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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.
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Withdrawal StatementThe authors have withdrawn this manuscript owing to its merge with BIORXIV/2023/540959. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The merged preprint can be found at doi.org/10.1101/2023.05.16.540959
Withdrawal StatementThe authors have withdrawn their manuscript because they have discovered errors in the alignment of parts of the data. Miscommunication with the sequencing center led to a misunderstanding of the pooling design and incorrect merging of sequencing read files from different individuals into single bam files. Thus bam alignments for each individual sample of four out of 12 batches are based on a majority of reads from that individual but also on reads from one or two other individuals. Individuals in eight other batches were not affected by this. However, for this paper, the entire data was jointly genotyped to produce vcf files, and the joint genotyping may thus indirectly affect samples of other batches. Revising the paper by teasing apart the affected and non-affected parts of the data is not an option, and they, therefore, withdraw the preprint. They are reanalyzing the dataset. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
Although long read sequencing has enabled obtaining high-quality and complete prokaryotic genomes from metagenomes, many challenges still remain to completely decompose a metagenome into its constituent genomes. These challenges include obtaining enough biomass, high-molecular weight DNA extraction, determining the appropriate depth of sequencing, and bioinformatics challenges to separate closely related genomes. This study focuses on decomposing an estuarine water metagenome from USGS Station 36 in the South San Francisco Bay into its constituent genomes and counting the number of organisms present. To achieve this, we developed a new bead-based DNA extraction method, a novel bin refinement method, and sequenced the sample with 150 Gbases of nanopore sequencing. With our results, we were able to estimate that there are [~]500 bacteria and archaeal species in our sample, obtain 68 high-quality bins (>90% complete, <5% contamination, [≤]5 contigs, no contigs shorter than 100 Kbases, and all ribosomal and necessary tRNA genes). Since we pre-filtered the sample at 11m and then collected directly on to a 0.1m filter, we also obtained many contigs of picoeukaryotes, environmental DNA of larger eukaryotes such as mammals, complete mitochondrial and chloroplast genomes, and detected [~]40,000 viral populations. This deep analysis of the taxonomy of the sample down to the strain and individual contig level allowed us to find that among picoeukaryotes, prokaryotes, and viruses there are likely only a few strains that comprise most of the species abundances. These results also indicate that to truly decompose a metagenome into its constituent genomes, we likely need 1Tbase of sequencing. If you are reading this preprint, know that this is the paper we wanted to write, but it will likely be shortened for submission to a journal.
The authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2022/497555. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: https://doi.org/10.1101/2022.06.24.497555
Withdrawal StatementbioRxiv has withdrawn this preprint following a formal investigation by the University of New Mexico Office of Research Integrity and Compliance.
Previous research indicates that a climatic signal is present in mitochondrial diversity, but absent in other genetic diversities (e.g., X-chromosomal) and cranial diversity. Such research included analysis which adjusted diversity for distance from Africa (i.e., to account for global expansion), and saw whether adjusted diversity is associated with climate. The indication of a climatic signal may be affected by the choice of African location from which distance is measured. To bypass this potential effect of location, some analyses in the present research only featured populations which are located outside of Africa. The present research (Studies 1 and 2) used various diversities which were sourced from previous research. Autosomal, X-chromosomal, mitochondrial, and cranial diversities were adjusted for distance from Africa. This adjustment was not used regarding Y-chromosomal diversity; a preprint suggested that Y-chromosomal diversity does not signify expansion from Africa. In Study 1, adjusted X-chromosomal diversity increased with minimum temperature. Conscious of replicability, Study 2 examined whether a climatic signal is present in X-chromosomal diversity when an alternative dataset is used; a signal was not detected. Other adjusted diversities, including (surprisingly) mitochondrial, were not found to be associated with minimum temperature. Data from one dataset, but not another, was supportive of a relationship between sex-biased migration and climate--it seems unclear if this relationship would underlie a climatic signal in X-chromosomal diversity. Therefore, it is uncertain i) if X-chromosomal diversity is associated with climate, and, ii) why there might be an association. Further replication research on this topic is called for.
The field of cancer research has greatly benefited from the wealth of new knowledge provided by research articles and preprints on platforms like Biorxiv. This study investigates the role of scientific figures and their accompanying captions in enhancing our comprehension of cancer. Leveraging the capabilities of Multimodal Large Language Models (MLLMs), we conduct a comprehensive analysis of both visual and linguistic data in biomedical literature. Our work introduces VLIB, a substantial scientific figure-caption dataset generated from cancer biology papers on Biorxiv. After thorough preprocessing, which includes figure-caption pair extraction, sub-figure identification, and text normalization, VLIB comprises over 500,000 figures from more than 70,000 papers, each accompanied by relevant captions. We fine-tune baseline MLLMs using our VLIB dataset for downstream vision-language tasks, such as image captioning and visual question answering (VQA), to assess their performance. Our experimental results underscore the vital role played by scientific figures, including molecular structures, histological images, and data visualizations, in conjunction with their captions, in facilitating knowledge translation through MLLMs. Specifically, we achieved a ROUGE score of 0.66 for VQA and 0.68 for image captioning, as well as a BLEU score of 0.72 for VQA and 0.70 for image captioning. Furthermore, our investigation highlights the potential of MLLMs to bridge the gap between artificial intelligence and domain experts in the field of cancer biology.
