Whitney Peterson (drakegrade4)

th elevated HBV risk, reinforcing the need to increase communication and education on condom use among key populations in Nigeria. Evaluating use of concurrent HIV antiretroviral therapy with anti-HBV activity may confirm the attenuated HBV prevalence for those living with HIV. Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub https//github.com/galadrielbriere/ClustOmics . The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub https//github.com/galadrielbriere/ClustOmics . GRAS, an important family of transcription factors, have played pivotal roles in regulating numerous intriguing biological processes in plant development and abiotic stress responses. Since the sequencing of the sorghum genome, a plethora of genetic studies were mainly focused on the genomic information. The indepth identification or genome-wide analysis of GRAS family genes, especially in Sorghum bicolor, have rarely been studied. A total of 81 SbGRAS genes were identified based on the S. bicolor genome. They were named SbGRAS01 to SbGRAS81 and grouped into 13 subfamilies (LISCL, DLT, OS19, SCL4/7, PAT1, SHR, SCL3, HAM-1, SCR, DELLA, HAM-2, LAS and OS4). SbGRAS genes are not evenly distributed on the chromosomes. According to the results of the gene and motif composition, SbGRAS members located in the same group contained analogous intron/exon and motif organizations. We found that the contribution of tandem repeats to the increase in sorghum GRAS members was slightly greater than that of fragment repeats. By quantitative (q) RT-PCR, the expression of 13 SbGRAS members in different plant tissues and in plants exposed to six abiotic stresses at the seedling stage were quantified. We further investigated the relationship between DELLA genes, GAs and grain development in S. bicolor. The paclobutrazol treatment significantly increased grain weight, and affected the expression levels of all DELLA subfamily genes. SbGRAS03 is the most sensitive to paclobutrazol treatment, but also has a high response to abiotic stresses. Collectively, SbGRAs play an important role in plant development and response to abiotic stress. This systematic analysis lays the foundation for further study of the functional characteristics