Biology subjects

Kartal, E.

Publications and source records attributed to Kartal, E..

6 recordsLinked to original sources

bioRxiv2025

Metalog: curated and harmonised contextual data for global metagenomics samples

Metagenomic sequencing enables the in-depth study of microbes and their functions in humans, animals and the environment. While sequencing data is deposited in public databases, the associated contextual data is often not complete and needs to be retrieved from primary publications. This lack of access to sample-level metadata like clinical data or in situ observations impedes cross-study comparisons and meta-analyses. We therefore created the Metalog database, a repository of manually curated metadata for metagenomics samples across the globe. It contains 73,082 samples from humans (including 58,506 of the gut microbiome), 10,703 animal samples, 5,146 ocean water samples, and 21,802 samples from other environmental habitats such as soil, sediment, or fresh water. Samples have been consistently annotated for a set of habitat-specific core features, such as demographics, disease status and medication for humans, host species and captivity status for animals, and filter sizes and salinity for marine samples. Additionally, all original metadata is provided in tabular form, simplifying focused studies e.g. into nutrient concentrations. Pre-computed taxonomic profiles facilitate rapid data exploration, while links to the SPIRE database enable genome-based analyses. The database is freely available for browsing and download at https://metalog.embl.de/.

microbiology↗

bioRxiv2025

A census of hidden and discoverable microbial diversity beyond genome-centric approaches

Cataloguing Earths biodiversity remains one of the most formidable challenges in biology, and the greatest diversity is expected to reside among the smallest organisms: microbes. Yet the ongoing census of microbial life is hampered by disparate sampling of Earths habitats, challenges in isolating uncultivated organisms, limited resolution in taxonomic marker gene amplicons, and incomplete recovery of metagenome-assembled genomes (MAGs). Here, we quantified discoverable bacterial and archaeal diversity in a comprehensive, curated cross-habitat dataset of 92,187 metagenomes. Clustering 502M sequences of 130 marker genes, we detected 705k bacterial and 27k archaeal species-level clades, the vast majority of which was hidden among unbinned contigs. At deeper taxonomic levels, we estimate that 10 archaeal and 145 bacterial novel phyla and around 80k novel genera are discoverable in current data. We identified soils and aquatic environments as novel lineage recovery hotspots, yet predict that discovery will remain in full swing across habitats as more data accrues. Finally, we show that prokaryotic diversity follows power laws, confirming century-old hypotheses on clade size patterns and suggesting that novel lineages arise within common (and fractal) evolutionary patterns, comparable to those among eukaryotic clades and viruses, along the full depth and breadth of the Tree of Life.

microbiology↗

bioRxiv2023

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are increasingly employed in microbiome studies to obtain a holistic view of molecular changes occurring within microbial communities exposed to different conditions. However, it is not always clear to what extent each omics data type contributes to our understanding of the community dynamics and whether they are concordant with each other. Here we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers, namely 16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics, and metabolomics. Using this controlled setting, we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions. Furthermore, different omics methods complement each other in their ability to capture functional changes in response to the drug perturbations. For example, while nearly all omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control and metabolomics revealed a decrease in polysaccharide uptake, likely caused by Bacteroidota depletion. Taken together, our study provides insights into how multi-omics datasets can be utilised to reveal complex molecular responses to external perturbations in microbial communities.

microbiology↗

bioRxiv2022

Cryogenic X-ray crystallographic studies of biomacromolecules at Turkish Light Source: Turkish DeLight

X-ray crystallography is a robust and powerful structural biology technique that provides high-resolution atomic structures of biomacromolecules. Scientists use this technique to unravel mechanistic and structural details of biological macromolecules (e.g. proteins, nucleic acids, protein complexes, protein-nucleic acid complexes, or large biological compartments). Since its inception, single-crystal cryo-crystallography has never been performed in Turkiye due to the lack of a single-crystal X-ray diffractometer. The X-ray diffraction facility recently established at the University of Health Sciences, Istanbul, Turkiye will enable Turkish and international researchers to easily perform high-resolution structural analysis of biomacromolecules from single crystals. Here, we describe the technical and practical outlook of a state-of-the-art home-source X-ray, using lysozyme as a model protein. The methods and practice described in this article can be applied to any biological sample for structural studies. Therefore, this article will be a valuable practical guide from sample preparation to data analysis.

biophysics↗

bioRxiv2021

Brain-inspired Weighted Normalization for CNN Image Classification

AO_SCPLOWBSTRACTC_SCPLOWWe studied a local normalization paradigm, namely weighted normalization, that better reflects the current understanding of the brain. Specifically, the normalization weight is trainable, and has a more realistic surround pool selection. Weighted normalization outperformed other normalizations in image classification tasks on Cifar10, Imagenet and a customized textured MNIST dataset. The superior performance is more prominent when the CNN is shallow. The good performance of weighted normalization may be related to its statistical effect of gaussianizing the responses.

neuroscience↗

bioRxiv2020

SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This revealed some biomarkers to be disease-specific, others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

bioinformatics↗