Biology subjects

Mangul, S.

Publications and source records attributed to Mangul, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A comprehensive benchmark of discrepancies across microbial genome reference databases

Metagenomic analysis of microbial communities relies significantly on the quality and completeness of reference genomes, which allow researchers to compare sequencing reads against reference genome collections to reveal essential community characteristics. However, the reliability of these analyses is often compromised by substantial discrepancies across existing reference resources, including differences in genome content, assembly fragmentation, taxonomic representation, and metadata completeness. While these inconsistencies are known to introduce bias, the extent of divergence between major databases remains largely unknown. Here, we present a comprehensive benchmark of discrepancies across multiple widely used microbial genome reference resources. We developed the Cross-DB Genomic Comparator (CDGC), which utilizes reference genome alignments to systematically capture discrepancies in genome assemblies across reference databases. Applying this framework, we found that 99% of viral genomes were identical across databases, indicating strong consistency in viral reference resources. In contrast, fungal genomes showed substantially greater variability: although 82% of assemblies exhibited at least 90% similarity, only 7% were identical across databases. More concerning, we identified a subset of 461 assemblies with less than 50% similarity, suggesting the presence of technical artifacts, incomplete assemblies, or damaged genome files that require closer examination. Collectively, these results demonstrate that systematic cross-database benchmarking provides a critical mechanism for refining the accuracy of individual reference databases and advancing efforts towards more unified and reliable universal reference genomes.

bioinformatics↗

bioRxiv2026

Leveraging a hybrid cross-disciplinary training model to accelerate global bioinformatics capacity

Disparities in formal bioinformatics training exacerbate the global skills gap, impeding the democratized application of advanced genomic technologies. To bridge this divide, we introduce a scalable, hybrid training framework designed to rapidly accelerate regional bioinformatics capacity. We exemplify this approach through the Eastern European Bioinformatics and Genomics (EEBG) workshop series -- a cross-disciplinary initiative that pairs international faculty with local institutions to deliver modular, hands-on curricula. Functioning as a structured knowledge-transfer pipeline, the series has catalyzed a sustainable educational ecosystem, evidenced by the establishment of multiple independent summer schools across the region. The assessment of the 2025 EEBG workshop in Krakow, Poland, validates the models viability; participant metrics confirm high efficacy in skill acquisition (mean satisfaction: 4.4/5.0) and community building. Crucially, the hybrid delivery mode dismantled geographic barriers, serving as a vital mechanism for maintaining scientific continuity for researchers facing displacement and crisis. Synthesizing these outcomes, we define the core features of a replicable blueprint for scientific readiness in resource-constrained environments. We conclude by presenting a strategic roadmap -- organized around infrastructure standardization, governance sustainability, and geographical expansion -- for adapting this regional proof-of-concept into a global export-ready model, offering a critical path toward ensuring universal access to genomic innovation.

scientific communication and education↗

bioRxiv2026

FASTR: Reimagining FASTQ via Compact Image-inspired Representation

MotivationHigh-throughput sequencing (HTS) enables population-scale genomics but generates massive datasets, creating bottlenecks in storage, transfer, and analysis. FASTQ, the standard format for over two decades, stores one byte per base and one byte per quality score, leading to inefficient I/O, high storage costs, and redundancy. Existing compression tools can mitigate some issues, but often introduce costly decompression or complex dependency issues. ResultsWe introduce FASTR, a lossless, computation-native successor to FASTQ that encodes each nucleotide together with its base quality score into a single 8-bit value. FASTR reduces file size by at least 2x while remaining fully reversible and directly usable for downstream analyses. Applying general-purpose compression tools on FASTR consistently yields higher compression ratios, 2.47, 3.64, and 4.8x faster compression, and 2.34, 1.96, 1.75x faster decompression than on FASTQ across Illumina, HiFi, and ONT reads. FASTR is machine-learning-ready, allowing reads to be consumed directly as numerical vectors or image-like representations. We provide a highly parallel software ecosystem for FASTQ-FASTR conversion and show that FASTR integrates with existing tools, such as minimap2, with minimal interface changes and no performance overhead. By eliminating decompression costs and reducing data movement, FASTR lays the foundation for scalable genomics analyses and real-time sequencing workflows. Availability and Implementationhttps://github.com/ALSER-Lab/FASTR

