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\begin{document}

\title{An Unemployed Gentleman Scholar}



\author[1]{Robert Edgar}%
\author[2]{Backstories}%
\affil[1]{Affiliation not available}%
\affil[2]{Backstories}%


\vspace{-1em}



  
  \date{May 11, 2024}


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This article was originally published at
\href{https://thewinnower.com/discussions/4-an-unemployed-gentleman-scholar}{The
Winnower} and is the backstory behind~\emph{Edgar, R. C. 2004. ``MUSCLE:
multiple sequence alignment with high accuracy and high throughput.''
Nucleic Acids Res no. 32 (5):1792-7. doi:
\href{https://dx.doi.org/10.1093/nar/gkh340}{10.1093/nar/gkh340}.}

First off I should say that my career path was not planned or plannable,
so you can't follow in my footsteps except to go where there is no path.

From a young age (7 years old), my vocation was to be a scientist, and I
quickly settled on theoretical physics because physics is everything -
any explanation of the natural world eventually bottoms out in the laws
of physics (yeah, I'm a hard-core reductionist and proud of it). At high
school and college, I was pretty good at math and physics, but once I
started my PhD I got a shock because I discovered that many people were
much smarter than me. A few months after starting my postdoc I concluded
that I would always be a mediocre physicist and would never make much of
a contribution, so I quit. That was very difficult because physics had
been my life's dream, but in retrospect physics was the wrong field for
me; I'm more of a natural programmer than a natural mathematician. Like
many failed physicists in the mid-80s, I got into the software business
and ended up starting my own company in San Francisco. I sold the
business to Intel in 1999, and in 2001 I was burned out and quit with no
idea what to do next.

I didn't want to start another business, which is what most
entrepreneurs do, and I didn't want to retire and play golf (or more
likely tennis). I knew about the human genome project and the importance
of software algorithms - it was very appealing to me as a software guy
that you could do important things just with strings of letters without
knowing all that tedious biochemistry. Biology=strcmp (). So while it
didn't occur to me that I might actively work on that stuff, I thought
it would be fun to learn something about it, and crashed a seminar at UC
Berkeley and met a newly-hired professor named Kimmen Sjolander. She was
looking for students to help her code up some algorithms for summer
research experience, and I volunteered. The result was a multiple
alignment program called SATCHMO (Edgar and Sjolander 2003), which
worked pretty well but was no better than CLUSTALW according to
BALIBASE, which was the only available benchmark at that time.

Being a competitive guy, I was dissatisfied with that, and started a
systematic project to figure out what did and didn't work in multiple
alignment algorithms, because it seemed to me that the programs had many
ideas in them, but it wasn't clear which of the ideas helped or hurt the
results. At that time, Gotoh's PRRx had the best accuracy (except maybe
for T-Coffee; I don't remember) but the code was unfriendly and slow and
hardly anybody used it. So I started from the PRRx algorithm and
systematically varied the elements of the algorithm with all the
alternatives I could find in the literature or dream up myself. That was
the line of work that led to MUSCLE (Edgar 2004). (As an aside, the
MAFFT people followed a similar strategy independently, and it's
remarkable how much the original MAFFT and MUSCLE v1 resembled each
other. MAFFT got published first and was better than my first attempt; I
had to "borrow' a couple of their ideas in order to do better than MAFFT
(Katoh et al. 2002). For example, we both had k-mer counting to build
the first tree, but I was using 3-mers in the usual alphabet and they
used 6-mers in a compressed alphabet, which gave slightly better
results.

So MUSCLE was a result of some financial independence I had after
selling my business. I've supported myself and my research from my
savings for the past decade. You can think of me as unemployed,
independent and/or a gentleman scholar of modest means (like, say, my
fellow-countryman Charles Darwin). It's hard to know how people have
perceived my unconventional status. Everyone feels misunderstood and
dismissed by peers sometimes (reviewers, editors, conference
organizers), so it's impossible to say whether I would have been better
accepted if I had a conventional affiliation. MUSCLE has been helpful
because many people have heard of it. That gave me some street cred
early on, and surely helped open other doors.

I don't see any lessons here to help young scientists, so in an attempt
to avoid becoming a bad influence I will defer to a more accomplished
scientist, Richard Hamming, who has much better advice in his lecture,
``You and Your Research'' (Hamming 1986)

\section*{References}

{\label{915999}}

Edgar, R. C. 2004. ``MUSCLE: multiple sequence alignment with high
accuracy and high throughput.'' Nucleic Acids Res no. 32 (5):1792-7.
doi: 10.1093/nar/gkh340.

Edgar, R. C., and K. Sjolander. 2003. ``SATCHMO: sequence alignment and
tree construction using hidden Markov models.'' Bioinformatics no. 19
(11):1404-11. doi: 10.1093/bioinformatics/btg158.

Hamming, R. You and Your Research 1986. Available from
\url{http://cmp.felk.cvut.cz/cmp/teaching/YouAndYourResearch.pdf}.

Katoh, K., K. Misawa, K. Kuma, and T. Miyata. 2002. ``MAFFT: a novel
method for rapid multiple sequence alignment based on fast Fourier
transform.'' Nucleic Acids Res no. 30 (14):3059-66. doi:
10.1093/nar/gkf436.

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