Ioannis Mouratidis

Penn State Milton S. Hershey Medical Center

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Ilias Georgakopoulos-Soares

Penn State Milton S. Hershey Medical Center

Nikol Chantzi

Pennsylvania State University

Candace S. Y. Chan

University of California, San Francisco

Nadav Ahituv

University of California, San Francisco

Michail Patsakis

Pennsylvania State University

Camille Moeckel

Penn State Milton S. Hershey Medical Center

Martin Hemberg

Brigham and Women's Hospital

Austin Montgomery

Pennsylvania State University

Andriani Charpidou

National and Kapodistrian University of Athens

Nikolaos Syrigos

National and Kapodistrian University of Athens

Cooperative Institutions

University of California, San Francisco

Pennsylvania State University

National and Kapodistrian University of Athens

Penn State Milton S. Hershey Medical Center

Sotiria General Hospital

Chest Diseases Hospital

University of Western Macedonia

National Technical University of Athens

Alexander Fleming Biomedical Sciences Research Center

Aristotle University of Thessaloniki

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Random forest classification algorithm for medical industry data

SHS Web of Conferences (2022)

Christodoulos Vlachas Lazaros Damianos Nikolaos Gousetis Ioannis Mouratidis Dimitrios Kelepouris

Medical industry produces a significant portion of data whereas by adopting various Machine Learning models it can make accurate predictions about public healthcare that can be generalised. Transfer learning improves traditional machine learning by transferring the knowledge learned in one or more tasks and by using it for learning improvement in a related target task. In the current study, transfer learning with random forests was applied. Four datasets of medical interest obtained from the University of California, Irvine (UCI) Machine Learning Repository were used i.e., the BUPA-Liver Disease Dataset, the Breast Cancer Wisconsin Dataset, the Cleveland Heart Disease Dataset, and the Pima Indians Diabetes dataset. To our knowledge, there has been no study that applied Random Forests and Transfer Learning for these datasets. According to our results, our proposed method could provide significant accuracy rates in terms of diagnosing these disorders. Specifically, the classification accuracy of each dataset was similar or higher compared to the majority of similar studies that applied Random Forests. Limitations and suggestions regarding future research are also presented.

Transfer of learning

10.1051/shsconf/202213903008

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Citations (7)

Frequentmers - a novel way to look at metagenomic next generation sequencing data and an application in detecting liver cirrhosis

BMC Genomics (2023)

Ioannis Mouratidis Nikol Chantzi Umair Khan Maxwell A. Konnaris Candace S. Y. Chan

Abstract Early detection of human disease is associated with improved clinical outcomes. However, many diseases are often detected at an advanced, symptomatic stage where patients are past efficacious treatment periods and can result in less favorable outcomes. Therefore, methods that can accurately detect human disease at a presymptomatic stage are urgently needed. Here, we introduce “frequentmers”; short sequences that are specific and recurrently observed in either patient or healthy control samples, but not in both. We showcase the utility of frequentmers for the detection of liver cirrhosis using metagenomic Next Generation Sequencing data from stool samples of patients and controls. We develop classification models for the detection of liver cirrhosis and achieve an AUC score of 0.91 using ten-fold cross-validation. A small subset of 200 frequentmers can achieve comparable results in detecting liver cirrhosis. Finally, we identify the microbial organisms in liver cirrhosis samples, which are associated with the most predictive frequentmer biomarkers.

Liver disease

10.1186/s12864-023-09861-w

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Citations (3)

MAFcounter: An efficient tool for counting the occurrences of k-mers in MAF files.

arXiv (Cornell University) (2024)

Michail Patsakis Kimonas Provatas Ioannis Mouratidis Ilias Georgakopoulos-Soares

Motivation: With the rapid expansion of large-scale biological datasets, DNA and protein sequence alignments have become essential for comparative genomics and proteomics. These alignments facilitate the exploration of sequence similarity patterns, providing valuable insights into sequence conservation, evolutionary relationships and for functional analyses. Typically, sequence alignments are stored in formats such as the Multiple Alignment Format (MAF). Counting k-mer occurrences is a crucial task in many computational biology applications, but currently, there is no algorithm designed for k-mer counting in alignment files. Results: We have developed MAFcounter, the first k-mer counter dedicated to alignment files. MAFcounter is multithreaded, fast, and memory efficient, enabling k-mer counting in DNA and protein sequence alignment files. Availability: The MAFcounter package and its Python bindings are released under GPL license as a multi-platform application and are available at: https://github.com/Georgakopoulos-Soares-lab/MAFcounter