bioinformatics↗

bioRxiv2025

Robust software development practices improve citations of RNA-seq tools

RNA sequencing (RNA-seq) has emerged as an exemplary technology in biology and clinical applications, offering a crucial complement to other transcriptomic profiling protocols due to its high sensitivity, precision, and accuracy in characterizing transcriptomes. However, the rapid proliferation of RNA-seq tools necessitates the adoption of robust software development practices. Such development underscores the critical need to examine how RNA-seq tools are developed, maintained, and distributed; and whether the data they generate is reproducible as all of these factors are essential for ensuring software reliability, transparency, and trust in scientific findings. We conducted a comprehensive assessment of 434 RNA-seq tools developed between 2008 and 2024, categorizing them based on the type of analysis they perform. Our evaluation encompassed their software development and distribution methodologies, as well as the attributes contributing to their widespread adoption and dependability within the biomedical community, which were quantified by factors such as package manager availability, containerization, multithreading support, documentation quality, and inclusion of example datasets. Our findings establish the first documented positive association between rigorous software development practices and their adoption of published RNA-seq tools as measured by citations (Mann-Whitney U test, p-value = 4.9 x 10-26). By identifying key characteristics of widely adopted software, our findings guide developing robust and user-friendly RNA-seq tools, thereby reinforcing the call for rigorous community-wide standards.

bioinformatics↗

bioRxiv2025

Assessing genomic reproducibility of read alignment tools

Genomic research relies on accurate and reproducible computational analyses of DNA sequencing data to draw reliable biological conclusions. Read mapping, the process of aligning reads to a reference genome, is central to many applications, including variant detection and comparative genomics. While several tools have been developed for this task, genomic reproducibility1, defined as the consistency of results across replicates, remains underexplored. Here, we address this question by introducing a methodology based on synthetic replicates of sequencing data, generated by perturbing the original reads through shuffling, reverse complementing, or combined shuffling and reverse complementing. Our approach is able to simulate variability observed across sequencing runs due to differences in library preparation techniques. We evaluated the reproducibility of eight alignment tools (BWA-MEM22, Bowtie23, HISAT24, minimap25, NextGenMap6, SNAP7, SMALT7,8 and Subread9) under these perturbations using whole-genome sequencing (WGS) data. Synthetic replicates were aligned and compared to the original sample to quantify discrepancies. Mapping accuracy changes ranged from 0.0001% to 4.4% for primary reads, which are alignments not marked as secondary, supplementary, or duplicates, and up to 12.2% for high-quality primary reads. For primary reads, the percentage of reads commonly mapped in both the original and synthetic replicate ranged from 91.66% to 100% relative to the total number of mapped reads in the original dataset. High-quality filtering improved consistency, though some tools still failed to recover more than 70% of the original alignments. Within the set of common reads, the incidence of inconsistent mappings was as high as 13.53% for primary reads and 6.73% for high-quality primary reads. Bowtie 2 was fully reproducible under the shuffling replicate, Subread was fully reproducible under the reverse-complement replicate, whereas NextGenMap exhibited only minor inconsistencies. By contrast, SNAP and minimap2 displayed the most significant variability under reverse complementing. We further demonstrate that alignment inconsistencies propagate to the downstream task of calling structural genomic variants. Using Manta for structural-variant (SV) calling, we observed that Bowtie 2, HISAT2, and minimap2 maintained perfect SV concordance between original and replicate alignments, whereas other tools exhibited lower concordance, with Subread scoring only 87% concordance. In conclusion, our comprehensive evaluation demonstrates that synthetic perturbations reveal critical differences in how alignment tools handle technical variability and how these differences propagate to downstream variant analyses, underscoring the necessity of incorporating reproducibility benchmarks into the selection and validation of read mappers to ensure robust and reliable genomic interpretations.

bioinformatics↗

bioRxiv2025

Patterns of population structure and genetic variation within the Saudi Arabian population

