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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2024

Biological insights from plasma proteomics of non-small cell lung cancer patients treated with immunotherapy

IntroductionImmune checkpoint inhibitors have made a paradigm shift in the treatment of non-small cell lung cancer (NSCLC). However, clinical response varies widely and robust predictive biomarkers for patient stratification are lacking. Here, we characterize early on-treatment proteomic changes in blood plasma to gain a better understanding of treatment response and resistance. MethodsPre-treatment (T0) and on-treatment (T1) plasma samples were collected from 225 NSCLC patients receiving PD-1/PD-L1 inhibitor-based regimens. Plasma was profiled using aptamer-based technology to quantify approximately 7000 plasma proteins per sample. Proteins displaying significant fold changes (T1:T0) were analyzed further to identify associations with clinical outcomes. Bioinformatic analyses of upregulated proteins were performed to determine potential cell origins and enriched biological processes. ResultsThe levels of 142 proteins were significantly increased in the plasma of NSCLC patients following ICI-based treatments. Soluble PD-1 exhibited the highest increase, with a positive correlation to tumor PD-L1 status. Bioinformatic analysis of the ICI monotherapy dataset revealed a set of 30 upregulated proteins that formed a single, highly interconnected network with CD8A serving as a central hub, suggesting T cell activation during ICI treatment. Notably, the T cell-related network was detected regardless of clinical benefit. Lastly, circulating proteins of alveolar origin were identified as potential biomarkers of limited clinical benefit, possibly due to a link with cellular stress and lung damage. ConclusionsOur study provides insights into the biological processes activated during ICI-based therapy, highlighting the potential of plasma proteomics to identify mechanisms of therapy resistance and potential biomarkers for outcome.

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bioRxiv2024

False discovery rate control: Moving beyond the Benjamini-Hochberg method

Bioinformatics studies often involve numerous simultaneous statistical tests, increasing the risk of false discoveries. To control the false discovery rate (FDR), these studies typically apply a statistical method called the Benjamini-Hochberg (BH) method. However, BH can be overly conservative, particularly in small-sample studies, and it does not take advantage of relevant structural information among the hypotheses, such as groupings. Group structures can arise, for example, when genomic features located in close proximity are co-regulated. Recent statistical developments have yielded group-adaptive BH methods that can leverage pre-existing group information to improve statistical power while maintaining FDR control. However, these methods remain underutilized in bioinformatics practice. In this study, we illustrate the practical application of group-adaptive BH methods using a previously published, moderately scaled microRNA (miRNA) dataset. Even under simple groupings based on chromosomal location, these methods identified more miRNAs with significantly deregulated expression (FDR-adjusted p-value < 0.05) compared to the traditional BH method. Most of the new discoveries are supported by prior literature and a related 2017 study. Although sensitivity to grouping strategy varied across methods, our control analysis indicated that, for most methods, the additional detections may be attributable to the incorporation of group information. Our results highlight the potential of specialized BH methods for controlling the FDR in omics studies with pre-defined group structures, and motivate further evaluation of their generalizability across diverse datasets.

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bioRxiv2024

GeneSelectR: An R Package Workflow for Enhanced Feature Selection from RNA Sequencing Data

MotivationHigh-dimensional Bulk RNA sequencing (RNAseq) datasets pose a considerable challenge in identifying biologically relevant features for downstream analyses and data mining efforts. The standard approach involves differential gene expression (DGE) analysis, but its effectiveness can be limited depending on the data due to its univariate nature. In complex datasets, an alternative approach involves employing a variety of machine learning (ML) tools, which attempt to understand non-linear relationships between features and focus on generalizability rather than statistical significance. This approach will result in the generation of multiple feature lists, which might exhibit similarities in terms of classification performance metrics. Therefore, there is an urgent need for a cohesive workflow that seamlessly integrates robust feature selection using diverse ML methods while also evaluating the biological relevance of the resulting feature lists. This combined approach would enable the prioritization of the best-performing list, considering both sets of criteria. ResultsWe introduce GeneSelectR, an open-source R package that innovatively combines ML and bioinformatic data mining approaches for enhanced feature selection. With GeneSelectR, features can be selected from a normalized RNAseq dataset with a variety of ML methods and user-defined parameters. This is followed by an assessment of their biological relevance with Gene Ontology (GO) enrichment analysis, along with a semantic similarity analysis of the resulting GO terms. Additionally, similarity coefficients and fractions of the GO terms of interest are calculated. With this, GeneSelectR optimizes ML performance and rigorously assesses the biological relevance of the various lists, offering a means to prioritize feature lists with regard to the biological question. When applied to the TCGA-BRCA dataset, the GeneSelectR workflow generated several feature lists using different ML methods and a DGE analysis. By leveraging the various functions in GeneSelectR, the different lists could be evaluated based on both ML performance and biological relevance. This comprehensive evaluation facilitated the selection of the best-performing list, which exhibited both strong machine learning performance and high relevance to the biological question while maintaining a manageable number of highly specific features. AvailabilityThe package is available on CRAN. To install it, run: install.packages( GeneSelectR) Contactdzhakparov@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2024

