Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics

In many metabolomics studies, NMR spectra are divided into bins of fixed width. This spectral quantification technique, known as uniform binning, is used to reduce the number of variables for pattern recognition techniques and to mitigate effects from variations in peak positions; however, shifts in peaks near the boundaries can cause dramatic quantitative changes in adjacent bins due to non-overlapping boundaries. Here we describe a new Gaussian binning method that incorporates overlapping bins to minimize these effects. A Gaussian kernel weights the signal contribution relative to distance from bin center, and the overlap between bins is controlled by the kernel standard deviation. Sensitivity to peak shift was assessed for a series of test spectra where the offset frequency was incremented in 0.5 Hz steps. For a 4 Hz shift within a bin width of 24 Hz, the error for uniform binning increased by 150%, while the error for Gaussian binning increased by 50%. Further, using a urinary metabolomics data set (from a toxicity study) and principal component analysis (PCA), we showed that the information content in the quantified features was equivalent for Gaussian and uniform binning methods. The separation between groups in the PCA scores plot, measured by the J ₂ quality metric, is as good or better for Gaussian binning versus uniform binning. The Gaussian method is shown to be robust in regards to peak shift, while still retaining the information needed by classification and multivariate statistical techniques for NMR-metabolomics data.     

Serpentine and non-serpentine ecotypes of Collinsia sparsiflora associate with distinct arbuscular mycorrhizal fungal assemblages

Although plant adaptation to serpentine soils has been studied for several decades, the mechanisms of plant adaptation to edaphic extremes are still poorly understood. Arbuscular mycorrhizal fungi (AMF) are common root symbionts that can increase the plant hosts' establishment and growth in stressful environments. However, little is known about the role plant-AMF interactions play in plant adaptation to serpentine. As a first step towards understanding this role, we examined the AMF assemblages associated with field populations of serpentine and non-serpentine ecotypes of California native plant Collinsia sparsiflora. We sampled roots of C. sparsiflora from three serpentine and three non-serpentine sites in close proximity (110 m to 1.94 km between sites) and analysed the small subunit ribosomal DNA gene amplified from root DNA extracts using AMF-specific primers. A total of 1952 clones from 24 root samples (four from each site) were sequenced. We used sequence similarity and phylogenetic analysis to determine operational taxonomic units (OTU) resulting in 19 OTUs representing taxa from six AMF genera, including one serpentine-specific OTU. We used Bray-Curtis similarity, multidimensional scaling and analysis of similarity to compare root sample AMF assemblages. These analyses clearly showed that plant ecotypes associated with distinct AMF assemblages; an Acaulospora OTU-dominated serpentine, and a Glomus OTU-dominated non-serpentine assemblages. Species diversity and evenness were significantly higher in serpentine assemblages. Finally, relate analysis showed a relationship between ecotype AMF assemblages and soil nutrients. This study reveals a strong relationship between AMF associates and plant adaptation to edaphic extremes.     

Characterization of Lysogens in Bacterioplankton Assemblages of the Southern California Borderland

Viruses cause significant mortality of marine microorganisms; however, their role in shaping the composition of microbial assemblages has not been fully elucidated. Because viruses may form lysogenic relationships with their hosts, temperate viruses may influence bacterial assemblage structures through direct lysis of hosts when induced by environmental stimuli or by homoimmunity (i.e., immunity to closely related viruses). We investigated the components of bacterioplankton assemblages that bore prophage using the lysogenic induction agent mitomycin C. Seawater was collected at two locations (the San Pedro Ocean Time Series Station and in the Santa Barbara Channel) in the Southern California Borderland and amended with mitomycin C. After 24-h incubation, the community structure of bacterioplankton was compared with unamended controls using automated rRNA intergenic spacer analysis. The addition of mitomycin C to seawater had effects on the community structure of bacterioplankton, stimulating detectable overall diversity and richness of fingerprints and causing the assemblages within incubations to become different to control assemblages. Most negatively impacted operational taxonomic units (OTU) in mitomycin C-amended incubations individually comprised a large fraction of total amplified DNA in initial seawater (5.3-23.3% of amplified DNA fluorescence) fingerprints, and data suggest that these include organisms putatively classified as members of the gamma-Proteobacteria, SAR11 cluster, and Synechococcus groups. The stimulation of assemblage richness by induction of lysogens, and the reduction in the contribution to total DNA of common OTU (and concomitant increase in rare OTU), suggests that temperate phage have the potential to strongly influence the diversity of bacterioplankton assemblages. Because lysogenic OTU may also be resistant to closely related lytic (i.e., free-living) viruses, the impact of lytic virioplankton on assemblages may only be pronounced transiently or when conditions causing lysogenic induction arise.     