Both the demographic history and cultural practices influence the pattern of genetic variation, sometimes in opposing manners. For Saudi Arabians, being situated at the hub connecting multiple continents is expected to increase heterogeneity and diversity due to cross-continental migrations. On the other hand, cultural practices such as endogamy to promote social stability may have also promoted regional isolations and reduced heterogeneity. To better understand the genomic impact of these potentially opposing forces, we genotyped and sequenced 3,352 and 302 individuals, respectively, from Saudi Arabians to study their population structure and admixture history, and patterns of genetic variation. We identified twelve genetic sub-clusters that correlated with geographical regions, differentiated by distinct components of ancestry based on comparisons to modern and ancient DNA references. These sub-clusters also showed variation across ranges of the genome covered in runs of homozygosity, reflecting potential differences in kinship or marital practices, as well as differences in population size changes over time. Using 25,488,981 variants found in whole genome sequencing, we found that the Saudi do not show the depletion of rare alleles typically observed in isolated populations, though they do show the expected pattern of enrichment of alleles bioinformatically annotated as deleterious when compared to Africans/African Americans and Non-Finnish Europeans from gnomAD. Saudi sub-clusters with greater inbreeding and lower effective population sizes showed greater enrichment of deleterious alleles as well. Taken together, our results suggest that Saudis history and culture impact its pattern of genetic variation and potentially to the population health. We also made available the allele frequency estimates of alleles discovered in our samples so to start a foundation on which to interpret medical- and pharmaco-genomic findings from these populations.

evolutionary biology↗

bioRxiv2024

Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data

Upwards of 40% of reads in sequencing datasets may be unmapped and discarded by standard protocols. Recent work has shown the utility of re-analyzing these unmapped reads to construct meaningful features, such as immune diversity repertoires or copy number variation in mtDNA and rDNA. While previous analyses of these features have produced significant correlations with diverse traits, they have generally been limited to analyses of RNA-sequencing data in phenotype-specific cohorts. Here, we explore whether associations can be identified using population-scale, whole-exome sequencing data in the UK BioBank. Using recently developed tools, we constructed multiple features including T-cell receptor diversity metrics, microbial load, and mtDNA and rDNA copy numbers for nearly 50,000 individuals in the UK BioBank. We first verify the validity of our method by showing that GWAS on these constructed traits results in replication of associations from studies in which the phenotypes were explicitly measured. Next, across several GWAS, we identified 21 novel independent significant loci in 11 genes, most of them in genes implicated in the innate immune response. Finally, we further analyzed the read-constructed features by establishing correlations to other population-level biobank traits such as immune disorders, metabolic disorders, neuropsychiatric disorders, and blood cell counts. Our results suggest that existing tools for feature construction from unmapped reads can offer novel information at the population level, and that these features can be used to establish novel genetic associations.

bioinformatics↗

bioRxiv2023

BioLLMBench: A Comprehensive Benchmarking of Large Language Models in Bioinformatics

The rapid advancements in artificial intelligence, particularly in Large Language Models (LLMs) such as GPT-4, Gemini, and LLaMA, have opened new avenues for computational biology and bioinformatics. We report the development of BioLLMBench, a novel framework designed to evaluate LLMs in bioinformatics tasks. This study assessed GPT-4, Gemini, and LLaMA through 2,160 experimental runs, focusing on 24 distinct tasks across six key areas: domain expertise, mathematical problem-solving, coding proficiency, data visualization, research paper summarization, and machine learning model development. Tasks ranged from fundamental to expert-level challenges, and each area was evaluated using seven specific metrics. A Contextual Response Variability Analysis was implemented to understand how model responses varied under different conditions. Results showed diverse performance: GPT-4 led in most tasks, achieving a 91.3% proficiency in domain knowledge, while Gemini excelled in mathematical problem-solving with a 97.5% proficiency score. GPT-4 also outperformed in machine learning model development, though Gemini and LLaMA struggled to generate executable code. All models faced challenges in research paper summarization, scoring below 40% using the ROUGE metric. Model performance variance increased when using a new chat window, though average scores remained similar. The study also discusses the limitations and potential misuse risks of these models in bioinformatics.

bioinformatics↗

bioRxiv2023

VISTA: An integrated framework for structural variant discovery

Structural variation (SV), refers to insertions, deletions, inversions, and duplications in human genomes. With advances in whole genome sequencing (WGS) technologies, a plethora of SV detection methods have been developed. However, dissecting SVs from WGS data remains a challenge, with the majority of SV detection methods prone to a high false-positive rate, and no existing method able to precisely detect a full range of SVs present in a sample. Previous studies have shown that none of the existing SV callers can maintain high accuracy across various SV lengths and genomic coverages. Here, we report an integrated structural variant calling framework, VISTA (Variant Identification and Structural Variant Analysis) that leverages the results of individual callers using a novel and robust filtering and merging algorithm. In contrast to existing consensus-based tools which ignore the length and coverage, VISTA overcomes this limitation by executing various combinations of top-performing callers based on variant length and genomic coverage to generate SV events with high accuracy. We evaluated the performance of VISTA on using comprehensive gold-standard datasets across varying organisms and coverage. We benchmarked VISTA using the Genome-in-a-Bottle (GIAB) gold standard SV set, haplotype-resolved de novo assemblies from The Human Pangenome Reference Consortium (HPRC)1,2, along with an in-house PCR-validated mouse gold standard set. VISTA maintained the highest F1 score among top consensus-based tools measured using a comprehensive gold standard across both mouse and human genomes. VISTA also has an optimized mode, where the calls can be optimized for precision or recall. VISTA-optimized is able to attain 100% precision and the highest sensitivity among other variant callers. In conclusion, VISTA represents a significant advancement in structural variant calling, offering a robust and accurate framework that outperforms existing consensus-based tools and sets a new standard for SV detection in genomic research.

