Bioinformatics

The last 10 years has seen rapid advances in sequencing technology, along with the explosion of publicly available large-scale datasets. Taking advantage of these resources to enhance traditional “wet lab” research requires a unique skillset that incorporates computer science, biology, and statistics.  In MCCB, we have developed a core team of accomplished computational biologists who work side-by-side with bench scientists to implement the latest bioinformatics pipelines.  This group allows MCCB researchers to be uniquely positioned to take full advantage of standard and cutting edge sequencing technologies, including ChIP-Seq, MNAse-Seq, ATAC-Seq, RNA-seq, and single-cell sequencing.  At the same time, the bioinformatics team has worked with MCCB labs to develop and implement novel software pipelines. 

Click below to see how MCCB labs have applied or developed bioinformatics approaches in their research programs. 

Click here to see our bioinformatics team.

Fazzio Lab

The Fazzio lab uses deep sequencing approaches to probe regulation of the epigenome and transcriptome in normal and cancerous cells.  In the context of this work, they have collaborated with the MCCB bioinformatics group to develop novel techniques and computational approaches, such as RED-Seq, which measures DNA accessibility throughout the genome in an unbiased manner. The lab continues to combine molecular and computational approaches to uncover mechanisms by which chromatin structure regulates gene expression networks.  

  • Yildirim et al. (2011) Mbd3/NURD complex regulates expression of 5-hydroxymethylcytosine marked genes in embryonic stem cells.  Cell, 147(7):1498-510. 
  • Chen et al. (2014) Unbiased chromatin accessibility profiling by RED-seq uncovers unique features of nucleosome variants in vivo.  BMC Genomics, 15:1104 

Green Lab

The lab actively collaborates with the bioinformatics team to analyze high-throughput data such as ChIP-Seq, CLIP-Seq and RNA-Seq. These productive collaborations have allowed the lab to perform genome-wide analyses to identify targets of transcription and splicing factors, and to apply transcriptome profiling to assess large-scale changes in gene expression upon gain or loss of specific proteins.  Together, these interactions with the MCCB bioinformatics team have greatly enhanced the research projects within the Green Lab and have produced important resources and tools for the scientific community-at-large.

Lawson Lab

The Lawson Lab has interacted extensively with Dr. Julie Zhu and the MCCB bioinformatics team to apply and develop computational pipelines in their efforts to study vascular development in the zebrafish.  These efforts include application of standard deep sequencing approaches (RNA-Seq, ChIP-Seq, miR-Seq) as well as development of bioinformatics packages for identifying poly-adenylation sites through the use of machine learning.  The Lawson Lab continues to work with the MCCB bioinformatics group in their work to identify the transcriptional regulatory networks important for defining endothelial cell identity.

  • Aday et al. (2011) Identification of cis regulatory features in the embryonic zebrafish genome through large-scale profiling of H3K4me1 and H3K4me3 binding sites. Dev Biol, 357(2):450-62. 
  • Nicoli et al. (2012) miR-221 is required for endothelial tip cell behaviors during vascular development. Dev Cell, 22(2):418-29. 
  • Sheppard et al. (2013) Accurate identification of polyadenylation sites from 3' end deep sequencing using a naive Bayes classifier.  Bioinformatics, 29(20):2564-71. 

Wolfe Lab

The Wolfe Lab studies the DNA-binding specificity of metazoan transcription factors, with a primary focus on Drosophila.  By working together with Dr. Julie Zhu, who heads the MCCB bioinformatics team, and in collaboration with the Brodsky Lab, the Wolfe Lab has applied its technical expertise in the bacterial one-hybrid system to construct Fly factor Survey, a database of Drosophila transcription factor specificities. The Wolfe Lab also works together with Dr. Zhu to create bioinformatics tools that aid in the design of artificial nucleases for efficient and precise gene editing.

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