MOTIVATION: We present Faucet, a two-pass streaming algorithm for assembly graph 
construction. Faucet builds an assembly graph incrementally as each read is 
processed. Thus, reads need not be stored locally, as they can be processed 
while downloading data and then discarded. We demonstrate this functionality by 
performing streaming graph assembly of publicly available data, and observe that 
the ratio of disk use to raw data size decreases as coverage is increased.
RESULTS: Faucet pairs the de Bruijn graph obtained from the reads with 
additional meta-data derived from them. We show these metadata-coverage counts 
collected at junction k-mers and connections bridging between junction 
pairs-contain most salient information needed for assembly, and demonstrate they 
enable cleaning of metagenome assembly graphs, greatly improving contiguity 
while maintaining accuracy. We compared Fauceted resource use and assembly 
quality to state of the art metagenome assemblers, as well as leading 
resource-efficient genome assemblers. Faucet used orders of magnitude less time 
and disk space than the specialized metagenome assemblers MetaSPAdes and 
Megahit, while also improving on their memory use; this broadly matched 
performance of other assemblers optimizing resource efficiency-namely, Minia and 
LightAssembler. However, on metagenomes tested, Faucet,o outputs had 14-110% 
higher mean NGA50 lengths compared with Minia, and 2- to 11-fold higher mean 
NGA50 lengths compared with LightAssembler, the only other streaming assembler 
available.
AVAILABILITY AND IMPLEMENTATION: Faucet is available at 
https://github.com/Shamir-Lab/Faucet.
CONTACT: rshamir@tau.ac.il or eranhalperin@gmail.com.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.