Synopsis generis Harrisonia

Ohne Zusammenfassung     

Synopsis generis Harrisonia

Ohne Zusammenfassung     

OMiR: Identification of associations between OMIM diseases and microRNAs

A large number of loci for genetic diseases have been mapped on the human genome and a group of hereditary diseases among them have thus far proven unsuccessful to clone. It is conceivable that such "unclonable" diseases are not linked to abnormalities of protein coding genes (PCGs), but of non-coding RNAs (ncRNAs). We developed a novel approach termed OMiR (OMIM and miRNAs), to test whether microRNAs (miRNAs) exhibit any associations with mapped genetic diseases not yet associated with a PCG. We found that "orphan" genetic disease loci were proximal to miRNA loci more frequently than to loci for which the responsible protein coding gene is known, thus suggesting that miRNAs might be the elusive culprits. Our findings indicate that inclusion of miRNAs among the candidate genes to be considered could assist geneticists in their hunt for disease genes, particularly in the case of rare diseases.     

Automatic extraction of mutations from Medline and cross-validation with OMIM

Mutations help us to understand the molecular origins of diseases. Researchers, therefore, both publish and seek disease-relevant mutations in public databases and in scientific literature, e.g. Medline. The retrieval tends to be time-consuming and incomplete. Automated screening of the literature is more efficient. We developed extraction methods (called MEMA) that scan Medline abstracts for mutations. MEMA identified 24,351 singleton mutations in conjunction with a HUGO gene name out of 16,728 abstracts. From a sample of 100 abstracts we estimated the recall for the identification of mutation-gene pairs to 35% at a precision of 93%. Recall for the mutation detection alone was >67% with a precision rate of >96%. This shows that our system produces reliable data. The subset consisting of protein sequence mutations (PSMs) from MEMA was compared to the entries in OMIM (20,503 entries versus 6699, respectively). We found 1826 PSM-gene pairs to be in common to both datasets (cross-validated). This is 27% of all PSM-gene pairs in OMIM and 91% of those pairs from OMIM which co-occur in at least one Medline abstract. We conclude that Medline covers a large portion of the mutations known to OMIM. Another large portion could be artificially produced mutations from mutagenesis experiments. Access to the database of extracted mutation-gene pairs is available through the web pages of the EBI (refer to http://www.ebi. ac.uk/rebholz/index.html).     

如何利用OMIM获取人类基因与遗传失调信息

本文介绍了利用美国国立生物技术信息中心(NCBI)联机人类孟德尔遗传系统(OMIM)获取人类基因与遗传疾病信息的方法与技巧。