Source data files underlying figure 2-4 in our paper

Nature Biotechnology FAQ - ResearchHelpDesk - Nature Biotechnology is interested in the best research from across the field of Biotechnology; our broad scope ensures that work published reaches the widest possible audience. All editorial decisions are made by a team of full-time professional editors. Nature Biotechnology is a monthly journal covering the science and business of biotechnology. It publishes new concepts in technology/methodology of relevance to the biological, biomedical, agricultural and environmental sciences as well as covers the commercial, political, ethical, legal, and societal aspects of this research. The first function is fulfilled by the peer-reviewed research section, the second by the expository efforts in the front of the journal. We provide researchers with news about business; we provide the business community with news about research developments. The core areas in which we are actively seeking research papers include: molecular engineering of nucleic acids and proteins; molecular therapy (therapeutics genes, antisense, siRNAs, aptamers, DNAzymes, ribozymes, peptides, proteins); large-scale biology (genomics, functional genomics, proteomics, structural genomics, metabolomics, etc.); computational biology (algorithms and modeling), regenerative medicine (stem cells, tissue engineering, biomaterials); imaging technology; analytical biotechnology (sensors/detectors for analytes/macromolecules), applied immunology (antibody engineering, xenotransplantation, T-cell therapies); food and agricultural biotechnology; and environmental biotechnology. A comprehensive list of areas of interest is shown below. Strategies for controlling gene expression Strategies for manipulating gene structure Strategies for gene containment Technologies for analyzing gene function (e.g., arrays, SAGE) Technologies for analyzing gene structure/organization (e.g., molecular beacons) Chemogenomics or chemical genetics Pharmacogenomics/SNPs Computational analysis Technologies for analyzing/identifying protein structure/function (e.g., 2-D gels, mass spectrometry, yeast two-hybrid, SPR, NMR, arrays and chips) Structural genomics Computational analysis Technologies for analyzing/profiling metabolites (chromatography, mass spectrometry) Computational analysis Bioinformatics; algorithms; data deconvolution Modeling and systems biology: kinetics-based models and constraints-based models Rational approaches for proteins/antibodies/enzymes/drugs Molecular evolution Molecular breeding approaches Genetic manipulation of species of interest to modify or allow the production of a commercially or therapeutically relevant compound Computational analysis Mammalian cells Insect cells Bacteria Fungi Plant cells Targeting strategies Viral and nonviral vector strategies Reporter molecules Imaging approaches/technologies for visualizing whole animals, cells, or single molecules Computational analysis Gene therapy (targeting, expression, integration, immunogenicity) Antisense RNAi DNAzymes and ribozymes Nanomaterials for use in drug delivery or as therapeutics Nanomaterials for use in industrial biotechnology Nanosensors Nanosystems for imaging molecules and cells Antibody engineering T-cell therapies Therapies exploiting innate immunity (e.g. complement) Antigen delivery vectors and approaches Nucleic acid vaccines Computational analysis Stem cells Tissue engineering Therapeutic cloning (somatic cell nuclear transfer) Xenotransplantation Biomaterials Approaches for detecting biological molecules Use of biological systems in detecting analytes Approaches for multiplexing and increasing throughput Selection/screening strategies for gene/proteins/drugs Microfluidics Engineering materials for biological application Molecular imprinting Biomimetics Nanotechnology Crop improvement (resistance to stress, disease, pests) Nutraceuticals Forest biotechnology Plant vaccines Plants as bioreactors Gene-containment strategies Transgenic animals Knockouts Reproductive cloning Biopharmaceutical and enzyme production Transgene targeting and expression strategies Bioremediation Biomining Phytoremediation Monitoring

This dataset contains images from the Peneda-Gerês National Park, Northern Portugal. The images were collected from the Flickr and Wikiloc platforms considering a time period from 2003 to 2017. In respect to the General Data Protection Regulation 2016/679, social media data protected by users’ rights was not downloaded nor analysed. Public data that would potentially contain personal information from social media users was kept anonymous through the study. Data was retrieved through the use of the freely available Flickr’s Application Programming Interface (API), indicating a time window and a bounding box with a pair of coordinates (in our case: minimum latitude: 41.653104; maximum lat.: 42.083595; min. longitude: -8.426270; max. lon.: -7.754076) around Peneda-Gerês. This information was then saved as an excel file with the following attributes: user-id, date taken, latitude, longitude, picture uniform resource locator (url).

