연구 배경

연구 배경에는 독자들이 논문의 내용을 이해하는 데에 필요하다고 생각되는 배경적 정보를 기술합니다. 또한 왜 이 실험을 하게 됐는지를 밟힙니다.

연구 배경은 다음과 같은 질문들에 대한 답을 제시해야 합니다:

어떤 질문/문제에 대한 연구인가?

연구 배경을 기술하면서 인용을 할 때에는 다음의 사항에 주의해야 합니다:

  • • 균형성: 해당 주제에 대한 상반된 의견들이 존재할 경우, 양쪽의 의견을 골고루 인용했는가?
  • • 최신 연구: 연구 분야마다 차이가 있을 수 있지만, 가능하면 출판된 지 10년이 넘지 않은 연구들을 인용하는 것이 좋습니다.
  • • 관련 연구: 자신의 연구와 관련이 있는 연구들을 인용하는 것이 무엇보다 중요합니다. 인용한 논문들은 반드시 논문의 주제와 밀접하게 연관된 연구여야 합니다.

연구 배경 자체를 서평처럼 써서는 안 됩니다. 독자들이 원할 경우에 보다 자세한 내용을 찾아볼 수 있도록 서평을 인용하는 것으로 충분합니다.

연구 배경을 소개한 다음에는 문제점이나 의문점에 대해 기술하고 연구의 목적을 밝힙니다. 대개의 경우 연구를 진행하는 이유는 기존 연구들에서 밝혀지지 않은 점들을 밝혀내거나 풀리지 않은 의문에 대한 답을 제시하기 위한 것입니다. 예를 들어, 어떤 약이 특정 모집단을 대상으로는 그 약효가 입증되었지만 다른 성격을 띤 모집단을 대상으로는 아직 실험이 이루어지지 않았다면, 아직 약효가 입증되지 않은 두 번째 모집단을 대상으로 실험을 진행해 약효와 안정성을 검증하는 것이 연구의 목적이 될 수 있습니다.

연구 배경의 마지막 부분에 포함되어야 할 내용은, 연구의 목적에 대한 분명하고 정확한 기술입니다. 그리고 (아주 간략하게!) 연구의 진행 방법에 대한 설명을 덧붙입니다.

Example

Source

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002,3:RESEARCH0034. Publisher full text

Background

Gene-expression analysis is increasingly important in many fields of biological research. Understanding patterns of expressed genes is expected to provide insight into complex regulatory networks and will most probably lead to the identification of genes relevant to new biological processes, or implicated in disease. Two recently developed methods to measure transcript abundance have gained much popularity and are frequently applied. Microarrays allow the parallel analysis of thousands of genes in two differentially labeled RNA populations [1], while real-time RT-PCR provides the simultaneous measurement of gene expression in many different samples for a limited number of genes, and is especially suitable when only a small number of cells are available [2,3,4]. Both techniques have the advantage of speed, throughput and a high degree of potential automation compared to conventional quantification methods, such as northern-blot analysis, ribonuclease protection assay, or competitive RT-PCR. Nevertheless, these new approaches require the same kind of normalization as the traditional methods of mRNA quantification.

Several variables need to be controlled for in gene-expression analysis, such as the amount of starting material, enzymatic efficiencies, and differences between tissues or cells in overall transcriptional activity. Various strategies have been applied to normalize these variations. Under controlled conditions of reproducible extraction of good-quality RNA, the gene transcript number is ideally standardized to the number of cells, but accurate enumeration of cells is often precluded, for example when starting with solid tissue. Another frequently applied normalization scalar is the RNA mass quantity, especially in northern blot analysis. There are several arguments against the use of mass quantity. The quality of RNA and related efficiency of the enzymatic reactions are not taken into account. Moreover, in some instances it is impossible to quantify this parameter, for example, when only minimal amounts of RNA are available from microdissected tissues. Probably the strongest argument against the use of total RNA mass for normalization is the fact that it consists predominantly of rRNA molecules, and is not always representative of the mRNA fraction. This was recently evidenced by a significant imbalance between rRNA and mRNA content in approximately 7.5% of mammary adenocarcinomas [5]. Also, it has been reported that rRNA transcription is affected by biological factors and drugs [6,7,8]. Further drawbacks to the use of 18S or 28S rRNA molecules as standards are their absence in purified mRNA samples, and their high abundance compared to target mRNA transcripts. The latter makes it difficult to accurately subtract the baseline value in real-time RT-PCR data analysis.

Statement of the problem

To date, internal control genes are most frequently used to normalize the mRNA fraction. This internal control - often referred to as a housekeeping gene - should not vary in the tissues or cells under investigation, or in response to experimental treatment. However, many studies make use of these constitutively expressed control genes without proper validation of their presumed stability of expression. But the literature shows that housekeeping gene expression - although occasionally constant in a given cell type or experimental condition - can vary considerably (reviewed in [9,10,11,12]). With the increased sensitivity, reproducibility and large dynamic range of real-time RT-PCR methods, the requirements for a proper internal control gene have become increasingly stringent.

Purpose and what was done

In this study, we carried out an extensive evaluation of 10 commonly used housekeeping genes in 13 different human tissues, and outlined a procedure for calculating a normalization factor based on multiple control genes for more accurate and reliable normalization of gene-expression data. Furthermore, this normalization factor was validated in a comparative study with frequently applied microarray scaling factors using publicly available microarray data.

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