Advertisement · 728 × 90
#
Hashtag

#HoffmanLab

Advertisement · 728 × 90
Bloom filter example. An example of a Bloom filter with three hash functions. The k-mers a and b have been inserted, but c and d have not. The three hash functions are represented with arrows, and the bits corresponding to the hashes for a and b have been set to 1. The Bloom filter indicates correctly that k-mer c has not been inserted since not all of its bits are set to 1. However, k-mer d is an example of a false positive: it has not been inserted, but since its bits were set to 1 by the insertion of a and b, the Bloom filter falsely reports that d has been seen already.

Bloom filter example. An example of a Bloom filter with three hash functions. The k-mers a and b have been inserted, but c and d have not. The three hash functions are represented with arrows, and the bits corresponding to the hashes for a and b have been set to 1. The Bloom filter indicates correctly that k-mer c has not been inserted since not all of its bits are set to 1. However, k-mer d is an example of a false positive: it has not been inserted, but since its bits were set to 1 by the insertion of a and b, the Bloom filter falsely reports that d has been seen already.

Reading "Efficient counting of k-mers in DNA sequences using a Bloom filter" by @pmelsted.bsky.social @jkpritch.bsky.social for #HoffmanLab journal club. I really like the clear diagram used to explain Bloom filters. 🧪🧬🖥️ bmcbioinformatics.biomedcentral.com/articles/10....

16 2 0 0

1/At the #HoffmanLab lab meeting, we often have tech talks describing useful tools for other lab members. Since they might also prove useful for others, we've been posting almost every #HoffmanLabTechTalk for years. 🧪🧬💻

44 9 2 2