Construction of a virtual Mycobacterium tuberculosis consensus genome and its application to data from a next generation sequencer

Background

Although Mycobacterium tuberculosis isolates are consisted of several different lineages and the epidemiology analyses are usually assessed relative to a particular reference genome, M. tuberculosis H37Rv, which might introduce some biased results. Those analyses are essentially based genome sequence information of M. tuberculosis and could be performed in sillico in theory, with whole genome sequence (WGS) data available in the databases and obtained by next generation sequencers (NGSs). As an approach to establish higher resolution methods for such analyses, whole genome sequences of the M. tuberculosis complexes (MTBCs) strains available on databases were aligned to construct virtual reference genome sequences called the consensus sequence (CS), and evaluated its feasibility in in sillico epidemiological analyses.

Results

The consensus sequence (CS) was successfully constructed and utilized to perform phylogenetic analysis, evaluation of read mapping efficacy, which is crucial for detecting single nucleotide polymorphisms (SNPs), and various MTBC typing methods virtually including spoligotyping, VNTR, Long sequence polymorphism and Beijing typing. SNPs detected based on CS, in comparison with H37Rv, were utilized in concatemer-based phylogenetic analysis to determine their reliability relative to a phylogenetic tree based on whole genome alignment as the gold standard. Statistical comparison of phylogenic trees based on CS with that of H37Rv indicated the former showed always better results that that of later. SNP detection and concatenation with CS was advantageous because the frequency of crucial SNPs distinguishing among strain lineages was higher than those of H37Rv. The number of SNPs detected was lower with the consensus than with the H37Rv sequence, resulting in a significant reduction in computational time. Performance of each virtual typing was satisfactory and accorded with those published when those are available.

Conclusions

These results indicated that virtual CS constructed from genome sequence data is an ideal approach as a reference for MTBC studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1368-9) contains supplementary material, which is available to authorized users.     

Genome-wide survey and analysis of microsatellites in giant panda (Ailuropoda melanoleuca), with a focus on the applications of a novel microsatellite marker system

Background

The giant panda (Ailuropoda melanoleuca) is a critically endangered species endemic to China. Microsatellites have been preferred as the most popular molecular markers and proven effective in estimating population size, paternity test, genetic diversity for the critically endangered species. The availability of the giant panda complete genome sequences provided the opportunity to carry out genome-wide scans for all types of microsatellites markers, which now opens the way for the analysis and development of microsatellites in giant panda.

Results

By screening the whole genome sequence of giant panda in silico mining, we identified microsatellites in the genome of giant panda and analyzed their frequency and distribution in different genomic regions. Based on our search criteria, a repertoire of 855,058 SSRs was detected, with mono-nucleotides being the most abundant. SSRs were found in all genomic regions and were more abundant in non-coding regions than coding regions. A total of 160 primer pairs were designed to screen for polymorphic microsatellites using the selected tetranucleotide microsatellite sequences. The 51 novel polymorphic tetranucleotide microsatellite loci were discovered based on genotyping blood DNA from 22 captive giant pandas in this study. Finally, a total of 15 markers, which showed good polymorphism, stability, and repetition in faecal samples, were used to establish the novel microsatellite marker system for giant panda. Meanwhile, a genotyping database for Chengdu captive giant pandas (n = 57) were set up using this standardized system. What’s more, a universal individual identification method was established and the genetic diversity were analysed in this study as the applications of this marker system.

Conclusion

The microsatellite abundance and diversity were characterized in giant panda genomes. A total of 154,677 tetranucleotide microsatellites were identified and 15 of them were discovered as the polymorphic and stable loci. The individual identification method and the genetic diversity analysis method in this study provided adequate material for the future study of giant panda.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1268-z) contains supplementary material, which is available to authorized users.     

REDHORSE-REcombination and Double crossover detection in Haploid Organisms using next-geneRation SEquencing data

Background

Next-generation sequencing technology provides a means to study genetic exchange at a higher resolution than was possible using earlier technologies. However, this improvement presents challenges as the alignments of next generation sequence data to a reference genome cannot be directly used as input to existing detection algorithms, which instead typically use multiple sequence alignments as input. We therefore designed a software suite called REDHORSE that uses genomic alignments, extracts genetic markers, and generates multiple sequence alignments that can be used as input to existing recombination detection algorithms. In addition, REDHORSE implements a custom recombination detection algorithm that makes use of sequence information and genomic positions to accurately detect crossovers. REDHORSE is a portable and platform independent suite that provides efficient analysis of genetic crosses based on Next-generation sequencing data.

