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Revolutionary in scope and application, the CRISPR Cas9 endonuclease system can be guided by 20-nt single guide RNA (sgRNA) to any complementary loci on the double-stranded DNA. Once the target site is located, Cas9 can then cleave the DNA and introduce mutations. Despite the power of this system, sgRNA is highly susceptible to off-target homologous attachment and can consequently cause Cas9 to cleave DNA at off-target sites. In order to better understand this flaw in the system, the human genome and Streptococcus pyogenes Cas9 (SpCas9) were used in a mathematical and computational study to analyze the probabilities of potential sgRNA off-target homologies. It has been concluded that off-target sites are nearly unavoidable for large-size genomes, such as the human genome. Backed by mathematical analysis, a viable solution is the double-nicking method which has the promise for genome editing specificity. Also applied in this study was a computational algorithm for off-target homology search that was implemented in Java to confirm the mathematical analysis.

A pathway is a collection of two or more proteins/molecules connected by their interactions within and around a cell. We study the informatics and evolutionary issues of pathways. Similar to the definition of homology in the comparison of nucleotide and protein sequences, we define homologous pathways as pathways that are evolved from the same ancestral pathway. We first present a survey of existing pathway databases and discuss their format of pathway representation. Then, our pathway representation, the SLIPR format, is presented. It is a semilinear graphic representation of nodes (proteins) and modes (interactions). Pathways in SLIPR format enable pathway comparisons for evolutionary relationship and large-scale pathway database searches. We also discuss how one can map out orthologous pathways, achieving a predictive power on functional assignment of novel genes, once the pathway is understood well-enough in a closely-related species.

Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet. An integrated heterogeneous network is first built to include molecular networks of multiple species and link together homologous proteins across multiple species. Based on this integrated network, a dimensionality reduction algorithm is introduced to obtain compact low-dimensional vectors to encode proteins in the network. Finally, we develop machine learning classification algorithms that take the vectors as input and make predictions by transferring annotations both within each species and across different species. Extensive experiments on five major species demonstrate that our integration of homology with molecular networks substantially improves the predictive performance over existing approaches.

There is growing use of ontologies for the measurement of cross-species phenotype similarity. Such similarity measurements contribute to diverse applications, such as identifying genetic models for human diseases, transferring knowledge among model organisms, and studying the genetic basis of evolutionary innovations. Two organismal features, whether genes, anatomical parts, or any other inherited feature, are considered to be homologous when they are evolutionarily derived from a single feature in a common ancestor. A classic example is the homology between the paired fins of fishes and vertebrate limbs. Anatomical ontologies that model the structural relations among parts may fail to include some known anatomical homologies unless they are deliberately added as separate axioms. The consequences of neglecting known homologies for applications that rely on such ontologies has not been well studied. Here, we examine how semantic similarity is affected when external homology knowledge is included. We measure phenotypic similarity between orthologous and non-orthologous gene pairs between humans and either mouse or zebrafish, and compare the inclusion of real with faux homology axioms. Semantic similarity was preferentially increased for orthologs when using real homology axioms, but only in the more divergent of the two species comparisons (human to zebrafish, not human to mouse), and the relative increase was less than 1% to non-orthologs. By contrast, inclusion of both real and faux random homology axioms preferentially increased similarities between genes that were initially more dissimilar in the other comparisons. Biologically meaningful increases in semantic similarity were seen for a select subset of gene pairs. Overall, the effect of including homology axioms on cross-species semantic similarity was modest at the levels of divergence examined here, but our results hint that it may be greater for more distant species comparisons.