Python

MIT License

Sequence (biology)

k-mer

File format

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Real-world survival outcomes to first-line chemoimmunotherapy and biomarker analysis in extensive-stage small-cell lung cancer

Research Square (Research Square) (2023)

Emmanouil Panagiotou Ioannis Vathiotis Austin Montgomery Camille Moeckel Maria Effrosyni Livanou

Abstract Background Recent approval of programmed death-ligand 1 (PD-L1) inhibitors in the first line of treatment has transformed the therapeutic landscape of extensive-stage small cell lung cancer (ES-SCLC); real-world (rw) evidence of efficacy is currently limited. Patients and Methods: We retrospectively assessed patients with SCLC, large cell neuroendocrine carcinoma (LCNEC) or combined histology receiving chemoimmunotherapy in the first-line treatment setting at “Sotiria” General Hospital for Chest Diseases, Athens, Greece. Patient demographics and disease characteristics were extracted using a standardized form. Kaplan-Meier curves were used to calculate rw progression-free survival (rwPFS) and rw overall survival (rwOS). Cox proportional hazards regression analysis was utilized to identify associations between patient characteristics and outcome. Results One hundred patients were included in the analysis. Median rwPFS was 7.2 months (95% CI, 6.7–12.0 months) and median rwOS was 14.4 months (95% CI, 9.4–18.6 months); efficacy metrics were similar between patients treated with durvalumab and atezolizumab. In the multivariate analysis, the number of metastatic sites was associated with increased risk of death (HR, 1.47; 95% CI, 1.11–1.94; p = 0.007), while BMI was associated with decreased risk (HR, 0.89; 95% CI, 0.81–0.97; p = 0.008). Exploratory biomarker analysis revealed a correlation between different prognostic scores (RMH, GRIM, LIPI, and EPSILoN) and rwOS. Conclusion Real-world data confirm the efficacy of first-line chemoimmunotherapy in patients with ES-SCLC. The association between prognostic scores and survival outcomes in ES-SCLC should be explored in prospective studies.

Chemoimmunotherapy

Durvalumab

Atezolizumab

Univariate analysis

10.21203/rs.3.rs-3621899/v1

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The rules of gymnastics in the first international Olympic Games in Athens (1896)

Vasileios Kaimakamis Th Laskari Ioannis Mouratidis Panagiotis Stefanidis

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Frequentmers - a novel way to look at metagenomic Next Generation Sequencing data and an application in detecting liver cirrhosis

medRxiv (Cold Spring Harbor Laboratory) (2023)

Ioannis Mouratidis Nikol Chantzi Umair Khan Maxwell A. Konnaris Candace S. Y. Chan

Liver disease

Human liver

10.1101/2023.09.19.23295771

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Citations (1)

Zimin patterns in genomes.

arXiv (Cornell University) (2024)

Nikol Chantzi Ioannis Mouratidis Ilias Georgakopoulos-Soares

Zimin words are words that have the same prefix and suffix. They are unavoidable patterns, with all sufficiently large strings encompassing them. Here, we examine for the first time the presence of k-mers not containing any Zimin patterns, defined hereafter as Zimin avoidmers, in the human genome. We report that in the reference human genome all k-mers above 104 base-pairs contain Zimin words. We find that Zimin avoidmers are most enriched in coding and Human Satellite 1 regions in the human genome. Zimin avoidmers display a depletion of germline insertions and deletions relative to surrounding genomic areas. We also apply our methodology in the genomes of another eight model organisms from all three domains of life, finding large differences in their Zimin avoidmer frequencies and their genomic localization preferences. We observe that Zimin avoidmers exhibit the highest genomic density in prokaryotic organisms, with E. coli showing particularly high levels, while the lowest density is found in eukaryotic organisms, with D. rerio having the lowest. Among the studied genomes the longest k-mer length at which Zimin avoidmers are observed is that of S. cerevisiae at k-mer length of 115 base-pairs. We conclude that Zimin avoidmers display inhomogeneous distributions in organismal genomes, have intricate properties including lower insertion and deletion rates, and disappear faster than the theoretical expected k-mer length, across the organismal genomes studied.

