discovery - Fred Hutchinson Cancer Research Center

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Robert Gentleman, Ph.D.
Computational Biologist
S earching
M I N I N G G E N O M I C D A T A F O R C A N C E R ’ S
MOLECULAR SECRETS
for the molecular roots Common of cancer duplicate is akin to reads after biological filtering research. Just a decade ago, scientists could only
looking for the proverbial > needle df1 in lapply(split(df1[, which is where Dr. Robert c("sequence", advances now "count")], enable efficient df1$lane), analysis of half head) a million genes.
Gentleman comes in. Technology $s_1_export.txt
and the need to sift through The new tools enable researchers to uncover novel potential
a deluge of genomic information has transformed biology from targets sequence for therapies count as well as to explore the underlying genetic
a purely lab-based science to an 101 information TAGGACGTGGAATATGGCAAGAAAACTGAAAATCA science as well. causes of many human 243 diseases.
102 GTAGGACGTGGAATATGGCAAGAAAACTGAAAATC 237
Gentleman, head of the Hutchinson Center’s Herbold There has also been an explosion in the amount of informa-
103 GTGGAAAATTTAGAAATGTCCACTGTAGGACGTGG 177
Computational Biology Program, 104creates CCATATTCCACGTCCTACAGTGGACATTTCTAAAT computing tools to tion available about the 165DNA
sequence of the human genome.
analyze massive amounts of data 105 from AGGACGTGGAATATGGCAAGAAAACTGAAAATCAT biological experiments Consequently, researchers 162 have identified a large number of
and uses mathematics and statistics 106 to CATGATTTTCAGTTTTCTTGCCATATTCCACGTCC generate new insights novel genes within these 158previously
unknown sequences. The
incorporating the existing
data. The approach
$s_2_export.txt
sequence count
challenge currently facing
scientists is to find a way to
helps researchers
251 GTAGGACGTGGAATATGGCAAGAAAACTGAAAATC 357 organize and catalog this vast
understand cancer at
its most fundamental
level by illuminating
252 TAGGACGTGGAATATGGCAAGAAAACTGAAAATCA
253 AGGACGTGGAATATGGCAAGAAAACTGAAAATCAT
254 ACTGTAGGACGTGGAATATGGCAAGAAAACTGAAA
255 CATGATTTTCAGTTTTCTTGCCATATTCCACGTCC
324
194
184
160
amount of information into a
usable form.
“Technology drove enor-
which cellular proteins 256 GCCATATTCCACGTCCTACAGTGGACATTTCTAAA 151 mous data sets, which drove
interact and how they
the need for clear, statistical
$s_3_export.txt
work together within a
thinking,” Gentleman said.
sequence count
cell. The process also saves both 401 time GTAGGACGTGGAATATGGCAAGAAAACTGAAAATC and money over other “We have no true comprehension 313 of cancer on a molecular level
lab methods.
402 TAGGACGTGGAATATGGCAAGAAAACTGAAAATCA yet. We need tools to 291 understand how the genome works and
A former auto mechanic might
403
seem
ACTGTAGGACGTGGAATATGGCAAGAAAACTGAAA
like an unlikely we need to know how 235 things interact. If we’re going to go in
404 AGGACGTGGAATATGGCAAGAAAACTGAAAATCAT 205
candidate to become a research
405
scientist.
GTGGAAAATTTAGAAATGTCCACTGTAGGACGTGG
But if you follow and poke something, we better know what the domino effect
188
Gentleman’s path from Hondas 406 to Harvard CTGTAGGACGTGGAATATGGCAAGAAAACTGAAAA to the Hutchinson will be.” 177
Center, his trajectory seems perfectly probable. The former He and his colleagues around the globe collaborate to write
grease monkey has a penchant for
$s_4_export.txt
challenges, so it makes sense free software for the entire scientific community to use. “This is
sequence count
that he’d choose a research endeavor
551 TAGGACGTGGAATATGGCAAGAAAACTGAAAATCA
that’s moving at breakneck a high-risk area, which is not where commercial software goes,”
266
speed. For brilliant young minds, 552biocomputing GTAGGACGTGGAATATGGCAAGAAAACTGAAAATC is the most he said. “We’re only interested 232 in being as close to the edge of
challenging field around. 553 AGGACGTGGAATATGGCAAGAAAACTGAAAATCAT scientific investigation 187 as we can be. We share to learn. Real
Advances in knowledge and
554
technology
ACTGTAGGACGTGGAATATGGCAAGAAAACTGAAA
have transformed change comes from people 164 working well together.”
555 CTGTAGGACGTGGAATATGGCAAGAAAACTGAAAA 161
556 TGTAGGACGTGGAATATGGCAAGAAAACTGAAAAT 139
$s_6_export.txt
sequence count
701 TAGGACGTGGAATATGGCAAGAAAACTGAAAATCA 395
702 GTAGGACGTGGAATATGGCAAGAAAACTGAAAATC 354
703 AGGACGTGGAATATGGCAAGAAAACTGAAAATCAT 263
704UNRAVELING ACTGTAGGACGTGGAATATGGCAAGAAAACTGAAA VAST DNA SEQUENCES, WRITTEN AS A-G-T-C 232 CODES, REQUIRES POWERFUL TOOLS.
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