Posters
Poster presentations at ISMB 2020 will be presented virtually. Authors will pre-record their poster talk (5-7 minutes) and will upload it to the virtual conference platform site along with a PDF of their poster.
All registered conference participants will have access to the poster and presentation through the conference and content until October 31, 2020. There are Q&A opportunities through a chat function to allow interaction between presenters and participants.
Preliminary information on preparing your poster and poster talk are available at:
https://www.iscb.org/ismb2020-general/presenterinfo#posters
Ideally authors should be available for interactive chat during the times noted below:
View Posters By Category
Poster Session A: July 13 & July 14 7:45 am - 9:15 am Eastern Daylight Time |
Session B: July 15 and July 16 between 7:45 am - 9:15 am Eastern Daylight Time |
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| July 14 between 10:40 am - 2:00 pm EDT |
Short Abstract: At USU’s high-performance computing | bioinformatics facility, we have developed a series of parallel-, multi-core-CPU-based open-source pipelines for large-scale -omics data analysis, which enables efficient and parallel analysis of multiple datasets in a short time. The facility provides access to high-performance compute resources, data analysis, hosting, programming expertise, backups and storage. The resources serve scientists at Utah State University and external academia / industry partners to master the informatics needs of their research in a proficient and cost-effective manner. Our unit has been contributing to different research projects by providing, (i) Support and expertise in programming and advanced data analysis, focusing primarily on high-throughput genomics technologies including microarrays, genotyping and next-generation sequencing (RNA-seq, ChIP-seq, SNP-seq, etc.), (ii) Virtual server environments, secure and public web portals, and a large suite of open source applications, (iii) Instruction of hands-on tutorials and workshops on a wide variety of informatics topics, (iv) Custom data analysis and consultation services, and (v) Establishment of research collaborations with experimental scientists from different departments. The facility is open to all researchers, USU as well as to external users anywhere in the world. Detailed information about the services, research, manuals developed, etc. is available at www.biosystems.usu.edu/core-labs/ and bioinfocore.usu.edu/.
Short Abstract: With the advent of Next-Generation Sequencing (NGS) technologies, numerous data is being generated every day, however, analysis remains a big hurdle to efficiently use the technology as this data requires complex multi-step processing and demands computational expertise from the user. While there is dedicated software available for individual step analysis but resources available for integrating this software into an automated workflow is limited.
To conquer this limitation, we have developed a Python package (pySeqRNA) which is capable of running the RNA-seq data analysis from start to finish reproducibly and efficiently. This package provides a uniform workflow interface and support for running python, and stand-alone tool on the High-Performance Computing Cluster (HPCC) as well as on local computers. This is a flexible pipeline that can handle complex experiments and samples, and whether a reference genome is available or not. pySeqRNA workflow consists of - quality check and preprocessing of raw sequence reads - accurate mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events - quantifying expression levels of genes, transcripts, and exons - differential analysis of gene expression among different biological conditions - biological interpretation of differentially expressed genes, including functional enrichment analysis.
Short Abstract: Reproducibility and portability are common problems in the analysis of genomic sequencing data. As many bioinformatic pipelines are usually designed to run only in particular and controlled High Performance Computing (HPC) environments, it is often a challenge for outside users to run pipelines designed by other organizations. The Center for Molecular Oncology (CMO) provides a centralized bioinformatic service for labs to perform analysis on IMPACT (a cancer specific panel) and Whole EXOME targeted sequencing data. Reproducibility and portability are needed to ensure any lab can replicate the analysis conducted at the CMO. For this purpose, the CMO has developed roslin-cwl, a pipeline that is reproducible, portable, and that meets the security specification of the HPC at Memorial Sloan Kettering. Roslin-cwl is written in Common Workflow Language (CWL), a standard specification for describing workflows, and executed using any executor such as TOIL. It uses Singularity containers which provide a fast and secure container environment for tools to run on the HPC. This presentation will highlight some of the challenges that hindered as well as open source technological advancements that facilitated the development of a portable and reproducible pipeline.
Short Abstract: Detecting biological significance is an essential step for any high-throughput sequence analysis. Once sequence reads are mapped and assembled, this is followed by different quantification analysis which ends up with a set of features (transcript/gene). Quickly exploring those features together from different angles along with statistical inference gives a good idea about the biology they are involved.
Doing these kinds of exploration for a particular organism requires an up to date annotation database. Currently available online/API platforms support either very few or only model organisms. Apart from that, reproducibility is a primary issue as databases continually updated.
To tackle these problems I am presenting SigBio-Shiny, a standalone interactive application based on R-Shiny which supports more than just model organisms with no requirement of manual database maintenance. It leverages available open-source resources such as Bioconductor’s AnnotationHub to collect the organism’s updated database in real-time with keeping track of what version of the database used. On top of this database, it helps with detecting biological significance on a set of genes by doing gene mapping, enrichment analysis of Gene Ontology (GO) and Pathway analysis.