Diversity and biogeography of bacterial assemblages in surface sediments across the San Pedro Basin, Southern California Borderlands

Sediment bacteria play important roles in the biogeochemistry of ocean sediments; however, factors influencing assemblage composition have not been extensively studied. We examined extractable sediment bacterial abundance, the composition of bacterial assemblages using a high-throughput molecular fingerprinting approach, and several sediment biogeochemical parameters (organic matter content and alkaline phosphatase activity), along a 35 km transect from Point Fermin, Southern California, to Santa Catalina Island, across the approximately 900-m-deep San Pedro Basin. Automated rRNA intergenic spacer analysis (ARISA) demonstrated that in two spatially isolated shallow (approximately < 60 m, on opposite sides of the channel) sediment environments, assemblages were more similar to each other than to deeper communities. Distinct communities existed in deeper and shallower sediments, and stations within the deep basin over 2 km apart contained remarkably similar assemblage fingerprints. The relative contribution to total amplified DNA fluorescence of operational taxonomic units (OTUs) was significantly correlated to that of other OTUs in few comparisons (2.7% of total), i.e. few bacterial types were found together or apart consistently. The relative proportions within assemblages of only a few OTU were significantly correlated to measured physicochemical parameters (organic matter content and wet/dry weight ratio of sediments) or enzyme (alkaline phosphatase) activities. A low percentage of shared OTU between shallow and deep sediments, and the presence of similar, but spatially isolated assemblages suggests that bacterial OTU may be widely dispersed over scales of a few kilometres, but that environmental conditions select for particular assemblages.     

Molecular diversity of bacteria in Yunnan yellow cattle (Bos taurs) from Nujiang region, China

The rumen content of four Yunnan Yellow Cattle (Bos taurs) were collected to determine the bacteria diversity by using 16S rRNA gene sequence analysis. A total of 129 sequences were examined and the sequences were referred as 107 OTU (Operational Taxonomy Unit) according to the similarity level of 97% in gene sequence. Similarity analysis revealed that Yunnan Yellow Cattle had 12 sequences (10 OTU) shared 97% or greater similarity with cultured rumen bacteria Butyrivibrio fibrisolvens, Succiniclasticum ruminis, Ruminococcus bromii, Clostridium proteoclasticum, Ruminococcus flavefaciens, Pseudobutyrivibrio ruminis, Jeotgalicoccus psychrophilus, and Prevotella ruminicola, which accounting for 9.3% of the total clones (9.2% of the total OTU). The further 12 sequences (9 OTU) shared 90–97% similarity with cultured bacteria Clostridium aminobutyricum, butyrate-producing bacterium, Schwartzia succinivorans, Prevotella ruminicola, Eubacterium ruminantium, Ruminococcus albus, and Clostridium termitidis, also accounting for 9.3% of the total sequences (8.3% of the total OTU). The remaining 105 sequences (90 OTU) shared less than 90% similarity with cultured bacteria, accounting for 81.4% of the total sequences (82.5% of the total OTU). According to the phylogenetic analysis, all sequences were phylogenetically placed within phyla of low G+C subdivision (accounting for 72.1 and 72.5% of the total clones and OTU, respectively) and CFB subdivision (Cytophaga-Flexibacter-Bacteroides; accounting for 27.9 and 27.5% of the total clones and OTU, respectively). Among the examined clones, rare bacteria Jeotgalicoccus psychrophilus was detected in the rumen of cattle.     

