June 2023
Intermediate to advanced
460 pages
6h 20m
Japanese
Content preview from Pythonではじめるバイオインフォマティクス ―可読性・拡張性・再現性のあるコードを書くために
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6章
ハミング距離を求める：点変異のカウント
ハミング距離とは序文で述べたRichard Hammingにちなんで名付けられたもので、ある文字列を別の文字列に変更するために必要な編集回数です。これは配列の類似性を測る指標の1つです。他にも第1章ではテトラヌクレオチド頻度、第5章ではGC含量に言及しています。後者はコーディング領域がGCリッチになる傾向があり実用的な情報ですが、テトラヌクレオチド頻度はそれほど有用ではありません。例えばAAACCCGGGTTTとCGACGATATGTCは全く異なる配列ですが、同じ塩基頻度になります。
$ ./dna.py AAACCCGGGTTT3 3 3 3$ ./dna.py CGACGATATGTC3 3 3 3
モノヌクレオチド頻度（各塩基の頻度）だけを見れば、これらの配列は同じように見えますが、全く機能の異なるタンパク質の配列になることは明らかです。図6-1の2配列のアライメントを見ると、12個の塩基のうち3個だけが一致していて25%しか似ていないことがわかります。
図6-1 基の一致を縦棒で示した2配列のアライメント
別の言い方をすると、一方の配列を他方の配列に変えるためには、12個の塩基のうち9個を変更する必要があります。これがハミング距離で、バイオインフォマティクスでは一塩基多型（SNPs、スニップスと発音）や一塩基変異（SNVs、スニーブスと発音）の関係がそれにあたります。ハミング距離では1つの塩基が別の塩基に置換することしか考慮しておらず、挿入や欠失を考慮する配列アライメントとは隔たりがあります。 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.