Download All Splice Samples At Once
Nevada Biernat
How is there not a "select all" when trying to download sounds off the app onto your HD?? Is there a way to do this? I "downloaded" like 300 samples from the webbrowser and now they are greyed out on the app and I have to click the cloud next to each individual sample to get them actually on my HD. Gotta be a better way to do it...
May 2023 Edit: It was confirmed to me by splice that the feature is no longer available. You can only sync your entire library at once which is asinine. If you are like me and only download a few select hits from different drum kits that means you will have like a single hat inside like 2-5 other folders depending on how the artist arranges the pack. For that reason I started just pasting them in my own organized sample library years ago. The lack of being able to download select samples sort of breaks this and tries to force you to keep everything in Splices folder.
I'm not sure if this has been asked before but is there any way drag multiple samples from the app to my DAW's sample folders at once? I'm pretty picky when it comes to sample organization and I haven't been able to come across an answer for this online yet. I did read that you need to hold down shift while selecting but that doesn't work.
With Splice Bridge, you can easily try out sounds in the context of your musical compositions before you buy them. By hearing samples in the way you intend to use them, you can choose the most suitable sounds, save time, save credits, and save your creative energy.
Splice is best known as a sample library. There are literally millions of samples in the database, all of which are royalty-free and can be used by anyone, anytime. Well-known musicians, producers, and sound designers often contribute sample packs to the ever-growing Splice library. Any samples you purchase via Sounds credits are yours to keep, even if you decide to cancel your plan.
SOUNDS by Native Instruments is comparable to Splice. It, too, offers a massive collection of high-quality, royalty-free samples for music production. Unlike Splice, Sounds.com focuses strictly on its sample library, excluding additional tools like plugins and tutorials.
A combined study is a custom study comprised of samples from multiple studies. The combined study featureenables you to combine samples from multiple studies to form a bigger study. This cohort of samples can then be queried or explored just like a traditional study, and can be returned to at a later date or shared with a collaborator.
A combined or merged study is a custom study comprised of samples from multiple studies. In the homepage of cbioportal, studies can be selected using the checkbox located on the left of the study. Once the studies are selected, they can be combined and explored using the "Explore Selected Studies" button. Alternatively, after the studies are selected, you can run queries on the combined study using the "Query by Gene" button.
A subset or sub cohort of a study can be created by specifying individual patients or samples. After a study is selected, user can click on the "Custom selection" button to create a new filter by specifying the sampleID or patientID that the user is interested to explore. Another way is to filter a set of patients using the charts on the study view and then view the IDs of the patients and samples that were selected or unselected based on the current filter.
A virtual study is a custom study comprised of samples from one or more existing studies. The virtual study feature allows you to define a custom cohort of samples that fit your specific genomic or clinical criteria of interest. These samples can be a subset of the data available in an existing study, or result from the combination of multiple existing studies. This cohort of samples can then be queried or explored just like a traditional study, and can be returned to at a later date or shared with a collaborator. For more information and examples, see our tutorial on virtual studies.
Group Comparison is a suite of analysis features which allows a user to compare clinical or genomic features of user-defined groups of samples. These groups can be defined based on any clinical or genomic features. For an overview, see our tutorial on group comparison.
The cBioPortal generally assumes that samples or patients that have the same ID are actually the same. This is important for cross-cancer queries, where each sample should only be counted once. If a sample is part of multiple cancer cohorts, its alterations are only counted once in the Mutations tab (it will be listed multiple times in the table, but is only counted once in the lollipop plot). However, other tabs (including OncoPrint and Cancer Types Summary) will count the sample twice - for this reason, we advise against querying multiple studies that contain the same samples (e.g., TCGA PanCancer Atlas and TCGA Firehose Legacy).
The Firehose Legacy dataset (formerly Provisional datasets) for each TCGA cancer type contains all data available from the Broad Firehose. The publication datasets reflect the data that were used for each of the publications. The samples in a published dataset are usually a subset of the firehose legacy dataset, since manuscripts were often written before TCGA completed their goal of sequencing 500 tumors.
Note that these calls are putative. We consider the deep deletions and amplifications as biologically relevant for individual genes by default. Note that these calls are usually not manually reviewed, and due to differences in purity and ploidy between samples, there may be false positives and false negatives.
For TCGA studies, the table in allthresholded.bygenes.txt (which is the part of the GISTIC output that is used to determine the copy-number status of each gene in each sample in cBioPortal) is obtained by applying both low- and high-level thresholds to to the gene copy levels of all the samples. The entries with value +/- 2 exceed the high-level thresholds for amplifications/deep deletions, and those with +/- 1 exceed the low-level thresholds but not the high-level thresholds. The low-level thresholds are just the 'ampthresh' and 'delthresh' noise threshold input values to GISTIC (typically 0.1 or 0.3) and are the same for every thresholds.
