Michelle Jonika

Genetics and Genomics Ph.D. Student · Blackmon Lab · Department of Biology · Texas A&M University

Current CV | Current Resume

As an evolutionary biologist, I enjoy the puzzle-like nature of coding genetics and genomics problems, specifically bringing this approach to the evolution of genomic content and sex chromosomes. Detail-orieted planning, project management, experimental design, annd clear communication are important to my critical thinking and problem solving strategies for research. I am pursuing a Ph.D. in Genetics and Genomics at Texas A&M University, and I defended my dissertation in January 2023 with a graduation date of Spring 2023. I am interested in career paths in industry intersecting bioinformatics, computational biology, genetics, genomics, and molecular biology. Check out some of my research projects below!

Education & Experience

Ph.D. in Genetics and Genomics

Graduate Certificate in Business

 Texas A&M University, College Station, TX

Dissertation: Patterns and Processes in the Evolution of Sequence Classes and Genomic Compartments — The content of genomes can be categorized into different sequence classes, into autosomes and sex chromosomes, coding and noncoding, repetitive and non-repetitive, to name but a few. Each of these classes of the genome have unique mechanisms that govern their evolution. In my dissertation work, I am studying the evolution of the genome on these three scales.
Advised by: Heath Blackmon

August 2018 - Present


Bioinformatics Intern | Bioinformatics and Data Science Team

 San Diego, California (Virtual)
  • Leveraged high-complexity data set to predict canine cancer types
  • Used machine learning (Random Forest) approaches to train and evaluate different models
  • Performed extensive data evaluation to curate sample metrics, obtain balanced training and testing sets, and identify meaningful model parameters
June 2022 - August 2022

Bayer Crop Science

Bioinformatics Intern | Genomics Discovery and Application Team

 St.Louis, Missouri (Virtual)
  • Identifying historic data to test for epistasis
  • Developing a statistical testing framework to identify interactions between introgressed loci
  • Three-month, full-time position exposure in an industry setting
  • Establishing multi-disciplinary connections with teams with expertise in data science, genomics, and precision breeding
May 2021 - August 2021

Bachelor of Science in Forensic and Investigative Science

Minor: Genetics

 Texas A&M University, College Station, TX

Undergraduate Thesis: Genes as Markers of Sex for Forensic Entomology — Identifying genes that can be used for a transcriptional approach to sex identification in blow fly species of forensic importance: Lucilia sericata (Diptera: Calliphoridae) (Meigen), Cochliomyia macellaria (Diptera: Calliphoridae) (Fabricius), and Chrysomya rufifacies (Diptera: Calliphoridae) (Macquart).
Advised by: Aaron M. Tarone

August 2014-May 2018

Gastrointestinal Laboratory (GI Lab) at Texas A&M

Undergraduate Research Technician

 Texas A&M University, College Station, TX

Investigated the effects of antibiotics on the gut health and microbioita of companion animals

January 2016-August 2018


Repetitive DNA

In most species, a large portion or even the majority of the genome can be made up of repetitive sequences (e.g., in humans, repetitive content is approximately 40%). Repetitive DNA can be further categorized into tandem or interspersed repeats. Tandem repeats are sequential sequences, including centromeric and telomeric, and simple sequence repeats. Interspersed repeats are sequences scattered throughout the genome and include transposable elements, retrotransposons, and transposons. Additionally, repetitive elements can be categorized by how proliferative the sequences are. In humans, moderately repetitive sequences compose ~30% of the genome, are present in 10-100,000 copies in the genome, and can be both tandem or interspersed. Moderately repetitive sequences encompass microsatellites, minisatellites, variable number tandem repeats (VNTRs), and transposable elements (LINES/SINES) and are located mostly in euchromatic regions. Highly repetitive sequences compose ~10% of the genome and are present in 1,000,000 copies or more in the genome, and are often found in tandem arrays. Highly repetitive sequences encompass macrosatellites and are located primarily in heterochromatic telomere and centromere regions. Each of these classes of DNA has unique evolutionary forces that govern its evolution. Repeats arise from a variety of biological mechanisms that result in extra copies being produced. For example, microsatellites, a type of tandem repeat, are highly polymorphic, which suggests a stepwise mutation model where variation is introduced by replication slippage. This slippage allows array length to change by only one or two repeats at a time, but occasionally there may be a jump of larger size. Contrasting the tandem repeats, interspersed repeats such as retrotransposons have vastly different mechanisms for evolution. These LINES and SINES replicate through RNA reinserted as DNA through reverse transcription.

