Probability-based collaborative filtering model for predicting gene–disease associations

Background

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses.

Methods

We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used.

Results

We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches.

Conclusions

PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

     

Pathway-based analyses

Background

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.

Methods

The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.

Results

Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.

Conclusions

The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

     

Comparison of the canine and human olfactory receptor gene repertoires

Background

Olfactory receptors (ORs), the first dedicated molecules with which odorants physically interact to arouse an olfactory sensation, constitute the largest gene family in vertebrates, including around 900 genes in human and 1,500 in the mouse. Whereas dogs, like many other mammals, have a much keener olfactory potential than humans, only 21 canine OR genes have been described to date.

Results

In this study, 817 novel canine OR sequences were identified, and 640 have been characterized. Of the 661 characterized OR sequences, representing half of the canine repertoire, 18% are predicted to be pseudogenes, compared with 63% in human and 20% in mouse. Phylogenetic analysis of 403 canine OR sequences identified 51 families, and radiation-hybrid mapping of 562 showed that they are distributed on 24 dog chromosomes, in 37 distinct regions. Most of these regions constitute clusters of 2 to 124 closely linked genes. The two largest clusters (124 and 109 OR genes) are located on canine chromosomes 18 and 21. They are orthologous to human clusters located on human chromosomes 11q11-q13 and HSA11p15, containing 174 and 115 ORs respectively.

Conclusions

This study shows a strongly conserved genomic distribution of OR genes between dog and human, suggesting that OR genes evolved from a common mammalian ancestral repertoire by successive duplications. In addition, the dog repertoire appears to have expanded relative to that of humans, leading to the emergence of specific canine OR genes.

     

SNP markers associated with body size and pelt length in American mink (<Emphasis Type="Italic">Neovison vison</Emphasis>)

Background

Identification of genes underlying production traits is a key aim of the mink research community. Recent availability of genomic tools have opened the possibility for faster genetic progress in mink breeding. Availability of mink genome assembly allows genome-wide association studies in mink.

Results

In this study, we used genotyping-by-sequencing to obtain single nucleotide polymorphism (SNP) genotypes of 2496 mink. After multiple rounds of filtering, we retained 28,336 high quality SNPs and 2352 individuals for a genome-wide association study (GWAS). We performed the first GWAS for body weight, behavior, along with 10 traits related to fur quality in mink.

Conclusions

Combining association results with existing functional information of genes and mammalian phenotype databases, we proposed WWC3, MAP2K4, SLC7A1 and USP22 as candidate genes for body weight and pelt length in mink.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

     

Outer membrane protein C (OmpC) of <Emphasis Type="Italic">Escherichia coli</Emphasis> induces neurodegeneration in mice by acting as an amyloid

Objectives

Involvement of the outer membrane protein C (OmpC) of Escherichia coli in neurodegeneration was investigated using a mouse model.

Results

OmpC formed protease-resistant fibres that exhibited the diagnostic features of an amyloid. The spectral shift in the Congo Red and the thioflavin T assays produced features similar to neurotoxic peptides. Intramuscular administration of OmpC in mice resulted in spongiform neurodegeneration of the brain through calcium-dependent apoptosis and also showed upregulation of apoptosis related genes. Immunolocalization of OmpC in brain demonstrated the direct involvement of the porin in neurodegeneration and formation of spongiform encephalopathy.

Conclusion

We have demonstrated the ability of OmpC of E. coli to induce neurodegeneration in mice.

     

Analysis of the conservation of synteny between Fugu and human chromosome 12

Background

The pufferfish Fugu rubripes (Fugu) with its compact genome is increasingly recognized as an important vertebrate model for comparative genomic studies. In particular, large regions of conserved synteny between human and Fugu genomes indicate its utility to identify disease-causing genes. The human chromosome 12p12 is frequently deleted in various hematological malignancies and solid tumors, but the actual tumor suppressor gene remains unidentified.

Results

We investigated approximately 200 kb of the genomic region surrounding the ETV6 locus in Fugu (fETV6) in order to find conserved functional features, such as genes or regulatory regions, that could give insight into the nature of the genes targeted by deletions in human cancer cells. Seven genes were identified near the fETV6 locus. We found that the synteny with human chromosome 12 was conserved, but extensive genomic rearrangements occurred between the Fugu and human ETV6 loci.

Conclusion

This comparative analysis led to the identification of previously uncharacterized genes in the human genome and some potentially important regulatory sequences as well. This is a good indication that the analysis of the compact Fugu genome will be valuable to identify functional features that have been conserved throughout the evolution of vertebrates.

     

Non-coding-regulatory regions of human brain genes delineated by bacterial artificial chromosome knock-in mice

Background

The next big challenge in human genetics is understanding the 98% of the genome that comprises non-coding DNA. Hidden in this DNA are sequences critical for gene regulation, and new experimental strategies are needed to understand the functional role of gene-regulation sequences in health and disease. In this study, we build upon our HuGX ('high-throughput human genes on the X chromosome’) strategy to expand our understanding of human gene regulation in vivo.

Results

In all, ten human genes known to express in therapeutically important brain regions were chosen for study. For eight of these genes, human bacterial artificial chromosome clones were identified, retrofitted with a reporter, knocked single-copy into the Hprt locus in mouse embryonic stem cells, and mouse strains derived. Five of these human genes expressed in mouse, and all expressed in the adult brain region for which they were chosen. This defined the boundaries of the genomic DNA sufficient for brain expression, and refined our knowledge regarding the complexity of gene regulation. We also characterized for the first time the expression of human MAOA and NR2F2, two genes for which the mouse homologs have been extensively studied in the central nervous system (CNS), and AMOTL1 and NOV, for which roles in CNS have been unclear.

Conclusions

We have demonstrated the use of the HuGX strategy to functionally delineate non-coding-regulatory regions of therapeutically important human brain genes. Our results also show that a careful investigation, using publicly available resources and bioinformatics, can lead to accurate predictions of gene expression.

     

Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

Background

Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.

Results

We applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.

Conclusions

These results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

Resolution effects in reconstructing ancestral genomes

Background

The reconstruction of ancestral genomes must deal with the problem of resolution, necessarily involving a trade-off between trying to identify genomic details and being overwhelmed by noise at higher resolutions.

Results

We use the median reconstruction at the synteny block level, of the ancestral genome of the order Gentianales, based on coffee, Rhazya stricta and grape, to exemplify the effects of resolution (granularity) on comparative genomic analyses.

Conclusions

We show how decreased resolution blurs the differences between evolving genomes, with respect to rate, mutational process and other characteristics.

     

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network

Objectives

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.

Results

From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.

Conclusions

These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.