About

Steffen Heber is a professor in the Department of Computer Science at NC State University and serves as the Director of Graduate Programs. He holds a joint appointment with the College of Sciences supporting the Bioinformatics Research Center. His academic background includes studying mathematics and biology at the University of Heidelberg in Germany, earning a Ph.D. in mathematics in 2001 from the German Cancer Research Center, and completing postdoctoral research at the University of California, San Diego before joining NC State in 2003. His research focuses on bioinformatics and computational biology, developing algorithms for the analysis, summarization, and quality control of complex biological data. His interests encompass data-driven storytelling, gene transcription and alternative splicing, translation, nature-inspired computation, and common intervals. Heber has received several honors, including the IBM Faculty Award, the Faculty Research and Professional Development Award from NC State, the Best Paper Award at ACMSE, and the Carol Miller Graduate Lecturer Award. Additionally, he has been recognized multiple times through the Thank a Teacher program at NC State.

Research topics

• Computer Science
• Artificial Intelligence
• Algorithm
• Mathematics
• Data Mining
• Mathematical optimization
• Statistics
• Biology

Selected publications

• Recruitment of bifunctional regulator thermospermine to methylated ribosomes directs xylem fate

  Science · 2026-02-12 · 1 citations

  articleOpen access

  Polyamines are often associated with ribosomes and are thought to stabilize their integrity. In Arabidopsis , the polyamine thermospermine (tSpm) affects xylem cell fate. tSpm induces translation of SUPPRESSOR-OF-ACAULIS51 (SAC51) and SAC51-LIKEs (SACLs), which inhibit heterodimerization of the xylem development proteins LONESOME-HIGHWAY (LHW) and TARGET-OF-MONOPTEROS5. Here, we report a methyltransferase, OVERACHIEVER, that methylates the peptidyl transferase center of the 25 S ribosomal RNA (rRNA). Residue m 3 U2952 promotes functional tSpm binding to a specific site connecting the P-site transfer RNA (tRNA) with rRNA residues in the peptidyl transferase center. This interaction enhances the translation of SACLs but inhibits that of LHW. Our study uncovers the dependency between a conserved rRNA base methylation and a polyamine in orchestrating cell fate decisions, highlighting a role for the ribosome chemical landscape in translational regulation.

  PublisherDOI

• Recruitment of bifunctional regulator thermospermine to methylated ribosomes directs xylem fate.

  Open MIND · 2025-12-20

  article

  Polyamines are often associated with ribosomes and are thought to stabilize their integrity. In Arabidopsis, the polyamine thermospermine (tSpm) affects xylem cell fate. tSpm induces translation of SUPPRESSOR-OF-ACAULIS51 (SAC51) and SAC51-LIKEs (SACLs), which inhibit heterodimerization of the xylem development proteins LONESOME-HIGHWAY (LHW) and TARGET-OF-MONOPTEROS5. Here, we report a methyltransferase, OVERACHIEVER, that methylates the peptidyl transferase center of the 25S ribosomal RNA (rRNA). Residue m3U2952 promotes functional tSpm binding to a specific site connecting the P-site transfer RNA (tRNA) with rRNA residues in the peptidyl transferase center. This interaction enhances the translation of SACLs but inhibits that of LHW. Our study uncovers the dependency between a conserved rRNA base methylation and a polyamine in orchestrating cell fate decisions, highlighting a role for the ribosome chemical landscape in translational regulation.

  DOI

• A New Discrete Whale Optimization Algorithm with a Spiral 3-opt Local Search for Solving the Traveling Salesperson Problem

  2022

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Mathematical optimization

  The whale optimization algorithm is a metaheuristic inspired by the hunting strategy of humpback whales. This paper proposes a new discrete spiral whale optimization algorithm (DSWOA) to solve the traveling salesperson problem (TSP). Our approach uses sequential consecutive crossover and spiral 3-opt search, a modified version of the popular 3-opt local search. Spiral 3-opt search works like the original 3-opt heuristic but only uses part of the tour to generate 3-opt moves. We show that spiral 3-opt achieves results similar to the original 3-opt technique and significantly reduces runtime. We evaluate DSWOA's performance on 19 TSP instances against six benchmark algorithms. Our results suggest that DSWOA produces TSP solutions that are as good or better than our competitors. For five of the six benchmark algorithms, we demonstrated statistically significant improvements.

