tinyFLP and tinyCAT: software for automatic peak selection and scoring of AFLP data tables

Amplified fragment length polymorphism (AFLP) is a well-established method for DNA fingerprinting. In the last years, fragment separation gradually changed from using gel to capillary electrophoresis, resulting in large, digital data sets. Automatic scoring softwares available to date are proprietary and expensive. tiny FLP/tiny CAT is a compact, open-source software for automatic scoring of AFLP data. It reads from PeakScanner^© generated tables, optimizes the conversion of peak data into a binary allelic matrix and removes operator-mediated ambiguities from the scoring process. The output is formatted for direct use in MrBayes and other phylogenetic applications.     

Optimizing automated AFLP scoring parameters to improve phylogenetic resolution

The amplified fragment length polymorphism (AFLP) technique is an increasingly popular component of the phylogenetic toolbox, particularly for plant species. Technological advances in capillary electrophoresis now allow very precise estimates of DNA fragment mobility and amplitude, and current AFLP software allows greater control of data scoring and the production of the binary character matrix. However, for AFLP to become a useful modern tool for large data sets, improvements to automated scoring are required. We design a procedure that can be used to optimize AFLP scoring parameters to improve phylogenetic resolution and demonstrate it for two AFLP scoring programs (GeneMapper and GeneMarker). In general, we found that there was a trade-off between getting more characters of lower quality and fewer characters of high quality. Conservative settings that gave the least error did not give the best phylogenetic resolution, as too many useful characters were discarded. For example, in GeneMapper, we found that bin width was a crucial parameter, and that although reducing bin width from 1.0 to 0.5 base pairs increased the error rate, it nevertheless improved resolution due to the increased number of informative characters. For our 30-taxon data sets, moving from default to optimized parameter settings gave between 3 and 11 extra internal edges with >50% bootstrap support, in the best case increasing the number of resolved edges from 14 to 25 out of a possible 27. Nevertheless, improvements to current AFLP software packages are needed to (1) make use of replicate profiles to calibrate the data and perform error calculations and (2) perform tests to optimize scoring parameters in a rigorous and automated way. This is true not only when AFLP data are used for phylogenetics, but also for other applications, including linkage mapping and population genetics.     

Influence of parameter settings in automated scoring of AFLPs on population genetic analysis

The use of procedures for the automated scoring of amplified fragment length polymorphisms (AFLP) fragments has recently increased. Corresponding software does not only automatically score the presence or absence of AFLP fragments, but also allows an evaluation of how different settings of scoring parameters influence subsequent population genetic analyses. In this study, we used the automated scoring package rawgeno to evaluate how five scoring parameters influence the number of polymorphic bins and estimates of pairwise genetic differentiation between populations (F_st). Steps were implemented in r to automatically run the scoring process in rawgeno for a set of different parameter combinations. While we found the scoring parameters minimum bin width and minimum number of samples per bin to have only weak influence on pairwise F_st values, maximum bin width and bin reproducibility had much stronger effects. The minimum average bin fluorescence scoring parameter affected F_st values in an only moderate way. At a range of scoring parameters around the default settings of rawgeno , the number of polymorphic bins as well as pairwise F_st values stayed rather constant. This study thus shows the particularities of AFLP scoring, be it either manual or automatical, can have profound effects on subsequent population genetic analysis.     

AFLP technology for DNA fingerprinting

The AFLP technique is a powerful DNA fingerprinting technology applicable to any organism without the need for prior sequence knowledge. The protocol involves the selective PCR amplification of restriction fragments of a total digest of genomic DNA, typically obtained with a mix of two restriction enzymes. Two limited sets of AFLP primers are sufficient to generate a large number of different primer combinations (PCs), each of which will yield unique fingerprints. Visualization of AFLP fingerprints after gel electrophoresis of AFLP products is described using either a conventional autoradiography platform or an automated LI-COR system. The AFLP technology has been used predominantly for assessing the degree of variability among plant cultivars, establishing linkage groups in crosses and saturating genomic regions with markers for gene landing efforts. AFLP fragments may also be used as physical markers to determine the overlap and positions of genomic clones and to integrate genetic and physical maps. Crucial characteristics of the AFLP technology are its robustness, reliability and quantitative nature. This latter feature has been exploited for co-dominant scoring of AFLP markers in sample collections such as F2 or back-cross populations using appropriate AFLP scoring software. This protocol can be completed in 2-3 d.     

