diff --git a/.vocs/search-index-4e8c166a.json b/.vocs/search-index-4e8c166a.json new file mode 100644 index 0000000..a247ee1 --- /dev/null +++ b/.vocs/search-index-4e8c166a.json @@ -0,0 +1 @@ +{"documentCount":32,"nextId":32,"documentIds":{"0":"docs/pages/blog/cutonestrand.mdx#how-data-analysis-can-help-to-fix-genetic-disorders","1":"docs/pages/blog/cutonestrand.mdx#introduction","2":"docs/pages/blog/cutonestrand.mdx#data-analysis-can-be-use-to-create-a-list-of-interesting-genomic-regions-for-gene-therapy","3":"docs/pages/blog/cutonestrand.mdx#generalization","4":"docs/pages/blog/cutonestrand.mdx#footnote-label","5":"docs/pages/blog/expdesign.mdx#experimental-design-in-the-context-of-next-generation-sequencing","6":"docs/pages/blog/expdesign.mdx#introduction","7":"docs/pages/blog/expdesign.mdx#next-generation-sequencing-specificities","8":"docs/pages/blog/expdesign.mdx#methodology","9":"docs/pages/blog/expdesign.mdx#conclusion","10":"docs/pages/blog/expdesign.mdx#to-go-further","11":"docs/pages/blog/gsea.mdx#a-quick-overview-of-gene-sets-enrichment-analysis","12":"docs/pages/blog/gsea.mdx#why-gsea-analysis-","13":"docs/pages/blog/gsea.mdx#principles","14":"docs/pages/blog/gsea.mdx#what-is-the-question-","15":"docs/pages/blog/gsea.mdx#the-method","16":"docs/pages/blog/gsea.mdx#to-go-further-with-theory","17":"docs/pages/blog/gsea.mdx#available-programs-for-practice","18":"docs/pages/blog/gsea.mdx#footnote-label","19":"docs/pages/blog/#blog","20":"docs/pages/projects/germlinevardb.mdx#germlinevardb","21":"docs/pages/projects/germlinevardb.mdx#why-this-app","22":"docs/pages/projects/germlinevardb.mdx#open-source","23":"docs/pages/projects/germlinevardb.mdx#can-i-use-it-for-clinical-purposes-","24":"docs/pages/projects/germlinevardb.mdx#footnote-label","25":"docs/pages/projects/#here-are-my-projects","26":"docs/pages/projects/#omicsverse","27":"docs/pages/projects/somavardb.mdx#somavardb","28":"docs/pages/projects/somavardb.mdx#why-this-app","29":"docs/pages/projects/somavardb.mdx#open-source","30":"docs/pages/projects/somavardb.mdx#can-i-use-it-for-clinical-purposes-","31":"docs/pages/projects/somavardb.mdx#footnote-label"},"fieldIds":{"title":0,"titles":1,"text":2},"fieldLength":{"0":[9,1,1],"1":[1,9,134],"2":[16,9,186],"3":[1,9,40],"4":[1,9,109],"5":[9,1,1],"6":[1,9,67],"7":[4,9,61],"8":[1,1,110],"9":[1,1,36],"10":[3,1,1],"11":[8,1,1],"12":[4,8,90],"13":[1,8,1],"14":[5,9,65],"15":[2,9,204],"16":[5,9,25],"17":[4,8,22],"18":[1,8,70],"19":[1,1,1],"20":[1,1,11],"21":[3,1,195],"22":[2,1,5],"23":[8,1,57],"24":[1,1,40],"25":[4,1,1],"26":[1,4,56],"27":[1,1,11],"28":[3,1,195],"29":[2,1,5],"30":[8,1,57],"31":[1,1,40]},"averageFieldLength":[3.53125,4.21875,59.3125],"storedFields":{"0":{"href":"/blog/cutonestrand#how-data-analysis-can-help-to-fix-genetic-disorders","html":"\n","isPage":true,"text":"\n","title":"How data analysis can help to fix genetic disorders","titles":[]},"1":{"href":"/blog/cutonestrand#introduction","html":"\n

Gene therapy as seen a major breakthrough with the development of CRISPR-Cas9 technology.\nThis revolutionary tool allows scientists to precisely edit genes, offering new hope for\ntreating genetic disorders and diseases. With the potential to correct genetic mutations at\nthe source, CRISPR-Cas9 opens up a world of possibilities for personalized medicine and targeted therapies.\nThe future of gene therapy looks brighter than ever,\nwith the promise of improved treatments and even potential cures for a wide range of conditions.

