Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Dufresnes PLOSONE2018 12-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Broad-scale genetic diversity of Cannabis for forensic applications|Broad-scale genetic diversity of Cannabis for forensic applications]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


''Cannabis'' (hemp and marijuana) is an iconic yet controversial crop. On the one hand, it represents a growing market for pharmaceutical and agricultural sectors. On the other hand, plants synthesizing the psychoactive THC produce the most widespread illicit drug in the world. Yet, the difficulty to reliably distinguish between ''Cannabis'' varieties based on morphological or biochemical criteria impedes the development of promising industrial programs and hinders the fight against narcotrafficking. Genetics offers an appropriate alternative to characterize drug vs. non-drug ''Cannabis''. However, forensic applications require rapid and affordable genotyping of informative and reliable molecular markers for which a broad-scale reference database, representing both intra- and inter-variety variation, is available. Here we provide such a resource for ''Cannabis'', by genotyping 13 microsatellite loci (STRs) in 1,324 samples selected specifically for fiber (24 hemp varieties) and drug (15 marijuana varieties) production. We showed that these loci are sufficient to capture most of the genome-wide diversity patterns recently revealed by [[DNA sequencing#High-throughput methods|next-generation sequencing]] (NGS) data. ('''[[Journal:Broad-scale genetic diversity of Cannabis for forensic applications|Full article...]]''')<br />
[[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 />
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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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