Assessment of Genetic Diversity of Biodiesel Species <Emphasis Type="Italic">Pongamia pinnata</Emphasis> Accessions using AFLP and Three Endonuclease-AFLP

Efficacy of two dominant molecular markers, namely, amplified fragment length polymorphism (AFLP) and three endonuclease (TE)-AFLP, were assessed in 20 individuals of the biodiesel species Pongamia pinnata. Four primer combinations generated a total of 254 and 194 bands in AFLP and TE-AFLP, respectively. Both techniques could unequivocally identify each accession used in this study. The Jaccard’s similarity coefficient ranged from 0.30 to 0.90 for AFLP and from 0.25 to 0.85 for TE-AFLP. The correlation coefficient between AFLP and TE-AFLP dendrogram was 0.56 which was low but significant (P < 0.001). Values of effective multiplex ratio, marker index, and resolving power were markedly higher in AFLP than in TE-AFLP. However, the band intensities across different lanes were uniform in TE-AFLP leading to easy and accurate scoring of gels which resulted in slightly higher bootstrap values with TE-AFLP data as compared to AFLP data. Inferences based on TE-AFLP data had similar level of biological relevance as compared to AFLP data when location and diameter of trees were taken in to consideration. However, the easy scorability of TE-AFLP profiles is extremely important and especially desirable in studies requiring genotyping of large number of individuals distributed across many gels.     

Automated Masking of AFLP Markers Improves Reliability of Phylogenetic Analyses

The amplified fragment length polymorphisms (AFLP) method has become an attractive tool in phylogenetics due to the ease with which large numbers of characters can be generated. In contrast to sequence-based phylogenetic approaches, AFLP data consist of anonymous multilocus markers. However, potential artificial amplifications or amplification failures of fragments contained in the AFLP data set will reduce AFLP reliability especially in phylogenetic inferences. In the present study, we introduce a new automated scoring approach, called “AMARE” (AFLP MAtrix REduction). The approach is based on replicates and makes marker selection dependent on marker reproducibility to control for scoring errors. To demonstrate the effectiveness of our approach we record error rate estimations, resolution scores, PCoA and stemminess calculations. As in general the true tree (i.e. the species phylogeny) is not known, we tested AMARE with empirical, already published AFLP data sets, and compared tree topologies of different AMARE generated character matrices to existing phylogenetic trees and/or other independent sources such as morphological and geographical data. It turns out that the selection of masked character matrices with highest resolution scores gave similar or even better phylogenetic results than the original AFLP data sets.     

Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models

Docking programs can generate subsets of a compound collection with an increased percentage of actives against a target (enrichment) by predicting their binding mode (pose) and affinity (score), and retrieving those with the highest scores. Using the QXP and GOLD programs, we compared the ability of six single scoring functions (PLP, Ligscore, Ludi, Jain, ChemScore, PMF) and four composite scoring models (Mean Rank: MR, Rank-by-Vote: Vt, Bayesian Statistics: BS and PLS Discriminant Analysis: DA) to separate compounds that are active against CDK2 from inactives. We determined the enrichment for the entire set of actives (IC50 < 10 microM) and for three activity subsets. In all cases, the enrichment for each subset was lower than for the entire set of actives. QXP outperformed GOLD at pose prediction, but yielded only moderately better enrichments. Five to six scoring functions yielded good enrichments with GOLD poses, while typically only two worked well with QXP poses. For each program, two scoring functions generally performed better than the others (Ligscore2 and Ludi for GOLD; QXP and Jain for QXP). Composite scoring functions yielded better results than single scoring functions. The consensus approaches MR and Vt worked best when separating micromolar inhibitors from inactives. The statistical approaches BS and DA, which require training data, performed best when distinguishing between low and high nanomolar inhibitors. The key observation that all hit rate profiles for all four activity intervals for all scoring schemes for both programs are significantly better than random, is evidence that docking can be successfully applied to enrich compound collections.     