MolecularWebXR is a new website for education, science communication and scientific peer discussion in chemistry and biology, based on modern web-based Virtual Reality (VR) and Augmented Reality (AR). With no installs as it is all web-served, MolecularWebXR enables multiple users to simultaneously explore, communicate and discuss concepts about chemistry and biology in immersive 3D environments, by manipulating and passing around objects with their bare hands and pointing at different elements with natural hand gestures. User may either be present in the same real space or distributed around the world, in the latter case talking naturally with each other thanks to built-in audio features. Although MolecularWebXR is most immersive when running in the web browsers of high-end AR/VR headsets, its WebXR core also allows participation by users with consumer devices such as smartphones, possibly inserted into cardboard goggles for deeper immersivity, or even in computers and tablets. MolecularWebXR comes with preset VR rooms that cover topics from general, inorganic and organic chemistry, biophysics and structural biology, and general biology; besides, new content can be added at will through moleculARwebs PDB2AR tool or by contacting the lead authors. We verified MolecularWebXRs ease of use and versatility by people aged 12-80 years old in entirely virtual sessions or in mixed real-virtual sessions at various science outreach events, in courses at the bachelor, masters and early doctoral levels, in scientific collaborations, and in conference lectures. MolecularWebXR is available for free use without registration at https://molecularwebxr.org, and a blog post version of this preprint with embedded videos is available at https://go.epfl.ch/molecularwebxr-blog-post.
Withdrawal StatementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2021/430776. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/2021.02.11.430776
Withdrawal StatementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2018/469114. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/469114
Withdrawal statementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2023/570712. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/2023.12.07.570712.
Withdrawal statementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2022/507166. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/2022.09.08.507166
In a recent preprint, Park, Metzger, and Thornton reanalyze 20 empirical protein sequence-function landscapes using a "reference-free analysis" (RFA) method they recently developed. They argue that these empirical landscapes are simpler and less epistatic than earlier work suggested, and attribute the difference to limitations of the methods used in the original analyses of these landscapes, which they claim are more sensitive to measurement noise, missing data, and other artifacts. Here, we show that these claims are incorrect. Instead, we find that the RFA method introduced by Park et al. is exactly equivalent to the reference-based least-squares methods used in the original analysis of many of these empirical landscapes (and also equivalent to a Hadamard-based approach they implement). Because the reanalyzed and original landscapes are in fact identical, the different conclusions drawn by Park et al. instead reflect different interpretations of the parameters describing the inferred landscapes; we argue that these do not support the conclusion that epistasis plays only a small role in protein sequence-function landscapes.
Withdrawal StatementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2021/471949. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/2021.12.10.471949.
Withdrawal statementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2024/592122. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: https://doi.org/10.1101/2024.05.01.592122.
Open science promotes the accessibility of scientific research and data, emphasising transparency, reproducibility, and collaboration. This study assesses the openness and FAIRness (Findable, Accessible, Interoperable, and Reusable) of data-sharing practices within the biosciences at the University of Edinburgh from 2014 to 2023. We analysed 555 research papers across biotechnology, regenerative medicine, infectious diseases, and non-communicable diseases. Our scoring system evaluated data completeness, reusability, accessibility, and licensing, finding a progressive shift towards better data-sharing practices. The fraction of publications that share all relevant data increased significantly, from 7% in 2014 to 45% in 2023. Data involving genomic sequences were shared more frequently than image data or data on human subjects or samples. The presence of data availability statement (DAS) or preprint sharing correlated with more and better data sharing, particularly in terms of completeness. We discuss local and systemic factors underlying the current and future Open data sharing. Evaluating the automated ODDPub (Open Data Detection in Publications) tool on this manually-scored dataset demonstrated high specificity in identifying cases where no data was shared. ODDPub sensitivity improved with better documentation in the DAS. This positive trend highlights improvements in data-sharing, advocating for continued advances and addressing challenges with data types and documentation.
The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionise drug discovery by extracting evidence from scientific literature for drug target identification and validation. This novel framework combines Named Entity Recognition (NER) for identifying gene/protein (target), disease, organism, and chemical/drug within scientific texts, and entity normalisation to map these entities to databases like Ensembl, Experimental Factor Ontology (EFO), and ChEMBL. Continuously operational, it has processed over 39 million abstracts and 4.5 million full-text articles and preprints to date, identifying more than 48.5 million unique associations that significantly help accelerate the drug discovery process and scientific research (> 29.9m distinct target-disease, 11.8m distinct target-drug and 8.3m distinct disease-drug relationships). The results are made accessible through the Open Targets Platform (https://platform.opentargets.org/) as well as Europe PMC website (SciLite web app) and annotations API (https://europepmc.org/annotationsapi).
Withdrawal StatementThe authors have withdrawn this manuscript because during the peer review process in the journal, it was discovered that one of the processed proteomic data groups (the chemical LTP group) was mistakenly processed from the raw data for the norepinephrine group, resulting in an apparent complete overlap between the two conditions. For a correct analysis, please refer to the currently published peer-reviewed publication through the following DOI: https://doi.org/10.1021/acs.jproteome.4c00180 Therefore, the authors do not wish this preprint to be cited as reference for the project. If you have any questions, please contact the corresponding author.