Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
165
datasets available to search
ShareScore release 0.7.1
Dataset results
165 results for “Mus musculus”
Genome indexes for Mus musculus (mm39)
<p><strong>BUILDING HISAT2 INDEXES IN CSC</strong><br> Here is the case for house mouse genome (mm39). The genome indexing step requires big memory and it might not be possible to carry out it on a laptop. Genome indexes for Mus musculus (mm39) were created using HISAT2 v2.2.1 on CSC (IT Center for Science), thanks to CSC-Puhti. </p> <p><strong>1. Create conda environment folder file to install the required packages, install and add the bin directory to the path.</strong><br> mkdir STRTN-env<br> conda-containerize new --prefix STRTN-env STRTN-env.yml<br> export PATH="<install_dir>/STRTN-env/bin:$PATH"</p> <p><strong>2. Load the required module.</strong><br> module load tykky<br> export PATH="<install_dir>/STRTN-env/bin:$PATH"<br> module load r-env<br> if test -f ~/.Renviron; then<br> sed -i '/TMPDIR/d' ~/.Renviron<br> fi<br> echo "TMPDIR=${WorkingDir_PATH}" >> ~/.Renviron<br> <br> <strong>3. Obtain the genome sequences of reference and ERCC spike-ins.</strong> <strong><em>You may add the ribosomal DNA repetitive unit for human (U13369) and mouse (BK000964).</em></strong><br> wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/bigZips/mm39.fa.gz<br> unpigz -c mm39.fa.gz | ruby -ne '$ok = $_ !~ /^>chrUn_/ if $_ =~ /^>/; puts $_ if $ok' > mouse_reference.fasta<br> wget https://tsapps.nist.gov/srmext/certificates/documents/SRM2374_putative_T7_products_NoPolyA_v2.FASTA<br> cat SRM2374_putative_T7_products_NoPolyA_v2.FASTA >> mouse_reference.fasta</p> <p><strong>4. Extract splice sites and exons from a GTF file.<em> Here we used wgEncodeGencodeBasicVM30 as the annotation file. You may additionally perform `hisat2_extract_snps_haplotypes_UCSC.py` to extract SNPs and haplotypes from a dbSNP file for human and mouse.</em></strong><br> wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/database/wgEncodeGencodeBasicVM30.txt.gz<br> unpigz -c wgEncodeGencodeBasicVM30.txt.gz | hisat2_extract_splice_sites.py - | grep -v ^chrUn > splice_sites.txt<br> unpigz -c wgEncodeGencodeBasicVM30.txt.gz | hisat2_extract_exons.py - | grep -v ^chrUn > exons.txt<br> <br> <strong>5. Build the HISAT2 index<em>. This outputs a set of files with suffixes. Here, `mouse_reference.1.ht2`, `mouse_reference.2.ht2`, ..., `mouse_reference.8.ht2` are generated.<br>In this case, `mouse_reference` is the basename used for `-i, --index`.</em></strong><br> hisat2-build mouse_reference.fasta --ss splice_sites.txt --exon exons.txt mouse_index/mouse_reference</p> <p><strong>6. Create the sequence dictionary for the reference and Spike-in sequences.<em> This is required for the Picard</em></strong> MergeBamAlignment program. Note that the original FASTA file (`mouse_reference.fasta` here) is also required.<br> picard CreateSequenceDictionary R=mouse_reference.fasta O=mouse_reference.dict<br> <br> <strong>7. Put the genome indexes, genome fasta file, sequence dictionary to same folder.</strong><br> mv mouse_reference.dict mouse_reference<br> mv mouse_reference.fasta mouse_reference</p>
Fig. 5 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 5. The hyperbolic regression of coefficient of variation (CV) on mean (X). The regression of the joint exponential grow curve: r = 0.86 (proportion of variance accounted for: 0.73).
Fig 2 in Sexual Size Dimorphism In Free-Living Populations Of Mus Musculus: Are Male House Mice Bigger?
Fig 2. Variation in SSD during the first five weeks of postnatal development in five mice populations. SSD is expressed as Lowich-Gibbons ratios of mean body weight (see under Material and Methods)
Fig. 1 in Sexual Size Dimorphism In Free-Living Populations Of Mus Musculus: Are Male House Mice Bigger?
