user: Does Jukebox contain many original songs?

assistant: <think>
Jukebox is the eleventh studio album by Australian pop vocal group Human Nature released in October 2014. It contains covers from the 1950s and 1960s, with one original song, "End of Days"</think>
No, it contains covers from the 1950s and 1960s, with only one original song.

user: Given these paragraphs about Multiomics, what is single-cell multiomics?

assistant: <think>
Multiomics, multi-omics, integrative omics, "panomics" or "pan-omics" is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome (i.e., a meta-genome and/or meta-transcriptome, depending upon how it is sequenced); in other words, the use of multiple omics technologies to study life in a concerted way. By combining these "omes", scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association. The OmicTools service lists more than 99 softwares related to multiomic data analysis, as well as more than 99 databases on the topic.

Systems biology approaches are often based upon the use of panomic analysis data. The American Society of Clinical Oncology (ASCO) defines panomics as referring to "the interaction of all biological functions within a cell and with other body functions, combining data collected by targeted tests ... and global assays (such as genome sequencing) with other patient-specific information."

Single-cell multiomics
A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics. This approach gives us an unprecedent resolution to look at multilevel transitions in health and disease at the single cell level. An advantage in relation to bulk analysis is to mitigate confounding factors derived from cell to cell variation, allowing the uncovering of heterogeneous tissue architectures.

Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification or physical separation of RNA and genomic DNA. They allow insights that cannot be gathered solely from transcriptomic analysis, as RNA data do not contain non-coding genomic regions and information regarding copy-number variation, for example. An extension of this methodology is the integration of single-cell transcriptomes to single-cell methylomes, combining single-cell bisulfite sequencing to single cell RNA-Seq. Other techniques to query the epigenome, as single-cell ATAC-Seq and single-cell Hi-C also exist.

A different, but related, challenge is the integration of proteomic and transcriptomic data. One approach to perform such measurement is to physically separate single-cell lysates in two, processing half for RNA, and half for proteins. The protein content of lysates can be measured by proximity extension assays (PEA), for example, which use DNA-barcoded antibodies. A different approach uses a combination of heavy-metal RNA probes and protein antibodies to adapt mass cytometry for multiomic analysis.

Multiomics and machine learning
In parallel to the advances in highthroughput biology, machine learning applications to biomedical data analysis are flourishing. The integration of multi-omics data analysis and machine learning has led to the discovery of new biomarkers. For example, one of the methods of the mixOmics project implements a method based on sparse Partial Least Squares regression for selection of features (putative biomarkers).


https://en.wikipedia.org/wiki/Multiomics</think>
The analysis of multilevel single-cell data. This approach gives us an unprecedent resolution to look at multilevel transitions in health and disease at the single cell level.

user: What is Cabernet Sauvignon?

assistant: <think>
Cabernet Sauvignon (French: [kabɛʁnɛ soviɲɔ̃]) is one of the world's most widely recognized red wine grape varieties. It is grown in nearly every major wine producing country among a diverse spectrum of climates from Australia and British Columbia, Canada to Lebanon's Beqaa Valley. Cabernet Sauvignon became internationally recognized through its prominence in Bordeaux wines, where it is often blended with Merlot and Cabernet Franc. From France and Spain, the grape spread across Europe and to the New World where it found new homes in places like California's Santa Cruz Mountains, Paso Robles, Napa Valley, New Zealand's Hawke's Bay, South Africa's Stellenbosch region, Australia's Margaret River, McLaren Vale and Coonawarra regions, and Chile's Maipo Valley and Colchagua. For most of the 20th century, it was the world's most widely planted premium red wine grape until it was surpassed by Merlot in the 1990s. However, by 2015, Cabernet Sauvignon had once again become the most widely planted wine grape, with a total of 341,000 hectares (3,410 km2) under vine worldwide</think>
Cabernet Sauvignon (French: [kabɛʁnɛ soviɲɔ̃]) is one of the world's most widely recognized red wine grape varieties.