Lucas Sinclair

A first-author publication in which we analyzed high-throughput sequencing data in novel ways, in particular with text mining.

We performed similarity searches of short DNA sequences against publicly available nucleotide database and proceeded to apply text-mining on the associated metadata of every hit.

Classifying the resulting vocabulary in a hierarchical-ontology method, enabled us to better contextualize the results of microbial ecology experiments through comparative statistics and quantitative descriptions.

One of the routine operations in medical bioinformatics is to read and write results from "genomic track" files. These files come in a wide variety of formats but all represent either a series of intervals along a one-dimensional genome or a discrete signal varying along the genome.

This library enables easy IO operations in Python in a fashion that is independent of the underlying format.

In this project, I designed, obtained funding for, and led a Python course aimed at teaching data analysis to graduate students at the University of Uppsala.

The course fit 20 PhDs students at a time (from all the STEM fields) and included theory, exercises, and a personal project.

It focused on writing programs to process, filter, clean, analyze, and visualize scientific data as well as the ability to automate the computational tasks that are used in day-to-day research.

In this project, I planned, developed, and deployed a Python web server acting as a public data repository. The results contained in the repository concern microbial community analyses from industrial digesters producing biogas.

To avoid uploading 2,000 line-long projects in this section, I opted to include a simple solution to a problem that I got recently as an interview question.

The technical product owner asked me to write a function that received two inputs, a triangle, and a point, in a 2D space. The function was to answer yes or no if the point was within the triangle.

To avoid uploading 2,000 line-long projects in this section, I included a simple solution to a typical interview question. These are good for quickly assessing a candidate's coding skills.

This problem is titled the "Inverse Fizz Buzz" and requires the candidate to write a function that finds the shortest sequence of consecutive numbers, which produce the required output when run through the fizz_buzz algorithm.

In this project, I designed and developed a Python pipeline that automates the cleaning, processing, analysis, and visualization of the data obtained from sequencing a series of microbial communities.

The program takes as input N files containing the raw output of the DNA sequencer and outputs a PDF report (example shown below) summarizing the salient characteristics of each sample as well as biodiversity indicators and a comprehensive comparison.

In this publication, I was tasked with performing the required bioinformatics and biostatistics on the ~100 samples sequenced as part of a comprehensive study of the Danube's microbial communities.

My tasks also included producing the various figures that the article needed.