Hypo, hype and ‘hyp’ human proteins

Genes with unknown function are called orphan genes while their transcripts and peptides are called hypothetical proteins. There are many genes and their associated proteins that remain uncharacterized in the human genome. A database of human hypothetical proteins with ascribed functions could be helpful for biologists to search for potential proteins of interest. In recent years, the rapid completion of genome sequences has created essential information to link genes to gene products. In order to better explain functions for un-annotated proteins we designed BioinformaTRICKS (an open source project) and used it to develop a database called HYPO

Prioritizing single-nucleotide polymorphisms and variants associated with clinical mastitis

Next-generation sequencing technology has provided resources to easily explore and identify candidate single-nucleotide polymorphisms (SNPs) and variants. However, there remains a challenge in identifying and inferring the causal SNPs from sequence data. A problem with different methods that predict the effect of mutations is that they produce false positives. In this hypothesis, we provide an overview of methods known for identifying causal variants and discuss the challenges, fallacies, and prospects in discerning candidate SNPs. We then propose a three-point classification strategy, which could be an additional annotation method in identifying causalities

One who shares, wins

There has recently been an increase in the number of open access journals showcasing the results of research, free of charge, in an affordable and easy to access online publication. In fact, it's a paradigm shift in publishing, and it has gained so much momentum and has become so favored institutionally that perhaps we can say that the one who shares, wins. We do wish to acknowledge the good that open access has achieved through journal readership, but we also want to mention here some of the problems and challenges brought about by these changes. These are issues we think authors should be aware of before submitting to open access journals

Multi-omic data integration and analysis using systems genomics approaches:methods and applications in animal production, health and welfare

International audienceAbstractIn the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare

Potential role of lncRNA cyp2c91-protein interactions on diseases of the immune system

With unprecedented increase in next generation sequencing (NGS) technologies, there has been a persistent interest on transcript profiles of long noncoding RNAs (lncRNAs) and protein-coding genes forming an interaction network. Apart from protein-protein interaction (PPI), gene network models such as Weighted Gene Co-expression Network Analysis are used to functionally annotate lncRNAs in identifying their potential disease associations. To address this, studies have led to characterizing transcript structures and understanding expression profiles mediating regulatory roles. In the current exploratory analysis, we show how a lncRNA - cyp2c91 contributes to the transcriptional regulation localized to cytoplasm thereby making refractory environment for transcription. By applying network methods and pathway analyses on genes related to a disease such as obesity and systemic lupus erythematosus, we show that we can gain deeper insight in biological processes such as the perturbances in immune system, and get a better understanding of the systems biology of diseases

In Silico screening for functional candidates amongst hypothetical proteins

<p>Abstract</p> <p>Background</p> <p>The definition of a hypothetical protein is a protein that is predicted to be expressed from an open reading frame, but for which there is no experimental evidence of translation. Hypothetical proteins constitute a substantial fraction of proteomes of human as well as of other eukaryotes. With the general belief that the majority of hypothetical proteins are the product of pseudogenes, it is essential to have a tool with the ability of pinpointing the minority of hypothetical proteins with a high probability of being expressed.</p> <p>Results</p> <p>Here, we present an <it>in silico </it>selection strategy where eukaryotic hypothetical proteins are sorted according to two criteria that can be reliably identified <it>in silico</it>: the presence of subcellular targeting signals and presence of characterized protein domains. To validate the selection strategy we applied it on a database of human hypothetical proteins dating to 2006 and compared the proteins predicted to be expressed by our selecting strategy, with their status in 2008. For the comparison we focused on mitochondrial proteins, since considerable amounts of research have focused on this field in between 2006 and 2008. Therefore, many proteins, defined as hypothetical in 2006, have later been characterized as mitochondrial.</p> <p>Conclusion</p> <p>Among the total amount of human proteins hypothetical in 2006, 21% have later been experimentally characterized and 6% of those have been shown to have a role in a mitochondrial context. In contrast, among the selected hypothetical proteins from the 2006 dataset, predicted by our strategy to have a mitochondrial role, 53-62% have later been experimentally characterized, and 85% of these have actually been assigned a role in mitochondria by 2008.</p> <p>Therefore our <it>in silico </it>selection strategy can be used to select the most promising candidates for subsequent <it>in vitro </it>and <it>in vivo </it>analyses.</p