Fig. 1. Map of the studied localities: 1 = Czech Republic, 2 = The Balkans, 3 = Iran, 4 = Jordan, 5 = hybrids. See Material and Methods for coordinates of the localities
Fig. 3 in Peroral Echinococcus multilocularis egg inoculation in Myodes glareolus, Mesocricetus auratus and Mus musculus (CD-1 IGS and C57BL/6j)
Fig. 3. Number of metacestodes of varying sizes in individual species at 6 wpi (M. glareolus at 8 wpi) after receiving 100 viable E. multilocularis eggs. A <1 mm, B 1 - Ý2 mm, C> 2 - Ý3 mm, D> 3 - Ý4 mm, E 4 - Ý5 mm, F> 5 mm. Data from current study and (Woolsey et al., 2015a; Woolsey et al., 2015b).
Fig. 2 in Peroral Echinococcus multilocularis egg inoculation in Myodes glareolus, Mesocricetus auratus and Mus musculus (CD-1 IGS and C57BL/6j)
Fig. 2. Mean establishment of E. multilocularis oncospheres in the different rodent intermediate hosts after receiving 100 viable eggs at 6 wpi (M. glareolus at 8 wpi). Data from current study and (Woolsey et al., 2015a; Woolsey et al., 2015b).
MinION 1D² Reads From Mus musculus GL261 Cell Lines
<p>Called FASTQ and raw FAST5 MinION cDNA reads (1D²) from a murine GL261 neuroblastoma cell line, cultured at the Malaghan Institute of Medical Research, sequenced on a R9.5 flow cell in August 2017 using the LSK309 1D² kit for ligating ONT adapters to cDNA generated using strand-switching primers.</p> <p>The called reads for the entire sequencing run are available:</p> <ul> <li>called_reads_1Dsq_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- called reads from both/all runs (1D²-corrected fastq files only).</li> <li>called_reads_uncorrected_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- uncorrected reads from both/all runs.</li> <li>metadata_called_reads_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- metadata associated with all called sequences (e.g. sequencing_summary.txt)</li> </ul> <p>This dataset only includes a subset of the total reads as raw signal / FAST5 files:</p> <ul> <li>Actb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse beta-actin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000029580;r=5:142903234-142903654;t=ENSMUST00000100497">ENSMUST00000100497.10</a>].</li> <li>Ubb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse ubiquitin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000019505;r=11:62551171-62553213;t=ENSMUST00000019649">ENSMUST00000019649.3</a>].</li> </ul>
Figure 1. A in Evolutionary History of the Subgenus Mus in Eurasia with Special Emphasis on the House Mouse Mus musculus
Figure 1. A sketch of the evolutionary patterns of lineage differentiation among species in the genus Mus based on molecular phylogenetic analysis of nuclear gene sequences (Suzuki et al., 2004; Shimada et al., 2010). The tree shows the four subgenera of the genus Mus and the four species groups (SGs) of the subgenus Mus: M. musculus, M. booduga, M. lepidoides, and M. caroli (previously termed as M. cervicolor SG), representing four geographic regions of the Palaearctic region, Indian subcontinent, Myanmar, and Southeast Asia, respectively. The taxon previously regarded as "M. cervicolor" in Thailand is here referred to as "M. sp.", due to uncertainty regarding the taxonomic status of the sampled specimens (see main text). The estimated divergence times for the subgenera and species groups are approximately 5 and 2.5 million years ago, respectively (Shimada et al., 2010). Specific habitat transitions from grasslands to forests and arid areas are marked for the species lineages of M. cookii and M. lepidoides. Predicted dispersal events between geographic regions are indicated with dotted arrows.
