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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Backman BMCBio2016 17.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:systemPipeR: NGS workflow and report generation environment|systemPipeR: NGS workflow and report generation environment]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
 
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


Next-generation sequencing (NGS) has revolutionized how research is carried out in many areas of biology and medicine. However, the analysis of NGS data remains a major obstacle to the efficient utilization of the technology, as it requires complex multi-step processing of big data, demanding considerable computational expertise from users. While substantial effort has been invested on the development of software dedicated to the individual analysis steps of NGS experiments, insufficient resources are currently available for integrating the individual software components within the widely used R/Bioconductor environment into automated [[Workflow|workflows]] capable of running the analysis of most types of NGS applications from start-to-finish in a time-efficient and reproducible manner. ('''[[Journal:systemPipeR: NGS workflow and report generation environment|Full article...]]''')<br />
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Latest revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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