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Resl, P. 2017: [Abstract:] From genome to function functional annotations in our favorite non-model organisms. - Fritschiana 85: 36-37. [RLL List # 251 / Rec.# 39869]
Abstract: High -throughput sequencing technologies have led to an immense increase of nucleic acid sequence data over the last decades. It is now easier than ever to sequence complete genomes or transcriptomes even for non- model organisms. However the ever -growing amount of data also creates many challenges when it comes to data analyses. One such challenge is how to assign functions to the many genes in newly sequenced genomes to understand links between genotype and phenotype. Traditionally, functional assignments focus on single (or few) genes and involve labori ous laboratory experiments the results of which are interpreted within the metabolic context of single, usually well -studied model organisms. Information on these functionally well -characterized genes forms the core of curated databases of protein functions such as Uni -ProtKB/Swiss -Prot ( T HE U NI P ROT C ONSORTIUM 2017). How ever, despite such joint international efforts, the functional characterization of genes cannot keep up with the speed at which new nucleic acid data are produced, and experimental evidence of gene function still largely stems from model organisms. Often, putative functions of large numbers of genes are therefore assigned compu- tationally, by comparing unknown genes to databases of w ell- characterized genes. Comparing whole- sequence similarity (BLAST best -hit approaches) or the similarity of functional domains or sequence motifs (usually based on Hidden Markov Models) are two possible approaches. A number of specialized databases such as SignalP (to identify signal peptides; N IELSEN 2017), CAZy ( L OMBARD et al. 2014; a database for carbohydrate active enzymes) or KEGG ( K ANEHISA et al . 2017; the encyclopedia of genes and genomes) among many others make it possible to put numbers of genes into a functional context. However , in non- model organisms a large number of genes or metabolic pathways may be underrepresented in the commonly utilized databases and thus remain uncharacterized with most analyses. In this talk, I will present functional annotation results of several lichen- forming fungal genomes with several functional annotation approaches and highlight some of the challenges associated with each of them. I will also introduce the Gene Ontology (GO; A SHBURNER et al . 2000) initiative, which aims to unify the vocabulary of gene product annotations.


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