Figures 2–4 in Evolutionary History of the Subgenus Mus in Eurasia with Special Emphasis on the House Mouse Mus musculus
Figures 2–4. Assessment of population genetic structure using concatenated sequences (4302 bp) of seven nuclear genes. (2) Positions of the analysed regions (open triangles) in seven genes on murine chromosome 8 (Nunome et al., 2010; Kodama et al., 2013). (3) Neighbour- Net network based on concatenated sequences from 98 Mus musculus, showing haplogroups representing the subspecies groups Mus musculus domesticus (DOM), Mus musculus castaneus (CAS), and Mus musculus musculus (MUS) as well as recombinant haplotypes (Re) (Kodama et al., 2013). In the network, the level of diversity of CAS is markedly higher than those of DOM and MUS, yielding five distinct phylogroups A–E. Scale bar indicates genetic divergence. (4) Approximate geographic ranges of the five subclusters of CAS. Localities where samples used in this analysis were collected are marked with open and filled circles, representing the mitochondrial haplogroup CAS-1 and all other types, respectively (Kodama et al., 2013). The phylogroups A–E of CAS showed rough geographical distributions and one of them, phylogroup D, comprised the haplotypes recovered from a large geographical area of Southeast Asia, south China, and Indonesia and can be characterized as the lineage dispersed with prehistoric human movement (arrow; Kodama et al., 2015). Note that subcluster D (arrow in Fig. 3) shows a broad distribution range in Southeast Asia and the southern part of East Asia. In the Neighbor-Net network, this subcluster exhibits limited divergence among haplotypes.
Data from: Strong effects of lab-to-field environmental transitions on the bacterial intestinal microbiota of Mus musculus are modulated by Trichuris muris infection
<p>Studies of controlled lab animals and natural populations represent two insightful extremes of microbiota research. We bridged these two approaches by transferring lab-bred female C57BL/6 mice from a conventional mouse facility to an acclimation room and then to an outdoor enclosure, to investigate how the gut microbiota changes with environment. Mice residing under constant conditions served as controls. Using 16S rRNA sequencing of fecal samples, we found that the shift in temperature and humidity, as well as exposure to a natural environment, increased microbiota diversity and altered community composition. Community composition in mice exposed to high temperatures and humidity diverged as much from the microbiota of mice housed outdoors as from the microbiota of control mice. Additionally, infection with the nematode <i>Trichuris muris</i> modulated how the microbiota responded to environmental transitions: The dynamics of several families were buffered by the nematodes, while invasion rates of two taxa acquired outdoors were magnified. These findings suggest that gut bacterial communities respond dynamically and simultaneously to changes within the host's body (e.g., the presence of nematodes) and to changes in the wider environment of the host.</p>
Comprehensive Context-specific Genome-scale Metabolic Models for Mus Musculus
<p>Comprehensive Context-specific Genome-scale Metabolic Models for Mus Musculus. The data consists of 28 models for the combination 2 mouse strains (WT and Ob/Ob), 2 diets (WT and HFD) and 7 tissues (Aorta, Heart, Liver, Skeletal Muscle, Hippocampus, Hypothalamus and Epididymal fat).</p>
Data from: Density matters: How population dynamics of house mice (Mus musculus) inform the epidemiology of Leptospira
<p>Rodents are maintenance hosts of numerous pathogens, and both their density and the pathogen prevalence determine the risk they pose to other animals or humans. However, density is often overlooked. We investigated a capture-mark-recapture-sampling strategy to study introduced mice (<em>Mus musculus</em>) and <em>Leptospira</em> as a model and demonstrate the advantages of a combined approach. We estimated population density and <em>Leptospira</em> prevalence in mice in a replicated longitudinal survey conducted between 2016 and 2018. Capture-mark-recapture sessions were undertaken at two sites in Spring and Autumn and blood and kidney samples were collected at the end of each session. Mouse density and areas of activity were estimated using spatially explicit capture-recapture (SECR) models and both were compared between <em>Leptospira</em> positive and negative mice. <em>Leptospira </em>exposure and shedding status were estimated using Microscopic Agglutination Test, and a combination of culture and <em>lipL32</em> PCR on kidneys. <em>Leptospira </em>prevalence was higher in spring (83% to 86%) than in autumn (31% to 37%) and mouse densities simultaneously varied from 3.6 to 55.9/ha. However, despite these variations in prevalence and density, the density of infected animals remained relatively constant over time (3 to 8/ha). Shedding or being seropositive was also associated with the activity of mice. Shedding or seropositive mice had a larger activity area, and seropositive mice were trapped on average one day earlier than seronegative mice. </p> <p><em>Synthesis and applications</em>. Our results show how understanding the population dynamics of pathogen-carrying rodents is critical in epidemiology. The wider movement patterns and easier encounters of positive mice highlight the possibility of biases in classical prevalence surveys and have implications for disease transmission within and between species. Importantly, and quite counter-intuitively, <em>Leptospira</em> prevalence was negatively associated with mouse density, resulting in a constant density of shedders that contradicts the conventional view of higher exposure risk at high rodent density. More broadly, such hybrid sampling designs can improve animal and disease control policies and better inform modelling studies by providing more parameter estimates than classical prevalence surveys.</p>
Fig. 7 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 7. Bivariate plot of MW and B with the discrimination equation and line.
