Carrie Ganote

Peptidergic signaling in Calanus finmarchicus (Crustacea, Copepoda): in silico identification of putative peptide hormones and their receptors using a de novo assembled transcriptome.

The copepod Calanus finmarchicus is the most abundant zooplankton species in the North Atlantic. While the life history of this crustacean is well studied, little is known about its peptidergic signaling systems despite the fact that these pathways are undoubtedly important components of its physiological/behavioral control systems. Here we have generated and used a de novo assembled transcriptome for C. finmarchicus (206,041 sequences in total) to identify peptide precursor proteins and receptors. Using known protein queries, 34 transcripts encoding peptide preprohormones and 18 encoding peptide receptors were identified. Using a combination of online software programs and homology to known arthropod isoforms, 148 mature peptides were predicted from the deduced precursors, including members of the allatostatin-A, allatostatin-B, allatostatin-C, bursicon, crustacean cardioactive peptide (CCAP), crustacean hyperglycemic hormone, diuretic hormone 31 (DH31), diuretic hormone 44 (DH44), FMRFamide-like peptide (myosuppressin, neuropeptide F [NPF] and extended FL/IRFamide subfamilies), leucokinin, neuroparsin, orcokinin, orcomyotropin, periviscerokinin, RYamide and tachykinin-related peptide (TRP) families. The identified receptors included ones for allatostatin-A, allatostatin-C, bursicon, CCAP, DH31, DH44, ecdysis-triggering hormone, NPF, short NPF, FMRFamide, insulin-like peptide, leucokinin, periviscerokinin, pigment dispersing hormone, and TRP. Developmental profiling of the identified transcripts in embryos, early nauplii, late nauplii, early copepodites, late copepodites, and adult females was also undertaken, with all showing the highest expression levels in the naupliar and copepodite stages. Collectively, these data radically expand the catalog of known C. finmarchicus peptidergic signaling proteins and provide a foundation for experiments directed at understanding the physiological roles served by them in this species.

STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq

Motivation Fusion genes created by genomic rearrangements can be potent drivers of tumorigenesis. However, accurate identification of functionally fusion genes from genomic sequencing requires whole genome sequencing, since exonic sequencing alone is often insufficient. Transcriptome sequencing provides a direct, highly effective alternative for capturing molecular evidence of expressed fusions in the precision medicine pipeline, but current methods tend to be inefficient or insufficiently accurate, lacking in sensitivity or predicting large numbers of false positives. Here, we describe STAR-Fusion, a method that is both fast and accurate in identifying fusion transcripts from RNA-Seq data. Results We benchmarked STAR-Fusion’s fusion detection accuracy using both simulated and genuine Illumina paired-end RNA-Seq data, and show that it has superior performance compared to popular alternative fusion detection methods. Availability and implementation STAR-Fusion is implemented in Perl, freely available as open source software at http://star-fusion.github.io, and supported on Linux. Contact bhaas@broadinstitute.org

A Voice for Bioinformatics

One of the challenges to adoption of HPC is the disjunction between those who need it and those who know it. Biology (specifically, genomics) is a growing field for computational use, but the typical biologist does not have an established informatics background. The National Center for Genome Analysis Support (NCGAS) aids users in getting past the initial shock of the command line and guides them toward savvy cluster use. NCGAS is initiating a push to become domain champions alongside Oklahoma States Brian Cougar. Our position at IU gives us a close relationship with XSEDE and we already fulfill a role in pushing users toward XSEDE resources when our local clusters are ill-suited to the job. We currently act as liaison between biologists and Jetstream, IU and TACCs research computing cloud. Typical issues include: Software installation; Software usage - what parameters do I choose, and how do I interpret the results; Batch job submission; Understanding how queues and job handlers work; Data movement, Spinning up VMs on Jetstream We will discuss how we have structured our support, and illustrate our impact on XSEDE resources.