\n

Autosomal-dominant disorders are among the diseases that could see gene treatments in the future.\nAs the name dominant implies, the presence of a single pathogenic mutated allele is sufficient for\nthe disease to appear, so some researchers are counting on crispr-cas9 technology to break the mutated allele.\nOnly the wild-type allele remains, and the disease is thus cured.\nAlthough the effectiveness of this approach looks promising 1 2 3, a number of issues still need\nto be addressed, two of which we will try to address in this article :

\n

How can the design of these personalized medicine treatments can be effective and quick for each patient ?

\nHow can we specifically target the mutated allele without breaking the functional allele or another part of\nthe genome ?

\n","isPage":false,"text":"\nGene therapy as seen a major breakthrough with the development of CRISPR-Cas9 technology.\nThis revolutionary tool allows scientists to precisely edit genes, offering new hope for\ntreating genetic disorders and diseases. With the potential to correct genetic mutations at\nthe source, CRISPR-Cas9 opens up a world of possibilities for personalized medicine and targeted therapies.\nThe future of gene therapy looks brighter than ever,\nwith the promise of improved treatments and even potential cures for a wide range of conditions.\nAutosomal-dominant disorders are among the diseases that could see gene treatments in the future.\nAs the name dominant implies, the presence of a single pathogenic mutated allele is sufficient for\nthe disease to appear, so some researchers are counting on crispr-cas9 technology to break the mutated allele.\nOnly the wild-type allele remains, and the disease is thus cured.\nAlthough the effectiveness of this approach looks promising 1 2 3, a number of issues still need\nto be addressed, two of which we will try to address in this article :\nHow can the design of these personalized medicine treatments can be effective and quick for each patient ? \nHow can we specifically target the mutated allele without breaking the functional allele or another part of\nthe genome ?\n","title":"Introduction","titles":["How data analysis can help to fix genetic disorders"]},"2":{"href":"/blog/cutonestrand#data-analysis-can-be-use-to-create-a-list-of-interesting-genomic-regions-for-gene-therapy","html":"\n

The targeted genome cleavage is achieved by targeting sequence-specific cleavage of S. pyogenes Cas9 (spCas9)\nendonuclease with a gRNA. In order for the gRNA to successfully direct Cas9 cleavage,\nthe corresponding target DNA sequence in the genome must be found next to a PAM site,\nalso known as a Protospacer Adjacent Motif. The canonical PAM is associated with the spCas9 nuclease is 5'-NGG-3'.\nWe are therefore going to try to draw up an exhaustive list of all the genomic regions that could be used for this\ngene therapy.

\n
    \n
  1. We start by selecting all the SNPs that are frequent in the population (> 5%), for which we can\nuse the gnomAD database 4. We want the list created to be usable to treat as many\npatients as possible, so we avoid SNPs that are too rare.
    2. \n
  2. Only SNPs that induce the disappearance or appearance of the 5‘-NGG-3’\nmotif will allow us to target only the mutated allele while preserving the WT. To do this, we wrote an in-house script in Python.
    3. \n
  3. We used the jvarkit tools suite to reconstitute the genomic context of these SNPs, i.e.\nto add the flanking sequences to the left and right of our SNPs of interest, according to the human reference genome.
    4. \n
  4. Finally, we used the FlashFry tool to calculate and predict efficiency and specificity\nscores for each of the positions we selected. We wanted to cut the diseased gene efficiently,\nwithout altering other regions of the genome.
    5. \n
\n

Using this method, we were able to draw up a list of genomic positions of interest in the treatment of Ryanodine receptor\ntype 1-related myopathies (RYR1-RM) of the ‘Autosomal-Dominant-Disorder’ type. 5\nThanks to next-generation sequencing, it is possible to obtain both genomic sequences of a patient\nat a reasonable cost. All the positions on our list for which the patient is heterozygous are therefore\ncandidates for gene therapy!

\n

Check out the analysis code here !