Results

We demonstrated the utility of REDHORSE using simulated data and real Next-generation sequencing data. The simulated dataset mimicked recombination between two known haploid parental strains and allowed comparison of detected break points against known true break points to assess performance of recombination detection algorithms. A newly generated NGS dataset from a genetic cross of Toxoplasma gondii allowed us to demonstrate our pipeline. REDHORSE successfully extracted the relevant genetic markers and was able to transform the read alignments from NGS to the genome to generate multiple sequence alignments. Recombination detection algorithm in REDHORSE was able to detect conventional crossovers and double crossovers typically associated with gene conversions whilst filtering out artifacts that might have been introduced during sequencing or alignment. REDHORSE outperformed other commonly used recombination detection algorithms in finding conventional crossovers. In addition, REDHORSE was the only algorithm that was able to detect double crossovers.

Conclusion

REDHORSE is an efficient analytical pipeline that serves as a bridge between genomic alignments and existing recombination detection algorithms. Moreover, REDHORSE is equipped with a recombination detection algorithm specifically designed for Next-generation sequencing data. REDHORSE is portable, platform independent Java based utility that provides efficient analysis of genetic crosses based on Next-generation sequencing data. REDHORSE is available at http://redhorse.sourceforge.net/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1309-7) contains supplementary material, which is available to authorized users.     

Microsatellite Markers Reveal Genetic Variation within Sclerotinia sclerotiorum Populations in Irrigated Dry Bean Crops in Brazil

Microsatellites are powerful markers to infer population genetic parameters. We used 10 microsatellite loci to characterize the genetic diversity and structure of 79 samples of Sclerotinia sclerotiorum isolated from four Brazilian dry bean populations and observed that eight of them were polymorphic within populations. We identified 102 different haplotypes ranging from 6 to 18 per locus. Analyses based on genetic diversity and fixation indices indicated variability among and within populations of 28.79% (F_ST = 28793) and 71.21%, respectively. To examine genetic relatedness among S. sclerotiorum isolates, we used internal spacer (ITS1‐5.8S‐ITS2) restriction fragment length polymorphism (PCR‐RFLP) and sequencing analysis. PCR‐RFLP analysis of these regions failed to show any genetic differences among isolates. However, we detected variability within the sequence, which does not support the hypothesis of clonal populations within each population. High variability within and among populations may indicate the introduction of new genotypes in the areas analysed, in addition to the occurrence of clonal and sexual reproduction in the populations of S. sclerotiorum in the Brazilian Cerrado.     

Chitinase III in pomegranate seeds (Punica granatum Linn.): a high-capacity calcium-binding protein in amyloplasts

Chitinases are a class of ubiquitous proteins that are widely distributed in plants. Defense is the major natural role for chitinases, primarily against fungal pathogens. Little is known regarding their non-defensive roles in seeds. In this study, a new class III chitinase from pomegranate seeds (pomegranate seed chitinase, PSC) was isolated and purified to homogeneity. The native state of PSC is a monomer with a molecular weight of approximately 30 kDa. This chitinase naturally binds calcium ions with high capacity and low affinity, suggesting that PSC is a calcium storage protein. Consistent with this idea, its amino acid sequence (inferred from cDNA) is rich in acidic amino acid residues, especially Asp, similar to reported calcium storage proteins. The presence of calcium considerably improves the stability of the protein but has little effect on its enzymatic activity. Transmission electron microscopy analyses indicate that, similar to phytoferritin, this enzyme is widely distributed in the stroma of amyloplasts of the embryonic cells, suggesting that amyloplasts in seeds could serve as an alternative plastid for calcium storage. Indeed, the transmission electron microscopy results showed that, within the embryonic cells, calcium ions are mainly distributed in the stroma of the amyloplasts, consistent with a role for PSC in calcium storage. Thus, the plant appears to have evolved a new plastid for calcium storage in seeds. During seed germination, the content of this enzyme decreases with time, suggesting that it is involved in the germination process.     

Application of long-range order to predict unfolding rates of two-state proteins

Predicting the experimental unfolding rates of two-state proteins and models describing the unfolding rates of these proteins is quite limited because of the complexity present in the unfolding mechanism and the lack of experimental unfolding data compared with folding data. In this work, 25 two-state proteins characterized by Maxwell et al. (Protein Sci 2005;14:602–616) using a consensus set of experimental conditions were taken, and the parameter long-range order (LRO) derived from their three-dimensional structures were related with their experimental unfolding rates ln(k(u)). From the total data set of 30 proteins used by Maxwell et al. (Protein Sci 2005;14:602–616), five slow-unfolding proteins with very low unfolding rates were considered to be outliers and were not included in our data set. Except all beta structural class, LRO of both the all-alpha and mixed-class proteins showed a strong inverse correlation of r = -0.99 and -0.88, respectively, with experimental ln(k(u)). LRO shows a correlation of -0.62 with experimental ln(k(u)) for all-beta proteins. For predicting the unfolding rates, a simple statistical method has been used and linear regression equations were developed for individual structural classes of proteins using LRO, and the results obtained showed a better agreement with experimental results.     

Learning generative models for protein fold families

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.