Thermodynamically optimal whole-genome tiling microarray design and validation

Comprehensive Analysis of SiNPs on the Genome-Wide Transcriptional Changes in Caenorhabditis elegans

Background

Large-scale production and application of amorphous silica nanoparticles (SiNPs) have enhanced the risk of human exposure to SiNPs. However, the toxic effects and the underlying biological mechanisms of SiNPs on Caenorhabditis elegans remain largely unclear.

Purpose

This study was to investigate the genome-wide transcriptional alteration of SiNPs on C. elegans.

Methods and Results

In this study, a total number of 3105 differentially expressed genes were identified in C. elegans. Among them, 1398 genes were significantly upregulated and 1707 genes were notably downregulated in C. elegans. Gene ontology analysis revealed that the significant change of gene functional categories triggered by SiNPs was focused on locomotion, determination of adult lifespan, reproduction, body morphogenesis, multicellular organism development, endoplasmic reticulum unfolded protein response, oocyte development, and nematode larval development. Meanwhile, we explored the regulated effects between microRNA and genes or signaling pathways. Pathway enrichment analysis and miRNA-gene-pathway-network displayed that 23 differential expression microRNA including cel-miR-85-3p, cel-miR-793, cel-miR-241-5p, and cel-miR-5549-5p could regulate the longevity-related pathways and inflammation signaling pathways, etc. Additionally, our data confirmed that SiNPs could disrupt the locomotion behavior and reduce the longevity by activating ins-7, daf-16, ftt-2, fat-5, and rho-1 genes in C. elegans.

Conclusion

Our study showed that SiNPs induced the change of the whole transcriptome in C. elegans, and triggered negative effects on longevity, development, reproduction, and body morphogenesis. These data provide abundant clues to understand the molecular mechanisms of SiNPs in C. elegans.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

Sequence characteristics define trade-offs between on-target and genome-wide off-target hybridization of oligoprobes

Off-target oligoprobe's interaction with partially complementary nucleotide sequences represents a problem for many bio-techniques. The goal of the study was to identify oligoprobe sequence characteristics that control the ratio between on-target and off-target hybridization. To understand the complex interplay between specific and genome-wide off-target (cross-hybridization) signals, we analyzed a database derived from genomic comparison hybridization experiments performed with an Affymetrix tiling array. The database included two types of probes with signals derived from (i) a combination of specific signal and cross-hybridization and (ii) genomic cross-hybridization only. All probes from the database were grouped into bins according to their sequence characteristics, where both hybridization signals were averaged separately. For selection of specific probes, we analyzed the following sequence characteristics: vulnerability to self-folding, nucleotide composition bias, numbers of G nucleotides and GGG-blocks, and occurrence of probe's k-mers in the human genome. Increases in bin ranges for these characteristics are simultaneously accompanied by a decrease in hybridization specificity-the ratio between specific and cross-hybridization signals. However, both averaged hybridization signals exhibit growing trends along with an increase of probes' binding energy, where the hybridization specific signal increases significantly faster in comparison to the cross-hybridization. The same trend is evident for the S function, which serves as a combined evaluation of probe binding energy and occurrence of probe's k-mers in the genome. Application of S allows extracting a larger number of specific probes, as compared to using only binding energy. Thus, we showed that high values of specific and cross-hybridization signals are not mutually exclusive for probes with high values of binding energy and S. In this study, the application of a new set of sequence characteristics allows detection of probes that are highly specific to their targets for array design and other bio-techniques that require selection of specific probes.

Respiratory Tularemia: Francisella Tularensis and Microarray Probe Designing

BACKGROUND

Francisella tularensis (F. tularensis) is the etiological microorganism for tularemia. There are different forms of tularemia such as respiratory tularemia. Respiratory tularemia is the most severe form of tularemia with a high rate of mortality; if not treated. Therefore, traditional microbiological tools and Polymerase Chain Reaction (PCR) are not useful for a rapid, reliable, accurate, sensitive and specific diagnosis. But, DNA microarray technology does. DNA microarray technology needs to appropriate microarray probe designing.

OBJECTIVE

The main goal of this original article was to design suitable long oligo microarray probes for detection and identification of F. tularensis.

METHOD

For performing this research, the complete genomes of F. tularensis subsp. tularensis FSC198, F. tularensis subsp. holarctica LVS, F. tularensis subsp. mediasiatica, F. tularensis subsp. novicida (F. novicida U112), and F. philomiragia subsp. philomiragia ATCC 25017 were studied via NCBI BLAST tool, GView and PanSeq Servers and finally the microarray probes were produced and processed via AlleleID 7.7 software and Oligoanalyzer tool, respectively.

RESULTS

In this in silico investigation, a number of long oligo microarray probes were designed for detecting and identifying F. tularensis. Among these probes, 15 probes were recognized as the best candidates for microarray chip designing.

CONCLUSION

Calibrated microarray probes reduce the biasis of DNA microarray technology as an advanced, rapid, accurate and cost-effective molecular diagnostic tool with high specificity and sensitivity. Professional microarray probe designing provides us with much more facility and flexibility regarding preparation of a microarray diagnostic chip.

Mango: combining and analyzing heterogeneous biological networks

BACKGROUND

Heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. Existing software for network analysis has limited scalability to large data sets or is only accessible to software developers as libraries. In addition, the polymorphic nature of the data sets requires a more standardized method for integration and exploration.

RESULTS

Mango facilitates large network analyses with its Graph Exploration Language, automatic graph attribute handling, and real-time 3-dimensional visualization. On a personal computer Mango can load, merge, and analyze networks with millions of links and can connect to online databases to fetch and merge biological pathways.

CONCLUSIONS

Mango is written in C++ and runs on Mac OS, Windows, and Linux. The stand-alone distributions, including the Graph Exploration Language integrated development environment, are freely available for download from http://www.complex.iastate.edu/download/Mango. The Mango User Guide listing all features can be found at http://www.gitbook.com/book/j23414/mango-user-guide.