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Bioinformatics & Computational Biology Postdoctoral Position  

A postdoctoral position in computational biology is available in the Singh laboratory at Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center. We are an integrative computational and experimental laboratory within the Department of Molecular and Cellular Medicine at Texas A&M University (TAMU) with an affiliation with Department of Biomedical Engineering at TAMU. The primary focus of our group is to utilize diverse, high-throughput functional assays and perturbations to uncover mechanisms of transcriptional and co-transcriptional regulation in normal and diseased states. We combine the strengths of computational methods and molecular assays to dissect the functional regulatory programs in mammalian cells. We have established multidisciplinary collaborations with researcher at Dana Farber Cancer Institute, Baylor College of Medicine, and Texas Children’s Hospital.

The candidate will have an opportunity to work on following two projects:

• Dysregulation of RNA Processing as Driver of Malignancies – Dysregulation of intronic polyadenylation (IPA) is emerging as a novel pathobiological phenomenon in cancer. Recognition of polyadenylation signals (pAS) present in introns of protein-coding genes can generate truncated mRNAs (IPA isoforms) that are either non-coding variants or transcripts with truncated open reading frames that lead to loss of C-terminal domains in the protein product. Our recent studies showed that expression of IPA isoforms is dysregulated in Chronic Lymphocytic Leukemia (CLL) (Lee* and Singh* et al, Nature 2018) and Multiple Myeloma (MM) patients (Singh et al, Nature Communications 2018). We showed that truncated mRNAs generated by IPA is a widespread phenomenon in CLL patients and predominantly inactivates tumor-suppressor genes (TSGs). Inactivation of TSGs by aberrant mRNA processing was more prevalent than the loss of such genes through genetic events. In contrast to CLL, MM patients displayed a striking loss of IPA isoforms that were expressed in plasma cells (PC, normal cell type for MM). We discovered that dysregulated IPA expression in MM patients is associated with shorter progression-free survival. Interestingly, IPA dysregulation impacted key genes of MM biology that are involved in response to lenalidomide therapy (a highly successful MM therapeutic). Still, the functional consequence of this loss of IPA isoform expression in MM remains unknown and requires in-depth investigation. Overall our studies highlight that mRNA events can be widespread contributors to cancer pathogenesis. Thus, it is critical to identify target genes subject to IPA dysregulation across malignancies and determine their role in tumorigenesis. To accomplish this, our lab is interested in characterizing the landscape of IPA across malignancies and interrogating its functional consequences.
• Computational modelling of chromatin directed gene expression profiles – Accumulation of genetic and epigenetic alternations leads to widespread changes in the gene expression programs in cancer. Aberrant activity of transcription factors (TFs) is instrumental in driving such gene expression changes by altering the chromatin accessibility landscape resulting in acquisition of hallmark capabilities of cancer: sustained proliferation, replicative immortality and apoptotic evasion. Data-driven computational methodlogies integrating the DNA sequence accessibility across promoters, intronic and intergenic enhancers can be effective to explain the gene expression profiles. We utilize learning framework to identify TFs that explain the gene expression either in a gene- or patient- specific manner.

Responsibilities: The candidate will be responsible for leading their own independent and collaborative research projects. As a Postdoc fellow, they will be expected to take initiative to lead projects and engage in scientific discussions with the group as required. It is expected that they will:

• Perform hypothesis driven computational research with a clear understanding and use of controls.
• Use computational methods to analyze large-scale sequencing data and formulate quantitative models.
• Development and dissemination of statistical methodology, software development, and application to large-scale genetic epidemiological studies. Specific areas of interest include multiclass regression model, nonparametric modeling and methods, multiple hypothesis testing and machine learning.
• Explore and bring novel approaches and ideas to their project by collaborating with other labs at TAMU and other collaborators.

Preferred Education: Recent PhDs with strong publications in computer science, bioinformatics, computational biology, machine learning and related fields will be considered. Training in statistics, and programming experience in a UNIX/Linux environment using programming languages such as Python, R, and/or Perl is required.

Preferred Experience: Experience in high-throughput analysis of next-generation sequencing data, familiarity with cluster computing and widely-used consortium datasets are advantageous.

Preferred Special Knowledge, Skills, and Abilities: The candidate should have expertise in computational biology and is expected to devise innovative analytical approaches to high-volume genomic, epigenomic, and gene expression data. The successful candidate will work independently and take a leading role in data analysis, interpretation, and visualization, and drafting of manuscripts for publication

Postdoctoral fellows are expected to have a strong work ethic, excellent organizational and communication skills, and critical thinking abilities.