遺伝子セット濃縮分析を用いたパスウェイ解析 (GSEA) ツール

遺伝子セット濃縮分析に多くのアプローチの一つである 遺伝子発現の分析 プロファイルデータとに記載されている ブロード研究所の労働者から.

基本的なコンセプトは、勉強という観察によって促された 個々の遺伝子 2ステートまたは表現型であるとの間の発現レベルにおける最も大きな違いを示す 機械論的洞察に欠け. 代わりに, それは取る方が理にかなっている 遺伝子のセット いくつかを共有する 生物学的なリンク, と質問をする - 全体集合は任意の統計を示してい 有意な富 発現差を持っているそれらの遺伝子の?

A 遺伝子セット 選択することができます, アプリオリ, e.gいくつかの理由で. 以上の影響を受けることが知られている遺伝子のセット- マイクロRNAまたは過少発現, 染色体上の位置に基づいて選択またはおそらくセット, 分子機能のためにまたは遺伝子, 細胞成分と / または生物学的プロセスはの統制語彙を使用して割り当てられている 遺伝子オントロジー.

GSEAのアプローチの1つの利点は、それがあなたを組み込むことが可能であるということです 完全なデータセット, 任意に選択された発現差のしきい値を持つだけでなく、それらの転写産物. "どのようにそれは完全なデータセットを使用するには、[OK]をすることができます - 私はこれを読んだ多くの人々が考えていることと確信している? 通常、私はだけで遺伝子を検討する >2 (または他の好きな値)-"アプローチが有効である理由は、低水準でまたは複製に大きな差があると発現している遺伝子がGSEAによって使用されるメインメトリ​​ックに寄与しない倍の差動式である, '濃縮スコア' (ES).

GSEAは最​​初のことで動作し ランキング ことにより、各遺伝子の発現値 信号対雑音 比 - 各表現型を表すサンプルの平均値の差を計算し、標準偏差の和によってそれらをスケーリング. これは生物学の間で異なった状態とほとんど変化の間で発現量に大きな差を持つ遺伝子が高度にランク付けされて複製されることを意味します.

次のステップは、つまり、ES, GSEAによって生成された主要な統計, 各遺伝子セットのために計算される - GSEAマニュアルに, 見事にソフトウェアを記録している, それは述べ:

"すべての遺伝子は、第1のノイズ比率へのシグナルによってランク付けされ, 次にESは遺伝子のランク付けされたドロップダウンリスト "歩く"ことによって計算される 増加 A 実行中の和 遺伝子が遺伝子セット内にあり、統計 減少 それはそれがないときに. ザ 大きさ インクリメントのに依存 相関 と遺伝子の 表現型. ESは、リストを歩行する際に遭遇ゼロからの最大偏差です. A 正の ESは、少なくとも遺伝子セット濃縮を示し トップ ランク付けされたリストの; A ESは、少なくとも遺伝子セット濃縮を示し ボトム ランク付けされたリストの。 "

ESの値は次のとおりです。 正規化 遺伝子セットのサイズに基づいて、次に 偽の発見率 計算され, 偽陽性の推定確率を与える. GSEAは非常にリラックスしたデフォルト値を使用します 25%, 生物学的に比較的多数のレプリケートと仮説生成に適している.

からのデータに働いていた科学者 非ヒト サンプルはまだGSEAを使用することができます, それだけには必要用心する - 遺伝子記号 "アールGSEAによって使用される翻訳"彼らの人間の同等物i.eから. マイクロアレイ上で表される、興味の種からの遺伝子のために使用される識別子は、そのための記号に変換されます ヒトのオーソログ, 分析で使用し. スブラマニアンら 主張する この変換は、ほとんど、あるいはされていることを 影響はありません GSEAの有用性について; それは、複数の非ヒト種で正常に使用されています, 結果の詳細を調査するときに、もちろん、これは心に留めておかなければなりません.

優れたための, 深い, 経路ツールのレビュー, 相談する:

カトリ, P., シロタ, M., & ビュート, A. J. (2012). パスウェイ解析のテン·イヤーズ: 現在のアプローチと卓越した課題. PLoSの計算生物学, 8(2), e1002375. 2:10.1371/journal.pcbi.1002375

パスウェイ解析のアドバイスもう一つの良いソース, 特にR統計パッケージに精通しているためである ここで.

参考文献

スブラマニアン, タマヨP, Mootha VK, ムケルジーS, エーベルトBL, ジレットマサチューセッツ, Paulovich, ポメロイSL, ゴラブTR, ランダーES, Mesirov J​​P (2005) 遺伝子セット濃縮分析: ゲノムワイドな発現プロファイルを解釈するための知識ベースのアプローチ. ProcのNATL ACADサイエンスU S 102:15545-15550

謝X, 呂J, Kulbokas EJ, ゴラブTR, Mootha V, リンドブラッド·藤K, ランダーES, Kellis M (2005) 体系的な人間のプロモーターで調節モチーフの発見と 3[素数] いくつかの哺乳類の比較によるUTRは. 自然 434:338-345

Posted in 経路解析 | 1 Response

学術科学の本を編集しての喜び

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. However, this paragon of science then never, ever replies to any future contacts. そう, you lower your sights and aim for good scientists, but not Nobel Prize winners. ついに, 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. However, 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? よく, 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, etc. 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 auそうor(s) – more delay – write the chapter yourself? – but it’s too far outside your own area of expertiそう. 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? However, 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!

そう, 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. ついに, 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 光救済 | 1 Response

転写因子の原因グルー耳の中に変異がどのように?

Acute otitis media, sometimes known as “glue ear”, is the most common bacterial infectionchildren and by 1 year of age about 60% of children will have had one episode. In some cases, children develop a chronic condition, which, despite the infection being cured, ザ “glue” doesn’t go away and causes deafness.  In an inherited マウス model of chronic glue ear the causative mutation has been shown to be in a gene encoding a 転写因子, Evi1.

The EVI1 protein has multiple domains, can repress or enhance expression of target genes and interact with many other proteins. 確かに, the multiplicity of known and potential interactions is a challenge to determining the role of the mutation.  There were clues, however, as to how this mutation might lead to disease from differences in phenotype e.g. mutant mice raised in a “clean” SPF animal facility were less likely to become deaf than those kept in the older, “dirty” animal house.

Did this mean that gene-environment interactions e.g. between immune system and microbes, influence disease susceptibility? It was also known that mutant mice showed high levels of influx of neutrophils into their middle ear cavities (inflammation), but it was unclear whether EVI1 was acting directly or indirectly in this process. Possible answers to these questions came recently from studies in cultured cells, showing that EVI1 can act as an inhibitor of one of the key proteins regulating inflammation, another transcription factor, nuclear factor kappa B (NFkB).  EVI1 binds to to one of the subunits of NFkB and interferes with a critical protein modification, acetylation.  However, EVI1 does not acetylate proteins directly, so other factors must be involved. What were those other factors?

I combined public and unpublished data using literature searchesopen source software e.g. iRefWeb in order to identify steps in the NFkB signalling pathways that might be disturbed by the mutation in EVI1.  The novel target proteins and starting points for drug development I discovered are suitable for testing in this preclinical model of chronic otitis media.

Read our testimonial from Dr Michael Cheeseman.

 

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Target discovery in childhood-onset asthma

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, however, 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, ザ knock-out mice were completely ノーマル. そう, 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. さらに, 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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遺伝子発現データの経路解析 – 男性の生殖能力削減 / 不妊

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, Prof. 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 経路解析, ターゲット発見 | 1 Response

継承された筋力低下におけるターゲット検出

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. Gonzalo Blanco

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