La Branca d'Anàlisi utilitzant l'anàlisi d'enriquiment conjunt de gens (GSEA) Eina

Gene anàlisi d'enriquiment conjunt és un dels molts enfocaments per a la l'anàlisi de l'expressió gènica dades del perfil i es descriu en un paperdels treballadors a l'Institut Broad.

El concepte bàsic va ser motivada per l'observació que l'estudi de gens individuals que mostra la diferència més significativa en el nivell d'expressió entre dos estats o fenotips és mancada d'una visió mecanicista. En lloc, té més sentit per prendre una conjunt de gens compartir algunes vincle biològic, i fer la pregunta - no tot el conjunt va mostrar estadísticament important enriquiment en aquells gens que tenen expressió diferencial?

La conjunt de gens pot ser elegit, a priori, per a un nombre de raons v.g.. el conjunt de gens coneguts per estar influenciada per sobre- o sota l'expressió d'un micro-ARN, o potser un conjunt escollit basat en la ubicació cromosòmica, o gens per als quals la funció molecular, component cel · lular i / o el procés biològic s'han assignat mitjançant els vocabularis controlats de la Ontologia de Gens.

Un avantatge de la GSEA enfocament és que és possible incorporar la seva conjunt complet de dades, no només les transcripcions amb un llindar d'expressió diferencial triat arbitràriament. Estic segur que moltes persones que llegeixen això estaran pensant - "Com pot estar bé per utilitzar el conjunt complet de dades? Normalment jo només consideraria gens amb >2 (O un altre valor preferit)-l'expressió diferencial vegades. "La raó és vàlida l'aproximació és que els gens expressats en nivells baixos o amb gran variació entre les rèpliques no contribueixen a la principal mètrica utilitzada per GSEA, el 'enriquiment Resultat' (ÉS).

GSEA treballa per primera classificació el valor de l'expressió de cada gen per senyal a soroll relació - el càlcul de la diferència entre els valors mitjans per a les mostres que representen cada fenotip i ajust a escala per la suma de les desviacions estàndard. Això significa que els gens amb grans diferències en el nivell d'expressió entre els diferents estats i poca variació entre repeticions biològics es classifiquen altament.

El següent pas és que l'ES, l'estadística primària generada per GSEA, es calcula per a cada conjunt de gens - en el manual d'GSEA, que documenta el programari excel · lent, s'afirma:

"Tots els gens es classifiquen primer per la seva relació senyal-soroll, llavors l'ES es calcula "caminar" per la llista ordenada de gens creixent 01:00 funcionament de summa estadística quan un gen es troba en el conjunt de gens i decreixent quan no és. La magnitud l'increment depèn de la correlació del gen amb una fenotip. L'ES és la màxima desviació de zero trobada en peu la llista. La positiu ÉS indica enriquiment conjunt de gens en el superior de la llista de classificació; 01:00 negatiu ÉS indica enriquiment conjunt de gens en el fons de la llista de classificació ".

Els valors són ÉS normalitzat sobre la base de gen grandària del conjunt i després un la taxa de fals descobriment es calcula, per donar una probabilitat estimada de falsos positius. GSEA utilitza un valor predeterminat molt relaxat 25%, que és adequat per a la generació d'hipòtesis amb un nombre relativament gran de repeticions biològica.

Els científics que treballen en les dades de non-human mostres poden seguir utilitzant GSEA, però caltenir cura - La símbols de gens utilitzats per GSEA són "traduït"De la seva I.E equivalents humana. identificadors utilitzats per als gens de les seves espècies d'interès representat al microarray es converteixen en símbols per a la seva ortòlegs humans, a continuació, s'utilitza en l'anàlisi. Subramanian i col · legues reclamar que aquesta conversió té poc o cap efecte sobre la utilitat de GSEA; s'ha utilitzat amb èxit en múltiples espècies no humanes, però, és clar, això s'ha de tenir en compte en la investigació de resultats en detall.

Per a una excel · lent, a fons, revisió d'eines via, consultar:

Khatri, P., Sirota, M., & Butte, La. J. (2012). Deu anys de Pathway Analysis: Enfocaments actuals i reptes pendents. PLoS Computational Biology, 8(2), e1002375. 02:00:10.1371/journal.pcbi.1002375

Una altra bona font de consells sobre la via d'anàlisi, especialment per a aquells que estan familiaritzats amb el paquet estadístic R és aquí.

Altres lectures

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ÉS, Mesirov JP (2005) Anàlisi d'enriquiment conjunt de gens: un enfocament basat en el coneixement per interpretar els perfils d'expressió de tot el genoma. Proc Natl Acad Sci U S A 102:15545-15550

Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ÉS, Kellis M (2005) Descobriment sistemàtic dels motius de reglamentació en humans i els promotors 3[principal] UTRs per comparació de diversos mamífers. Naturalesa 434:338-345

Publicat en Pathway anàlisi | 1 Response

Alegries de l'edició de llibres de ciència acadèmica

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. So, you lower your sights and aim for good scientists, but not Nobel Prize winners. Finally, 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? 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, 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 author(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 wastedand 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!

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. Finally, 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!

 

Publicat en Alleujament de Llum | 1 Response

Com funciona una mutació en un factor de transcripció causa otitis?

Acute otitis media, sometimes known as “glue ear”, is the most common bacterial infection in children 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, the “glue” doesn’t go away and causes deafness.  In an inherited mouse model of chronic glue ear the causative mutation has been shown to be in a gene encoding a transcription factor, Evi1.

The EVI1 protein has multiple domains, can repress or enhance expression of target genes and interact with many other proteins. Indeed, 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 searches and open 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.

 

Publicat en Pathway anàlisi, Target descobriment | Deixeu un comentari

Target discovery in childhood-onset asthma

Asthma is caused by a combination of environmental and genetic 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, the knock-out mice were completely normal. 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

Publicat en Target descobriment | Deixeu un comentari

Pathway anàlisi de dades d'expressió gènica – Reducció de la fertilitat masculina / esterilitat

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.

Publicat en Pathway anàlisi, Target descobriment | 1 Response

Descobriment de l'objectiu en la debilitat muscular hereditari

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, Dr. 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; Myh4 and Pmp22. 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 Dr. Gonzalo Blanco

Publicat en Target descobriment | Deixeu un comentari
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