MOTIVATION: The deluge of current sequenced data has exceeded Moore's Law, more 
than doubling every 2 years since the next-generation sequencing (NGS) 
technologies were invented. Accordingly, we will able to generate more and more 
data with high speed at fixed cost, but lack the computational resources to 
store, process and analyze it. With error prone high throughput NGS reads and 
genomic repeats, the assembly graph contains massive amount of redundant nodes 
and branching edges. Most assembly pipelines require this large graph to reside 
in memory to start their workflows, which is intractable for mammalian genomes. 
Resource-efficient genome assemblers combine both the power of advanced 
computing techniques and innovative data structures to encode the assembly graph 
efficiently in a computer memory.
RESULTS: LightAssembler is a lightweight assembly algorithm designed to be 
executed on a desktop machine. It uses a pair of cache oblivious Bloom filters, 
one holding a uniform sample of [Formula: see text]-spaced sequenced [Formula: 
see text]-mers and the other holding [Formula: see text]-mers classified as 
likely correct, using a simple statistical test. LightAssembler contains a light 
implementation of the graph traversal and simplification modules that achieves 
comparable assembly accuracy and contiguity to other competing tools. Our method 
reduces the memory usage by [Formula: see text] compared to the 
resource-efficient assemblers using benchmark datasets from GAGE and 
Assemblathon projects. While LightAssembler can be considered as a gap-based 
sequence assembler, different gap sizes result in an almost constant assembly 
size and genome coverage.
AVAILABILITY AND IMPLEMENTATION: 
https://github.com/SaraEl-Metwally/LightAssembler CONTACT: 
sarah_almetwally4@mans.edu.egSupplementary information: Supplementary data are 
available at Bioinformatics online.