I’m analyzing the “alien mummy” DNA so you don’t have to.

Understanding the Analysis of “Alien Mummy” DNA: A Data Scientist’s Perspective

In recent discussions surrounding purported “alien mummy” specimens found in Peru, much attention has been given to anatomical features—such as unusual finger bones and hip structures. These morphological observations are intriguing, but as a data scientist with background in molecular biology and bioinformatics, my focus has been on the genetic data associated with these specimens. Specifically, I have undertaken a detailed analysis of the DNA sequences purportedly extracted from these mummies, aiming to assess their authenticity, potential origins, and what they reveal scientifically.

This article provides an overview of the process and findings from my ongoing investigation. My goal is to offer a transparent, scientifically grounded perspective that complements the broader conversation.

Background and Motivation

The claims about ancient mummies with unusual biological features have sparked significant public interest. While anatomical analyses are important, my expertise and interest are in evaluating the genetic evidence. In particular, I was motivated by a proactive approach inspired by physicist Avi Loeb’s principle that “we don’t have to wait for definitive proof—we can analyze the data ourselves.” Accordingly, I began examining the publicly available genetic sequences linked to these specimens.

Data Collection and Initial Steps

Several Redditors shared links to purported genetic sequence data from the mummies. The sequences are accessible from publicly available repositories, and I have begun processing these datasets locally—from raw read files to initial classifications—using bioinformatics pipelines that I assembled specifically for this purpose.

Given the complexity of ancient DNA analysis, I anticipated that these steps would be computationally intensive. Indeed, some processes—such as tasks involving alignment and assembly—can take hours to complete. Over the past week, I have dedicated considerable time to building and running these pipelines, inspecting results at each stage to ensure accuracy.

Pipeline Overview

The core of my analysis involves:

• Quality Control: Assessing raw sequence quality using tools like FastQC, ensuring the data are suitable for downstream analysis.
• Alignment: Mapping reads to the human reference genome (e.g., hg38) using Bowtie2, which helps identify segments that match known human DNA.
• Unaligned Read Analysis: Focusing on reads that do not align to the human genome, which could contain evidence of other organisms or contamination.
• Taxonomic Classification: Using tools such as Kraken2 to classify DNA sequences and identify potential non-human origins.
• De Novo Assembly: Constructing longer contiguous sequences (contigs) from unaligned reads