\n","isPage":false,"text":"\nThe targeted genome cleavage is achieved by targeting sequence-specific cleavage of S. pyogenes Cas9 (spCas9)\nendonuclease with a gRNA. In order for the gRNA to successfully direct Cas9 cleavage,\nthe corresponding target DNA sequence in the genome must be found next to a PAM site,\nalso known as a Protospacer Adjacent Motif. The canonical PAM is associated with the spCas9 nuclease is 5'-NGG-3'.\nWe are therefore going to try to draw up an exhaustive list of all the genomic regions that could be used for this\ngene therapy.\n\nWe start by selecting all the SNPs that are frequent in the population (> 5%), for which we can\nuse the gnomAD database 4. We want the list created to be usable to treat as many\npatients as possible, so we avoid SNPs that are too rare.\nOnly SNPs that induce the disappearance or appearance of the 5‘-NGG-3’\nmotif will allow us to target only the mutated allele while preserving the WT. To do this, we wrote an in-house script in Python.\nWe used the jvarkit tools suite to reconstitute the genomic context of these SNPs, i.e.\nto add the flanking sequences to the left and right of our SNPs of interest, according to the human reference genome.\nFinally, we used the FlashFry tool to calculate and predict efficiency and specificity\nscores for each of the positions we selected. We wanted to cut the diseased gene efficiently,\nwithout altering other regions of the genome.\n\nUsing this method, we were able to draw up a list of genomic positions of interest in the treatment of Ryanodine receptor\ntype 1-related myopathies (RYR1-RM) of the ‘Autosomal-Dominant-Disorder’ type. 5\nThanks to next-generation sequencing, it is possible to obtain both genomic sequences of a patient\nat a reasonable cost. All the positions on our list for which the patient is heterozygous are therefore\ncandidates for gene therapy!\nCheck out the analysis code here !\n","title":"Data analysis can be use to create a list of interesting genomic regions for gene therapy","titles":["How data analysis can help to fix genetic disorders"]},"3":{"href":"/blog/cutonestrand#generalization","html":"\n

Of course, the implementation of gene therapy has to deal with other obstacles and questions,\nbut this approach can be generalised to other Autosomal-Dominant-Disorders and enable carers to\nscreen the genome extensively in order to create a short list of regions\nof interest for this type of gene therapy !

\n
","isPage":false,"text":"\nOf course, the implementation of gene therapy has to deal with other obstacles and questions,\nbut this approach can be generalised to other Autosomal-Dominant-Disorders and enable carers to\nscreen the genome extensively in order to create a short list of regions\nof interest for this type of gene therapy !\n","title":"Generalization","titles":["How data analysis can help to fix genetic disorders"]},"4":{"href":"/blog/cutonestrand#footnote-label","html":"\n
    \n
  1. \n

    Anzalone A.V, Koblan L.W and Liu D.R . Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors DOI ↩

    \n
    2. \n
  2. \n

    F Chemello, A.C Chai, H Li, C Rodriguez-Caycedo, E Sanchez-Ortiz, A Atmanli, A.A Mireault, N Liu,\nR Bassel-Duby, E.N Olson. Precise correction of Duchenne muscular dystrophy exon\ndeletion mutations by base and prime editing DOI ↩

    \n
    3. \n
  3. \n

    Kelly Godbout, Joël Rousseau, Jacques P Tremblay. Successful Correction by Prime Editing of a\nMutation in the RYR1 Gene Responsible for a Myopathy DOI ↩

    \n
    4. \n
  4. \n

    The Genome Aggregation Database (gnomAD) ↩

    \n
    5. \n
  5. \n

    Mathilde Beaufils, Margaux Melka, Julie Brocard, Clement Benoit, Nagi Debbah, Kamel Mamchaoui,\nNorma B. Romero, Anne Frédérique Dalmas-Laurent, Susana Quijano-Roy, Julien Fauré, John Rendu\nand Isabelle Marty. Functional benefit of CRISPR-Cas9-induced allele deletion for RYR1 dominant mutation ↩