bioinformatics↗

bioRxiv2023

Analytical code sharing practices in biomedical research

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten papers organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability (p=2.71x10-9). Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses (p=1.15*10-07). In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

scientific communication and education↗

bioRxiv2023

A rigorous benchmarking of alignment-based HLA callers for RNA-seq data

Accurate identification of human leukocyte antigen (HLA) alleles is essential for various clinical and research applications, such as transplant matching and drug sensitivities. Recent advances in RNA-seq technology have made it possible to impute HLA types from sequencing data, spurring the development of a large number of computational HLA typing tools. However, the relative performance of these tools is unknown, limiting the ability for clinical and biomedical research to make informed choices regarding which tools to use. Here we report the study design of a comprehensive benchmarking of the performance of 12 HLA callers across 682 RNA-seq samples from 8 datasets with molecularly defined gold standard at 5 loci, HLA-A, -B, -C, -DRB1, and -DQB1. For each HLA typing tool, we will comprehensively assess their accuracy, compare default with optimized parameters, and examine for discrepancies in accuracy at the allele and loci levels. We will also evaluate the computational expense of each HLA caller measured in terms of CPU time and RAM. We also plan to evaluate the influence of read length over the HLA region on accuracy for each tool. Most notably, we will examine the performance of HLA callers across European and African groups, to determine discrepancies in accuracy associated with ancestry. We hypothesize that RNA-Seq HLA callers are capable of returning high-quality results, but the tools that offer a good balance between accuracy and computational expensiveness for all ancestry groups are yet to be developed. We believe that our study will provide clinicians and researchers with clear guidance to inform their selection of an appropriate HLA caller.

bioinformatics↗

bioRxiv2023

Community structure and temporal dynamics of SARS-CoV-2 epistatic network allows for early detection of emerging variants with altered phenotypes

The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

bioinformatics↗

bioRxiv2022

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

bioinformatics↗

bioRxiv2022

TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles

Metagenomic taxonomic profiling aims to predict the identity and relative abundance of taxa in a given whole genome sequencing metagenomic sample. A recent surge in computational methods that aim to accurately estimate taxonomic profiles, called taxonomic profilers, have motivated community driven efforts to create standardized benchmarking datasets and platforms, standardized taxonomic profile formats, as well as a benchmarking platform to assess tool performance. While this standardization is essential, there is currently a lack of tools to visualize the standardized output of the many existing taxonomic profilers, and benchmarking studies rely on a single value metrics to compare performance of tools and compare to benchmarking datasets. Here we report the development of TAMPA (Taxonomic metagenome profiling evaluation), a robust and easy-to-use method that allows scientists to easily interpret and interact with taxonomic profiles produced by the many different taxonomic profiler methods beyond the standard metrics used by the scientific community. We demonstrate the unique ability of TAMPA to provide important biological insights into the taxonomic differences between samples otherwise missed by commonly utilized metrics. Additionally, we show that TAMPA can help visualize the output of taxonomic profilers, enabling biologists to effectively choose the most appropriate profiling method to use on their metagenomics data. TAMPA is available on GitHub, Bioconda and Galaxy Toolshed at https://github.com/dkoslicki/TAMPA, and is released under the MIT license.

bioinformatics↗

bioRxiv2022

Unlocking the microblogging potential for science and medicine

Microblogging platform Twitter allows researchers to showcase their work, receive constructive feedback, find jobs, and build scientific collaborations. While existing literature has analyzed the benefits of Twitter in the development and distribution of scientific knowledge, most of the studies only took into account a limited number of researchers, which affected the generalizability of derived results. Our study analyzed the activity of 6,000 biomedical scientists on Twitter using data-driven approaches, a third of whom were female. Furthermore, we estimated that up to a quarter of the members of the scientific community are engaged on Twitter. While the number of male scientists joining the microblogging platform every year has decreased, the number of female scientists has remained roughly the same. Scientists are very selective in who they are following as compared to the general public. We also found that the type of tweets and retweets one posts may affect the number of followers, specifically, that a moderate to high level of professionalism and a high level of positivity is correlated with followers count. Moreover, female scientists send fewer negative tweets as compared to male scientists (31.1% for females and 34.7% for males). Our analysis could provide insights and launch a conversation on the advantages and limitations of using Twitter for disseminating scientific information and engaging in constructive discussion and collaborations within the scientific community.

