Posters - Schedules

Cacti are a young and diverse group of angiosperms widely known for their biochemical, anatomical and physiological adaptations to arid and semi-arid environments. Despite being a numerous family with ca. 1800 species, Cactaceae genomic and transcriptomic resources are rather limited, with only 6 low-coverage and highly fragmented genomes available.
In this work, we sequenced and analyzed the root tip transcriptome of Pachycereus pringlei, a mexican columnar cactus, at three different developmental stages.The P. pringlei transcriptome was de-novo assembled, and 63.7% of the assembled contigs were annotated.
Here we will present some of the results obtained from the analysis of the un-annotated contigs, including their coding potential assessment, differential expression, and some insights derived from comparative analysis with transcriptomes from other Cactaceae species.
Our analyses suggest the existence of lineage-specific transcripts, i.e., putative orphan genes, with differential regulation across root development, which might be involved in the regulation of root growth and development.

Mutations in the mitochondrial genome have been linked to aging in humans, primates and rodents and cause a range of neuromuscular diseases in human. We show here that the mitochondrial genome of embryonic, adult, and aged mouse brain from two different strains contains a diversity of single nucleotide variants affecting both, coding and non-coding regions with no overt age-related increase in abundance. We also detected de novo variants in oocytes and in adult liver and found that in half of the human samples analyzed, over 60% of the mitochondrial genome copies may bear lesions such as a group of base substitutions of low heteroplasmy or clustered intergenic deletions.

The advent of next generation sequencing (NGS) technologies has revolutionized the field of genomic sciences by cutting down the cost and time associated with standard sequencing methods. This advancement has not only provided us with an abundance of data but has also presented us with a challenge of analyzing it. The paramount challenge in analyzing the copious amount of data is using the optimal resources for the myriad tools available. To overcome this, we propose an “Automated workflow for sequencing data analysis”.

This workflow is based on nextflow scripting language and uses docker to manage and deploy the run environment. We have developed workflows to be used for germline DNA-seq and RNA-seq data. The DNA-seq workflow consists of four analysis phases - Quality Control, Mapping to reference genome & quality score recalibration, variant calling & variant recalibration and annotation. An important feature of the DNA-seq workflow is that it uses the combination of multiple variant callers (GATK Haplotypecaller, DeepVariant, VarScan2, Freebayes and Strelka2) and presents us with a consensus output from different variant callers. This consensus calculation is implemented to get more dependable variants. The RNA-seq workflow consists of four phases - Quality control, Mapping to reference genome, read quantification & gene expression calculation. The RNA-seq pipeline also performs GO analysis and KEGG pathway analysis and generates a detailed report of the analysis.

The objective was to develop a scalable workflow which integrates the fragmented tools and enhances reproducibility of the results. The ease of deployment and usage facilitated by the workflow should enable computational reproducibility providing researchers more time for downstream analysis. The source code, instructions for installation and usage of the tool are available to the public through our gitlab repository -
https://gitlab.utu.fi/bioinformatics/automated-workflows/.