Name: Dr. Reema Singh
Type: User
Company: Vaccine and Infectious Disease Organization (VIDO)
Bio: A computational scientist with more than 14 years of research experience | Ph.D. In Computational Biology and Bioinformatics
Twitter: ReemaSingh28
Location: Saskatoon, Canada
Blog: https://ca.linkedin.com/in/reema-singh-phd-30b03758; https://www.researchgate.net/profile/Reema_Singh3
Dr. Reema Singh's Projects
Identify Differential Expressed Genes in DgcA null mutant and Knock-out samples in Dictyostelium discoideum. The same workflow has been used for the differential expression analysis of Polysphondylium pallidum.
A pipeline that assembles short reads into full scaffolds and automatically assigns molecular epidemiological and AMR information to the assembled genomes.
Graphical User Interface (GUI) for the Gen2Epi computational pipeline named Gen2EpiGUI. Gen2Epi facilitates an understandable analysis of N. gonorrhoeae WGS data for users with limited bioinformatics skills.
A generic platform for the analysis of high throughput transcriptomics, proteomics, and metabolomics data.
De-novo Transcriptomics Assembly workflow for four Dictyostelium species (e.g.- Dictyostelium discoideum, Polysphondylium pallidum, Dictyostelium Lacteum and Dictyostelium Fasciculatum). This is the standard assembly workflow that should ideally work on any organism.Before normalizing first concatenate all RNAseq data across all samples into a single set of inputs to generate a single reference transcriptomics assembly. Combine all left reads in one file and all right reads in another file. In order to reduce the number, raw reads were normalized using in silico digital normalization implemented in trinity at 50X coverage. The reads were assembled with Trinity using kmer parameter of 25.