Postdoctoral Research Fellow Position

Field: Bioinformatics, Transcriptomics, Genomics

Where: University of Bologna, Italy

Title: Development of Single-Cell Transcriptional Gene Networks to investigate Cancer Heterogeneity

Duration: 24 months

Supervisor: Federico M. Giorgi, Assistant Professor of Bioinformatics with a background in molecular biology, please send any enquiry directly to

Required Degree: PhD in Bioinformatics, Genomics, ComputationalBiology or equivalent

Required Skills: NGS data analysis, R programming, Biostatistics

Infrastructure: personal workstation Intel Xeon E3,64GB RAM, 256 SSD + 8TB HDD. Access to HPCcluster Cineca Marconi (~80,000 working hours/year)

Working Environment: the lab is located in the historical center of Bologna, a dynamic student city, at the heart of the department of biotechnology, with strong interactions with experimental labs. The working group is young, it values scientific freedom and it endorses collaborations,internal and external. The candidate will have the optional chance to participate in the teaching of bioinformatics at the University of Bologna.

Project Abstract

Cancer is a malignant disease characterized by uncontrolled proliferation and specific molecular mechanisms that make it difficult to be treated pharmacologically or surgically1. Furthermore, Cancer possesses an intrinsic heterogeneity, i.e. phenotypical, clinical and molecular differences between cancers originating from different tissues2, across different subtypes3 originating from the same tissue in different patients4 and between different cells within the same tumor micronvironment5.

Characterizing Cancer heterogeneity requires the analysis of large quantities of tumoral molecular data, such as that collected by The Cancer Genome Atlas (TCGA) project6, which collected RNA-Seq, RPPA, CNV, SNV, methylation and clinical data on a patient-by-patient basis across more than 15,000 individuals 30 tumor types. Also, it will require the analysis of Single-Cell data, both publicly available (e.g. from the Human Cell Atlas project7) or generated directly in Dr. Giorgi’s lab in the context of brain tumors.

The project will investigate complex cancer-specific gene-gene (e.g. TF-targets) relationships as network representations. These gene networks serve as both biological representations and mathematical tools to better understand tumor development across several cancer types. We will build tissue context-specific transcriptional networks using established and novel algorithms based on motif binding prediction, co-expression, experimental data and literature mining. These networks will be used to drive experimental validation of novel genetic and protein/protein interactions through internal collaborations with experimental labs within the department. We will also leverage network representations as a tool to increase the signal/noise ratio in intrinsically problematic Transcriptomics datasets, such as low-quality samples, low coverage micro-RNAs, early detection screenings or single-cell quantitative assays8.


  1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011 Mar 4;144(5):646-74. doi: 10.1016/j.cell.2011.02.013.
  2. Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C. Emerging landscape of oncogenic signatures across human cancers. Nat Genet. 2013 Oct;45(10):1127-33. doi: 10.1038/ng.2762.
  3. Comprehensive molecular portraits of human breast tumours. Cancer Genome Atlas Network. Nature. 2012 Oct 4;490(7418):61-70. doi: 10.1038/nature11412. Epub 2012 Sep 23.
  4. Alvarez MJ, Subramaniam PS, … Califano A. A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors. Nat Genet. 2018 Jul;50(7):979-989. doi: 10.1038/s41588-018-0138-4. Epub 2018 Jun 18.
  5. Feig C1, Gopinathan A, Neesse A, Chan DS, Cook N, Tuveson DA. The pancreas cancer microenvironment. Clin Cancer Res. 2012 Aug 15;18(16):4266-76. doi: 10.1158/1078-0432.CCR-11-3114.
  6. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 Oct;45(10):1113-20. doi: 10.1038/ng.2764.
  7. Orit Rozenblatt-Rosen, Michael J.T. Stubbington, Aviv Regev, Sarah A. Teichmann: The Human Cell Atlas: from vision to reality. Nature; 2017 Oct 18. PMID: 29072289
  8. Brennecke P1, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, Baying B, Benes V, Teichmann SA, Marioni JC, Heisler MG. Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013 Nov;10(11):1093-5. doi: 10.1038/nmeth.2645. Epub 2013 Sep 22.

Further Reading

  • Giorgi FM, Lachmann A, Garcia GL, Califano A. ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics 2016.
  • Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A. Dissecting the mutational landscape of cancer by integrative inference and analysis of protein activity. Nature Genetics 2016.
  • Giorgi FM, Lachmann A, Alvarez MJ, Califano A. Detection and removal of spatial bias in multi-well assays. Bioinformatics 2016.
  • Del Fabbro C, Scalabrin S, Morgante M and Giorgi FM. An extensive evaluation of read trimming effects on Illumina NGS data analysis. PLOS ONE, 2013.
  • Giorgi FM, Del Fabbro C, Licausi F. Comparative study of RNA-seq- and Microarray- derived coexpression networks in Arabidopsis thaliana. Bioinformatics 2013.
  • Giorgi FM, Bolger AM, Lohse M and Usadel B. Algorithm-driven Artifacts in median polish summarization of Microarray data, BMC Bioinformatics 2010.