Recovering microbial genomes from metagenomes in hypersaline environments: The Good,the Bad and the Ugly

Current metagenomic tools allow the recovery of microbial genomes directly from the environment. This can be accomplished by binning metagenomic contigs according to their coverage and tetranucleotide frequency, followed by an estimation of the bin quality. The public availability of bioinformatics tools, together with the decreasing cost of next generation sequencing, are democratizing this powerful approach that is spreading from specialized research groups to the general public. Using metagenomes from hypersaline environments, as well as mock metagenomes composed of Archaea and Bacteria frequently found in these systems, we have analyzed the advantages and difficulties of the binning process in these extreme environments to tackle microbial population diversity. These extreme systems harbor relatively low species diversity but high intraspecific diversity, which can compromise metagenome assembly and therefore the whole binning process. The main goal is to compare the output of the binning process with what is previously known from the analyzed samples, based on years of study using different approaches. Several scenarios have been analyzed in detail: (i) a good quality bin from a species highly abundant in the environment; (ii) an intermediate quality bin with incongruences that can be solved by further analyses and manual curation, and (iii) a low-quality bin to investigate the failure to recover a very abundant microbial genome as well as some possible solutions. The latter can be considered the “great metagenomics anomaly” and is mainly due to assembly problems derived from the microdiversity of naturally co-existing populations in nature.     

宏基因组重叠群分箱方法研究综述

宏基因组学技术可以直接从环境中提取微生物的全部遗传物质,而不需要像传统方法一样在培养基上纯培养。这种技术的出现为科学家对微生物群落的结构和功能的认识提供了重要的方法,同时对疾病的诊治、环境的治理以及生命的认识具有重大的意义。从环境中提取出微生物全部遗传物质,对其进行测序从而得到它们的reads片段,通过reads组装工具可以进一步组装成重叠群片段。对重叠群片段进行分箱,可以从宏基因组样本中重建出更多完整的基因。分箱效果的好坏直接影响到后续的生物分析,因此如何将这些含有不同微生物基因混合的重叠群序列进行有效的分箱成为了宏基因组学研究的热点和难点。机器学习方法被广泛应用于宏基因组重叠群分箱,通常分为有监督重叠群分类方法和无监督重叠群聚类方法。该综述针对宏基因组重叠群分箱方法进行了较为全面的阐述,深入剖析了重叠群分类方法与聚类方法,发现其存在分类准确率较低、分箱时间较长、难以从复杂数据集中重建更多微生物基因等问题,并对未来重叠群分箱方法的研究和发展进行了展望。作者建议可以使用半监督学习、集成学习以及深度学习方法,并采用更有效的数据特征表示等途径来提高分箱效果。     

Characterization of Thrombolitic Bioherm Cyanobacterial Assemblages in a Meromictic Marl Lake (Fayetteville Green Lake,New York)

The Fayetteville Green Lake is a small (0.258 km²) meromictic marl lake that is host to a thrombolitic bioherm inhabited by coccoid and filamentous cyanobacteria. To date there has been only limited study of bioherm cyanobacterial community ecology, and none focusing on their molecular diversity. Samples of the bioherm were collected along vertical and spatial transects on a portion of the thrombolite in early fall 2010. Cyanobacterial assemblage spatial variability and operational taxonomic unit composition was analyzed by automated rRNA intergenic spacer analysis (ARISA). A total of 123 cyanobacterial ARISA operational taxonomic units were observed across all fingerprints. Cyanobacterial assemblages were variable across depth and spatial gradients, and may be structured by gradients in light availability and habitat stability. Cyanobacterial community fingerprints were more taxonomic unit rich, diverse, and had greater fingerprint similarity in deeper samples than those at the surface. Several operational taxonomic units were common to all samples taken, while the majority of assemblage components were heterogeneous between transects and depths. Hence, our results suggest that cyanobacterial communities on the thrombolite in Green Lake represent a mixture of taxa that are selected for by prevailing physicochemical conditions, while other taxa are selected for on spatial scales of meters and may represent more specialized cyanobacteria on the thrombolite. Moreover, our data suggest that the depth-dependent structure of bioherm cyanobacterial assemblages may be a consequence of habitat conditions, which may include light intensity and quality, temperature variation and habitat stability.     

On plotting species abundance distributions

1. There has been a revival of interest in species abundance distribution (SAD) models, stimulated by the claim that the log-normal distribution gave an underestimate of the observed numbers of rare species in species-rich assemblages. This led to the development of the neutral Zero Sum Multinomial distribution (ZSM) to better fit the observed data. 2. Yet plots of SADs, purportedly of the same data, showed differences in frequencies of species and of statistical fits to the ZSM and log-normal models due to the use of different binning methods. 3. We plot six different binning methods for the Barro Colorado Island (BCI) tropical tree data. The appearances of the curves are very different for the different binning methods. Consequently, the fits to different models may vary depending on the binning system used. 4. There is no agreed binning method for SAD plots. Our analysis suggests that a simple doubling of the number of individuals per species in each bin is perhaps the most practical one for illustrative purposes. Alternatively rank-abundance plots should be used. 5. For fitting and testing models exact methods have been developed and application of these does not require binning of data. Errors are introduced unnecessarily if data are binned before testing goodness-of-fit to models.     

