使用基因集合富集分析的途徑分析 (GSEA) 工具

基因集富集分析的許多方法之一 基因表達分析 描述文件數據,是描述在 工人在Broad研究院.

通過觀察,學習的基本概念提示 單個基因 最重要的兩個國家或表型表達水平之間的差異 在機械性的認識不足. 代替, 它採取一個更有意義 設置的基因 分享一些 生物鏈接, 問的問題 - 整套任何統計顯示 顯著富集 在這些差異表達的基因,?

基因組 可以選擇, 先驗, 的數量的原因,例如. 受到超過已知的基因集- 或下一個微RNA的表達, 或者一組基於染色體位置選擇, 或基因,這些基因的分子功能, 細胞成分和 / 或生物過程已分配使用的受控詞表 基因本體論.

到GSEA方法的優點之一是,它是可能把您的 完整的數據集, 不只是這些謄本,一個任意選擇的差異表達閾值. 我相信,很多人讀這將思考 - “怎麼會這樣確定使用完整的數據集? 一般情況下我只考慮基因 >2 (或其他喜愛的價值)-倍差異表達的原因。“的方法是有效的,是不利於基因表達水平低或存在較大差異複製的主要指標使用GSEA, '富集得分“ (ES).

GSEA的工作原理是第一 排行 每個基因的表達值 信號噪聲 比 - 計算代表每個表型的樣品的平均值之間的差異,和縮放它們的總和的標準偏差. 這意味著,有很高的基因表達水平有較大的差異,在不同的國家和生物之間的變化不大複製.

接下來的步驟是,在ES, GSEA所產生的主要統計, 計算每個基因組 - 中的GSEA手冊, 它記載了出色的軟件, 它指出:

“所有的基因的信號噪音比第一排名, 那麼,ES的計算方法是“走”下來的基因排序名單 增加正在運行的總和 統計當一個基因的基因組中,並 減少 它時,它是不. “ 大小 的增量取決於 相關 基因與 . ES是從零走在列表中遇到的最大偏差. 一 積極 ES表示基因組富集 頂部 位列榜單; 一 ES表示基因組富集 底部 位列榜單。“

ES值 基於基因組大小,然後一個 虛假的發現率 計算, 得到的估計的誤報的概率. GSEA使用一個非常寬鬆的默認值 25%, 這是適合於產生假設與相對大量的生物學重複.

工作的科學家提供的數據 非人類的 樣本仍然可以使用GSEA, 但需要提防 - 在 基因符號 使用GSEA“翻譯“從他們的人力現金等價物即. 他們的芯片上表示的利息從你種的基因使用的標識符轉換成符號 人類的同源基因, 然後在分析中使用. Subramanian和他的同事們 聲稱 這種轉換具有很少或 沒有影響 GSEA的效用; 它已成功地用於在多個非人類物種, 但當然,這必須牢記的詳細調查結果時,.

對於一個優秀的, 深入, 審查通路工具, 參考:

Khatri, P., 希洛塔, M., & 巴特, 一. Ĵ. (2012). 十年的途徑分析: 目前的做法和重大挑戰. PLoS計算生物學, 8(2), e1002375. 二:10.1371/journal.pcbi.1002375

另一個很好的來源,途徑分析建議, 特別是對那些熟悉的R統計軟件包 這裡.

延伸閱讀

SUBRAMANIAN一個, 塔馬約P, Mootha VK, 慕克吉小號, 艾伯特BL, 吉列MA, Paulovich一個, 波默羅伊SL, 戈盧布TR, 蘭德ES, Mesirov J​​P (2005) 基因集富集分析: 以知識為基礎的方法來解釋全基因組表達譜. PROC NATL科學院學報U S A 102:15545-15550

謝興, 呂君昌, Kulbokas EJ, 戈盧布TR, Mootha V, 琳達巴德托博士K表, 蘭德ES, Kellis中號 (2005) 系統發現人的發起人和監管圖案 3[總理] 通過比較幾種哺乳動物的非編碼區. 性質 434:338-345

Posted in 路徑分析 | 1 Response

編輯學術科普讀物的樂趣

Image courtesy of ningmilo / FreeDigitalPhotos.net

Or: “A beginner’s guide to herding cats”.

Consider this scenario: you are an academic scientist, in a busy research institute and your boss is invited to edit a book, but declines due to pressure of work; then suggests that it would look good on your CV. You agree, it would look good on your CV, so you commit yourself to editing your first multi-author academic science book.

So why is that a problem?

Getting authors on board

You want the best people to write the chapters. You Google some big-name experts and invite them to contribute a chapter to your book. They almost all decline, or fail to reply to your email. But, somewhat to your amazement, one agrees. 然而, this paragon of science then never, ever replies to any future contacts. So, you lower your sights and aim for good scientists, but not Nobel Prize winners. 最後, you get enough authors together to write the chapters around the topic that the publishers have given you – phew!

