Publicaciones de Rogue Scholar

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Publicado in GigaBlog

This week in GigaScience we published research revealing a previously hidden diversity of symbiotic bacteria from the genus Rickettsia , spanning a wide range of arthropod hosts. Using accidentally amplified sequence data from a barcoding database as a starting point, the results will help to better understand the co-evolution of these intimate symbioses.

Publicado in GigaBlog

In this data-driven era, research is faced with new challenges, from sharing, storing and accessing data, including how to better integrate data to answer big questions in science. With many data repositories available, it is hard to maintain them all – some repositories are forced to close – meaning loss of access to invaluable datasets.

Publicado in GigaBlog

The mock metagenome, MAGs and breaking the first rule of Long Read Club Nick showing us some of his experiments with an early antecedent Short Read Club… Out today in GigaScience is a new “mock metagenome” Data Note from the Nick Loman lab in Birmingham showcasing the latest long-read sequencing technologies from Oxford Nanopore.

Publicado in GigaBlog

Out today is the winner of our ICG13 Prize, presenting work that can aid in revealing new biologically relevant findings and missed genes from previously generated transcriptome assemblies. Teaching old data new tricks, and maximising every last nugget of information from previously funded research.

Publicado in GigaBlog

Navigating Pangenome’s Labyrinth In the two decades since the first genomes were sequenced, with the exponential growth of new and closely related genomes it has become increasingly difficult to visualise and compare their structure. Particularly with the large diversity and difference in genes within microbial genomes.

Publicado in GigaBlog

** GigaScience has Tapeworms and Scabies! And Reproducible Research. **While there has been recent controversy (and hashtags in response) from some of the more conservative sections of the medical community calling those who use or build on previous data “research parasites”, as data publishers we strongly disagree with this.