  PublisherDOI

• RiboSimR: A Tool for Simulation and Power Analysis of Ribo-seq Data

  Lecture notes in computer science · 2020 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Data Mining
  PublisherDOI

• RiboStreamR: a web application for quality control, analysis, and visualization of Ribo-seq data

  BMC Genomics · 2019-06-01 · 19 citations

  articleOpen accessSenior authorCorresponding

  BACKGROUND: Ribo-seq is a popular technique for studying translation and its regulation. A Ribo-seq experiment produces a snap-shot of the location and abundance of actively translating ribosomes within a cell's transcriptome. In practice, Ribo-seq data analysis can be sensitive to quality issues such as read length variation, low read periodicities, and contaminations with ribosomal and transfer RNA. Various software tools for data preprocessing, quality assessment, analysis, and visualization of Ribo-seq data have been developed. However, many of these tools require considerable practical knowledge of software applications, and often multiple different tools have to be used in combination with each other. RESULTS: We present riboStreamR, a comprehensive Ribo-seq quality control (QC) platform in the form of an R Shiny web application. RiboStreamR provides visualization and analysis tools for various Ribo-seq QC metrics, including read length distribution, read periodicity, and translational efficiency. Our platform is focused on providing a user-friendly experience, and includes various options for graphical customization, report generation, and anomaly detection within Ribo-seq datasets. CONCLUSIONS: RiboStreamR takes advantage of the vast resources provided by the R and Bioconductor environments, and utilizes the Shiny R package to ensure a high level of usability. Our goal is to develop a tool which facilitates in-depth quality assessment of Ribo-seq data by providing reference datasets and automatically highlighting quality issues and anomalies within datasets.

  PublisherOA PDFDOI

• PeakPass: Automating ChIP-Seq Blacklist Creation

  Lecture notes in computer science · 2019-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

• PeakPass: Automating ChIP-Seq Blacklist Creation

  Journal of Computational Biology · 2019-12-19 · 20 citations

  articleSenior author

  ChIP-Seq blacklists contain genomic regions that frequently produce artifacts and noise in ChIP-Seq experiments. To improve signal-to-noise ratio, ChIP-Seq pipelines often remove data points that map to blacklist regions. Existing blacklists have been compiled in a manual or semiautomated way. In this article we describe PeakPass, an efficient method to generate blacklists, and demonstrate that blacklists can increase ChIP-Seq data quality. PeakPass leverages machine learning and attempts to automate blacklist generation. PeakPass uses a random forest classifier in combination with genomic features such as sequence, annotated repeats, complexity, assembly gaps, and the ratio of multimapping to uniquely mapping reads to identify artifact regions. We have validated PeakPass on a large data set and tested it for the purpose of upgrading a blacklist to a new reference genome version. We trained PeakPass on the ENCODE blacklist for the hg19 human reference genome, and created an updated blacklist for hg38. To assess the performance of this blacklist, we tested 42 ChIP-Seq replicates from 24 experiments using 10 ChIP-Seq quality metrics including relative strand coefficient, standardized standard deviation, and enrichment of reads in promoter regions. Using the blacklist generated by PeakPass resulted in a statistically significant improvement for nine of these metrics.