A first annotation, in the context of cultural ecosystem services (CES), was performed by dividing the photographs of the dataset into “Indoor” and “Outdoor” classes. Only the “Outdoor” pictures were included in this study, since CES are directly connected to nature and environment, which in turn are related to the outside/outdoor. The “Outdoor” images were further divided into two main classes, “Natural” and “Human”, depending on whether the image was dominated by natural or man-made elements. Lastly, a finer annotation for outdoor images was also provided, which encompasses the following six classes: “Species”, “Landscape”, “Nature”, “Human activities”, “Human structures” and “Posing”. “Species” pictures respectively pertained to close-up shots of animals or plants in the wild, translating CES pertaining to biodiversity appreciation. “Landscape” pictures show wide-open shots of nature in the wild, often with a visible horizon most often representing people’s enjoyment of landscape aesthetics. “Human activities” include pictures where people engage in by recreational activities, for instance related to sports such as ski or cycling. “Human structures” include those pictures where man-made structures dominate in the wild, e.g., historical monuments and churches, capturing situations of cultural heritage and spiritual enrichment. “Posing” refers to pictures with people looking at the camera, with recognizable faces, testifying social enjoyment and sense of identity. Finally, “Nature” pictures capture natural elements with no particular feature (such as species) but with an intermediate shot (differing from wide-open shots attributed to landscapes), expressing the appreciation of nature by people.

spatial gene expression of drosophila embryo from: https://www.nature.com/articles/s41592-022-01480-9 and originally https://www.nature.com/articles/s41586-019-1773-3

Nature Biotechnology Impact Factor 2024-2025 - ResearchHelpDesk - Nature Biotechnology is interested in the best research from across the field of Biotechnology; our broad scope ensures that work published reaches the widest possible audience. All editorial decisions are made by a team of full-time professional editors. Nature Biotechnology is a monthly journal covering the science and business of biotechnology. It publishes new concepts in technology/methodology of relevance to the biological, biomedical, agricultural and environmental sciences as well as covers the commercial, political, ethical, legal, and societal aspects of this research. The first function is fulfilled by the peer-reviewed research section, the second by the expository efforts in the front of the journal. We provide researchers with news about business; we provide the business community with news about research developments. The core areas in which we are actively seeking research papers include: molecular engineering of nucleic acids and proteins; molecular therapy (therapeutics genes, antisense, siRNAs, aptamers, DNAzymes, ribozymes, peptides, proteins); large-scale biology (genomics, functional genomics, proteomics, structural genomics, metabolomics, etc.); computational biology (algorithms and modeling), regenerative medicine (stem cells, tissue engineering, biomaterials); imaging technology; analytical biotechnology (sensors/detectors for analytes/macromolecules), applied immunology (antibody engineering, xenotransplantation, T-cell therapies); food and agricultural biotechnology; and environmental biotechnology. A comprehensive list of areas of interest is shown below. Strategies for controlling gene expression Strategies for manipulating gene structure Strategies for gene containment Technologies for analyzing gene function (e.g., arrays, SAGE) Technologies for analyzing gene structure/organization (e.g., molecular beacons) Chemogenomics or chemical genetics Pharmacogenomics/SNPs Computational analysis Technologies for analyzing/identifying protein structure/function (e.g., 2-D gels, mass spectrometry, yeast two-hybrid, SPR, NMR, arrays and chips) Structural genomics Computational analysis Technologies for analyzing/profiling metabolites (chromatography, mass spectrometry) Computational analysis Bioinformatics; algorithms; data deconvolution Modeling and systems biology: kinetics-based models and constraints-based models Rational approaches for proteins/antibodies/enzymes/drugs Molecular evolution Molecular breeding approaches Genetic manipulation of species of interest to modify or allow the production of a commercially or therapeutically relevant compound Computational analysis Mammalian cells Insect cells Bacteria Fungi Plant cells Targeting strategies Viral and nonviral vector strategies Reporter molecules Imaging approaches/technologies for visualizing whole animals, cells, or single molecules Computational analysis Gene therapy (targeting, expression, integration, immunogenicity) Antisense RNAi DNAzymes and ribozymes Nanomaterials for use in drug delivery or as therapeutics Nanomaterials for use in industrial biotechnology Nanosensors Nanosystems for imaging molecules and cells Antibody engineering T-cell therapies Therapies exploiting innate immunity (e.g. complement) Antigen delivery vectors and approaches Nucleic acid vaccines Computational analysis Stem cells Tissue engineering Therapeutic cloning (somatic cell nuclear transfer) Xenotransplantation Biomaterials Approaches for detecting biological molecules Use of biological systems in detecting analytes Approaches for multiplexing and increasing throughput Selection/screening strategies for gene/proteins/drugs Microfluidics Engineering materials for biological application Molecular imprinting Biomimetics Nanotechnology Crop improvement (resistance to stress, disease, pests) Nutraceuticals Forest biotechnology Plant vaccines Plants as bioreactors Gene-containment strategies Transgenic animals Knockouts Reproductive cloning Biopharmaceutical and enzyme production Transgene targeting and expression strategies Bioremediation Biomining Phytoremediation Monitoring

