Estimating differential expression from multiple indicators
Kuupäev
2015-03-16
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Üksikud erandid välja arvatud, on genoom meie keha igas rakus täpselt ühesugune. Ometi koosneme paljudest erineva kuju, füsioloogia ja ülesandega rakkudest. Kirjeldatud variatsiooni aluseks on genoomis paiknevate geenide koordineeritud aktiivsus. Teisisõnu, igas rakus on oma programm, mille järgi reguleeritakse geenide aktiivsust, mis omakorda määrab raku morfoloogia ja funktsiooni.
Geeni aktiivsust ehk geeni ekspressiooni on võimalik mõõta erinevate tehnoloogiate abil. Üheks levinumaks vahendiks on juba kaks aastakümmet tagasi välja arendatud DNA-mikrokiipide tehnoloogia, mis võimaldab korraga mõõta kümnete tuhandete geenide ekspressiooni. Mikrokiipide oluliseks eeliseks teiste alternatiivsete tehnoloogiate ees on aastate jooksul välja töötatud standardid andmete hoiustamiseks ja analüüsimiseks. Lisaks on mikrokiibiga mõõdetud geeni ekspressiooni andmete hulk avalikes andmebaasides suurem kui teiste tehnoloogiate puhul. Seega on mikrokiipide abil tehtud eksperimentide suur hulk heaks platvormiks olemasolevate andmete kasutamiseks uute hüpoteeside püstitamisel ja lisandväärtust loovate bioinformaatiliste tööriistade arendamisel. Samas eeldab see paindlikke lahendusi, mis aitavad võimalikult efektiivselt andmetes peituvat potentsiaali ära kasutada.
Käesolev töö pakuv välja uudse diferentsiaalse ekspressiooni analüüsi meetodoloogia (Differential Expression from Multiple Indicators, DEMI) mikrokiibi abil tehtud eksperimendist saadud andmete analüüsimiseks. Antud metodoloogia loomise motivatsiooniks oli kasutada kõrgtihedusega mikrokiibi tehnoloogia korduvmõõtmistes (samaaegselt mõõdetakse ühte geeni mitmest erinevast kohast) leiduvat informatsiooni senisest suuremal määral, et suurendada meetodi tundlikust. Võrdlemisel teiste meetoditega leidsime, et DEMI sooritus on stabiilselt hea, sõltumata mikrokiibi platvormist ja replikaatide arvust. Biloogiliste replikaatide vähesus on tihti probleemiks näiteks kliiniliste proovide puhul, mille hankimine on keeruline või pilootkatsete tegemisel piiratud ressursside tingimuses. Lisaks võimaldab DEMI analüüsida erineva ülesehitusega geeni ekspressiooni eksperimente. Sealhulgas eksperimente, kus on vaja analüüsida ajast või doosist sõltuvat geeni ekspressioonidünaamikat olukorras, kus replikaadid puuduvad, või tuvastamaks genoomseid piirkondi, kus on toimunud kõrvutiasetsevate geenide aktiivsuse ühesuunaline ekspressiooni muutus (näiteks vähis esinevate epigeneetiliste mõjutuste toimel). Kokkuvõttes pakub DEMI paindliku lahenduse olemasolevate ja uute andmete analüüsimiseks ning võimaldab teadlastel küsida andmetelt veel esitamata küsimusi uuest vaatenurgast.
With very few exceptions the genome is identical in every cell of our body. Nevertheless, our body consists of many different cells with unique shape, physiology and behavior. This variation in cell types is achieved by coordinated activity of every gene in the genome. In other words, every cell has it’s own program that regulates the activity of the genes, which subsequently determines the cell’s morphology and functionality. There are several technologies that can be used for measuring the activity of a gene i.e. gene expression. One of the best-known and widely used technologies is DNA-microarray developed more than two decades ago. The advantage of microarrays is that they can measure gene expression of tens of thousands of genes simultaneously. Additionally, good standards for data housing and data analysis have been established and currently the amount of microarray data in public warehouses from experiments measuring gene expression is unmatched. Therefore the reuse of available data to find answers to alternative hypothesis and the development of added-value bioinformatics tools requires flexible solutions that best exploit the potential hidden in the data. This work focused on developing a new framework for differential expression analysis of microarray data, termed Differential Expression from Multiple Indicators (DEMI). Our motivation was to utilize the information stored in the repeated measurements on microarrays (a gene’s expression is measured from multiple locations) to increase the sensitivity of the analysis. In comparison to other well-established methods, DEMI demonstrated a good and stable performance regardless of the microarray platform and sample size. This is especially important when samples are hard to obtain, like in clinical trials, or due to limitation in the resource, which is often the case in pilot studies. Furthermore, DEMI can handle experiments with non-conventional design, like time- or dose-dependent differential expression analysis with no replicates and identify genomic regions with unidirectional changes in gene expression levels of neighboring genes (e.g. a decrease in gene expression levels of neighboring genes can result due to large-scale epigenetic changes caused by a cancer). All in all, DEMI provides a flexible solution for differential expression analysis and is able to answer new questions from already published data.
With very few exceptions the genome is identical in every cell of our body. Nevertheless, our body consists of many different cells with unique shape, physiology and behavior. This variation in cell types is achieved by coordinated activity of every gene in the genome. In other words, every cell has it’s own program that regulates the activity of the genes, which subsequently determines the cell’s morphology and functionality. There are several technologies that can be used for measuring the activity of a gene i.e. gene expression. One of the best-known and widely used technologies is DNA-microarray developed more than two decades ago. The advantage of microarrays is that they can measure gene expression of tens of thousands of genes simultaneously. Additionally, good standards for data housing and data analysis have been established and currently the amount of microarray data in public warehouses from experiments measuring gene expression is unmatched. Therefore the reuse of available data to find answers to alternative hypothesis and the development of added-value bioinformatics tools requires flexible solutions that best exploit the potential hidden in the data. This work focused on developing a new framework for differential expression analysis of microarray data, termed Differential Expression from Multiple Indicators (DEMI). Our motivation was to utilize the information stored in the repeated measurements on microarrays (a gene’s expression is measured from multiple locations) to increase the sensitivity of the analysis. In comparison to other well-established methods, DEMI demonstrated a good and stable performance regardless of the microarray platform and sample size. This is especially important when samples are hard to obtain, like in clinical trials, or due to limitation in the resource, which is often the case in pilot studies. Furthermore, DEMI can handle experiments with non-conventional design, like time- or dose-dependent differential expression analysis with no replicates and identify genomic regions with unidirectional changes in gene expression levels of neighboring genes (e.g. a decrease in gene expression levels of neighboring genes can result due to large-scale epigenetic changes caused by a cancer). All in all, DEMI provides a flexible solution for differential expression analysis and is able to answer new questions from already published data.
Kirjeldus
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Märksõnad
geeniekspressioon, mikrokiibid, analüüs, uurimismeetodid, gene expression, DNA chips, analysis, inquiry methods