  PublisherDOI

• Using a Novel Negative Selection Inspired Anomaly Detection Algorithm to Identify Corrupted Ribo-seq and RNA-seq Samples

  2019-09-04 · 2 citations

  articleOpen accessSenior author

  RNA-seq and Ribo-seq are popular techniques for quantifying cellular transcription and translation. These experiments use next-generation sequencing to produce genome-wide high-resolution snapshots of the total populations of mRNAs and translating ribosomes within the investigated samples. When performed in concert, these experiments yield valuable information about protein synthesis rates and translational efficiency. Due to their intricate experimental protocols and demanding data processing requirements, quality control and analysis of such experiments are often challenging. Therefore, methods for accurately assessing data quality, and for identifying contaminated samples, are greatly needed. In the following we use a novel negative selection inspired algorithm called Boundary Detection Using Nearest Neighbors (BDUNN), for the identification of corrupted samples. Our algorithm constructs a detector set and reduced training set that defines the boundaries between normal data points and potential anomalies. Subsequently, a nearest neighbor algorithm is used to classify unseen observations. We compare the performance of BDUNN with other popular negative selection and one-class classification algorithms, and show that BDUNN is capable of accurately and efficiently detecting anomalies in standard anomaly detection datasets and simulated RNA-seq and Ribo-seq data sets. Furthermore, we have implemented our method within an existing R Shiny platform for analyzing RNA-seq an Ribo-seq datasets, which permits downstream analysis of anomalous samples.

  PublisherOA PDFDOI

• Identifying the Signatures of Missing Transcripts

  2018-10-01

  articleSenior author

  In eukaryotes, alternative splicing (AS) is a fundamental mechanism of post-transcriptional regulation. AS occurs within the majority of multi-exon genes and has important implications for physiology, development, and diseases. AS might generate a plethora of different transcript isoforms from a single gene and lead to differential expression of transcript isoforms across tissues, developmental time points and disease-states. The increasing amount of available RNA-seq data has resulted in various transcript assembly algorithms. However, these algorithms are far from perfect, often yielding highly incomplete, and sometimes erroneous, transcriptome reconstructions. In our presentation we discuss how gene and alignment features, as well as other metrics, could be used to detect un-annotated alternative splicing events. The goal of our research is to identify genes that show reliable evidence for `missing' splice variants in their transcript catalogue.

  PublisherDOI

• A parallel island model for biogeography-based classification rule mining in julia

  Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018-07-06

  articleSenior author

  In this paper, we present a distributed island model implementation of biogeography-based optimization for classification rule mining (island BBO-RM). Island BBO-RM is an evolutionary algorithm for rule mining that uses Pittsburgh style classification rule encoding, which represents an entire ruleset (classifier) as a single chromosome. Our algorithm relies on biogeography-based optimization (BBO), an optimization technique that is inspired by species migration pattern between habitats. Biogeography-based optimization has been reported to perform well in various applications ranging from function optimization to image classification. A major limitation of evolutionary rule mining algorithms is their high computational cost and running time. To address this challenge, we have applied a distributed island model to parallelize the rule extraction phase via BBO. We have explored several different migration topologies and data windowing techniques. Our algorithm is implemented in Julia, a dynamic programming language designed for high-performance and parallel computation. Our results show that our distributed implementation is able to achieve considerable speedups when compared to a serial implementation. Without data windowing, we obtain speedups up to a factor of nine without a loss of classification accuracy. With data windowing, we obtain speedups up to a factor of 30 with a small loss of accuracy in some cases.

  PublisherDOI

Recent grants

• EAGER: Role of alternative splicing in Arabidopsis immune response

  NSF · $299k · 2009–2011

Frequent coauthors

• Jörg D. Hoheisel

  German Cancer Research Center

  27 shared

• Jens Stoye

  Bielefeld University

  21 shared

• Martin Vingron

  Max Planck Institute for Molecular Genetics

  17 shared

• Marcus Frohme

  Technical University of Applied Sciences Wildau

  13 shared

• Brian E. Howard

  Research Triangle Park Foundation

  11 shared

• Anna N. Stepanova

  North Carolina State University

  10 shared

• David C. Muddiman

  North Carolina State University

  10 shared

• Elena Casacuberta

  Institut de Biologia Evolutiva

  9 shared

Awards & honors

• IBM Faculty Award
• Faculty Research and Professional Development Award from NC…
• Best Paper Award at ACMSE
• Carol Miller Graduate Lecturer Award
• Thank a Teacher program recognitions from NC State