scientific communication and education↗

bioRxiv2022

Data availability of open T-cell receptor repertoire data, a systematic assessment

The improvement of next-generation sequencing technologies has promoted the field of immunogenetics and produced numerous immunogenomics data. Modern data-driven research has the power to promote novel biomedical discoveries through secondary analysis of such data. Therefore, it is important to ensure data-driven research with great reproducibility and robustness for promoting a precise and accurate secondary analysis of the immunogenomics data. In scientific research, rigorous conduct in designing and conducting experiments is needed, specifically in scientific and articulate writing, reporting and interpreting results. It is also crucial to make raw data available, discoverable, and well described or annotated in order to promote future re-analysis of the data. In order to assess the data availability of published T cell receptor (TCR) repertoire data, we examined 11,918 TCR-Seq samples corresponding to 134 TCR-Seq studies ranging from 2006 to 2022. Among the 134 studies, only 38.1% had publicly available raw TCR-Seq data shared in public repositories. We also found a statistically significant association between the presence of data availability statements and the increase in raw data availability (p=0.014). Yet, 46.8% of studies with data availability statements failed to share the raw TCR-Seq data. There is a pressing need for the biomedical community to increase awareness of the importance of promoting raw data availability in scientific research and take immediate action to improve its raw data availability enabling cost-effective secondary analysis of existing immunogenomics data by the larger scientific community.

scientific communication and education↗

bioRxiv2021

The systematic assessment of completeness of public metadata accompanying omics studies

Recent advances in high-throughput sequencing technologies have made it possible to collect and share a massive amount of omics data, along with its associated metadata. Enhancing metadata availability is critical to ensure data reusability and reproducibility and to facilitate novel biomedical discoveries through effective data reuse. Yet, incomplete metadata accompanying public omics data may hinder reproducibility and reusability by reducing sample interpretability and limiting secondary analyses. In this study, we performed a comprehensive assessment of metadata completeness shared in both scientific publications and/or public repositories by analyzing over 253 studies encompassing over 164 thousands samples, including both human and non-human mammalian studies. We observed that studies often omit over a quarter of important phenotypes, with an average of only 74.8% of them shared either in the text of publication or the corresponding repository. Notably, public repositories alone contained 62% of the metadata, surpassing the textual content of publications by 3.5%. Only 11.5% of studies completely shared all phenotypes, while 37.9% shared less than 40% of the phenotypes. Studies involving non-human samples were more likely to share metadata than studies involving human samples. We observed similar results on the extended dataset spanning 2.1 million samples across over 61,000 studies from the Gene Expression Omnibus repository. The limited availability of metadata reported in our study emphasizes the necessity for improved metadata sharing practices and standardized reporting. Finally, we discuss the numerous benefits of improving the availability and quality of metadata to the scientific community and beyond, supporting data-driven decision-making and policy development in the field of biomedical research. This work provides a scalable framework for evaluating metadata availability and may help guide future policy and infrastructure development.

bioinformatics↗

bioRxiv2021

Virtual meetings promise to eliminate the geographical and administrative barriers and increase accessibility, diversity, and inclusivity

The COVID-19 pandemic brought a new set of unprecedented challenges not only for healthcare, education, and everyday jobs but also in terms of academic conferences. In this study, we investigate the effect of the broad adoption of virtual platforms for academic conferences as a response to COVID-19 restrictions. We show that virtual platforms enable higher participation from underrepresented minority groups, increased inclusion, and broader geographic distribution. We also discuss emerging challenges associated with the virtual conference format resulting in a decreased engagement of social activities, limited possibilities of cross-fertilization between participants, and reduced peer-to-peer interactions. Lastly, we conclude that a novel comprehensive approach needs to be adopted by the conference organizers to ensure increased accessibility, diversity, and inclusivity of post-pandemic conferences. Our findings provide evidence favoring a hybrid format for future conferences, marrying the strength of both in-person and virtual platforms.

scientific communication and education↗