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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Sequences01:29

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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Arithmetic Sequences01:30

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An arithmetic sequence is a structured arrangement of numbers where each term is derived by adding a constant value, known as the common difference, to the previous term. This consistent pattern allows for the efficient computation of any term within the sequence as well as the cumulative sum of multiple terms. The formula for finding the nth term of an arithmetic sequence is:Here, aₙ represents the nth term of the sequence, a is the first term, d is the common difference, and n is the...
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Detection of Copy Number Alterations Using Single Cell Sequencing
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A Single-Cell Sequencing Guide for Immunologists.

Peter See1, Josephine Lum1, Jinmiao Chen1

  • 1Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore.

Frontiers in Immunology
|November 9, 2018
PubMed
Summary

Choosing the right single-cell RNA sequencing (scRNA-seq) platform is crucial for immunology research. This study compares four popular scRNA-seq platforms, aiding researchers in selecting the best method and integrating diverse datasets for cell population discovery.

Keywords:
10X genomics chromiumMARS-seqSMART-seqdendritic cellsfluidigm C1immunologysingle-cell RNA sequencing

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Area of Science:

  • Immunology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is increasingly utilized in immunology.
  • A wide array of scRNA-seq technologies presents challenges in platform selection for researchers.
  • Choosing the appropriate scRNA-seq protocol is critical for addressing specific biological questions.

Purpose of the Study:

  • To compare the advantages and limitations of four common scRNA-seq platforms.
  • To guide researchers in selecting suitable scRNA-seq platforms for diverse experimental applications.
  • To provide insights into integrating datasets from different scRNA-seq platforms and identifying novel cell populations.

Main Methods:

  • Comparative analysis of four widely used single-cell RNA sequencing (scRNA-seq) platforms.
  • Evaluation of platform suitability for various experimental immunology applications.
  • Exploration of bioinformatics strategies for dataset integration and unbiased cell population identification.

Main Results:

  • Detailed comparison of the strengths and weaknesses of four scRNA-seq platforms.
  • Assessment of the applicability of each platform for specific immunological research questions.
  • Methodologies for combining data from disparate scRNA-seq platforms are discussed.

Conclusions:

  • The study clarifies the suitability of different scRNA-seq platforms for specific research needs in immunology.
  • Guidance is provided for selecting optimal scRNA-seq protocols and integrating multi-platform datasets.
  • Bioinformatic approaches for discovering novel single-cell populations are highlighted.