Endophytic fungi associated with leaves of Betulaceae in Japan

Diversity and species composition of endophytic fungi on leaves of 11 tree species in Betulaceae were studied, with reference to climatic, tree species, and seasonal variations. A total of 186 fungal isolates were obtained from 190 leaves collected in a subalpine forest, a cool temperate forest, and a subtropical forest in Japan, and were divided into 46 operational taxonomic units (OTUs) according to the base sequences of D1-D2 region of large subunit rDNA. The 2 most frequent OTUs were Muscodor sp. and Nemania sp. in Xylariaceae, followed by Gnomonia sp., Glomerella acutata , Apiosporopsis sp., Asteroma sp., and Cladosporium cladosporioides . The similarities of OTU composition in endophytic fungal assemblages on leaves of Betulaceae were generally low among the forests of different climatic regions. Fungal OTU compositions were relatively similar between 2 Betula species in the subalpine forest, whereas 7 tree species in the cool temperate forest were divided into 3 groups according to the similarity of endophytic fungal assemblages on the leaves, with 4 Carpinus species assigned into 2 of the 3 groups. The similarity of endophytic fungal assemblages between August and October was relatively high in the subalpine forest, whereas the seasonal changes were generally greater (i.e., the similarities among sampling dates were lower) in the cool temperate forest.     

Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm’s ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a ¹H NMR-based experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification.     

Karst pools in subsurface environments: collectors of microbial diversity or temporary residence between habitat types

We studied bacterial diversity and community composition in three shallow pools of a Swiss karst cave system with contrasting hydrological and hydrochemical properties. The microbial assemblages in the pools were remarkably different, and only one operational taxonomic unit of 16S rRNA genes (OTU, 97% similarity) was shared between the three of them (total OTU number in all pools: 150). Unexpectedly high microbial phylotype richness was found even in the two pools without groundwater contact and with low concentrations of organic carbon and total cell numbers (< 10⁴ ml^?1). One of these seepage water fed systems harboured 15 distinct OTUs from several deeply branching lineages of the candidate phylum OP3, whereas representatives of this group were not detected in the other two pools. A tentative phylogeographic analysis of available OP3‐related sequences in the context of our data set revealed that there was generally little agreement between the habitats of origin of closely related sequence types. Two bacterial clades affiliated with the obligate methylamine utilizer Methylotenera mobilis were only found in the pool that was exposed to repeated flooding events. These bacteria formed relatively stable populations of up to 6% of total cell counts over periods of several months irrespective of inundation by groundwater. This suggests that karst water may provide a means of transport for these bacteria from terrestrial to freshwater habitats.     

Effect of long-term nitrogen fertilization on mycorrhizal fungi associated with a dominant grass in a semiarid grassland

We studied the diversity of arbuscular mycorrhizal fungi (AMF) in semiarid grassland and the effect of long-term nitrogen (N) fertilization on this fungal community. Root samples of Bouteloua gracilis were collected at the Sevilleta National Wildlife Refuge (New Mexico, USA) from control and N-amended plots that have been fertilized since 1995. Small subunit rDNA was amplified using AMF specific primers NS31 and AM1. The diversity of AMF was low in comparison with other ecosystems, only seven operational taxonomic units (OTU) were found in B. gracilis and all belong to the genus Glomus. The dominant OTU was closely related to the ubiquitous G. intraradices/G. fasciculatum group. N-amended plots showed a reduction in the abundance of the dominant OTU and an increase in AMF diversity. The greater AMF diversity in roots from N-amended plots may have been the result of displacement of the dominant OTU, which facilitated detection of uncommon AMF. The long-term implications of AMF responses to N enrichment for plant carbon allocation and plant community structure remain unclear.     

