACs (either BAMHI or ECORI partial digest inserts) generated a total of 1853 BAC alignments for 517 mapped chicken genome STS markers. These data are publicly available, and they have been used in the assembly of a first generation BAC contig map of the chicken genome.