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms

BackgroundIntra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS. ResultsWe characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm. ConclusionsThese findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.

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bioRxiv2024

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics. Author SummaryAll life processes involve the consumption, creation, and interconversion of metabolites. Metabolomics is the comprehensive study of these small molecules, often using mass spectrometry, to provide critical information of health and disease. Automated processing of such metabolomics data is desired, especially for the bioinformatics community with familiar tools and infrastructures. Despite of Pythons popularity in bioinformatics and machine learning, the Python ecosystem in computational metabolomics still misses a complete data pipeline. We have developed an end-to-end computational metabolomics data processing pipeline, based on the raw data preprocessor Asari [1]. Our pipeline takes experimental data in .mzML or .raw format and outputs annotated feature tables for subsequent biological interpretation. We demonstrate the application of this pipeline to multiple metabolomics and lipidomics datasets. Accompanying the pipeline, we have designed a set of reusable data structures, released as the MetDataModel package, which shall promote more consistent terminology and software interoperability in this area.

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bioRxiv2024

Function-Assigned Masked Superstrings as a Versatile and Compact Data Type for ????-Mer Sets

The design of efficient dynamic data structures for large k-mer sets belongs to central challenges of sequence bioinformatics. Recent advances in compact k-mer set representations via Spectrum-Preserving String Sets (SPSS), culminating with the masked superstring framework, have provided data structures of remarkable space efficiency for wide ranges of k-mer sets. However, the possibility to perform set operations with the resulting indexes has remained limited due to the static nature of the underlying compact representations. Here, we develop f-masked superstrings, a concept combining masked superstrings with custom demasking functions f to enable k-mer set operations based on index merging. Combined with the FMSI index for masked superstrings, we obtain a memory-efficient k-mer membership index and compressed dictionary supporting set operations via Burrows-Wheeler Transform merging. The framework provides a promising theoretical solution to a pressing bioinformatics problem and highlights the potential of f-masked superstrings to become an elementary data type for k-mer sets.

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bioRxiv2024

MetagenomicKG: a knowledge graph for metagenomic applications

MotivationThe sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. ResultsWe present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. Availability and ImplementationThe source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available at Github: https://github.com/KoslickiLab/MetagenomicKG. We also host a Neo4j instance: http://mkg.cse.psu.edu:7474 for accessing and querying this graph. Contactdmk333@psu.edu Supplementary informationavailable at Bioinformatics online.

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bioRxiv2024

BetaAlign: a deep learning approach for multiple sequence alignment

The multiple sequence alignment (MSA) problem is a fundamental pillar in bioinformatics, comparative genomics, and phylogenetics. Here we characterize and improve BetaAlign, the first deep learning aligner, which substantially deviates from conventional algorithms of alignment computation. BetaAlign draws on natural language processing (NLP) techniques and trains transformers to map a set of unaligned biological sequences to an MSA. We show that our approach is highly accurate, comparable and sometimes better than state-of-the-art alignment tools. We characterize the performance of BetaAlign and the effect of various aspects on accuracy; for example, the size of the training data, the effect of different transformer architectures, and the effect of learning on a subspace of indel-model parameters (subspace learning). We also introduce a new technique that leads to improved performance compared to our previous approach. Our findings further uncover the potential of NLP-based approaches for sequence alignment, highlighting that AI-based methodologies can substantially challenge classic tasks in phylogenomics and bioinformatics.