The promoter region is located near the transcription start sites, which regulate the transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, recognition of the promoter region is an important area of interest in the field of bioinformatics. Over the past years, many new promoter prediction programs (PPPs) have emerged. PPPs aim to identify promoter regions in a genome using computational methods. Promoter prediction is a supervised learning problem that consists of three main steps to extract features: 1) CpG islands 2) Structural features 3) Content features

Supplementary Data for Fukushima and Pollock (2020, Nature Communications 11:4459) https://www.nature.com/articles/s41467-020-18090-8

Contains preprocessed single-cell data for sketching single-cell samples. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy.

• The HIV Vaccine Trials Network (HVTN) Flow Cytometry dataset (hvtn_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZZZV (http://flowrepository.org/id/FR-FCM-ZZZV).
• The preeclampsia CyTOF dataset (preeclampsia_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZYRQ (http://flowrepository.org/id/FR-FCM-ZYRQ).
• The NK-Cell CyTOF dataset (nk_cell_preprocessed.h5ad) from Ref. (https://www.nature.com/articles/ncomms14825) was originally downloaded from (https://github.com/eiriniar/CellCnn).
• The multiple sclerosis (MS) single-cell RNA sequencing dataset of peripheral blood samples (ms_preprocessed.h5ad) from Ref. (https://www.nature.com/articles/s41467-019-14118-w) was originally accessed from the Gene Expression Omnibus using the accession code GSE138266 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266).

Current studies in genetics very often refer to notions from information science. The concept of genetic information is still disputed because it attributes semantic traits to what seem to be regular biochemical entities. Some researchers maintain that the use of information in biology is just metaphorical and maybe even misleading. In this paper, we offer an analysis of the nature and characteristics of the use of information in proteins, protein families, and their sequences. It is argued that the foundation of the metaphorical view is relatively weak given the current findings in bioinformatics, and it is shown that the present understanding of genetics fits well into the context of the modern philosophy of information. Here, we propose an extension of Floridi’s conceptual model of information to include genetic information better. In addition, we discuss how to understand the qualitative aspects of genetic information and how to measure its quantitative aspects and present a joint s...

A subset of the Haber dataset that only contains the regions. It explicitly does not include the large cells.

https://www.nature.com/articles/nature24489

List of 8383 taxon names with the number of public records in BOLD. The column "type" indicates if the original name was found at BOLD or if an alternative name from TPL (TPLsynonym or TPLaccepted) was found at BOLD.

Distances measured between distinctive parts of amino acid residues surrounding the ligand.

Nature Reviews Molecular Cell Biology CiteScore 2024-2025 - ResearchHelpDesk - Molecular cell biology is a marriage of two distinct, yet complementary, disciplines. In its traditional sense, the term 'molecular biology' refers to the study of the macromolecules essential to life — nucleic acids and proteins. The field of cell biology is a natural extension of this, integrating what we know at the molecular level into an understanding of processes and interactions at the cellular level. Only by combining both fields can we paint a broad picture of essential biological processes such as how cells divide, grow, communicate and die. Nature Reviews Molecular Cell Biology features Reviews, Perspective articles and Comments on a broad range of topics, and highlights important primary papers and technological progress. Reviews, Perspectives and Comments are commissioned by the editorial team. The scope of the journal includes: Cell signalling (signalling networks, ion channels, gap junctions) Membrane dynamics (membrane organization, endocytosis, exocytosis, organelle biogenesis) Cell adhesion (adhesion molecules, extracellular matrix) Cytoskeletal dynamics (cell motility, molecular motors, actin, microtubules, intermediate filaments) Developmental and stem cell biology Cell growth and division (cell cycle, cytokinesis, cancer) Cell death (apoptosis, necrosis, autophagy, ageing) Cellular microbiology (host–pathogen interactions) Plant cell biology Gene expression (transcription, splicing, RNA stability, translation, RNA interference, circadian rhythms) Nucleic-acid metabolism (DNA repair, recombination and replication, RNA biogenesis) Chromosome biology and nuclear architecture (chromatin, chromosome structure, transposons) Nuclear transport (import and export of molecules to and from the nucleus) Protein structure and metabolism (structure-function relationships, quality control, post-translational modifications, folding, translocation, degradation) Bioenergetics (respiration, photosynthesis, organelle biochemistry) Technology and techniques (imaging, proteomics, systems biology, bioinformatics)