    \n
    6. \n
\n
","isPage":false,"text":"\n\n\nAnzalone A.V, Koblan L.W and Liu D.R . Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors DOI ↩\n\n\nF Chemello, A.C Chai, H Li, C Rodriguez-Caycedo, E Sanchez-Ortiz, A Atmanli, A.A Mireault, N Liu,\nR Bassel-Duby, E.N Olson. Precise correction of Duchenne muscular dystrophy exon\ndeletion mutations by base and prime editing DOI ↩\n\n\nKelly Godbout, Joël Rousseau, Jacques P Tremblay. Successful Correction by Prime Editing of a\nMutation in the RYR1 Gene Responsible for a Myopathy DOI ↩\n\n\nThe Genome Aggregation Database (gnomAD) ↩\n\n\nMathilde Beaufils, Margaux Melka, Julie Brocard, Clement Benoit, Nagi Debbah, Kamel Mamchaoui,\nNorma B. Romero, Anne Frédérique Dalmas-Laurent, Susana Quijano-Roy, Julien Fauré, John Rendu\nand Isabelle Marty. Functional benefit of CRISPR-Cas9-induced allele deletion for RYR1 dominant mutation ↩\n\n\n","title":"Footnotes","titles":["How data analysis can help to fix genetic disorders"]},"5":{"href":"/blog/expdesign#experimental-design-in-the-context-of-next-generation-sequencing","html":"\n","isPage":true,"text":"\n","title":"Experimental design in the context of next generation sequencing","titles":[]},"6":{"href":"/blog/expdesign#introduction","html":"\n

Coming from a biotechnology background, I was sensitized to the problems of experimental design. I then spent a few years as a bioinformatician, leaving this task to my platform colleagues.

\n

As a bioinformatician, you focus on producing quality pipelines and algorithms for processing sequencer output.\nBut we sometimes forget that, without good experimental design, we run the risk of being unable to draw conclusions,\nor even of unwittingly producing false positives.

\n

Here's a brief overview of the main considerations for experience designers.

\n","isPage":false,"text":"\nComing from a biotechnology background, I was sensitized to the problems of experimental design. I then spent a few years as a bioinformatician, leaving this task to my platform colleagues.\nAs a bioinformatician, you focus on producing quality pipelines and algorithms for processing sequencer output.\nBut we sometimes forget that, without good experimental design, we run the risk of being unable to draw conclusions,\nor even of unwittingly producing false positives. \nHere's a brief overview of the main considerations for experience designers.\n","title":"Introduction","titles":["Experimental design in the context of next generation sequencing"]},"7":{"href":"/blog/expdesign#next-generation-sequencing-specificities","html":"\n

We need to bear in mind that everything starts with biological material, from which we want to extract informative biological sequences, to answer a precise question.\nThe difficulty lies in extracting useful information without confusing it with other factors: background noise, bias...

\n

The sample is first taken from its environment, by which it will have been impacted.\nThe interaction of the biological material with its environment,\nas well as the sampling carried out, already constitute a first source of variance.

\n
","isPage":false,"text":"\nWe need to bear in mind that everything starts with biological material, from which we want to extract informative biological sequences, to answer a precise question.\nThe difficulty lies in extracting useful information without confusing it with other factors: background noise, bias...\nThe sample is first taken from its environment, by which it will have been impacted.\nThe interaction of the biological material with its environment,\nas well as the sampling carried out, already constitute a first source of variance.\n","title":"Next generation sequencing specificities","titles":["Experimental design in the context of next generation sequencing"]},"8":{"href":"/blog/expdesign#methodology","html":"
\n
    \n
  1. Clearly define the question posed and the technology chosen to answer it.
    \nMany confounding factors will depend on the technology chosen. They will have a greater or lesser impact depending on the question asked.
    2. \n
  2. Control the experimental variables
    \nVariables to control can include sample preparation techniques, sequencing parameters and environmental conditions. Ensure that the impact of this factors\non your data is minimized and controled to isolate the impact of the specific variables of interest, enhancing ythe reliability of the results.
    3. \n
  3. Randomization techniques
    \nApplying randomization techniques will prevent your experiment to capture systemic bias in your data.
    4. \n
  4. Replication Strategies
    \nReplicating the experiment with different samples or under different conditions enhances the robustness of the results.\nIt allows for the identification of consistent patterns and aids in generalizing findings to a broader context.
    5. \n
  5. Factorial design
    \nFactorial design is a statistical experimental method that explores the effects of multiple independent variables simultaneously.\nIt allows researchers to assess the individual effects of each factor and any potential interactions between them, providing a more comprehensive understanding\nof experimental outcomes.
    6. \n
\n
","isPage":false,"text":"\n\nClearly define the question posed and the technology chosen to answer it. \nMany confounding factors will depend on the technology chosen. They will have a greater or lesser impact depending on the question asked.\nControl the experimental variables \nVariables to control can include sample preparation techniques, sequencing parameters and environmental conditions. Ensure that the impact of this factors\non your data is minimized and controled to isolate the impact of the specific variables of interest, enhancing ythe reliability of the results.\nRandomization techniques \nApplying randomization techniques will prevent your experiment to capture systemic bias in your data.\nReplication Strategies\nReplicating the experiment with different samples or under different conditions enhances the robustness of the results.\nIt allows for the identification of consistent patterns and aids in generalizing findings to a broader context.\nFactorial design\nFactorial design is a statistical experimental method that explores the effects of multiple independent variables simultaneously.\nIt allows researchers to assess the individual effects of each factor and any potential interactions between them, providing a more comprehensive understanding\nof experimental outcomes.\n\n","title":"Methodology","titles":[]},"9":{"href":"/blog/expdesign#conclusion","html":"
\n

