Gene fusions and their products (RNA and protein) were once thought to be unique features to cancer. However, chimeric RNAs can also be found in normal cells. Here, we performed, curated and analyzed nearly 300 RNA-Seq libraries covering 30 different non-neoplastic human tissues and cells as well as 15 mouse tissues. A large number of fusion transcripts were found. Most fusions were detected only once, while 291 were seen in more than one sample. We focused on the recurrent fusions and performed RNA and protein level validations on a subset. We characterized these fusions based on various features of the fusions, and their parental genes. They tend to be expressed at higher levels relative to their parental genes than the non-recurrent ones. Over half of the recurrent fusions involve neighboring genes transcribing in the same direction. A few sequence motifs were found enriched close to the fusion junction sites. We performed functional analyses on a few widely expressed fusions, and found that silencing them resulted in dramatic reduction in normal cell growth and/or motility. Most chimeras use canonical splicing sites, thus are likely products of ‘intergenic splicing’. We also explored the implications of these non-pathological fusions in cancer and in evolution.

RNA-Seq made possible the global identification of fusion transcripts, i.e. “chimeric RNAs”. Even though various software packages have been developed to serve this purpose, they behave differently in different datasets provided by different developers. It is important for both users and developers to have an unbiased assessment of the performance of existing fusion detection tools. Toward this goal, we compared the performance of 12 well-known fusion detection software packages. We evaluated the sensitivity, false discovery rate, computing time and memory usage of these tools in four different datasets (positive, negative, mixed and test). We conclude that some tools are better than others in terms of sensitivity, positive prediction value, time consumption and memory usage. We also observed small overlaps of the fusions detected by different tools in the real dataset (test dataset). This could be due to false discoveries by various tools, but could also be due to the reason that none of the tools are inclusive. We have found that the performance of the tools depends on the quality, read length and number of reads of the RNA-Seq data. We recommend that users choose the proper tools for their purpose based on the properties of their RNA-Seq data.

Plants produce an array of peptides as part of their innate defense mechanism against pathogens. The potential use of these peptides for various therapeutic purposes is increasing per diem. In order to excel in this research, the community requires web repositories that provide reliable and accurate information about these phyto-peptides. This work is an attempt to bridge the gaps in plant-based peptide research. PlantPepDB is a manually curated database that consists of 3848 plant-derived peptides among which 2821 are experimentally validated at the protein level, 458 have experimental evidence at the transcript level, 530 are predicted and only 39 peptides are inferred from homology. Incorporation of physicochemical properties and tertiary structure into PlantPepDB will help the users to study the therapeutic potential of a peptide, thus, debuts as a powerful resource for therapeutic research. Different options like Simple, Advanced, PhysicoChem and AA composition search along with browsing utilities are provided in the database for the users to execute dynamic search and retrieve the desired data. Interestingly, many peptides that were considered to possess only a single property were found to exhibit multiple properties after careful curation and merging the duplicate data that was collected from published literature and already available databases. Overall, PlantPepDB is the first database comprising detailed analysis and comprehensive information of phyto-peptides from a broad functional range which will be useful for peptide-based applied research. PlantPepDB is freely available at http://www.nipgr.ac.in/PlantPepDB/.

Cancer therapies are limited by the development of drug resistance, and mutations in drug targets is one of the main reasons for developing acquired resistance. The adequate knowledge of these mutations in drug targets would help to design effective personalized therapies. Keeping this in mind, we have developed a database “CancerDR”, which provides information of 148 anti-cancer drugs, and their pharmacological profiling across 952 cancer cell lines. CancerDR provides comprehensive information about each drug target that includes; (i) sequence of natural variants, (ii) mutations, (iii) tertiary structure, and (iv) alignment profile of mutants/variants. A number of web-based tools have been integrated in CancerDR. This database will be very useful for identification of genetic alterations in genes encoding drug targets, and in turn the residues responsible for drug resistance. CancerDR allows user to identify promiscuous drug molecules that can kill wide range of cancer cells. CancerDR is freely accessible at http://crdd.osdd.net/raghava/cancerdr/

We report the de novo assembled 20.05-Mb draft genome of the red yeast Rhodosporidium toruloides MTCC 457, predicted to encode 5,993 proteins, 4 rRNAs, and 125 tRNAs. Proteins known to be unique to oleaginous fungi are present among the predicted proteins. The genome sequence will be valuable for molecular genetic analysis and manipulation of lipid accumulation in this yeast and for developing it as a potential host for biofuel production.