Bioinformatics

Author(s):  
Mark A. Ragan

Bioinformatics has emerged as new discipline at the interface of molecular bioscience with mathematics, computer science and information technology. Bioinformatics is driven by data arising from high-throughput technologies in molecular bioscience. To enable biological discovery, bioinformatics draws on and extends technologies for data capture, management, integration and mining, computing, and communication technology. The rise of genomics has been a key driver for bioinformatics. Genomics, however, was never an end unto itself, but rather was intended to enable the understanding of complex biological systems. Bioinformatics continues to evolve in support of its constituent domains and, increasingly, their integration into genome-scale molecular systems biology. This article presents bioinformatics first from the perspective of computer science and information technology, then from the perspective of bioscience. In practice these perspectives often merge, making bioinformatics a rich, vibrant area of multidisciplinary research and application.

Author(s):  
Mark A. Ragan

Bioinformatics has emerged as new discipline at the interface of molecular bioscience with mathematics, computer science, and information technology. Bioinformatics is driven by data arising from high-throughput technologies in molecular bioscience. To enable biological discovery, bioinformatics draws on and extends technologies for data capture, management, integration and mining, computing, and communication technology. The rise of genomics has been a key driver for bioinformatics. Genomics, however, was never an end unto itself, but rather was intended to enable the understanding of complex biological systems. Bioinformatics continues to evolve in support of its constituent domains and, increasingly, their integration into genome-scale molecular systems biology. This chapter presents bioinformatics first from the perspective of computer science and information technology, then from the perspective of bioscience. In practice these perspectives often merge, making bioinformatics a rich, vibrant area of multidisciplinary research and application.


2015 ◽  
Vol 112 (34) ◽  
pp. 10810-10815 ◽  
Author(s):  
Laurence Yang ◽  
Justin Tan ◽  
Edward J. O’Brien ◽  
Jonathan M. Monk ◽  
Donghyuk Kim ◽  
...  

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5′UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1636
Author(s):  
Daniel E. Carlin ◽  
Forrest Kim ◽  
Trey Ideker ◽  
Jill P. Mesirov

We present a unified GenomeSpace recipe that combines the results of a high throughput CRISPR genetic screen and a biological network to return a subnetwork that suggests a mechanistic explanation of the screen’s results. The explanatory subnetwork is found by network propagation, a popular systems biology approach.  We demonstrate our pipeline on an alpha toxin screen, revealing a subnetwork that is both highly interconnected and highly enriched for hits in the screen.


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