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bioRxiv2024

PIMMS-Dash: Accessible analysis, interrogation, and visualisation of high-throughput transposon insertion sequencing (TIS) data

MotivationCurrent methods for visualising and interrogating high-throughput transposon insertion mutagenesis sequencing (TIS) data requires a significant time investment in learning bioinformatics, often producing static figures that do not facilitate real time analysis. SummaryWe have created an accessible web-based browser tool for visualisation and downstream analysis of high-throughput TIS data. This includes multiple interactive and sortable tables to aid the user to identify genes of interest, enabling the user to gain a greater understanding of the genes contributing to fitness in their experimental work. PIMMS-Dash permits researchers, with any level of bioinformatics knowledge, to interrogate data sets and generate publication quality figures. AvailabilityPIMMS-Dash is freely available and is accessible online at https://pimms-dashboard-uon.azurewebsites.net

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bioRxiv2024

CAIM: Coverage-based Analysis for Identification of Microbiome

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic (WMS) approach. In this study, we developed a new bioinformatics tool, CAIM, for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consitently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similality of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and primary 44 liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers. Key Points- Metagenomic coverage is an important index to obtain highly accurate species identification by reducing false positives from whole shotgun metagenomic data. - Comparative analyses of CAIM and other bioinformatics tools for species identification on many mock community whole shotgun metagenomic datasets generated by short-read and long-read sequencing and synthetic datasets were performed, showing that CAIM has a very good performance compared with the other tools. - Using the metagenomic coverage approach through CAIM improves the predictive power of species biomarkers identified from in stool samples of colorectal cancer and primary liver datasets.

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bioRxiv2024

Global biogeography of N2-fixing microbes: nifH amplicon database and analytics workflow

Marine nitrogen (N) fixation is a globally significant biogeochemical process carried out by a specialized group of prokaryotes (diazotrophs), yet our understanding of their ecology is constantly evolving. Although marine dinitrogen (N2)-fixation is often ascribed to cyanobacterial diazotrophs, indirect evidence suggests that non-cyanobacterial diazotrophs (NCDs) might also be important. One widely used approach for understanding diazotroph diversity and biogeography is polymerase chain reaction (PCR)-amplification of a portion of the nifH gene, which encodes a structural component of the N2-fixing enzyme complex, nitrogenase. An array of bioinformatic tools exists to process nifH amplicon data, however, the lack of standardized practices has hindered cross-study comparisons. This has led to a missed opportunity to more thoroughly assess diazotroph biogeography, diversity, and their potential contributions to the marine N cycle. To address these knowledge gaps a bioinformatic workflow was designed that standardizes the processing of nifH amplicon datasets originating from high-throughput sequencing (HTS). Multiple datasets are efficiently and consistently processed with a specialized DADA2 pipeline to identify amplicon sequence variants (ASVs). A series of customizable post-pipeline stages then detect and discard spurious nifH sequences and annotate the subsequent quality-filtered nifH ASVs using multiple reference databases and classification approaches. This newly developed workflow was used to reprocess nearly all publicly available nifH amplicon HTS datasets from marine studies, and to generate a comprehensive nifH ASV database containing 7909 ASVs aggregated from 21 studies that represent the diazotrophic populations in the global ocean. For each sample, the database includes physical and chemical metadata obtained from the Simons Collaborative Marine Atlas Project (CMAP). Here we demonstrate the utility of this database for revealing global biogeographical patterns of prominent diazotroph groups and highlight the influence of sea surface temperature. The workflow and nifH ASV database provide a robust framework for studying marine N2 fixation and diazotrophic diversity captured by nifH amplicon HTS. Future datasets that target understudied ocean regions can be added easily, and users can tune parameters and studies included for their specific focus. The workflow and database are available, respectively, in GitHub (https://github.com/jdmagasin/nifH-ASV-workflow) and Figshare (https://doi.org/10.6084/m9.figshare.23795943.v1).

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bioRxiv2024

varVAMP: automated pan-specific primer design for tiled full genome sequencing and qPCR of highly diverse viral pathogens.

Time- and cost-saving surveillance of viral pathogens is achieved by tiled sequencing in which a viral genome is amplified in overlapping PCR amplicons and qPCR. However, designing pan-specific primers for viral pathogens that have high genomic variability represents a major challenge. Here, we present a bioinformatics command-line tool, called varVAMP (variable virus amplicons). It relies on multiple sequence alignments of highly variable virus sequences and enables automatic pan-specific primer design for qPCR or tiled amplicon whole genome sequencing. The varVAMP software guarantees pan-specificity by two means: it designs primers in regions with minimal variability and introduces degenerate nucleotides into primer sequences to compensate for common sequence variations. We demonstrate varVAMPs utility by designing and evaluating novel pan-specific primer schemes suitable for sequencing the genomes of SARS-CoV-2, Hepatitis E virus, rat Hepatitis E virus, Hepatitis A virus, Borna-disease-virus-1, and Poliovirus. Moreover, we established highly sensitive and specific Poliovirus qPCR assays that could potentially simplify current Poliovirus surveillance. Importantly, wet-lab and bioinformatic techniques established for SARS-CoV-2 tiled amplicon sequencing were readily transferable to these new primer schemes and will allow sequencing laboratories to extend their established methodology to other human pathogens.

