Identification of gene-oriented exon orthology between human and mouse

<p>Abstract</p> <p>Background</p> <p>Gene orthology has been well studied in the evolutionary area and is thought to be an important implication to functional genome annotations. As the accumulation of transcriptomic data, alternative splicing is taken into account in the assignments of gene orthologs and the orthology is suggested to be further considered at transcript level. Whether gene or transcript orthology, exons are the basic units that represent the whole gene structure; however, there is no any reported study on how to build exon level orthology in a whole genome scale. Therefore, it is essential to establish a gene-oriented exon orthology dataset.</p> <p>Results</p> <p>Using a customized pipeline, we first build exon orthologous relationships from assigned gene orthologs pairs in two well-annotated genomes: human and mouse. More than 92% of non-overlapping exons have at least one ortholog between human and mouse and only a small portion of them own more than one ortholog. The exons located in the coding region are more conserved in terms of finding their ortholog counterparts. Within the untranslated region, the 5' UTR seems to have more diversity than the 3' UTR according to exon orthology designations. Interestingly, most exons located in the coding region are also conserved in length but this conservation phenomenon dramatically drops down in untranslated regions. In addition, we allowed multiple assignments in exon orthologs and a subset of exons with possible fusion/split events were defined here after a thorough analysis procedure.</p> <p>Conclusions</p> <p>Identification of orthologs at the exon level is essential to provide a detailed way to interrogate gene orthology and splicing analysis. It could be used to extend the genome annotation as well. Besides examining the one-to-one orthologous relationship, we manage the one-to-multi exon pairs to represent complicated exon generation behavior. Our results can be further applied in many research fields studying intron-exon structure and alternative/constitutive exons in functional genomic areas.</p

miRNA arm selection and isomiR distribution in gastric cancer

BACKGROUND: MicroRNAs (miRNAs) are small non-protein-coding RNAs. miRNA genes need several biogenesis steps to form function miRNAs. However, the precise mechanism and biology involved in the mature miRNA molecules are not clearly investigated. In this study, we conducted in-depth analyses to examine the arm selection and isomiRs using NGS platform. METHODS: We sequenced small RNAs from one pair of normal and gastric tumor tissues with Solexa platform. By analyzing the NGS data, we quantified the expression profiles of miRNAs and isomiRs in gastric tissues. Then, we measured the expression ratios of 5p arm to 3p arm of the same pre-miRNAs. And, we used Kolmogorov-Smirnov (KS) test to examine isomiR pattern difference between tissues. RESULTS: Our result showed the 5p arm and 3p arm miRNA derived from the same pre-miRNAs have different tissue expression preference, one preferred normal tissue and the other preferred tumor tissue, which strongly implied that there could be other mechanism controlling mature miRNA selection in addition to the known hydrogen-bonding selection rule. Furthermore, by using the KS test, we demonstrated that some isomiR types preferentially occur in normal gastric tissue but other types prefer tumor gastric tissue. CONCLUSIONS: Arm selections and isomiR patterns are significantly varied in human cancers by using deep sequencing NGS data. Our results provided a novel research topic in miRNA regulation study. With advanced bioinformatics and molecular biology studies, more robust conclusions and insight into miRNA regulation can be achieved in the near future

Detection of interacting transcription factors in human tissues using predicted DNA binding affinity

BACKGROUND: Tissue-specific gene expression is generally regulated by combinatorial interactions among transcription factors (TFs) which bind to the DNA. Despite this known fact, previous discoveries of the mechanism that controls gene expression usually consider only a single TF. RESULTS: We provide a prediction of interacting TFs in 22 human tissues based on their DNA-binding affinity in promoter regions. We analyze all possible pairs of 130 vertebrate TFs from the JASPAR database. First, all human promoter regions are scanned for single TF-DNA binding affinities with TRAP and for each TF a ranked list of all promoters ordered by the binding affinity is created. We then study the similarity of the ranked lists and detect candidates for TF-TF interaction by applying a partial independence test for multiway contingency tables. Our candidates are validated by both known protein-protein interactions (PPIs) and known gene regulation mechanisms in the selected tissue. We find that the known PPIs are significantly enriched in the groups of our predicted TF-TF interactions (2 and 7 times more common than expected by chance). In addition, the predicted interacting TFs for studied tissues (liver, muscle, hematopoietic stem cell) are supported in literature to be active regulators or to be expressed in the corresponding tissue. CONCLUSIONS: The findings from this study indicate that tissue-specific gene expression is regulated by one or two central regulators and a large number of TFs interacting with these central hubs. Our results are in agreement with recent experimental studies