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"Tichifunga ndima idzi pamusoro peMultiomics, chii chinonzi single-cell multiomics? Multiomics, multi-omics, integrative omics, "" panomics"" kana "" pan-omics "" inzira yekuongorora biological iyo iyo data seti yakawanda "" omes"", senge genome, proteome, transcriptome, epigenome, metabolome, uye microbiome (kureva, meta-genome uye/kana meta-transcriptome, zvichienderana nekuti inotevedzana sei); nemamwe mazwi, kushandiswa kweakawanda omics. Nekubatanidza aya "omes", masayendisiti anogona kuongorora data rakakura rebhayoloji kuti awane hukama hutsva pakati pezvinhu zvipenyu, kunongedza akakodzera biomarker uye kuvaka akadzama mamaki echirwere uye physiology. Mukuita izvi, multiomics. inobatanidza data yakasiyana-siyana yeomics kuti iwane hukama hunoenderana ne geno-pheno-envirotype kana hukama.Sevhisi yeOmicTools inonyora anopfuura makumi mapfumbamwe nepfumbamwe esoftware ane chekuita nekuongororwa kwedata remultiomic, pamwe nemadatabase anodarika makumi mapfumbamwe nemapfumbamwe emusoro wenyaya.

Maitiro eSystem biology anowanzo akavakirwa pakushandiswa kwepanomic yekuongorora data. IAmerican Society of Clinical Oncology (ASCO) inotsanangura panomics ichitaura nezve "" kupindirana kwezvinhu zvese zvipenyu mukati mesero uye nemamwe mabasa emuviri, kubatanidza data inounganidzwa neyakanangwa bvunzo ... uye pasi rose (senge genome sequencing) rumwe ruzivo rwemurwere."

Single-cell multiomics
Bazi remunda we multiomics ndeyekuongororwa kweakawanda-sero-sero data, inonzi single-cell multiomics. Iyi nzira inotipa sarudzo isati yamboonekwa yekutarisa kuchinjika kwakawanda muhutano uye chirwere padanho resero rimwe chete. Chakanakira chine chekuita nekuwongorora kuwanda ndechekudzikamisa zvinokatyamadza zvinotorwa kubva kusero kuenda kune masero akasiyana, zvichibvumira kufumurwa kwehterogeneous tissue architecture.

Nzira dzekuenzanisa-sero genomic uye transcriptomic ongororo inogona kuve yakavakirwa pakusimudzwa panguva imwe chete kana kupatsanurwa kwemuviri kweRNA uye genomic DNA. Vanobvumira maonero asingaunganidzwe chete kubva kuongororo yezvinyorwa, sezvo RNA data isina matunhu asina kukodha genomic uye ruzivo nezve kukopa-nhamba kusiyanisa, semuenzaniso. Kuwedzerwa kweiyi nzira ndeyekubatanidzwa kwe-single-cell transcriptomes kune imwechete-sero methylomes, kusanganisa single-sero bisulfite sequencing kune imwechete sero RNA-Seq. Mamwe matekiniki ekubvunza epigenome, seyo imwe-sero ATAC-Seq uye imwechete-sero Hi-C iripo.

Imwe yakasiyana, asi ine hukama, dambudziko nderekubatanidzwa kweproteomic uye transcriptomic data. Imwe nzira yekuita kuyerwa kwakadai ndeyekupatsanura muviri-sero lysates muviri, kugadzira hafu yeRNA, uye hafu yemapuroteni. Protein yemukati yemalysates inogona kuyerwa neproximity extension assays (PEA), semuenzaniso, inoshandisa DNA-barcoded antibodies. Nzira yakasiyana inoshandisa musanganiswa weheavy-metal RNA probes neprotein antibodies kuti igadzirise mass cytometry yekuongorora kwemultiomic.

Multiomics uye kudzidza muchina
Mukufambirana nekufambira mberi kweiyo highthroughput biology, mashandisirwo ekudzidza muchina kune biomedical data kuongororwa ari kubudirira. Iko kubatanidzwa kweakawanda-omics data kuongororwa uye kudzidza muchina kwakatungamira mukuwanikwa kwetsva biomarkers. Semuenzaniso, imwe yenzira dzemusanganiswaOmics purojekiti inoshandisa nzira yakavakirwa pane sparse Partial Least Squares regression yekusarudzwa kwezvinhu (putative biomarkers).


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Ongororo yeakawanda nhanho-sero data. Iyi nzira inotipa sarudzo isati yamboonekwa yekutarisa kuchinjika kwakawanda muhutano uye chirwere padanho resero rimwe chete.