Null model analyses of temporal patterns of bird assemblages and their foraging guilds revealed the predominance of positive and random associations

Patterns of species associations have been commonly used to infer interactions among species. If species positively co‐occur, they may form predominantly neutral assemblages, and such patterns suggest a relatively weak role for compensatory dynamics. The main objective of this study was to test this prediction on temporal samples of bird assemblages (n = 19, 10–57 years) by the presence/absence and quantitative null models on assemblage and guild levels. These null model outcomes were further analyzed to evaluate the effects of various data set characteristics on the outcomes of the null models. The analysis of two binary null models in combination with three association indices revealed 20% with significant aggregations, 61% with random associations, and only 19% with significant segregations (n = 95 simulations). The results of the quantitative null model simulations detected more none‐random associations: 61% aggregations, 6% random associations, and 33% segregations (n = 114 simulations). Similarly, quantitative analyses on guild levels showed 58% aggregations, 20% segregations, and 22% random associations (n = 450 simulations). Bayesian GLMs detected that the outcomes of the binary and quantitative null models applied to the assemblage analyses were significantly related to census plot size, whereas the outcomes of the quantitative analyses were also related to the mean population densities of species in the data matrices. In guild‐level analyses, only 9% of the GLMs showed a significant influence of matrix properties (plot size, matrix size, species richness, and mean species population densities) on the null model outcomes. The results did not show the prevalence of negative associations that would have supported compensatory dynamics. Instead, we assume that a similar response of the majority of species to climate‐driven and stochastic factors may be responsible for the revealed predominance of positive associations.     

A COMPARISON OF GENETIC AND PHENOTYPIC CORRELATIONS

Genetic variances and correlations lie at the center of quantitative evolutionary theory. They are often difficult to estimate, however, due to the large samples of related individuals that are required. I investigated the relationship of genetic- and phenotypic-correlation magnitudes and patterns in 41 pairs of matrices drawn from the literature in order to determine their degree of similarity and whether phenotypic parameters could be used in place of their genetic counterparts in situations where genetic variances and correlations cannot be precisely estimated. The analysis indicates that squared genetic correlations were on average much higher than squared phenotypic correlations and that genetic and phenotypic correlations had only broadly similar patterns. These results could be due either to biological causes or to imprecision of genetic-correlation estimates due to sampling error. When only those studies based on the largest sample sizes (effective sample size of 40 or more) were included, squared genetic-correlation estimates were only slightly greater than their phenotypic counterparts and the patterns of correlation were strikingly similar. Thus, much of the dissimilarity between phenotypic- and genetic-correlation estimates seems to be due to imprecise estimates of genetic correlations. Phenotypic correlations are likely to be fair estimates of their genetic counterparts in many situations. These further results also indicate that genetic and environmental causes of phenotypic variation tend to act on growth and development in a similar manner.     

Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill

Biomonitoring underpins the environmental assessment of freshwater ecosystems and guides management and conservation. Current methodology for surveys of (macro)invertebrates uses coarse taxonomic identification where species‐level resolution is difficult to obtain. Next‐generation sequencing of entire assemblages (metabarcoding) provides a new approach for species detection, but requires further validation. We used metabarcoding of invertebrate assemblages with two fragments of the cox1 “barcode” and partial nuclear ribosomal (SSU) genes, to assess the effects of a pesticide spill in the River Kennet (southern England). Operational taxonomic unit (OTU) recovery was tested under 72 parameters (read denoising, filtering, pair merging and clustering). Similar taxonomic profiles were obtained under a broad range of parameters. The SSU marker recovered Platyhelminthes and Nematoda, missed by cox1, while Rotifera were only amplified with cox1. A reference set was created from all available barcode entries for Arthropoda in the BOLD database and clustered into OTUs. The River Kennet metabarcoding produced matches to 207 of these reference OTUs, five times the number of species recognized with morphological monitoring. The increase was due to the following: greater taxonomic resolution (e.g., splitting a single morphotaxon “Chironomidae” into 55 named OTUs); splitting of Linnaean binomials into multiple molecular OTUs; and the use of a filtration‐flotation protocol for extraction of minute specimens (meiofauna). Community analyses revealed strong differences between “impacted” vs. “control” samples, detectable with each gene marker, for each major taxonomic group, and for meio‐ and macrofaunal samples separately. Thus, highly resolved taxonomic data can be extracted at a fraction of the time and cost of traditional nonmolecular methods, opening new avenues for freshwater invertebrate biodiversity monitoring and molecular ecology.     