Cost-effective fluorescent amplified fragment length polymorphism (AFLP) analyses using a three primer system

The amplified fragment length polymorphism (AFLP) technique is a widely used multi-purpose DNA fingerprinting tool. The ability to size-separate fluorescently labelled AFLP fragments on a capillary electrophoresis instrument has provided a means for high-throughput genome screening, an approach particularly useful in studying the molecular ecology of nonmodel organisms. While the 'per-marker-generated' costs for AFLP are low, fluorescently labelled oligonucleotides remain costly. We present a cost-effective method for fluorescently end-labelling AFLPs that should make this tool more readily accessible for laboratories with limited budgets. Both standard fluorescent AFLPs and the end-labelled alternatives presented here are repeatable and produce similar numbers of fragments when scored using both manual and automated scoring methods. While it is not recommended to combine data using the two approaches, the results of the methods are qualitatively comparable, indicating that AFLP end-labelling is a robust alternative to standard methods of AFLP genotyping. For researchers commencing a new AFLP project, the AFLP end-labelling method outlined here is easily implemented, as it does not require major changes to PCR protocols and can significantly reduce the costs of AFLP studies.     

Codominant scoring of AFLP in association panels

A study on the codominant scoring of AFLP markers in association panels without prior knowledge on genotype probabilities is described. Bands are scored codominantly by fitting normal mixture models to band intensities, illustrating and optimizing existing methodology, which employs the EM-algorithm. We study features that improve the performance of the algorithm, and the unmixing in general, like parameter initialization, restrictions on parameters, data transformation, and outlier removal. Parameter restrictions include equal component variances, equal or nearly equal distances between component means, and mixing probabilities according to Hardy–Weinberg Equilibrium. Histogram visualization of band intensities with superimposed normal densities, and optional classification scores and other grouping information, assists further in the codominant scoring. We find empirical evidence favoring the square root transformation of the band intensity, as was found in segregating populations. Our approach provides posterior genotype probabilities for marker loci. These probabilities can form the basis for association mapping and are more useful than the standard scoring categories A, H, B, C, D. They can also be used to calculate predictors for additive and dominance effects. Diagnostics for data quality of AFLP markers are described: preference for three-component mixture model, good separation between component means, and lack of singletons for the component with highest mean. Software has been developed in R, containing the models for normal mixtures with facilitating features, and visualizations. The methods are applied to an association panel in tomato, comprising 1,175 polymorphic markers on 94 tomato hybrids, as part of a larger study within the Dutch Centre for BioSystems Genomics.     

Successful analysis of AFLPs from non-lethally sampled wing tissues in butterflies

We demonstrate the successful generation and analysis of amplified fragment length polymorphism (AFLP) profiles from small samples of wing tissue in two butterfly species. With slight modifications of commercial DNA extraction and AFLP kit protocols, we produced highly repeatable AFLP profiles from these non-destructive tissue samples. Error rates were comparable to those previously reported for AFLPs generated from lethally obtained thoracic tissue. Furthermore, AFLP profiles obtained from thoracic and wing tissues of the same individuals were identical. Our results indicate that AFLP analysis is a viable method of obtaining genetic data from threatened populations of butterflies, and potentially other insects, using small, non-destructive tissue samples.     

Phylogenetic signal in AFLP data sets

AFLP markers provide a potential source of phylogenetic information for molecular systematic studies. However, there are properties of restriction fragment data that limit phylogenetic interpretation of AFLPs. These are (a) possible nonindependence of fragments, (b) problems of homology assignment of fragments, (c) asymmetry in the probability of losing and gaining fragments, and (d) problems in distinguishing heterozygote from homozygote bands. In the present study, AFLP data sets of Lactuca s.l. were examined for the presence of phylogenetic signal. An indication of this signal was provided by carrying out tree length distribution skewness (g1) tests, permutation tail probability (PTP) tests, and relative apparent synapomorphy analysis (RASA). A measure of the support for internal branches in the optimal parsimony tree (MPT) was made using bootstrap, jackknife, and decay analysis. Finally, the extent of congruence in MPTs for AFLP and internal transcribed spacer (ITS)-1 data sets for the same taxa was made using the partition homogeneity test (PHT) and the Templeton test. These analytical studies suggested the presence of phylogenetic signal in the AFLP data sets, although some incongruence was found between AFLP and ITS MPTs. An extensive literature survey undertaken indicated that authors report a general congruence of AFLP and ITS tree topologies across a wide range of taxonomic groups, suggesting that the present results and conclusions have a general bearing. In these earlier studies and those for Lactuca s.l., AFLP markers have been found to be informative at somewhat lower taxonomic levels than ITS sequences. Tentative estimates are suggested for the levels of ITS sequence divergence over which AFLP profiles are likely to be phylogenetically informative.     