Gene density

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Eligibility for screening with low-dose CT in a real-world cohort of patients with lung cancer in Greece: A brief report

Lung Cancer (2023)

Maria Effrosyni Livanou Nikolaos Syrigos Austin Montgomery Camille Moeckel Emmanouil Panagiotou

10.1016/j.lungcan.2023.107424

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Absent from DNA and protein: genomic characterization of nullomers and nullpeptides across functional categories and evolution

Genome biology (2021)

Ilias Georgakopoulos-Soares Ofer Yizhar‐Barnea Ioannis Mouratidis Martin Hemberg Nadav Ahituv

Abstract Nullomers and nullpeptides are short DNA or amino acid sequences that are absent from a genome or proteome, respectively. One potential cause for their absence could be their having a detrimental impact on an organism. Results Here, we identify all possible nullomers and nullpeptides in the genomes and proteomes of thirty eukaryotes and demonstrate that a significant proportion of these sequences are under negative selection. We also identify nullomers that are unique to specific functional categories: coding sequences, exons, introns, 5′UTR, 3′UTR, promoters, and show that coding sequence and promoter nullomers are most likely to be selected against. By analyzing all protein sequences across the tree of life, we further identify 36,081 peptides up to six amino acids in length that do not exist in any known organism, termed primes. We next characterize all possible single base pair mutations that can lead to the appearance of a nullomer in the human genome, observing a significantly higher number of mutations than expected by chance for specific nullomer sequences in transposable elements, likely due to their suppression. We also annotate nullomers that appear due to naturally occurring variants and show that a subset of them can be used to distinguish between different human populations. Analysis of nullomers and nullpeptides across vertebrate evolution shows they can also be used as phylogenetic classifiers. Conclusions We provide a catalog of nullomers and nullpeptides in distinct functional categories, develop methods to systematically study them, and highlight the use of variability in these sequences in other analyses

Proteome

Coding region

10.1186/s13059-021-02459-z

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Citations (22)

Characterization of hairpin loops and cruciforms across 118,065 genomes spanning the tree of life

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Nikol Chantzi Camille Moeckel Candace S. Y. Chan Akshatha Nayak Guliang Wang

Inverted repeats (IRs) can form alternative DNA secondary structures called hairpins and cruciforms, which have a multitude of functional roles and have been associated with genomic instability. However, their prevalence across diverse organismal genomes remains only partially understood. Here, we examine the prevalence of IRs across 118,065 complete organismal genomes. Our comprehensive analysis across taxonomic subdivisions reveals significant differences in the distribution, frequency, and biophysical properties of perfect IRs among these genomes. We identify a total of 29,589,132 perfect IRs and show a highly variable density across different organisms, with strikingly distinct patterns observed in Viruses, Bacteria, Archaea, and Eukaryota. We report IRs with perfect arms of extreme lengths, which can extend to hundreds of thousands of base pairs. Our findings demonstrate a strong correlation between IR density and genome size, revealing that Viruses and Bacteria possess the highest density, whereas Eukaryota and Archaea exhibit the lowest relative to their genome size. Additionally, the study reveals the enrichment of IRs at transcription start and termination end sites in prokaryotes and Viruses and underscores their potential roles in gene regulation and genome organization. Through a comprehensive overview of the distribution and characteristics of IRs in a wide array of organisms, this largest-scale analysis to date sheds light on the functional significance of inverted repeats, their contribution to genomic instability, and their evolutionary impact across the tree of life.

Characterization

Tree (set theory)

10.1101/2024.09.29.615628

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Citations (1)