Experimental design is not so complicated in terms of methodology, and is essential to get the most out of sequencing your biological samples.\nHowever, a thorough knowledge of the different technologies available and of experimental design techniques and strategies is required to apply it effectively...

\n
","isPage":false,"text":"\nExperimental design is not so complicated in terms of methodology, and is essential to get the most out of sequencing your biological samples.\nHowever, a thorough knowledge of the different technologies available and of experimental design techniques and strategies is required to apply it effectively...\n","title":"Conclusion","titles":[]},"10":{"href":"/blog/expdesign#to-go-further","html":"
\n\n","isPage":false,"text":"\n\n","title":"To go further","titles":[]},"11":{"href":"/blog/gsea#a-quick-overview-of-gene-sets-enrichment-analysis","html":"\n","isPage":true,"text":"\n","title":"A quick overview of Gene sets enrichment analysis","titles":[]},"12":{"href":"/blog/gsea#why-gsea-analysis-","html":"\n

Genome wide expression analysis has become become a mainstay of genomics research. However, there is still a wide range of tools for interpreting these gene expression profiles.\nThey all have pros, cons, and are still evolving. This, coupled with the fact that these studies rely on the testing of a large number of hypotheses and relatively small sample sizes, lead that whole-genome expression studies in particular, to be often not reproducible.\nThis is why reproducibility is one of the major challenges facing studies involving whole-genome expression data. 1, 2, 3

\n

Finally, interpreting lists of thousands of differentially expressed genes is a tedious exercise for the biologist.

\n

The GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies,\nwhile facilitating their interpetation.

\n","isPage":false,"text":"\nGenome wide expression analysis has become become a mainstay of genomics research. However, there is still a wide range of tools for interpreting these gene expression profiles.\nThey all have pros, cons, and are still evolving. This, coupled with the fact that these studies rely on the testing of a large number of hypotheses and relatively small sample sizes, lead that whole-genome expression studies in particular, to be often not reproducible.\nThis is why reproducibility is one of the major challenges facing studies involving whole-genome expression data. 1, 2, 3\nFinally, interpreting lists of thousands of differentially expressed genes is a tedious exercise for the biologist.\nThe GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies,\nwhile facilitating their interpetation.\n","title":"Why GSEA Analysis ?","titles":["A quick overview of Gene sets enrichment analysis"]},"13":{"href":"/blog/gsea#principles","html":"\n","isPage":false,"text":"\n","title":"Principles","titles":["A quick overview of Gene sets enrichment analysis"]},"14":{"href":"/blog/gsea#what-is-the-question-","html":"\n

Let's says you have ranked a gene list L according to your favourite metric. The GSEA tries to answer the following question :

\n

Given a gene set S : Does the genes belonging to S tends to occur toward the top (or the bottom) of the list L,\nin which case the gene set is correlated with the phenotypic class distinction.

\n

Of course we will do as many independant tests as we have genes sets to try.\nA multiple testing correction should then be considered.