ORCHIDEE‐STICS,a process‐based model of sugarcane biomass production: calibration of model parameters governing phenology

Agro‐Land Surface Models (agro‐LSM) combine detailed crop models and large‐scale vegetation models (DGVMs) to model the spatial and temporal distribution of energy, water, and carbon fluxes within the soil–vegetation–atmosphere continuum worldwide. In this study, we identify and optimize parameters controlling leaf area index (LAI) in the agro‐LSM ORCHIDEE‐STICS developed for sugarcane. Using the Morris method to identify the key parameters impacting LAI, at eight different sugarcane field trial sites, in Australia and La Reunion island, we determined that the three most important parameters for simulating LAI are (i) the maximum predefined rate of LAI increase during the early crop development phase, a parameter that defines a plant density threshold below which individual plants do not compete for growing their LAI, and a parameter defining a threshold for nitrogen stress on LAI. A multisite calibration of these three parameters is performed using three different scoring functions. The impact of the choice of a particular scoring function on the optimized parameter values is investigated by testing scoring functions defined from the model‐data RMSE, the figure of merit and a Bayesian quadratic model‐data misfit function. The robustness of the calibration is evaluated for each of the three scoring functions with a systematic cross‐validation method to find the most satisfactory one. Our results show that the figure of merit scoring function is the most robust metric for establishing the best parameter values controlling the LAI. The multisite average figure of merit scoring function is improved from 67% of agreement to 79%. The residual error in LAI simulation after the calibration is discussed.     

How sophisticated should a scoring function be to ensure successful docking, scoring and virtual screening?

To estimate how sophisticated should an empirical scoring function be to ensure successful docking, scoring and virtual screening a new scoring function NScore (naive score) has been developed and tested. NScore is an extremely simple function and has the minimum possible number of parameters; nevertheless, it allows all the main effects determining the ligand–protein interaction to be taken into account. The fundamental difference of NScore from the currently used empirical functions is that all its parameters are selected on the basis of general physical considerations, without any adjustment or training with the use of experimental data on ligand–protein interaction. The results of docking and scoring with the use of NScore in an independent test sets of proteins and ligands have proved to be as good as those yielded by the ICM, GOLD, and Glide software packages, which use sophisticated empirical scoring functions. With respect to some parameters, the results of docking with the use of NScore are even better than those obtained using other functions. Since no training set is used in the development of NScore, this scoring function is indeed versatile in that it does not depend on the specific goal or target. We have performed virtual screening for ten targets and obtained results almost as good as those yielded by the Glide and better than GOLD and DOCK software. Figure Average percent of known actives found vs percent of the ranked database screened (x axis) for NScore (NScore, black)- an extremely simple function where all parameters are selected on the basis of general physical considerations, without any adjustment or training with the use of experimental data, Glide XP (XP, red), Glide SP (SP, green), DOCK (DOCK, blue), GOLD GoldScore1x (gold1x, cyan), and GOLD ChemScore1x (chem1x, magenta). Grey lines (rand show results expected by chance.     

