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# System prepended metadata

title: Identifying the Genetic Basis of Antibiotic Resistance

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Mari Hoffman, Jacqueline Moore
Professor Eetemadi
ECS 124
10 December 2020

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Identifying the Genetic Basis of Antibiotic Resistance
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## I. Abstract
The frequent, current use of antibiotics to treat bacterial diseases has led bacteria to undergo selective pressure by acquiring genetic mutations that favor their survival through antibiotic resistance. The goal of our project is to analyze sequencing data on bacteria that have evolved to become resistant to antibiotic usage (treatment group) and bacteria that have evolved under normal conditions (control group) in order to understand the relevant mutations, genes, and pathways that confer antibiotic resistance. We were provided with sequence data that included six pairs of .fastq files, three of which relate to the treatment group and the other three to the control group. 
	The six given .fastq files were trimmed to improve read quality using trimmomatic. The resultant forward and reverse .fastq files were aligned with the MG1655 E.coli reference genome using bowtie2. Using samtools, we indexed the files and identified potential locations of SNPs. Then, with BCFtools, we called SNPs, indexed our sequencing data, and identified SNPs that are present in all of the treatment genomes and none of the control genomes. These SNPs might correspond to specific E.coli genes that influence the bacteria’s observed antibiotic resistance.
	[Results summary here -- ~100 words]


## II. Method
**Acquire Reference Genome with EcoCyc**
Downloading the E.coli reference genome was the critical first step of our project methodology. Using the EcoCyc Genome Database, we pulled the complete genome of E.coli str. K-12 substr. MG1655 (GenBank U00096.3) as a single .fasta file. We also used EcoCyc to visualize our SNPs and allow for identification of the specific gene affected by the SNP.

**Quality Control with Trimmomatic and FastQC:**
To visualize our reads and ensure good quality, we used FastQC quality control software. We then used Trimmomatic to trim off the adaptor sequence and any sequences that had a low quality score. The implementation of Trimmomatic returned one pair of zipped .fastq files, one forward and one reverse, for each of the six sample files.

![Figure 1](https://i.imgur.com/U5STdQs.png) 
Figure 1: Per base sequence quality on control sample 1 forward read


