We’ll be attending PAG XXV, the largest ag-genomics meeting in the world, January 14-18 in San Diego, CA, USA. PAG is one of our favorite events to meet researchers from all over the world within plant and animal genomics. As always, we’ve prepared scientific activities for you, which you can read more about below.
Workshop: Metagenomics and more – bioinformatics solutions for plant and animal research
Monday, January 16. 12:50-3:00 pm
Royal Palm Salon 5-6
QIAGEN bioinformatics offers genome scientists continuity in their research. CLC Genomics Workbench and a series of specialized modules integrate all NGS data analysis capabilities required to power modern plant and animal research into a single platform – from DNA-seq and variant detection, advanced RNA-seq and epigenomics capabilities, to a comprehensive toolbox for metagenomics or plant pathogen analysis. The direct integration with Ingenuity Pathway Analysis (IPA) allows researchers to explore the biological consequences of the results of RNA-seq or epigenomics experiments.
For this workshop we have picked two NGS application areas that we will explore in greater depth: Dr. Jamie Hill (Product owner, CLC Genomics Workbench, QIAGEN) will focus on RNA-seq capabilities and best practices. And Dr. Mark Borodovski is presenting on gene finding and annotation of metagenomes or pathogen genomes.
Make lasting expressions with your research – one integrated solution for your RNA-seq analysis
Speaker: Jamie Hill, Senior Bioinformatics Scientist, QIAGEN Aarhus
For few areas of genomics, do best practices evolve as quickly and continuously as for RNA-seq applications. As a consequence of the rapid development within RNA-seq, researchers struggle to ensure that their analysis pipelines meet the latest standards. In the daily routine users often run a mix of different bioinformatics tools for the respective analysis step they perform best, from read mapping through isoform quantification to the detection of differential abundance. However, integrating and testing the best performers among a growing number of analysis solutions is complex and time consuming.
RNA-seq analysis is a declared focus area for QIAGEN bioinformatics. Users of CLC Genomics Workbench and Biomedical Genomics Workbench rely on us to constantly evaluate emerging bioinformatics approaches and integrate leading approaches into our solutions in a way that follows modern design control and quality assurance criteria. In our workshop we will share best practices as well as some of the recent improvements and underlying methods implemented into our RNA-seq solution.
MetaGeneMark: Analysis of NGS sequences with CLC Genomic Workbench
Speaker: Mark Borodovsky, Georgia Institute of Technology, Atlanta, GA, USA, Gene Probe, Inc., Atlanta, GA, USA
Gene prediction and annotation plays central role in genomics. However, in spite of much attention, open problems still exist and stimulate development of new algorithmic solutions in all categories of gene finding. Particularly, gene prediction in short assemblies of NGS reads e.g. in short metagenomic sequences, is far from trivial.
The gene finder, MetaGeneMark, has been frequently used in individual labs for analysis of short sequences. It has also been used as a part of comprehensive pipelines, such as DOE JGI IMG/M pipeline for annotation of environmental metagenomes.
Implementation of MetaGeneMark in the CLC Genomic Workbench, its applications and the theory behind MetaGeneMark is the subject of this presentation.
P0079 – Variant identification workflow for chromosome scale assembly
Presenter: Marta Matvienko
Location: Grand Exhibit Hall
NGS assemblies of plant genomes often consist of thousands of contigs. Sequencing the segregating progenies is regularly used to anchor the de novo assembled contigs into chromosomescale assemblies. The analysis of sequencing data from segregating progenies usually involves custom scripting, and requires advanced bioinformatics skills. Here we present a userfriendly workflow that can be performed in CLC Genomics Workbench, enabling biologists to proceed with this type of data analysis.
We used the publicly available ddRADSeq data for sacred lotus, Nelumbo nucifera (Liu et al, 2016), and the corresponding genomic assembly consisting of 3,602 contigs. The alignments, as well as all variant calling and variant filtering were performed in CLC Genomics Workbench. The variants were called using the Fixed Ploidy Variant caller, filtered against control reads of the other parent, and selected for homozygosity and variant quality. This part of the workflow produced a known variants track, which was used to call variants in the progenies. We further filtered the variant tracks using the workbench’s comparative tools, and ended up with 4K variants detected in at least 70% of F2 samples.
To assess the quality of variant calls, we exported the data from CLC Genomics Workbench and submitted them to the MadMapper program, which clustered contigs into chromosomes. Most of the genomic assembly (72.5%) was clustered into 9 lotus chromosomes; a similar number, 70.6% was anchored by Liu et al. This confirmed the quality of the marker data for chromosomescale assemblies outputted by the workbench using the user-friendly workflow.