Fragment length distributions and collision probabilities for AFLP markers

AFLP is a DNA fingerprinting technique frequently used in plant and animal sciences. A drawback of the technique is the occurrence of multiple DNA fragments of the same length in a single AFLP lane, which we name a collision. In this article we quantify the problem. The well-known birthday problem plays a role. Calculation of collision probabilities requires a fragment length distribution (fld). We discuss three ways to estimate the fld: based on theoretical considerations, on in-silico determination using DNA sequence data from Arabidopsis thaliana, or on direct estimation from AFLP data. In the latter case we use a generalized linear model with monotone smoothing of the fragment length probabilities. Collision probabilities are calculated from two perspectives, assuming known fragment counts and assuming known band counts. We compare results for a number of fld's, ranging from uniform to highly skewed. The conclusion is that collisions occur often, with higher probabilities for higher numbers of bands, for more skewed distributions, and, to a lesser extent, for smaller scoring ranges. For a typical plant genome an AFLP with 19 bands is likely to contain the first collision. Practical implications of collisions are discussed. AFLP examples from lettuce and chicory are used for illustration.     

Evaluating CASP4 predictions with physical energy functions

Physical energy scoring functions based on implicit solvation models are tested by evaluating predictions from the most recent CASP4 competition. The best performing scoring functions are identified along with the best protocol for preparing structures before energies are evaluated. Ranking of structures with the best scoring functions is compared across CASP4 targets to establish when physical scoring functions can be expected to reliably distinguish structures that are most similar to the native fold in a set of misfolded or unfolded protein conformations. The results are used to interpret previous studies where scoring functions were tested on the standard decoy sets by Park, Levitt, and Baker. We show that the best physical scoring functions can be applied successfully in automated consensus scoring applications where a single best conformation has to be selected from a set of structures from different sources. Finally, the potential for better protein structure scoring functions is discussed with a suggestion for an empirically parameterized linear combination of energy components.     

The status of AFLP in the genomics era and a pipeline for converting AFLPs into single-locus markers

Even though next-generation sequencing (NGS) has now become the predominant state-of-the-art technique for genotyping populations, amplified fragment length polymorphism (AFLP) DNA fingerprinting is still a relevant method, thanks to its versatility, cost-effectiveness, independence of prior sequence information and broad applicability. Even though the number of AFLP studies reached its peak in 2012, it is still applied extensively for phylogenetic analysis, genotyping or identifying non-model species, which often feature complex and large genomes. For these purposes, tools continue to be developed for designing AFLP studies, scoring AFLPs or handling AFLP data. Moreover, AFLP studies embrace the NGS technology; for example, the whole-genome sequence of model species is used to design more efficient AFLP studies for non-model species. Conversely, in complexity reduction of polymorphic sequences and restriction site-associated DNA sequencing studies, polymorphisms are often found to be present in many restriction sites, which can still be studied as AFLPs. We discuss the latest advances in AFLP-based studies in the era of NGS and anticipate that AFLP will remain a relevant method in the near future, even for species with a known genome, owing to its many promising new features such as methylation-sensitive-AFLP. Here, we also present an optimized pipeline for converting AFLP markers into single-locus markers, which can be applied in both traditional AFLP and NGS studies.     

Mitigating scoring errors in microsatellite data from wild populations

Microsatellite data are widely used to test ecological and evolutionary hypotheses in wild populations. In this paper, we consider three typical sources of scoring errors capable of biasing biological conclusions: stuttering, large‐allele dropout and null alleles. We describe methods to detect errors and propose conventions to mitigate scoring errors and report error rates in studies of wild populations. Finally, we discuss potential bias in ecological or evolutionary conclusions based on data sets containing these scoring errors.     

玉米相对饱和遗传连锁图谱构建与一种新的AFLPs共显性分析方法探讨

利用一个F2作图群体(X178×B73),首先构建了一个含有130个SSRs的玉米连锁框架图,然后用119个AFLPs位点增加图谱密度,得到一个全长1659·3cM,标记间平均间距6·66cM的玉米相对饱和连锁图。同时,对SSRs和AFLPs的一些遗传特性进行了分析,探讨了AFLP标记进行共显性分析的一种新方法。分析表明SSRs和AFLPs分子标记具有多态性和可靠性高等特点,是构建高密度分子标记遗传连锁图的有效技术。加密的玉米遗传连锁图谱为比较基因组研究、数量性状位点(quantitativetraitloci,QTLs)克隆、杂种优势机理研究以及标记辅助选择等提供了技术基础。