{"id":103868,"date":"2023-10-11T02:00:00","date_gmt":"2023-10-11T07:00:00","guid":{"rendered":"https:\/\/www.unmc.edu\/newsroom\/?p=103868"},"modified":"2024-03-27T09:44:54","modified_gmt":"2024-03-27T14:44:54","slug":"unmc-team-develops-cloud-based-genomic-data-analysis-tools","status":"publish","type":"post","link":"https:\/\/www.unmc.edu\/newsroom\/2023\/10\/11\/unmc-team-develops-cloud-based-genomic-data-analysis-tools\/","title":{"rendered":"UNMC team develops cloud-based genomic data analysis tools\u00a0"},"content":{"rendered":"
\n

The explosion of research data being generated in the past 10 years has presented several computing challenges from its production to consumption.  <\/p>\n\n\n\n

\u201cRaw data in itself does not have much value unless it’s cleaned, normalized, analyzed and correlated for knowledge extraction,\u201d said Babu Guda, PhD, director of the bioinformatics and systems biology core and professor and vice chair in the UNMC Department of Genetics, Cell Biology and Anatomy. <\/p>\n\n\n\n

To alleviate this challenge, Dr. Guda and Jordan Rowley, PhD, co-director of the bioinformatics and systems biology graduate program and assistant professor in UNMC’s genetics, cell biology and anatomy department, jointly created a cloud-based learning module that has been selected by the National Institutes of Health as the premier model for cloud-based learning as it relates to ATAC-seq and single cell ATAC-seq data analysis.<\/p>\n\n\n\n

Assay for Transposase-Accessible Chromatin sequencing, or ATAC-seq, allows identification of chromatin accessibility and transcription factor footprints genome-wide, Dr. Rowley said. <\/p>\n\n\n\n

In conjunction with paired-end sequencing, ATAC-seq data also can be analyzed in a specific manner to provide genome-wide maps of nucleosome occupied chromatin and Transpose Hypersensitive Sites, which correlate with transcription factor occupancy. <\/p>\n\n\n\n

\u201cDue to the wealth of information it provides, ATAC-seq has quickly become a popular tool,\u201d he said. <\/p>\n\n\n\n

\u201cYour team did an outstanding job in working with the Google Professional Services team for developing a very high-quality cloud-based learning module, which includes many advanced features for data analysis and data visualization such as sequence quality plots, genome viewer and sequence motifs,\u201d Lakshmi Kumar Matukumalli, program director in the NIH Networks and Development Programs Division for Research Capacity Building, wrote in a letter to Drs. Rowley and Guda. <\/p>\n\n\n\n

Said Dr. Guda, \u201cIt is an honor to be selected and recognized for our leadership in carrying out these types of projects that have such national importance.\u201d <\/p>\n\n\n\n

The software platform Drs. Guda and Rowley created will help harness and translate the immense volume of data generated in research and provide insight and answers to complex questions. <\/p>\n\n\n\n

\u201cWe need to have an entire ecosystem built to handle storage capacity, high performance computers, the bandwidth for data transfer and skilled personnel who can help researchers mine their data,\u201d Dr. Guda said. <\/p>\n\n\n\n

To democratize access to this ecosystem, Dr. Guda said the NIH came up with the idea of having a cloud-based central location for publicly available data storage that would help smaller institutions access the data the same way larger institutions do and have access to the experts who can help them utilize these resources. <\/p>\n\n\n\n

That is when Drs. Guda and Rowley applied for a grant through the parent INBRE award to develop their cloud module that would be applied to ATAC-seq. <\/p>\n\n\n\n

The Cloud-ATAC module was developed using Jupyter notebooks on the Google Cloud Platform with the help of Avinash Veerappa, PhD, a member of Dr. Guda\u2019s laboratory.  <\/p>\n\n\n\n

The open-source software is installed and available on the NIH\/NIGMS Sandbox GitHub<\/a> repository.\u00a0\u00a0<\/p>\n\n\n\n

Along with Cloud-ATAC, users can access 11 other cloud-based modules<\/a> developed by NIH-backed projects to analyze a variety of research data.\u00a0\u00a0<\/p>\n\n\n\n

By providing self-paced interactive tutorials and example analyses on the cloud, the NIH Sandbox provides the means and training for performing advanced data analysis regardless of an individual researcher\u2019s background or local computational resources.  <\/p>\n\n\n\n

\u201cThese modules breakdown current barriers and offer an exciting opportunity for everyone to leverage the power of genomics in their research,\u201d Dr. Rowley said. <\/p>\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"

The effort was led by UNMC bioinformatics experts Babu Guda, PhD, and Jordan Rowley, 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