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bioRxiv2024

Compositional restrictions in the flanking regions give potential specificity and strength boost to binding in short linear motifs

Short linear motif (SLiM)-mediated protein-protein interactions play important roles in several biological processes where transient binding is needed. They usually reside in intrinsically disordered regions (IDRs), which makes them accessible for interaction. Although information about the possible necessity of the flanking regions surrounding the motifs is increasingly available, it is still unclear if there are any generic amino acid attributes that need to be functionally preserved in these segments. Here, we describe the currently known ligand-binding SLiMs and their flanking regions with biologically relevant residue features and analyse them based on their simplified characteristics. Our bioinformatics analysis reveals several important properties in the widely diverse motif environment that presumably need to be preserved for proper motif function, but remained hidden so far. Our results will facilitate the understanding of the evolution of SLiMs, while also hold potential for expanding and increasing the precision of current motif prediction methods. Author summaryProtein-protein interactions between short linear motifs and their binding domains play key roles in several molecular processes. Mutations in these binding sites have been linked to severe diseases, therefore, the interest in the motif research field has been dramatically increasing. Based on the accumulated knowledge, it became evident that not only the short motif sequences themselves, but their surrounding flanking regions also play crucial roles in motif structure and function. Since most of the motifs tend to be located within highly variable disordered protein regions, searching for functionally important physico-chemical properties in their proximity could facilitate novel discoveries in this field. Here we show that the investigation of the motif flanking regions based on different amino acid attributes can provide further information on motif function. Based on our bioinformatics approach we have found so far hidden features that are generally present within certain motif categories, thus could be used as additional information in motif searching methods as well.

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bioRxiv2024

GRanges: A Rust Library for Genomic Range Data

MotivationThe Rust programming language is a fast, memory-safe language that is increasingly used in computational genomics and bioinformatics software development. However, it can have a steep learning curve, which can make writing specialized, high performance bioinformatics software difficult. ResultsGRanges is a Rust library that provides an easy-to-use and expressive way to load genomic range data into memory, compute and process overlapping ranges, and summarize data in a tidy way. The GRanges library outperforms established tools like plyranges and bedtools. AvailabilityThe GRanges library is available at https://github.com/vsbuffalo/granges and https://crates.io/crates/granges.

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bioRxiv2024

b-move: faster bidirectional character extensions in a run-length compressed index

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.s r-index and Nishimoto and Tabeis move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient bidirectional character extensions in run-length compressed space. It achieves bidirectional character extensions up to 8 times faster than the br-index, closing the performance gap with FM-index-based alternatives, while maintaining the br-indexs favorable memory characteristics. For example, all available complete E. coli genomes on NCBIs RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. Thus, b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license. FundingLore Depuydt: PhD Fellowship FR (1117322N), Research Foundation - Flanders (FWO) Luca Renders: PhD Fellowship SB (1SE7822N), Research Foundation - Flanders (FWO) Travis Gagie: NSERC Discovery Grant RGPIN-07185-2020 to Travis Gagie and NIH grant R01HG011392 to Ben Langmead

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bioRxiv2024

SeqLengthPlot: An easy-to-use Python-based Tool for Visualizing and Retrieving Sequence Lengths from fasta files with a Tunable Splitting Point

MotivationAccurate sequence length profiling is essential in bioinformatics, particularly in genomics and proteomics. Existing tools like SeqKit and the Trinity toolkit, among others provide basic sequence statistics but often fall short in offering comprehensive analytics and plotting options. For instance, SeqKit is a very complete and fast tool for sequence analyses, that delivers useful metrics (e.g., number of sequences, average, minimum, maximum length), and can returns the range of sequence shorter or longer (one side, not both at once) on a given lengths. Similarly, Trinitys utility pearl-based scripts provide detailed contig length distributions (e.g., N50, median, and average lengths) but do not encompass the total number of sequences nor offer graphical representations of data. ResultsGiven that key sequence analysis tasks are distributed among separate tools, we introduce SeqLengthPlot: an easy-to-use Python-based script that fills existing gaps in bioinformatics tools on sequence length profiling, crucial. SeqLengthPlot generates comprehensive statistical summaries, filtering and automatic sequences retriving from the input FASTA (nucleotide and proteins) file into two distinct files based on a tunable, user-defined sequence length, as well as the plots or dynamic visualizations of the corresponding sequences. Availability and implementationThe detailed SeqLengthPlot pipeline is available on GitHub at https://github.com/danydguezperez/SeqLengthPlot, released under the GPL-3.0 license. Additional datasets used as sources or compiled as use cases are publicy available through the Mendeley Data repository: DATASET_Ss_SE.1: http://dx.doi.org/10.17632/pmxwfjyyvy.1, DATASET_Ss_SE.2: http://dx.doi.org/10.17632/3rtbr7c9s8.1, DATASET_Ss_SE.3: http://dx.doi.org/10.17632/wn5kbk5ryy.1, DATASET_Ss_SE.4: http://dx.doi.org/10.17632/sh79mdcm2c.1 and DATASET_Ss_SE.5: http://dx.doi.org/10.17632/zmvvff35dx.1.

