If iI were to have access to funds for research, would learning this technique be a boon for me? Or are next-gen sequencing methods all the range now? My knowledge of both are limited.
Sanger sequencing is still used in the labs today - and not only on the side. Next-generation sequencing has its strength when it comes to sequencing very large amounts of DNA (basically whole genomes or exomes).
Sanger sequencing is used when you want to sequence smaller regions or portions of a genome/plasmid. Typical read length is (depeding on the machine and the skills of the operator) 600-900bp. This is enough to verify site directed mutagenesis, the presence of inserts you cloned as well that they are correct (if the insert is longer you will typically nest the single sequence reactions to cover the whole sequence) or look for a polymorphism. There are endless possibilities.
Additionally it is relatively cheap to sequence single samples (which is not really working on the next-gen machines), one sample comes between 8 and 10$ (at least the last time I sent this out). And you don't even have to do it yourself as there are a lot of service providers available. The technique itself is not hard to learn either.
No its not Depends on the scale you are workin on
The dideoxy termination method of DNA sequencing (often called Sanger sequencing after the technique’s inventor, Fred Sanger) has been the workhorse of pretty much every molecular biology lab for the last 30 years. However, over the last few years the method has been increasingly supplanted by so-called next-generation sequencing technologies, which allow incredibly rapid generation of large amounts of sequence data.
Sanger sequencing is still widely used for small-scale experiments and for “finishing” regions that can’t be easily sequenced by next-gen platforms (e.g. highly repetitive DNA), but most people see next-gen as the future of genomics.
The article also addresses issues on read length and cost between NGS and Sanger.
For Sanger sequencing these reads are routinely 800-1000 base pairs long; next-gen methods produce much larger quantities of sequence, but in the form of much smaller reads (the two best-performing platforms generate 35-75 base pair reads, while a third, lower-throughput platform can manage 400 base pairs). Read length is absolutely crucial when it comes to assembling accurate sequence, especially for genomes as complex and repetitive as the human genome. If a repetitive region is much longer than a platform’s read length, it can’t really be accurately assembled – so human genomes sequenced with current next-gen platforms actually consist of hundreds of thousands of accurately sequenced fragments interspersed by gaps....