Out researchers have developed a range of statistical analysis packages, software programmes and visualisation tools to facilitate genetic research.
The DietMetab Data tool provides blood metabolomics associations with self-reported dietary intakes (EPIC FFQ) within the TwinsUK population.
The human gut is inhabited by a complex and metabolically active microbial ecosystem regulating host health. While many studies have focused on the effect of individual microbial taxa, the metabolic potential of the entire gut microbial ecosystem has been largely under-explored.
Visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns.
Power of variance component multipoint linkage analysis studies through simulations.
Author: Mario Falchi (DTR) and Cesare Cappio Borlino (Shardna)
YAMP: a containerized workflow enabling reproducibility in metagenomics research
“Yet Another Metagenomics Pipeline” (YAMP), a ready-to-use containerized workflow that, using state-of-the-art tools, processes raw shotgun metagenomics sequencing data up to the taxonomic and functional annotation. YAMP is implemented in Nextflow and is accompanied by a Docker and a Singularity container. Although YAMP was developed to be ready to use by nonexperts, bioinformaticians will appreciate its flexibility, modularization, and simple customization.
YAMP can be executed on any UNIX-like system and offers seamless support for multiple job schedulers as well as for the Amazon AWS cloud. The YAMP script, parameters, and documentation are available at https://github.com/alesssia/YAMP.
If you use YAMP for research purpose, please cite:
Visconti, Alessia, et al. “YAMP: a containerized workflow enabling reproducibility in metagenomics research” GigaScience 7(7) (2018), DOI:10.1093/gigascience/giy072
PopPAnTe: Population and Pedigree Association Testing for Quantitative Data
PopPAnTe is a user-friendly framework enable pairwise association testing of quantitative -omics variables in family-based study. Relationships between individuals can be either described by known family structures of any size and complexity, or by genetic similarity matrices (GSM) inferred from genome-wide genetic data. This approach is particularly useful when some degree of hidden relatedness (including population stratification) is expected, but extensive genealogical information is missing or incomplete. For instance, genealogical information going back more than three or four generations may be difficult to be retrieved for individuals recruited in large-scale biobank started in genetic isolates.
PopPAnTe models the data in a variance component framework to keep into account the resemblance among individuals, supports region-based testing, assesses the significance of the association through a formal likelihood ratio testing as well as through an adaptive permutation procedure, and performs basic data pre- and post-processing.
PopPAnTe is now at version 1.0.2 (released on March 20th, 2018)
If you use PopPAnTe for research purpose, please cite:
Visconti, Alessia, et al. “PopPAnTe: population and pedigree association testing for quantitative data.” BMC genomics 18.1 (2017): 150, DOI:10.1186/s12864-017-3527-7
famCNV: copy number variant association for quantitative traits in families
famCNV is a user-friendly Java program that enables genome-wide association of copy number variants with quantitative phenotypes in families of arbitrary size and complexity. It uses intensity signals such as the log ratio of observed to expected signal intensity (LRR) from Illumina genotyping arrays.
The quantitative trait of interest is analysed in a variance component framework to model the resemblance among relatives and to remove possible bias from familiarity.
famCNV is now at version 2.0. This new version allows a more flexible input, and quantile normalisation can be automatically applied to each phenotype to improve normality of the response variables. Moreover, its output includes also beta and percentage of variance explained. The significance of the results is also now tested both through formal likelihood testing and through an empirical adaptive procedure. Version 2.0 is also more efficient in its implementation, allowing multithreading.
An open resource for phenotype-expression associations and interactive exploration of GxE effects on gene expression.