Chromosome Stability

Chromosome number is a fundamental aspect of genome organization and is available for more than 10,000 species. Eukaryotes show a wide diversity in the number of chromosomes within their genome. The evolution of chromosome number has been recalcitrant to the formation of rules or generalizations that explain variation in patterns and rates across large clades. What is clear is that within clades, fusions and fissions are two dominant mechanisms in reshaping karyotypes. We use these terms for simplicity to describe single chromosome number changes. However, in reality, fusions decreasing chromosome number captures two different molecular processes: first, Robertsonian translocation followed by the loss of nonessential DNA, and second, the fusion of telomeres from two chromosomes followed by the inactivation of one centromere . In contrast, fissions increasing chromosome number can happen in just the way we might imagine through fissions in the centromere region and gaining of new telomeric sequences.

It has often been assumed that fissions and fusions should be deleterious or underdominant. As such, they should only fix in a population if there is a low effective population size. Understanding why some lineages across the tree of life experience rapid rates of chromosome number evolution is important, as changes in chromosome number can directly or indirectly impact things such as the evolution of haplodiploidy, recombination rates, reproductive isolation, and gene transcription. Many things may shape karyotype evolution across the tree of life. However, my dissertation focuses on two such traits – centromeric structure and range size.

Centromeric structure may modulate the fitness effect of fusions and fissions as holocentric centromeres are diffuse, and spindle fibers attach along the entire length of the chromosome. It has been hypothesized that species with this type of centromere should have little difficulty segregating chromosomes that have experienced fusions or fissions. In contrast, species with monocentric chromosomes have a single, localized centromere and chromosomal fragments generated from fusions or fissions may lack centromeres. These chromosomal fragments will not be able to segregate normally and will likely be deleterious. Therefore, holocentricity can reduce or eliminate selective pressure against and underdominance of chromosome rearrangements allowing for a higher rate of chromosome number evolution.

Effective population size may modulate the fate of fission and fusion mutations. Population size plays an important role in the efficacy of selection. In species with a small effective population size, drift is the dominant cause of allele frequency change and the loss or fixation of mutations. In contrast, in species with a large effective population size, selection is the dominant cause of allele frequency change and the loss or fixation of mutations. We use range size as a proxy for effective population size to test whether fusions and fissions are more likely to fix in populations with low effective population size. Ultimately, understanding changes in chromosome number is important to better understanding divergence, adaptation, and speciation as well as fundamental diversity patterns across the tree of life.

Sex Chromosome Evolution

Just as the architecture of the genome can be divided into several classes, chromosomes can be classified into autosomes and sex chromosomes. Autosomes are those chromosomes that segregate randomly with respect to sex. In contrast, sex chromosomes segregate differentially among the sexes. In XY systems, the females are homogametic, meaning that all gametes produced carry an X chromosome. However, males are heterogametic and produce two types of gametes, those carrying an X and those carrying a Y chromosome. For ZW systems, this pattern is reversed, females are heterogametic and males are homogametic. For the remainder of this description, I will describe patterns in terms of X and Y chromosomes; however, these are almost universally applicable to the Z and W chromosome in female heterogametic systems.

Sex chromosomes have evolved independently across many lineages of plants and animals. The evolution of sex chromosomes can be divided into two stages: the transition into chromosomal sex determination and the evolution of sex chromosomes once chromosomal sex determination has evolved. The theory surrounding the origin of chromosomal sex determination is somewhat balkanized between the animal and plant literature, likely because of differences in the ancestral sex determination landscape of the two clades.

The canonical model of sex chromosome evolution begins with a pair of homomorphic sex chromosomes that are identical outside of the locus acting as the master switch for sex determination. Some chromosomes in empirical systems have been shown to still be in this state, with the best example being the fugu fish, a species where males and females differ by only a single nucleotide polymorphism (SNP). However, most sex chromosomes experience a process of divergence between the X and Y chromosome with eventual decay of the Y chromosome. This divergence and decay are only possible in regions where the X and Y have suppressed recombination. One widely accepted hypothesis for the evolution of reduced recombination is sexually antagonistic variation. A sexually antagonistic locus is one where two alleles segregate in a population, and one benefits males but is deleterious to females, while the second benefits females but is deleterious to males. The presence of a sexually antagonistic locus on the sex chromosomes will lead to indirect selection for inversions that span the sex-determining locus and the sexually antagonistic locus. The selection force is indirect in that it is selected due to its impact on recombination rates rather than a direct effect of the structural variation on fitness. Over time, through a series of inversions, recombination can be suppressed along the majority of the sex chromosomes. In these regions where recombination is no longer occurring, a combination of a reduction in effective population size, Muller’s ratchet, and genetic hitchhiking will occur and lead to a loss of functional gene content on the Y chromosome. As genes become non-functional, deletion biases can lead to a reduction in the overall size of the Y chromosome.