Fig. 2 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 2. Map of Hungary showing the collection regions. 1–5: geographic regions (see Table 1)
Fig. 4 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 4. Bivariate plot of individual scores on PC1 and PC2.
The genetics of immune and infection phenotypes in wild mice, Mus musculus domesticus
<p><span>Wild animals are under constant threat from a wide range of micro- and macroparasites in their environment. Animals make immune responses against parasites, and these are important in affecting the dynamics of parasite populations. Individual animals vary in their anti-parasite immune responses. Genetic polymorphism of immune-related loci contributes to inter-individual differences in immune responses, but most of what we know in this regard comes from studies of humans or laboratory animals; there are very few such studies of wild animals naturally infected with parasites. Here we have investigated the effect of Single Nucleotide Polymorphisms (SNPs) in immune-related loci (the MHC, and loci coding for cytokines and Toll-like receptors) on a wide range of immune and infection phenotypes in UK wild house mice, <em>Mus musculus domesticus</em>. We found strong associations between SNPs in various MHC and cytokine-coding loci on both immune measures (antibody concentration and cytokine production) and on infection phenotypes (infection with mites, worms and viruses). Our study provides a comprehensive view of how polymorphism of immune-related loci affects immune and infection phenotypes in naturally infected wild rodent populations.</span></p>
An invasive appetite: Combining molecular and stable isotope analyses to reveal the diet of introduced house mice (Mus musculus) on a small, subtropical island
<p>House mice (<em>Mus musculus</em>) pose a conservation threat on islands, where they adversely affect native species' distributions, densities, and persistence. On Sand Island of Kuaihelani, mice recently began to depredate nesting adult mōlī (Laysan Albatross, <em>Phoebastria immutabilis</em>). Efforts are underway to eradicate mice from Sand Island, but knowledge of mouse diet is needed to predict ecosystem response and recovery following mouse removal. We used next-generation sequencing to identify what mice eat on Sand Island, followed by stable isotope analysis to estimate the proportions contributed by taxa to mouse diet. We collected paired fecal and hair samples from 318 mice between April 2018 to May 2019; mice were trapped approximately every eight weeks among four distinct habitat types to provide insight into temporal and spatial variation. Sand Island's mice mainly consume arthropods, with nearly equal (but substantially smaller) contributions of C<sub>3 </sub>plants, C<sub>4</sub> plants, and mōlī. Although seabird tissue is a small portion of mouse diet, mice consume many detrital-feeding arthropods in and around seabird carcasses, such as isopods, flesh flies, ants, and cockroaches. Additionally, most arthropods and plants eaten by mice are non-native. Mouse diet composition differs among habitat types but changes minimally throughout the year, indicating that mice are not necessarily limited by food source availability or accessibility. Eradication of house mice may benefit seabirds on Sand Island, but it is unclear how arthropod and plant communities may respond and change. Non-native and invasive arthropods and plants previously consumed (and possibly suppressed) by mice may be released post-eradication, which could prevent recovery of native taxa. Comprehensive knowledge of target species' diet is a critical component of eradication planning. Dietary information should be used both to identify and to monitor which taxa may respond most strongly to invasive species removal and to assess if proactive, pre-eradication management activities are warranted.</p>
Data from: Strong effects of lab-to-field environmental transitions on the bacterial intestinal microbiota of Mus musculus are modulated by Trichuris muris infection
Open the record for dataset details and reuse information.
The genetics of immune and infection phenotypes in wild mice, Mus musculus domesticus
Open the record for dataset details and reuse information.
An invasive appetite: Combining molecular and stable isotope analyses to reveal the diet of introduced house mice (Mus musculus) on a small, subtropical island
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.