Hi, I'm Matthew
Genome Analyst | Bioinformatician | Web Developer
About Me
I am a Genome Analyst and Bioinformatics Professional with over 7 years of experience in genomic data analysis, variant interpretation, and healthcare analytics. My career began in the wet lab, where I mastered molecular biology techniques and DNA sequencing at institutions like the Clinical Genomics Centre and The Centre for Applied Genomics. This hands-on experience provided a strong foundation in genomic research and clinical diagnostics.
Building on this foundation, I transitioned into variant interpretation at the Laboratory Medicine Programs at UHN and Hamilton Health Sciences, analyzing complex genetic data to support clinical decision-making. To address the increasing complexity of genomic datasets, I expanded my expertise into bioinformatics and programming. Proficient in Python, Flask, and MongoDB, I have developed custom pipelines and web-based tools to streamline workflows, enhance data visualization, and improve data accessibility.
Today, I bring a unique blend of wet lab expertise, variant interpretation proficiency, and technical skills to deliver innovative, data-driven solutions in genomics. My passion lies in leveraging technology to optimize genomic workflows and advance healthcare outcomes.
Skills and Technologies
Programming
Bioinformatics and Genomics
Institutes I've Worked At
My Projects
Sneaker Release Monitor
A Python script that scrapes Nike's website for upcoming sneaker releases, sends email alerts for new products, and calculates urgency based on release time.
Technologies: Python, BeautifulSoup, Pandas, SMTP
def get_time_left(release_date_str):
date_match = re.search(r'(\d{1,2}-\d{1,2}) at (\d{1,2}:\d{2} [a,p].m.)', release_date_str)
if not date_match:
raise ValueError(f"Unexpected date format: {release_date_str}")
date_part, time_part = date_match.groups()
time_part = time_part.replace('.', '')
release_date = datetime.strptime(f"{datetime.now().year}-{date_part} {time_part}", "%Y-%m-%d %I:%M %p")
time_left = release_date - datetime.now()
return f"{time_left.days} days {time_left.seconds//3600} hours {time_left.seconds%3600//60} minutes"Sentiment Analysis - Subreddit Comments
This project leverages Reddit's API to scrape and preprocess subreddit data, applying sentiment analysis using TF-IDF and word2vec models to develop a generalized framework for real-time trend insights and community sentiment monitoring.
Technologies: Python, SciKitLearn, nltk, MongoDB
LYFE Sci: Variant Interpretation Database
A web-based application for analyzing genomic variants.
Technologies: Python, Flask, MongoDB, Bootstrap, Celery
REVEL and BayesDel Scoring Framework
A framework leveraging REVEL and BayesDel scores to evaluate genetic variant pathogenicity. The pipeline automates data retrieval, processing, and scoring for genomic variants.
Technologies: Python, Pandas, Google Colab, REVEL, BayesDel
def get_url_for_location(chromosome, location, range_dict):
if chromosome in range_dict:
for range_info in range_dict[chromosome]:
for range_str, url in range_info.items():
start, end = map(int, range_str.split('-'))
if start <= location <= end:
return url
return NoneTTC Security Incidents Analysis
A Python-based project analyzing security incidents across Toronto's transit network by integrating TTC open data and real-time Twitter feeds. The analysis identifies trends and patterns in safety-related events for buses, subways, and streetcars.
Technologies: Python, Pandas, Regex, Google Colab, Flourish, Twitter API
# Filter tweets mentioning 'incident'
incident_tweets = tweets_df[tweets_df['Text'].str.contains('incident', case=False)]
# Extract route and line information
for tweet, timestamp in zip(incident_tweets['Text'], incident_tweets['Datetime']):
for route, line_name in ttc_lines.items():
if re.search(rf'\b{route}\b', tweet):
print(f"Tweet mentions route {route} ({line_name}) at {timestamp}")Hemoglobin Mutation Database for Hamilton Health Sciences
A Flask-based web application developed in collaboration with Hamilton Health Sciences, designed to aggregate and organize patient data with alpha or beta globin mutations. This database enables advanced searching and filtering at the sample level, facilitating precise data analysis and clinical decision-making. Inspired by Ithanet but tailored for institutional needs, it aims to improve operational efficiency and patient care.
Technologies: Flask, Python, MongoDB, Pandas, HTML, CSS, JavaScript
SubtitleSyncTool
A Python script that adjusts subtitle timings in SRT files, allowing precise synchronization for video playback. The tool is customizable with adjustable offsets and ensures seamless formatting.
Technologies: Python, Datetime, File I/O
def adjust_subtitle_timing(srt_content, offset_seconds):
"""
Adjust subtitle timings by a specified offset in seconds.
Args:
srt_content (str): The content of the SRT file.
offset_seconds (int): The number of seconds to adjust the timing.
Returns:
str: The adjusted SRT content.
"""
offset_duration = timedelta(seconds=offset_seconds)
adjusted_content = []
for line in srt_content.splitlines():
if '-->' in line:
start_str, end_str = line.split(' --> ')
start_dt = parse_srt_timestamp(start_str)
end_dt = parse_srt_timestamp(end_str)
start_dt += offset_duration
end_dt += offset_duration
line = f"{format_srt_timestamp(start_dt)} --> {format_srt_timestamp(end_dt)}"
adjusted_content.append(line)
return "\n".join(adjusted_content)HHSC Genomic Report Generator
A highly customized Python script developed for Hamilton Health Sciences (HHSC) to generate patient-specific genomic reports by integrating data from SoftGenetics Geneticist Assistant. The tool automates file monitoring, data processing, and report creation tailored to HHSC's needs.
Technologies: Python, Pandas, Python-docx, BeautifulSoup
CNV Heatmap Generator
A Python-based tool designed to generate copy number variation (CNV) heatmaps from NextGene CNV tool output. This script allows users to filter genes of interest, analyze CNV gains and losses, and visualize the data in an intuitive heatmap format.
Technologies: Python, Pandas, Matplotlib, Seaborn, NumPy
### Features **Customizable Gene Selection:** - Analyze any set of genes of interest specified by the user. **Dynamic Heatmap Creation:** - Categorizes CNV data into gains, losses, and normal regions. - Generates intuitive heatmaps with customizable color schemes. **File Integration:** - Reads data from Excel files and outputs gene-specific CSVs and a consolidated heatmap. **Error Handling:** - Validates input data format and ensures compatibility with predefined workflows.
### Workflow 1. Specify the input Excel file and sheet containing CNV data. 2. Provide a list of genes to analyze (e.g., ATM, BRCA1). 3. The script processes the data, saves sorted gene-specific CSVs, and generates a heatmap PNG. 4. The heatmap displays CNV gains, losses, and normal regions, annotated with gene descriptions and numeric values.
### Example Output - **CSV Files:** Sorted data for each gene of interest. - **Heatmap PNG:** Intuitive visual representation of CNV gains and losses, categorized as: - Gains (blue) - Losses (red) - Normal regions (white).