Though many forces lead to the degeneration of the Y chromosome, some forces lead to its maintenance. New genes may be generated on the Y chromosome through duplication or translocation. Purifying selection can also maintain the Y chromosome when it contains essential genes. This impact is seen when we examine the rate of gene loss from Y chromosomes. Initially, gene loss is very high but slows overtime, genes are conserved due to purifying selection, and new, “essential” genes are transferred to the Y and maintained in palindromic sequences. Despite this interplay of retention and degradation of the Y chromosome, a pseudo-autosomal region (PAR) persists in most species. The PAR is a small region of homology between the sex chromosomes that maintains recombination and is essential to facilitate proper pairing and segregation of the sex chromosomes during meiosis.

Sex chromosomes are the battleground of the genome. It is only in the sex chromosomes that alternative versions of a gene (one benefitting males and one benefitting females) can increase the fitness of organisms. The unique nature of sex chromosomes has made them a focus of evolutionary biology since their discovery more than a century ago.

Mammalian Pseudoautosomal Region (PAR) Evolution

The classic model of sex chromosome evolution begins with a pair of homomorphic sex chromosomes and leads to eventual decay of the Y chromosome in regions of suppressed recombination. Despite degradation of the Y chromosome, a pseudo-autosomal region (PAR) persists in most species. The PAR is a region of homology between sex chromosomes that maintains recombination and is essential for segregation of sex chromosomes during meiosis. What happens to sex chromosomes once the PAR becomes very small? The fragile Y hypothesis predicts that as PAR sizes become smaller, the risk of aneuploidy increases, increasing the probability of Y chromosome loss, achiasmatic meiosis, or rejuvenation through translocation or fusion of autosomal material. While these outcomes are perceived as rare due to their absence in most mammals, each of them is quite common across the tree of life and all have occurred in mammals. The unique nature of sex chromosomes has made them a focus of evolutionary biology since their discovery more than a century ago, but PAR size has been estimated in just 9 species of mammals. For this reason, many questions have remained unanswered due to limitations of available data and analysis tools.

Current methods to estimate PAR sizes are time consuming and costly. For this reason, I developed an approach to train and deploy a convolutional neural network (CNN) to accurately predict PAR size. I am currently using my developed pipeline to generate training datasets and hope to train and deploy the CNN in the next few months. The trained CNN will allow for estimation of PAR size for hundreds of species both with publicly available genomic sequences and through de-novo sequencing of DNA available from collaborators. Additionally, this tool will be made broadly available to the community as a publicly available genome annotation tool to improve all future genome assemblies. With vastly increased numbers of PAR size estimates, we can for the first time begin to understand the dynamics of this genomic trait and determine whether achiasmatic meiosis, fusions of autosomes to sex chromosomes, and Y loss are associated with small PAR sizes.


Check out my Google Scholar page


J.M. Alfieri, M.M. Jonika, J.N. Dulin, H. Blackmon. 2023. Tempo and Mode of Genome Structure Evolution in Insects. Genes. 14(2):336.[PDF]


M. Pitonak, M. Aceves, P.A. Kumar, G. Dampf, P. Green, A. Tucker, V. Dietz, D. Miranda, S. Letchuman, M.M. Jonika, D. Bautista, H. Blackmon, J.N. Dulin. 2022. Effects of Biological Sex Mismatch on Neural ProgenitorCell Transplantation for Spinal Cord Injury in Mice. Nature Communications. 13(1):1-12. [PDF]

M.M. Jonika, J.M. Alfieri, T. Sylvester, A.R. Buhrow, H. Blackmon. 2022. Why Not Y Naught. Heredity 1-4 [PDF]

J.M. Alfieri, G. Wang, M.M. Jonika, C.A. Gill, G.N. Athrey, H. Blackmon. 2022. A Primer for Single-Cell Sequencing in Non-Model Organisms. Genes 13(2):380 [PDF]