\n","isPage":false,"text":"\nLet's says you have ranked a gene list L according to your favourite metric. The GSEA tries to answer the following question : \nGiven a gene set S : Does the genes belonging to S tends to occur toward the top (or the bottom) of the list L,\nin which case the gene set is correlated with the phenotypic class distinction.\nOf course we will do as many independant tests as we have genes sets to try.\nA multiple testing correction should then be considered.\n","title":"What is the question ?","titles":["A quick overview of Gene sets enrichment analysis","Principles"]},"15":{"href":"/blog/gsea#the-method","html":"\n

Step 1 : Compute an enrichment score (ES)
\nThis score reflects the degree to which the set S is overrepresented at the extremes top (ES > 0) or bottom (ES < 0) of the ranked list R. It is calculated by walking down the list L,\nincreasing the running sum when we encounter a gene in S and decreasing it when we encounter genes not in S. Finally the maximum deviation from zero encountered during the random walk is kept as ES.

\nStep 2 : Estimation of significance level of ES
\nThe nominal P-Value is estimates the statistical significance of the ES by using an empirical phenotype-based permutation test that\npreserves the correlation structure of the gene expression data. Phenotype labels are permuted and the ES recomputed to generate a null distribution for the ES. The empirical nominal P Value of the observed ES is then calculated relative to\nthis null distribution. Permutation of class labels preserves gene-gene correlations and thus, provide a more biologically reasonable assessment of significance than would be obtained by permuting genes.

\nStep 3 : Adjustment of Multiple Hypothesis Testing.
\nThe enrichment scores for each set are normalized and a false discovery rate is calculated

\n

It is usefull to keep in mind that

\n","isPage":false,"text":"\nStep 1 : Compute an enrichment score (ES)\nThis score reflects the degree to which the set S is overrepresented at the extremes top (ES > 0) or bottom (ES < 0) of the ranked list R. It is calculated by walking down the list L,\nincreasing the running sum when we encounter a gene in S and decreasing it when we encounter genes not in S. Finally the maximum deviation from zero encountered during the random walk is kept as ES. \nStep 2 : Estimation of significance level of ES\nThe nominal P-Value is estimates the statistical significance of the ES by using an empirical phenotype-based permutation test that\npreserves the correlation structure of the gene expression data. Phenotype labels are permuted and the ES recomputed to generate a null distribution for the ES. The empirical nominal P Value of the observed ES is then calculated relative to\nthis null distribution. Permutation of class labels preserves gene-gene correlations and thus, provide a more biologically reasonable assessment of significance than would be obtained by permuting genes.\nStep 3 : Adjustment of Multiple Hypothesis Testing.\nThe enrichment scores for each set are normalized and a false discovery rate is calculated\nIt is usefull to keep in mind that\nThat the GSEA official software will not complain if you provide them with raw gene expression data. However your results will get totally incorrect results.\nYou can perform a pre ranked GSEA, which can be very helpful for performing gene set enrichment analysis on data that do not conform to the typical GSEA scenario. For example, it can be used when the ranking metric choices provided by GSEA are not appropriate for the data, or when a ranked list of genomic features deviates from traditional gene expression data (e.g., GWAS results, ChIP seq, etc.). Also if you lack computing power and have access to pre-ranked list, this solution can be your best option.\nClearly define the question your are trying to address and choose the appropriate ranking metrics.\nGenes sets curation could be useful as a pre-processing step. Indeed you do not need to perform test on genes set you are not interested in. It will increase your need for computing resources and create supplementary noise for the Adjustment of Multiple Hypothesis Testing procedure.\n\n","title":"The Method","titles":["A quick overview of Gene sets enrichment analysis","Principles"]},"16":{"href":"/blog/gsea#to-go-further-with-theory","html":"\n

There exists a variant of GSEA called FGSEA for Fast Gene Set Enrichment Analysis.

\n

Another common approach to perform pathways analysis is the Gene Ontology Enrichment analysis.

\n","isPage":false,"text":"\nThere exists a variant of GSEA called FGSEA for Fast Gene Set Enrichment Analysis.\nAnother common approach to perform pathways analysis is the Gene Ontology Enrichment analysis.\n","title":"To go further with theory","titles":["A quick overview of Gene sets enrichment analysis","Principles"]},"17":{"href":"/blog/gsea#available-programs-for-practice","html":"\n\n\n
","isPage":false,"text":"\n\nOfficial Broad institute tools\nWEB-based GEne SeT AnaLysis Toolkit\nGUI\nR package\nFast Gene Set Enrichment Analysis (Pre-ranked only)\n\n\n","title":"Available programs for practice","titles":["A quick overview of Gene sets enrichment analysis"]},"18":{"href":"/blog/gsea#footnote-label","html":"\n
    \n
  1. \n

