Sungear: interactive visualization and functional analysis of genomic datasets

Sungear is a software system that supports a rapid, visually interactive and biologist-driven comparison of large datasets. The datasets can come from microarray experiments (e.g. genes induced in each experiment), from comparative genomics (e.g. genes present in each genome) or even from non-biological applications (e.g. demographics or baseball statistics). Sungear represents multiple datasets as vertices in a polygon. Each possible intersection among the sets is represented as a circle inside the polygon. The position of the circle is determined by the position of the vertices represented in the intersection and the area of the circle is determined by the number of elements in the intersection. Sungear shows which Gene Ontology terms are over-represented in a subset of circles or anchors. The intuitive Sungear interface has enabled biologists to determine quickly which dataset or groups of datasets play a role in a biological function of interest. AVAILABILITY: A live online version of Sungear can be found at http://virtualplant-prod.bio.nyu.edu/cgi-bin/sungear/index.cgi     

Identification and comparison of cutinases for synthetic polyester degradation

Cutinases have been exploited for a broad range of reactions, from hydrolysis of soluble and insoluble esters to polymer synthesis. To further expand the biotechnological applications of cutinases for synthetic polyester degradation, we perform a comparative activity and stability analysis of five cutinases from Alternaria brassicicola (AbC), Aspergillus fumigatus (AfC), Aspergillus oryzae (AoC), Humicola insolens (HiC), and the well-characterized Fusarium solani (FsC). Of the cutinases, HiC demonstrated enhanced poly(ε-caprolactone) hydrolysis at high temperatures and under all pH values, followed by AoC and AfC. Both AbC and FsC are least stable and function poorly at high temperatures as well as at acidic pH conditions. Surface charge calculations and phylogenetic analysis reveal two important modes of cutinase stabilization: (1) an overall neutral surface charge within the “crowning area” by the active site and (2) additional disulfide bond formation. These studies provide insights useful for reengineering such enzymes with improved function and stability for a wide range of biotransformations.     

Rational design of temperature-sensitive alleles using computational structure prediction

Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate "top 5" list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions.     

Optimising non-destructive sampling methods to study nitrogen use efficiency throughout the growth-cycle of giant C4 crops

Plant and Soil - The improvement of nitrogen use efficiency (NUE) of crops allows crop nitrogen (N) demands to be met while reducing N supply, and so reducing excess N which has potential negative...     

Identification of Small Exonic CNV from Whole-Exome Sequence Data and Application to Autism Spectrum Disorder

Copy number variation (CNV) is an important determinant of human diversity and plays important roles in susceptibility to disease. Most studies of CNV carried out to date have made use of chromosome microarray and have had a lower size limit for detection of about 30 kilobases (kb). With the emergence of whole-exome sequencing studies, we asked whether such data could be used to reliably call rare exonic CNV in the size range of 1–30 kilobases (kb), making use of the eXome Hidden Markov Model (XHMM) program. By using both transmission information and validation by molecular methods, we confirmed that small CNV encompassing as few as three exons can be reliably called from whole-exome data. We applied this approach to an autism case-control sample (n = 811, mean per-target read depth = 161) and observed a significant increase in the burden of rare (MAF ≤1%) 1–30 kb CNV, 1–30 kb deletions, and 1–10 kb deletions in ASD. CNV in the 1–30 kb range frequently hit just a single gene, and we were therefore able to carry out enrichment and pathway analyses, where we observed enrichment for disruption of genes in cytoskeletal and autophagy pathways in ASD. In summary, our results showed that XHMM provided an effective means to assess small exonic CNV from whole-exome data, indicated that rare 1–30 kb exonic deletions could contribute to risk in up to 7% of individuals with ASD, and implicated a candidate pathway in developmental delay syndromes.