Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disorder characterized by the degeneration of both the upper and lower motor neurons, resulting in progressive muscle weakness and eventual paralysis. Upon symptom onset, disease progression is often rapid, and there is a lack of treatment options available. There are limited established risk factors for ALS, including male sex, family history, and older age. Although there are several susceptibility genes reported, the overall genetic contribution to ALS still appears weak. Furthermore, ALS cases are most often sporadic, indicating that lifestyle, environmental, and disrupted physiological processes are likely involved in disease development. ALS patients are often hypermetabolic, with a lower body mass index (BMI) before disease onset. An emerging hypothesis that is gaining momentum concerns defects in energy metabolism and immune responses, both of which are clinically distinct and targetable for therapeutic interventions. By now, it is widely accepted that gut microbiota orchestrates energy homeostasis through modulating energy-harvesting efficiency, fat storage, and immune responses. Environment, lifestyle, and diet all interact and influence the composition of the microbiome. Changes in the gut microbiome have recently been suggested in ALS. Animal models have indicated a change in the microbiome composition prior to and after disease onset. Additionally, human studies have suggested differences in microbial composition between those with ALS and healthy controls, and metabolome-derived metabolites may play a role in disease progression. However, many previous studies investigating the role of the human microbiome in ALS contain small samples size, cross-sectional design, and often do not include a proper comparison group. In this project, we aim to explore the relationship between the gut microbiome composition and function and ALS disease onset, progression, and survival. We will characterize the composition and functional potential of the gut microbiome using stool samples. Extracted DNA will be sequenced using shotgun metagenomics, and we will use standardized tools in the field to taxonomically profile the sequencing reads using MetaPhlAn4, and explore the functional potential of the present species using HUMANn3. Other samples will be sequenced using 16S rRNA sequencing of the conserved 16S bacterial gene. These samples will be trimmed, denoised, and taxonomically profiled. Further, we will develop a computational method to analyze shotgun metagenomic sequencing samples, with the aim of comprehending and elucidating the functional landscape of the gut microbiome in ALS patients and its linkage to patient phenotypes. Theoretically, DNA-sequenced reads are mapped back to reference genomes, sorted/binned to the functions of mapped genes, and the abundance of each gene/gene function is then estimated by the total number of mapped reads. Novel quantification methods for metagenomics utilize a wide range of mapping algorithms and references. However, these tools do not specifically tackle the issue of ambiguous reads mapped to multiple locations in the reference. In this project, we will address the issue of ambiguous reads for the functional quantification method in metagenomics.