Keeping up with the rapidly growing literature has become virtually impossible 
for most scientists. This can have dire consequences. First, we may waste 
research time and resources on reinventing the wheel simply because we can no 
longer maintain a reliable grasp on the published literature. Second, and 
perhaps more detrimental, judicious (or serendipitous) combination of knowledge 
from different scientific disciplines, which would require following disparate 
and distinct research literatures, is rapidly becoming impossible for even the 
most ardent readers of research publications. Text mining - the automated 
extraction of information from (electronically) published sources - could 
potentially fulfil an important role - but only if we know how to harness its 
strengths and overcome its weaknesses. As we do not expect that the rate at 
which scientific results are published will decrease, text mining tools are now 
becoming essential in order to cope with, and derive maximum benefit from, this 
information explosion. In genomics, this is particularly pressing as more and 
more rare disease-causing variants are found and need to be understood. Not 
being conversant with this technology may put scientists and biomedical 
regulators at a severe disadvantage. In this review, we introduce the basic 
concepts underlying modern text mining and its applications in genomics and 
systems biology. We hope that this review will serve three purposes: (i) to 
provide a timely and useful overview of the current status of this field, 
including a survey of present challenges; (ii) to enable researchers to decide 
how and when to apply text mining tools in their own research; and (iii) to 
highlight how the research communities in genomics and systems biology can help 
to make text mining from biomedical abstracts and texts more straightforward.