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How AI Uses DNA to Find New Medicines

1Why DNA Matters for Finding Medicines2Turning DNA Into Data a Computer Can Read3How AI Learns Patterns in DNA4From DNA Patterns to Disease Clues5From Target to Candidate Medicine6What AI Can and Cannot Do Here
Turning DNA Into Data a Computer Can Read

From Scattered Reads to One Sequence

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Watch the fragments arrive scattered, each one short and starting at a different place. The trick is that they overlap. Where the tail of one read matches the head of another, they can be joined. As the overlaps are found, the fragments lock together into one long strand. Any stretch that no fragment covered stays as a gap, and where two reads disagree, the assembler has to choose. What comes out is a single consensus sequence — that is the genome that gets stored.
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No sequencing machine reads a whole chromosome from end to end in one pass. Instead it reads many short fragments, each only a few hundred letters long, taken from different places along the DNA. The output of a sequencing run is therefore a large pile of short reads, not one tidy sequence.

To recover the original sequence, the reads are assembled. The key observation is that the reads overlap: because the fragments were taken from random positions, the end of one read usually matches the beginning of another. Assembly finds those overlaps and chains the reads together, like reconstructing a sentence from many overlapping slips of paper that each contain only a few words. Where reads disagree, the assembler has to decide which letter is correct, and regions that no read covers remain gaps.

The result is a consensus sequence: one continuous string of letters representing the DNA that was sequenced. This assembled sequence, not the raw pile of reads, is what gets stored and shared as a genome.

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