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bioRxiv2024

Efficient De Novo Assembly and Recovery of Microbial Genomes from Complex Metagenomes Using a Reduced Set of k-mers

In recent years, the analysis of metagenomic data to recover unculturable microbes has revolutionized microbial genomics by rapidly expanding the reference genome catalog. Central to this, are the computational approaches of de novo assembly and genome binning that enable large-scale reference-independent recovery of microbial genomes from the metagenomic sequencing data. Despite the advancements in bioinformatics approaches to address the computational challenges inherent to these tasks, the limitation of computational resources continues to be a significant barrier to harvesting the full potential of these techniques. Consequently, there is a stressed need to devise strategies involving the fine-tuning of the employed parameters for the effective utilization of the available metagenomic tools. As most of the available metagenome assembly tools are based on the de Bruijn graph framework that relies on a parameter k, selecting an appropriate subset of k-mers has become a common approach in bioinformatics for efficient computations. In this study, we propose a reduced set of k-mers, optimized to strike a balance between computational efficiency and the quality of the high- and low-complexity metagenome assemblies. Utilizing this set of k-mers with MEGAHIT reduces the metagenome assembly time by half compared to the default set, thus greatly reducing the associated computational cost. In addition, it also brings the promise to improve large-scale genome binning studies that adopt this set in the future as we observed an increase in the total number of the recovered genomes as well as obtained higher proportions of high- and medium-quality genomes recovered from the reduced k-mers-based metagenome assemblies.

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bioRxiv2024

Application of a new data management system to the study ofthe gut microbiome of children who are small for theirgestational age

Microbiome studies aim to answer the following questions: which organisms are in the sample and what is their impact on the patient or the environment? To answer these questions, investigators have to perform comparative analyses on their classified sequences based on the collected metadata, such as treatment, condition of the patient, or the environment. The integrity of sequences, classifications, and metadata is paramount for the success of such studies. Still, the area of data management for the preliminary study results appears to be neglected. Here, we present the development of a central data management system with an accessible web interface for the study of the gut microbiome in children who are small for their gestational age. We have called this system MetagenomicsDB. The web interface allows users, regardless of their bioinformatics expertise, to operate the system. The interface contains links to external resources in order to facilitate literature search, statistical analyses, and assessments of the potential pathogenicity of taxa. Preliminary study results are automatically quality-controlled and subsequently imported into a relational database. After exploration and optional filtering by the user, data are exported in a format directly suitable for follow-up analyses. Compared to a more conventional approach of storing the data in plain files, the automated quality control and database storage offered by MetagenomicsDB provides an enhanced quality assurance of the produced study results. This is especially true in a collaborative setting. Also, the automation of the data transfer from the format of the input data to the format needed for downstream analyses makes basic statistical and bioinformatic analyses accessible. The web interface not only allows us to perform our internal analyses, but will also facilitate transparent sharing of the complete study results at publication time with reviewers and the general public. We demonstrated the viability of this approach by making a subset of our preliminary study results already publicly accessible: https://www.bioinformatics.uni-muenster.de/tools/metagenomicsDB In the context of our study, our system provided more flexibility to conduct study- specific analyses and to integrate specific external resources than existing and necessarily more generic solutions. Our system is not yet ready to be widely applicable out-of-the-box for any microbiome study. However, we expect that due to the modular concept, the tests, and the extensive description of the underlying rationale, large parts of our implementation can be adapted for future projects in a fraction of the time needed to develop a new data management system from scratch. Thus, we report our endeavors in order to motivate the application of data management systems at the scale of single studies in microbiome research.

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