Probability binning comparison: a metric for quantitating multivariate distribution differences

BACKGROUND: While several algorithms for the comparison of univariate distributions arising from flow cytometric analyses have been developed and studied for many years, algorithms for comparing multivariate distributions remain elusive. Such algorithms could be useful for comparing differences between samples based on several independent measurements, rather than differences based on any single measurement. It is conceivable that distributions could be completely distinct in multivariate space, but unresolvable in any combination of univariate histograms. Multivariate comparisons could also be useful for providing feedback about instrument stability, when only subtle changes in measurements are occurring. METHODS: We apply a variant of Probability Binning, described in the accompanying article, to multidimensional data. In this approach, hyper-rectangles of n dimensions (where n is the number of measurements being compared) comprise the bins used for the chi-squared statistic. These hyper-dimensional bins are constructed such that the control sample has the same number of events in each bin; the bins are then applied to the test samples for chi-squared calculations. RESULTS: Using a Monte-Carlo simulation, we determined the distribution of chi-squared values obtained by comparing sets of events from the same distribution; this distribution of chi-squared values was identical as for the univariate algorithm. Hence, the same formulae can be used to construct a metric, analogous to a t-score, that estimates the probability with which distributions are distinct. As for univariate comparisons, this metric scales with the difference between two distributions, and can be used to rank samples according to similarity to a control. We apply the algorithm to multivariate immunophenotyping data, and demonstrate that it can be used to discriminate distinct samples and to rank samples according to a biologically-meaningful difference. CONCLUSION: Probability binning, as shown here, provides a useful metric for determining the probability with which two or more multivariate distributions represent distinct sets of data. The metric can be used to identify the similarity or dissimilarity of samples. Finally, as demonstrated in the accompanying paper, the algorithm can be used to gate on events in one sample that are different from a control sample, even if those events cannot be distinguished on the basis of any combination of univariate or bivariate displays. Published 2001 Wiley-Liss, Inc.     

MICROSATELIGHT--pipeline to expedite microsatellite analysis

MICROSATELIGHT is a Perl/Tk pipeline with a graphical user interface that facilitates several tasks when scoring microsatellites. It implements new subroutines in R and PERL and takes advantage of features provided by previously developed freeware. MICROSATELIGHT takes raw genotype data and automates the peak identification through PeakScanner. The PeakSelect subroutine assigns peaks to different microsatellite markers according to their multiplex group, fluorochrome type, and size range. After peak selection, binning of alleles can be carried out 1) automatically through AlleloBin or 2) by manual bin definition through Binator. In both cases, several features for quality checking and further binning improvement are provided. The genotype table can then be converted into input files for several population genetics programs through CREATE. Finally, Hardy-Weinberg equilibrium tests and confidence intervals for null allele frequency can be obtained through GENEPOP. MICROSATELIGHT is the only freely available public-domain software that facilitates full multiplex microsatellite scoring, from electropherogram files to user-defined text files to be used with population genetics software. MICROSATELIGHT has been created for the Windows XP operating system and has been successfully tested under Windows 7. It is available at http://sourceforge.net/projects/microsatelight/.     

Considerations for measuring genetic variation and population structure with multilocus fingerprinting

Multilocus DNA fingerprinting provides a cost-effective means to rapidly assay genetic variation at many loci. While this makes the technique particularly attractive for studies of evolution and conservation biology, fingerprint data can be difficult to interpret. Measurement errors inherent with the technique force investigators to group similar-sized alleles (bands) into discrete bins before estimating genetic parameters. If too little error is accounted for in this process homologous alleles will not be grouped in a common bin, whereas overestimated error can produce bins with homoplasic alleles. We used simulations and empirical data for two frog species ( Rana luteiventris and Hyla regilla ) to demonstrate that mean band-sharing ( S¯_xy ) and heterozygosity ( H ¯_E) are a function of both bin width and band profile complexity (i.e. number and distribution of bands). These estimators are also sensitive to the number of lanes included in the analysis when bin width is wide and a floating bin algorithm is employed. Multilocus estimates of H ¯_E were highly correlated with S¯_xy and thus provide no additional information about genetic variation. Estimates of population subdivision ( F ^ and Φ^_ST) appeared robust to changes in bin size. We also examined the issue of statistical independence for band-sharing data when comparisons are made among all samples. This analysis indicated that the covariance between band-sharing statistics was very small and not statistically different from zero. We recommend that sensitivity analyses for bin size be used to improve confidence in the biological interpretation of multilocus fingerprints, and that the covariance structure for band-sharing statistics be examined.