Getting authors to agree a deadline

Assuming it’s not unreasonable, everyone is usually relaxed about the deadline set. 然而, the real challenge is:

Getting them to meet the deadline

  1. This should be easy, right? Scientists are grown-up, professional people. Aren’t they? Well, sort of. In reality, academics typically over-commit themselves, doing not only research and teaching, but also writing grant funding applications, papers, reviews, book chapters, etc, 等等. After all, the scientific mission statement is “publish OR be damned.”
  2. As the deadlines go past – “wooshh”, like passing cars, half your authors have submitted their chapters, the rest not. Now another sticky moment arrives – these are meant to be cutting edge reviews. State-of-the-Art. But this delay now means that the ‘good’ authors work is rapidly reaching its sell-by date. You may have to go crawling back to them to ask for updates. Which they are usually not too unhappy about, but you hate the loss of face.
  3. One more thing that I forgot to mention; as the editor, you have to READ these chapters. Worse still, you are expected to produce cogent critiques – what the author needs to add, remove, expand or contract. Even if the topic is on the fringe of your main expertise.

What happens if authors go AWOL?

What do you do when one of your authors decides that they are NOT going to write their chapter? Not simply procrastinate, fail to meet deadlines, but stop all communication. Disappear off the map. So, now you’re stuck – find another auSoor(s) – more delay – write the chapter yourself? – but it’s too far outside your own area of expertise. So, eventually, you find someone else. Which means yet more delay.

Writing your own chapter

Oh, yes, you forgot that you agreed to write one of the chapters yourself. Oops. Oh well, not a problem. Offer co-authorship to one of your PhD students – they’ll be falling over themselves to get another publication on their CV. Or maybe not: no, they are not interested after all; obviously suspecting (correctly) that your aim is to let them write the whole thing, then submit the chapter to you for a little light editorial polishing.

Pleading with the publishers for more time

  1. You now hold the dubious record for the longest gestation period of a multi-author academic book in human history, excluding the Bible.
  2. ‘Please, sir, I want some more.’
  3. The publishers are not impressed, but quietly resigned, telling you to go away and come back when you meet a new deadline.

Losing your marbles and giving up completely

It’s all taking SO LONG – too few authors have submitted first drafts of their chapters. You start to get desperate – the original deadline was so long ago that you’ve forgotten it – the “new” deadline is also now history. You consider giving the whole thing up – apologise to the authors and the publishers and say the book can’t be finished. But your co-editor and the authors who have delivered on time are indignant – naturally enough they don’t want to see their work wasted – and insist that you go back to the recalcitrant scientists with a big stick. How do you threaten authors with a stick by email? Or by phone? 然而, a combination of the metaphorical big stick, pleas for mercy and piling on the guilt eventually work and all the chapters are delivered! Hooray.

Hooray!

So, now, you’re on the last lap. Or the last dregs – the soul-destroying process of assembling the index and proofreading. Once, a sub-editor with a scientific background might have written an index, but not now. Academic publishers want their pound of flesh, so this task is delegated to authors and editors. Authors select keywords from their chapters, with varying degrees of enthusiasm or accuracy, then the editor attempts to assemble them into something useful to the reader. 最後, a draft proof arrives by email. You are now heartily sick of every word, but a final spurt of enthusiasm drives you on and the book is finished.

One more thing – did I forget? – you don’t get paid – but you are given a few free copies of your own book. Such fun!

 

Posted in 淺浮雕 | 1 Response

如何定義一個轉錄因子引起膠耳中的突變?

急性化膿性中耳炎, 有時被稱為“膠耳“, 是最常見的細菌 感染孩子 和 1 年樹齡約 60% 孩子都會有一個情節. 在某些情況下, 孩子養成 慢性病 條件, 哪, 儘管被治愈的感染, 在 “膠水” 不走,並導致 . 在一個繼承 鼠標 模型 慢性膠耳致病突變已被證明是在一個基因編碼 轉錄因子, EVI1.

EVI1蛋白有多個域, 可以抑制或增強靶基因的表達,並與許多其他的蛋白質. 的確, 已知的和潛在的相互作用的多重性是一個挑戰,以確定突變的作用. 有 線索, 然而, 這種突變可能導致疾病的不同表型,例如. 突變小鼠中提出一個“乾淨”SPF動物設施不太可能成為聾子比保存在舊, “臟”動物之家.

難道這意味著, 基因與環境相互作用 例如. 免疫系統和微生物之間, 影響疾病的易感性? 它也被稱為進入中耳腔,突變小鼠表現出較高的水平,中性粒細胞的大量湧入 (), 但目前還不清楚是否EVI1在這個過程中直接或間接. 可能這些問題的答案,近日從 在培養的細胞中的研究, 顯示出 EVI1 可以作為一個 抑製劑 調節炎症反應的關鍵蛋白之一, 另一個轉錄因子, 核因子-κB (核轉錄因子). EVI1結合NFkB的亞基干擾的一個關鍵的蛋白質修飾, 乙酰. 然而, EVI1不直接乙酰化蛋白質, 所以其他的因素都必須參與. 什麼是那些 其他因素?