Essential proteins are vital for the life and reproduction of organisms and play a crucial role in maintaining cellular functions. If the destruction of a certain protein would lead to lethality or infertility, it can be classified as essential to an organism, meaning the organism cannot survive without it. Compared to non-essential proteins, essential proteins are more likely to persist in biological evolution. For instance, essential proteins make excellent targets for the development of new potential drugs and vaccines aimed at treating and preventing diseases.

With the advent of high-throughput technologies, such as the yeast two-hybrid system and mass spectrometry analysis, various protein-protein interaction (PPI) data become available, facilitating the study of essential proteins at the network level.

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
   You can unpack by simply doing
   tar -zxf Data.tar.gz -C ./
   This will create a folder called Data with the uncompressed material inside
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

BioEmergences-wt4: Zebrafish wild-type embryo during Gastrulation. Raw data 3D+time: nuclei + membrane channels (VTK). Digital cell lineage (computed with BioEmergences workflow https://www.nature.com/articles/ncomms9674).

The house mouse (Mus musculus), commensal to humans, has spread globally via human activities, leading to secondary contact between genetically divergent subspecies. This pattern of genetic admixture can provide insights into the selective forces at play in this well-studied model organism. Our analysis of 163 house mouse genomes, mainly from East Asia, revealed substantial admixture between the subspecies castaneus and musculus, particularly in Japan and southern China. We revealed, despite the admixture, that all Y chromosomes in the East Asian samples belonged to the musculus-type haplogroup, potentially explained by genomic conflict under sex ratio distortion due to varying copy numbers of ampliconic genes on sex chromosomes. We also investigated the influence of natural selection on the post-hybridization of the subspecies castaneus and musculus in Japan. Even though the genetic background of most Japanese samples closely resembles the subspecies musculus, certain genomic regions overrepresented the castaneus-like genetic components, particularly in immune-related genes. Furthermore, a large genomic block containing a vomeronasal/olfactory receptor gene cluster predominantly harbored castaneus-type haplotypes in the Japanese samples, highlighting the possible role of olfaction-based recognition in shaping hybrid genomes.

Datasets used in CERES publication.Meyers, Bryan, et al. Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nature Genetics. 2017.External data:CCLE_copynumber_2013-12-03.seg.txtCCLE copy number data downloaded from https://data.broadinstitute.org/ccle_legacy_data/dna_copy_number/CCLE_copynumber_2013-12-03.seg.txtCCLE_RNAseq_081117.rpkm.gctCCLE gene expression data downloaded from https://data.broadinstitute.org/ccle/CCLE_RNAseq_081117.rpkm.gctccle2maf_081117.txtCCLE mutation data downloaded from https://data.broadinstitute.org/ccle/ccle2maf_081117.txtAchilles_v2.20.2_GeneSolutions.gctRNAi DEMETER gene dependencies downloaded from https://portals.broadinstitute.org/achilles/datasets/12/download/Achilles_v2.20.2_GeneSolutions.gctCCDS.current.txtCCDS gene annotations downloaded from ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/archive/15/CCDS.current.txtc2.all.v6.0.symbols.gmt MSigDB genesets downloaded from http://software.broadinstitute.org/gsea/msigdb/download_file.jsp?filePath=/resources/msigdb/6.0/c2.all.v6.0.symbols.gmtavana_rs2.txt and gecko_rs2.txtDoench-Root scores for sgRNAs provided by authors of Doench, et al. Nat. Biotechnol. 2016.

wang_pool_normalized.counts.txtDownloaded from supplement of Wang, et al. Cell. 2017.

All exotic and invasive taxa detected using DAISIE

The Paired Omics Data Platform is a community-based initiative standardizing links between genomic and metabolomics data in a computer readable format to further the field of natural products discovery. The goals are to link molecules to their producers, find large scale genome-metabolome associations, use genomic data to assist in structural elucidation of molecules, and provide a centralized database for paired datasets. This dataset contains the projects in http://pairedomicsdata.bioinformatics.nl/.

The JSON documents adhere to the http://pairedomicsdata.bioinformatics.nl/schema.json JSON schema.