Genomics and molecular epidemiology of Cryptosporidium species

Cryptosporidium is one of the most widespread protozoan parasites that infects domestic and wild animals and is considered the second major cause of diarrhea and death in children after rotavirus. So far, around 20 distinct species are known to cause severe to moderate infections in humans, of which Cryptosporidium hominis and Cryptosporidium parvum are the major causative agents. Currently, ssurRNA and gp60 are used as the optimal markers for differentiating species and subtypes respectively. Over the last decade, diagnostic tools to detect and differentiate Cryptosporidium species at the genotype and subtype level have improved, but our understanding of the zoonotic and anthroponotic transmission potential of each species is less clear, largely because of the paucity of high resolution whole genome sequencing data for the different species. Defining which species possess an anthroponotic vs. zoonotic transmission cycle is critical if we are to limit the spread of disease between animals and humans. Likewise, it is unclear to what extent genetic hybridization impacts disease potential or the emergence of outbreak strains. The development of high resolution genetic markers and whole genome sequencing of different species should provide new insights into these knowledge gaps. The aim of this review is to outline currently available molecular epidemiology and genomics data for different species of Cryptosporidium.

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.

NextGen sequencing reveals short double crossovers contribute disproportionately to genetic diversity in Toxoplasma gondii

Background

Toxoplasma gondii is a widespread protozoan parasite of animals that causes zoonotic disease in humans. Three clonal variants predominate in North America and Europe, while South American strains are genetically diverse, and undergo more frequent recombination. All three northern clonal variants share a monomorphic version of chromosome Ia (ChrIa), which is also found in unrelated, but successful southern lineages. Although this pattern could reflect a selective advantage, it might also arise from non-Mendelian segregation during meiosis. To understand the inheritance of ChrIa, we performed a genetic cross between the northern clonal type 2 ME49 strain and a divergent southern type 10 strain called VAND, which harbors a divergent ChrIa.

Results

NextGen sequencing of haploid F1 progeny was used to generate a genetic map revealing a low level of conventional recombination, with an unexpectedly high frequency of short, double crossovers. Notably, both the monomorphic and divergent versions of ChrIa were isolated with equal frequency. As well, ChrIa showed no evidence of being a sex chromosome, of harboring an inversion, or distorting patterns of segregation. Although VAND was unable to self fertilize in the cat, it underwent successful out-crossing with ME49 and hybrid survival was strongly associated with inheritance of ChrIII from ME49 and ChrIb from VAND.

Conclusions

Our findings suggest that the successful spread of the monomorphic ChrIa in the wild has not been driven by meiotic drive or related processes, but rather is due to a fitness advantage. As well, the high frequency of short double crossovers is expected to greatly increase genetic diversity among progeny from genetic crosses, thereby providing an unexpected and likely important source of diversity.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1168) contains supplementary material, which is available to authorized users.

Semantically linking and browsing PubMed abstracts with gene ontology

BACKGROUND

The technological advances in the past decade have lead to massive progress in the field of biotechnology. The documentation of the progress made exists in the form of research articles. The PubMed is the current most used repository for bio-literature. PubMed consists of about 17 million abstracts as of 2007 that require methods to efficiently retrieve and browse large volume of relevant information. The State-of-the-art technologies such as GOPubmed use simple keyword-based techniques for retrieving abstracts from the PubMed and linking them to the Gene Ontology (GO). This paper changes the paradigm by introducing semantics enabled technique to link the PubMed to the Gene Ontology, called, SEGOPubmed for ontology-based browsing. Latent Semantic Analysis (LSA) framework is used to semantically interface PubMed abstracts to the Gene Ontology.

RESULTS

The Empirical analysis is performed to compare the performance of the SEGOPubmed with the GOPubmed. The analysis is initially performed using a few well-referenced query words. Further, statistical analysis is performed using GO curated dataset as ground truth. The analysis suggests that the SEGOPubmed performs better than the classic GOPubmed as it incorporates semantics.

CONCLUSIONS

The LSA technique is applied on the PubMed abstracts obtained based on the user query and the semantic similarity between the query and the abstracts. The analyses using well-referenced keywords show that the proposed semantic-sensitive technique outperformed the string comparison based techniques in associating the relevant abstracts to the GO terms. The SEGOPubmed also extracted the abstracts in which the keywords do not appear in isolation (i.e. they appear in combination with other terms) that could not be retrieved by simple term matching techniques.

A unified framework for finding differentially expressed genes from microarray experiments

BACKGROUND

This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework.

RESULTS

The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets.

CONCLUSION

This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

Detection and tracking of rotated and scaled targets by use of Hilbert-wavelet transform

In a recent work, we demonstrated the usefulness of the Hilbert transform in identifying the in-plane rotation angle between two objects. Here we use the Hilbert-wavelet bases instead of the Hilbert transform in the determination of the exact angle of rotation. We describe the design of the two-dimensional Hilbert-wavelet filter based on the spectral-factorization method to generate a Hilbert-transform pair of orthogonal wavelet bases. We compare the relative performance of the Hilbert transform and the Hilbert wavelet to identify both in-plane and out-of-plane rotation angles. We demonstrate that the Hilbert wavelet offers better rotation-angle determination than the Hilbert transform. We present correlation based rotated and scaled object identification and tracking using Hilbert or Hilbert-wavelet transformed infrared image sequences. We also demonstrate reduced data handling and improved tracking of distorted objects using the Hilbert-wavelet transform.