M.L. Pimsler, C.E. Hjelmen, M.M. Jonika, A. Sharma, S. Fu, M. Bala, S.H. Sze, J.K. Tomberlin, A.M. Tarone. 2021. Sexual Dimorphism in Growth Rate and Gene Expression Throughout Immature Development in Wild Type Chrysomya rufifacies (Diptera: Calliphoridae). Frontiers in Ecology and Evolution 9:368 [PDF]


S. Ruckman* (Co-first author), M.M. Jonika* (Co-first author), C. Casola, H. Blackmon. 2020. Chromosome Number Evolves at Equal Rates in Holocentric and Monocentric Clades. PLOS Genetics 16(10):e1009076 [PDF]

M.M Jonika, J. Lo, H. Blackmon. 2020. Mode and Tempo of Microsatellite Evolution across 300 Million Years of Insect Evolution. Genes 11, 945 [PDF]

M.M. Jonika, C.E. Hjelmen, A.M. Faris, A.M. Tarone. 2020. An Evaluation of Differentially Spliced Genes As Markers of Sex for Forensic Entomology. Journal of Forensic Science. 65:1579-1587 [PDF]


J. Lo, M.M. Jonika, H. Blackmon. 2019. micRocounter: Microsatellite Characterization in Genome Assemblies. G3: Genes | Genomes | Genetics 9(10):3101-3104 [PDF]

R.D. Perkins, J.R. Gamboa, M.M. Jonika, J. Lo, A. Shum, R.H Adams, H. Blackmon. 2019. A Database of Amphibian Karyotypes. Chromosome Research 27:313-319 [PDF]

B.C. Guard, J.B. Honneffer, A.E. Jergens M.M. Jonika, L.Toresson, Y.A. Lawrece, C.B. Webb, S. Hill, J.A. Lidbury, J.M. Steiner, J.S. Suchodolski. 2019. Longitudinal Assessment of Microbial Dysbiosis, Fecal Unnconjugated Bile Acid Concentrations, and Disease Activity in Dogs with Steroid-Responsive Chronic Inflammatory Enteropathy. Journal of Veterinary Internal Medicine 33(3):1295-1305 [PDF]

Coding Contributions

Software Contributions


  • Genomics: NGS analysis and pipeline development(SRA, Trimmomatic, bwa, samtools, GATK), Genome Assembly(HiFiasm, QUAST, BUSCO, BlobToolKit) RNASeq(FastQC, bowtie2, DESeq2), GWAS(PLINK, GEMMA), Genomic Prediction(rrBLUP)
  • Genetics: Evolutionary Biology (Genome structure evolution, Mammalian Sex Chromosome evolution, Morphometrics), Veterinary Medicine (Clinical Data Evaluation, Cancer Prediction), Crop Science (Epistasis, Introgression)
  • Molecular Biology: Primer optimization, gDNA/DNA extraction, RNA extraction, PCR, qPCR, Gel visualization/imaging, Flow Cytometry
  • Chemistry: GC-MS, DART-MS, Drug Database Deisgn, LC-MS
  • Programming: R, Unix/Linux, tidyverse, conda, Python, LaTeX, HTML/CSS, R Shiny
  • Code/Project Management: Git/Github, VSCode, BitBucket, Docker, JIRA
  • Data Science: Large dataset management (>20 Gb), Machine Learning, Bayesian statistics, Phylogenetics, Simulations, Data Visualization, Software Development, Amazon Web Services (AWS)/Cloud Computing, HPC Cluster Computing
  • Soft Skills: Project management, Public Speaking and Communication, Leadership, Multi-disciplinary collaboration, Adaptive problem solving, Multi-tasking, Self-Motivated, Time Management, Strategic Planning


Teaching Assistant

Python for Biologists | Spring 2023 (Texas A&M)

Anatomy and Physiology I | Spring 2022 (Texas A&M)

Critical Writing in Biology | Fall 2020, Spring 2021 (Texas A&M)

Introduction to Genetics Laboratory | Spring 2019, Spring 2023 (Texas A&M)

Guest Lecturer

Forensic Genetics | Topic: Genetic Testing | September 2022 (Texas A&M)

Bioinformatics | Topic: Genetic Privacy | November 2021 (Utah Valley University)

Bioinformatics | Topic: Genetic Privacy | October 2019 (Texas A&M)

Awards & Certifications