    Timothy E. Sweeney , Winston A. Haynes , Francesco Vallania , John P. Ioannidis\nand Purvesh Khatri. (2017). Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Research, Volume 45(Issue 1), Page Range. DOI ↩

    \n
    2. \n
  2. \n

    Steven N Goodman , Daniele Fanelli , John P A Ioannidis. (2016). *What does research reproducibility mean? *. Sci Transl Med, 8(341),12. DOI ↩

    \n
    3. \n
  3. \n

    Lianbo Yu. (2020). RNA-Seq Reproducibility Assessment of the Sequencing Quality Control Project. Cancer Inform, Volume 19 DOI ↩

    \n
    4. \n
\n
","isPage":false,"text":"\n\n\nTimothy E. Sweeney , Winston A. Haynes , Francesco Vallania , John P. Ioannidis\nand Purvesh Khatri. (2017). Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Research, Volume 45(Issue 1), Page Range. DOI ↩\n\n\nSteven N Goodman , Daniele Fanelli , John P A Ioannidis. (2016). *What does research reproducibility mean? *. Sci Transl Med, 8(341),12. DOI ↩\n\n\nLianbo Yu. (2020). RNA-Seq Reproducibility Assessment of the Sequencing Quality Control Project. Cancer Inform, Volume 19 DOI ↩\n\n\n","title":"Footnotes","titles":["A quick overview of Gene sets enrichment analysis"]},"19":{"href":"/blog#blog","html":"\n
","isPage":true,"text":"\n","title":"Blog","titles":[]},"20":{"href":"/projects/germlinevardb#germlinevardb","html":"\n

A graphical interface to explore your germline genomic variation database.


\n\"\"/

\n","isPage":true,"text":"\n A graphical interface to explore your germline genomic variation database.\n\n","title":"GermlineVarDB","titles":[]},"21":{"href":"/projects/germlinevardb#why-this-app","html":"\n

Few hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:

\n\n

Turning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:

\n\n

With this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.

\n

However, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.

\n

SomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.

\n","isPage":false,"text":"\nFew hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:\n\nLack of resources and investment capacity to anticipate rapid technological change.\nThe data professions, which are still in their infancy, have not yet fully penetrated these structures.\nCumbersome legal and administrative constraints on the accreditation of IT solutions for clinical use.\n\nTurning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:\n\nThe cost of the outsourced service is ultimately much higher than in-house.\nUsers (clinical biologists) work free of charge to sort the databases built by these external service providers from the raw data sent to them by the hospitals to which these clinicians belong.\nThe hospital will therefore once again pay for access to the Ias that have been trained thanks to its work.\n\nWith this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.\nHowever, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.\nSomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.\n","title":"Why this app","titles":["GermlineVarDB"]},"22":{"href":"/projects/germlinevardb#open-source","html":"\n\n","isPage":false,"text":"\n\nLive demo\nSource code\n\n","title":"Open-source","titles":["GermlineVarDB"]},"23":{"href":"/projects/germlinevardb#can-i-use-it-for-clinical-purposes-","html":"\n

Unfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time.

\nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.

\n
","isPage":false,"text":"\nUnfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time. \nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.\n","title":"Can I use it for clinical purposes ?","titles":["GermlineVarDB"]},"24":{"href":"/projects/germlinevardb#footnote-label","html":"\n
    \n
  1. \n

    Tuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩

    \n
    2. \n
  2. \n

    Cyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩

    \n
    3. \n
  3. \n

    Małgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL ↩

    \n
    4. \n
\n
","isPage":false,"text":"\n\n\nTuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩\n\n\nCyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩\n\n\nMałgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL ↩\n\n\n","title":"Footnotes","titles":["GermlineVarDB"]},"25":{"href":"/projects#here-are-my-projects","html":"\n","isPage":true,"text":"\n","title":"Here are my projects","titles":[]},"26":{"href":"/projects#omicsverse","html":"\n

The initial aim of the OmicsVerse portal is to host demo versions of the various data applications I create.

\n

Application source code and documentation for administrators can be found on my github.

\n

The following sections & pages will host detailed documentation of users.

\n

If you have a genomics-oriented data application to host, don't hesitate to contact me. provided there are resources available.

