Biology subjects

Alser, M.

Publications and source records attributed to Alser, M..

11 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

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.

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bioRxiv2022

TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally-inefficient and memory-hungry; bottlenecking the entire genome analysis pipeline. However, for many applications, the majority of reads do no match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first fast and widely-applicable pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCalls key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. TargetCall filters out all off-target reads before basecalling; and the highly-accurate but slow basecalling is performed only on the raw signals whose noisy reads are labeled as on-target. Our thorough experimental evaluations using both real and simulated data show that TargetCall 1) improves the end-to-end basecalling performance of the state-of-the-art basecaller by 3.31 x while maintaining high (98.88%) sensitivity in keeping on-target reads, 2) maintains high accuracy in downstream analysis, 3) precisely filters out up to 94.71% of off-target reads, and 4) achieves better performance, sensitivity, and generality compared to prior works. We freely open-source TargetCall to aid future research in pre-basecalling filtering at https://github.com/CMU-SAFARI/TargetCall.

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bioRxiv2022

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding exact-matching seeds as the conventional hashing methods assign distinct hash values for different seeds, including highly similar seeds. Finding only exact-matching seeds causes either 1) increasing the use of the costly sequence alignment or 2) limited sensitivity. We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches. BLEND 1) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and 2) provides the proper mechanisms for using seeds as sets with the SimHash technique to find fuzzy seed matches efficiently. We show the benefits of BLEND when used in read overlapping and read mapping. For read overlapping, BLEND is faster by 2.4x-83.9x (on average 19.3x), has a lower memory footprint by 0.9x-14.1 x (on average 3.8x), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is fasterby 0.8x-4.1x (on average 1.7x) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND.

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bioRxiv2022

A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.

genomics↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes

AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4x. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants. Code AvailabilityAirLift source code and readme describing how to reproduce our results are available at https://github.com/CMU-SAFARI/AirLift.

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bioRxiv2020

Metalign: Efficient alignment-based metagenomic profiling via containment min hash

Whole-genome shotgun sequencing enables the analysis of microbial communities in unprecedented detail, with major implications in medicine and ecology. Predicting the presence and relative abundances of microbes in a sample, known as "metagenomic profiling", is a critical first step in microbiome analysis. Existing profiling methods have been shown to suffer from poor false positive or false negative rates, while alignment-based approaches are often considered accurate but computationally infeasible. Here we present a novel method, Metalign, that addresses these concerns by performing efficient alignment-based metagenomic profiling. We use a containment min hash approach to reduce the reference database size dramatically before alignment and a method to estimate organism relative abundances in the sample by resolving reads aligned to multiple genomes. We show that Metalign achieves significantly improved results over existing methods on simulated datasets from a large benchmarking study, CAMI, and performs well on in vitro mock community data and environmental data from the Tara Oceans project. Metalign is freely available at https://github.com/nlapier2/Metalign, along with the results and plots used in this paper, and a docker image is also available at https://hub.docker.com/repository/docker/nlapier2/metalign.

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bioRxiv2018

MiCoP: Microbial Community Profiling method for detecting viral and fungal organisms in metagenomic samples

BackgroundHigh throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.\n\nResultsHere we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project.\n\nConclusionsMiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation is publicly available on GitHub at: https://github.com/smangul1/MiCoP

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