Supervisor: Mohammed Kanchwala

General tasks

• Test and run NextFlow pipelines such as RNA-seq, ATAC-seq, ChIP-seq, Single-cell RNA-seq, and Single-cell ATAC-seq to perform analysis for high-throughput sequencing data.

• Utilize AWS to run memory and core intensive pipelines and processes.

Gene Regulatory Network Inference from MIX-Seq single-cell transcriptomics

• Understand the overall goal of the method that how different cell lines can be mixed and profiled for drug response at the same time.

• Use SNP profiling to resolve cell line identity by using UMAP and differential expression.

• Perform cell-cycle analysis of the single-cell RNA-seq data to quantify the drug’s impact on cell-cycle phase.

• Reproduce the results that were generated by the paper by downloading, interpreting, and analyzing the data.

• Further build on the it by implementing SCENIC workflow to infer the gene regulatory networks present.

GENIE to further infer co-expression network from MIX-seq data

• Run GENIE instead of modified version of GENIE3 used in SCENIC pipeline.

• Analyze and compare the results generated by SCENIC using GENIE3 and one produced by GENIE.

Develop RNA-seq pipeline in basic shell script

• Install various tools and learn its parameters to run in shell.

• Use tools such as FastQC and TrimGalore to get Quality Control reports.

• Employ alignment tool such as STAR for RNA mapping and quantification tool such as Salmon.

• Troubleshoot installation and compatibility errors such that the tools can be used in a self-made pipeline.