Biology subjects

Rabbani, L.

Publications and source records attributed to Rabbani, L..

3 recordsLinked to original sources

bioRxiv2020

An Algorithm to Build a Multi-genome Reference

1MotivationNew DNA sequencing technologies have enabled the rapid analysis of many thousands of genomes from a single species. At the same time, the conventional approach of mapping sequencing reads against a single reference genome sequence is no longer adequate. However, even where multiple high-quality reference genomes are available, the problem remains how one would integrate results from pairwise analyses. ResultTo overcome the limits imposed by mapping sequence reads against a single reference genome, or serially mapping them against multiple reference genomes, we have developed the MGR method that allows simultaneous comparison against multiple high-quality reference genomes, in order to remove the bias that comes from using only a single-genome reference and to simplify downstream analyses. To this end, we present the MGR algorithm that creates a graph (MGR graph) as a multi-genome reference. To reduce the size and complexity of the multi-genome reference, highly similar orthologous1 and paralogous2 regions are collapsed while more substantial differences are retained. To evaluate the performance of our model, we have developed a genome compression tool, which can be used to estimate the amount of shared information between genomes. Availabilityhttps://github.com/LeilyR/Multi-genome-Reference.git Contactweigel@tue.mpg.de

bioinformatics↗

bioRxiv2018

snakePipes enable flexible, scalable and integrative epigenomic analysis

The scale and diversity of epigenomics data has been rapidly increasing and ever more studies now present analyses of data from multiple epigenomic techniques. Performing such integrative analysis is time-consuming, especially for exploratory research, since there are currently no pipelines available that allow fast processing of datasets from multiple epigenomic assays while also allow for flexibility in running or upgrading the workflows. Here we present a solution to this problem: snakePipes, which can process and perform downstream analysis of data from all common epigenomic techniques (ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq) in a single package. We demonstrate how snakePipes can simplify integrative analysis by reproducing and extending the results from a recently published large-scale epigenomics study with a few simple commands. snakePipes are available under an open-source license at https://github.com/maxplanck-ie/snakepipes.

genomics↗

bioRxiv2017

A rainfall-manipulation experiment with 517 Arabidopsis thaliana accessions

The gold standard for studying natural selection and adaptation in the wild is to quantify lifetime fitness of individuals from natural populations that have been grown together in a common garden, or that have been reciprocally transplanted. By combining fitness values with species traits and genome sequences, one can infer selection coefficients at the genetic level. Here we present a rainfall-manipulation experiment with 517 whole-genome sequenced natural accessions of the plant Arabidopsis thaliana spanning the global distribution of the species. The experiments were conducted in two field stations in contrasting climates, in the Mediterranean and in Central Europe, where we built rainout shelters and simulated high and low rainfall. Using custom image analysis we quantified fitness- and phenology-related traits for 23,154 pots, which contained about 14,500 plants growing independently, and over 310,000 plants growing in small populations (max. 30 plants). This large field experiment dataset, which associates fitness and ecologically-relevant traits with genomes, will provide an important resource to test eco-evolutionary genetic theories and to understand the potential evolutionary impacts of future climates on an important plant model species.

evolutionary biology↗