For mRNA and microRNA expression data, we typically compute the relative expression of an individual gene in a tumor sample to the gene's expression distribution in a reference population of samples. That reference population is all profiled samples (by default for mRNA), or normal samples (when specified), or all samples that are diploid for the gene in question (discontinued). The returned value indicates the number of standard deviations away from the mean of expression in the reference population (Z-score). The normalization method is described here. Please note that the expression results by querying a gene with the default setting (z-score threshold of 2) oftentimes are not meaningful. Since the z-scores were usually calculated compared to other tumor samples, high or low expression does not necessarily mean that the gene is expressed irregularly in tumors. The data is useful for correlation analysis, for example, pick a threshold based on overall expression (using Plots tab) and compare survival data between expression high and low groups.
We have RNASeqV2 mRNA expression data for normal samples of 16 TCGA PanCan Atlas Cohorts. The data was curated from GDC, and can be downloaded from our Datahub or Data Set page. This data is not directly queriable in portal; they are only used as reference data for calculating the "relavtive to normal expression z-score" profile. Example: ERBB2 expression z-scores relative to normal expression.
The first step is to define your sample set. There are two slightly different approaches you can take to defining your sample set, depending on whether you are selecting based on a positive criteria (samples with TP53 mutations) or a negative criteria (samples without a KRAS mutation).
Specifically in the case of TCGA samples with two mutations in the same gene, you can also obtain access to the aligned sequencing reads from the GDC and check if the mutations are in cis or in trans (if the mutations are close enough to each other).
cBioPortal supports Onco Query Language (OQL) which can be used to query over/under expression of a gene. When writing a query, select an mRNA expression profile. By default, samples with expression z-scores >2 or 2. Review for the OQL specification page or tutorial slides for more specifics and examples.
To compare outcomes in patients with high vs low expression of a gene (excluding those patients with intermediate levels of expression), we will follow a 2 step process that builds on the approach described above in How can I query/explore a select subset of samples?, utilizing OQL to first identify and then stratify that cases of interest.
Some studies include data from one or more targeted sequencing platforms which do not include all genes. For samples sequenced on these smaller panels, cBioPortal will indicate that a particular gene was not included on the sequencing panel used for that sample. Alteration frequency calculations for each gene also take this information into account. Hover over a sample in OncoPrint to see the gene panel name, and click on that gene panel name to view a list of the genes included on that panel.
The calculations on the Mutual Exclusivity tab are performed using all samples included in the query. A sample is defined as altered or unaltered for each gene based on the OQL utilized in the query - by default, this will be non-synonymous mutations, fusions, amplifications and deep deletions.
Primary immunodeficiency (PID) refers to a group of heterogeneous genetic disorders with a weakened immune system. Mendelian susceptibility to mycobacterial disease (MSMD) is a subset of PID in which patients exhibit defects in intrinsic and innate immunity. It is a rare congenital disorder characterized by severe and recurrent infections caused by weakly virulent mycobacteria or other environmental mycobacteria. Any delay in definitive diagnosis poses a major concern due to the confounding nature of infections and immune deficiencies. Here, we report the clinical, immunological, and genetic characteristics of two siblings (infants) with recurrent infections. There was a history of death of two other siblings in the family after BCG vaccination. Whole exome sequencing of the two affected surviving infants along with their consanguineous parents identified a novel, homozygous single nucleotide splice acceptor site variant in intron 2 of the interferon gamma receptor 2 (IFNGR2) gene. Sanger sequencing of DNA obtained from blood and fibroblasts confirmed the variant. The patients underwent bone marrow transplantation from their father as a donor. RT-PCR and Sanger sequencing of the cDNA of patients from blood samples after transplantation showed the expression of both wild type and mutant transcript expression of IFNGR2. To assess partial or complete expression of IFNGR2 mutant transcripts, fibroblasts were cultured from skin biopsies. RT-PCR and Sanger sequencing of cDNA obtained from patient fibroblasts revealed complete expression of mutant allele and acquisition of a cryptic splice acceptor site in exon 3 that resulted in deletion of 9 nucleotides in exon 3. This led to an in-frame deletion of three amino acids p.(Thr70-Ser72) located in a fibronectin type III (FN3) domain in the extracellular region of IFNGR2. This illustrates individualized medicine enabled by next generation sequencing as identification of this mutation helped in the clinical diagnosis of MSMD in the infants as well as in choosing the most appropriate therapeutic option.
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