我結合公共和未發表的數據使用 文獻檢索開源軟件 例如. iRefWeb 步驟,以確定核轉錄因子信號轉導通路,可能會干擾EVI1突變. 小說 目標 蛋白質和出發點 藥物開發 我發現,適合在此進行測試 臨床前模型 慢性化膿性中耳炎.

請閱讀我們的告別賽 博士邁克爾·奇斯曼.

 

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目標發現在兒童期發病的哮喘

Asthma is caused by a combination of environmentalgenetic influences, but the specific factors are poorly understood. A significant “hit” detected in a genome-wide association scan (GWAS) for childhood asthma led a client to believe that one gene might be partially responsible. Proving that this genetic association really was causing asthma was, 然而, difficult. Firstly, no one knew the function of the protein made by the gene and secondly, changing genes in humans to test a hypothesis, rather than as therapy, is technically challenging & ethically questionable, especially in children. Fortunately, mice share about 90% of their genes with humans, so scientists “knocked-out” the equivalent gene, then tested whether these animals behaved like children with asthma. The short answer is – they didn’t. In lung-function tests that would have had asthmatics reaching for their inhalers, 在 knock-out mice were completely 正常. So, what was going on? Were mice not enough like humans? Was this the wrong gene?

For this project, I went back to first principles – what was the evidence supporting the idea that this gene was responsible for increased asthma risk? Digging through the online literature, in particular papers from other groups studying the same gene and supplementary material not available in print, there were suggestions that the genetic effects were more complex. I found evidence that two other genes nearby were either more or less transcriptionally active in asthmatics and so might play a role in susceptibility to asthma. Furthermore, using data from the ENCODE project, I found that the regulatory element predicted to control these genes was conserved in mice, so it would be possible to test the predictions experimentally.

This suggested a novel therapeutic target – altering the activity of a cluster of genes, rather than just one, might alter disease risk.

Testimonial

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基因表達數據的途徑分析 – 男性生育能力下降 / 不育症

A group of animals that can breed and produce fertile offspring is one of the definitions of a species.

This means that the biological mechanisms of fertility and infertility are of interest not only to evolutionary biologists, but also to clinicians and of course to the wider public. At the Institute of Molecular Genetics in Prague, 教授. Jiri Forejt is studying what controls fertility in the hybrid offspring produced by the mating of mouse sub-species. He wanted to know why some male mice were infertile – he knew that genes in one particular genome region were important, but not how those genes influenced the expression of the rest of the genome.

This is where I was recruited into the team, to help with identifying the classes of genes disrupted in mice with reduced fertility. Scientists in his group had produced Affymetrix gene expression results from the testes of fertile, sub-fertile and infertile mice and I analysed these data genome-wide for differentially-expressed transcripts. Using the Broad Institute’s marvellous GSEA tool, I assessed the statistical evidence that specific Gene Ontology terms and pathways were over-represented and also whether differential genes were localised to particular genome regions. This analysis uncovered evidence that specific, functionally related sets of genes were over-represented in the expression data and helped to develop novel hypotheses about the causes of reduced fertility.

Posted in 路徑分析, 目標發現 | 1 Response

的目標發現在遺傳性肌肉無力

Muscle weakness can be caused by a rare inherited disease called myofibrillar myopathy. Gonzalo Blanco’s team found a mouse model of this disease and wanted to identify the underlying cause of the severe muscle weakness. Their aim was to discover potential therapeutic targets to translate into pre-clinical and clinical studies.

Before I became involved, the disease had been mapped to a large region of one chromosome and Dr Blanco’s team were planning to use conventional positional cloning methods to find the mutation. I proposed that a faster approach would be to use next-generation sequencing targeted at genes in the region. I designed a set of probes to enrich specific DNA fragments and I worked with a bioinformatician, 博士. Michelle Simon, to design a software pipeline to find and characterise mutations.

At the end of the design process, the pipeline was used to identify mutations in the muscle weakness mutants and predict that they altered the coding sequences of two genes; Myh4Pmp22. Two lines of evidence suggested that the mutation in Myh4, which codes for a muscle myosin protein, was the most likely cause of the weakness. Firstly, our colleagues found that mice carrying only the myosin mutation still had the trait and secondly, abnormal protein aggregates from affected mice contained large amounts of the myosin.

Scientists at the MRC’s Mammalian Genetics Unit have used the same approach, that Michelle Simon and I pioneered, to find mutations in other disease models.

Publication in Human Molecular Genetics

Testimonial from 博士. 貢薩洛·布蘭科

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