\n","isPage":false,"text":"\nThe initial aim of the OmicsVerse portal is to host demo versions of the various data applications I create.\nApplication source code and documentation for administrators can be found on my github.\nThe following sections & pages will host detailed documentation of users.\nIf you have a genomics-oriented data application to host, don't hesitate to contact me. provided there are resources available.\n","title":"OmicsVerse","titles":["Here are my projects"]},"27":{"href":"/projects/somavardb#somavardb","html":"\n

A graphical interface to explore your somatic genomic variation database.

\n\"\"/

\n","isPage":true,"text":"\n A graphical interface to explore your somatic genomic variation database.\n\n","title":"SomaVarDB","titles":[]},"28":{"href":"/projects/somavardb#why-this-app","html":"\n

Few hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:

\n\n

Turning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:

\n\n

With this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.

\n

However, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.

\n

SomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.

\n","isPage":false,"text":"\nFew hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:\n\nLack of resources and investment capacity to anticipate rapid technological change.\nThe data professions, which are still in their infancy, have not yet fully penetrated these structures.\nCumbersome legal and administrative constraints on the accreditation of IT solutions for clinical use.\n\nTurning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:\n\nThe cost of the outsourced service is ultimately much higher than in-house.\nUsers (clinical biologists) work free of charge to sort the databases built by these external service providers from the raw data sent to them by the hospitals to which these clinicians belong.\nThe hospital will therefore once again pay for access to the Ias that have been trained thanks to its work.\n\nWith this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.\nHowever, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.\nSomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.\n","title":"Why this app","titles":["SomaVarDB"]},"29":{"href":"/projects/somavardb#open-source","html":"\n\n","isPage":false,"text":"\n\nLive demo\nSource code\n\n","title":"Open-source","titles":["SomaVarDB"]},"30":{"href":"/projects/somavardb#can-i-use-it-for-clinical-purposes-","html":"\n

Unfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time.

\nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.

\n
","isPage":false,"text":"\nUnfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time. \nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.\n","title":"Can I use it for clinical purposes ?","titles":["SomaVarDB"]},"31":{"href":"/projects/somavardb#footnote-label","html":"\n
    \n
  1. \n

    Tuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩

    \n
    2. \n
  2. \n

    Cyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩

    \n
    3. \n
  3. \n

    Małgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL ↩

    \n
    4. \n
\n
","isPage":false,"text":"\n\n\nTuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩\n\n\nCyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩\n\n\nMałgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL 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design in the context of next generation sequencing","titles":[]},"1":{"href":"/blog/expdesign#introduction","html":"\n

Coming from a biotechnology background, I was sensitized to the problems of experimental design. I then spent a few years as a bioinformatician, leaving this task to my platform colleagues.

\n

As a bioinformatician, you focus on producing quality pipelines and algorithms for processing sequencer output.\nBut we sometimes forget that, without good experimental design, we run the risk of being unable to draw conclusions,\nor even of unwittingly producing false positives.

\n

Here's a brief overview of the main considerations for experience designers.

\n","isPage":false,"text":"\nComing from a biotechnology background, I was sensitized to the problems of experimental design. I then spent a few years as a bioinformatician, leaving this task to my platform colleagues.\nAs a bioinformatician, you focus on producing quality pipelines and algorithms for processing sequencer output.\nBut we sometimes forget that, without good experimental design, we run the risk of being unable to draw conclusions,\nor even of unwittingly producing false positives. \nHere's a brief overview of the main considerations for experience designers.\n","title":"Introduction","titles":["Experimental design in the context of next generation sequencing"]},"2":{"href":"/blog/expdesign#next-generation-sequencing-specificities","html":"\n

We need to bear in mind that everything starts with biological material, from which we want to extract informative biological sequences, to answer a precise question.\nThe difficulty lies in extracting useful information without confusing it with other factors: background noise, bias...

\n

The sample is first taken from its environment, by which it will have been impacted.\nThe interaction of the biological material with its environment,\nas well as the sampling carried out, already constitute a first source of variance.

\n
","isPage":false,"text":"\nWe need to bear in mind that everything starts with biological material, from which we want to extract informative biological sequences, to answer a precise question.\nThe difficulty lies in extracting useful information without confusing it with other factors: background noise, bias...\nThe sample is first taken from its environment, by which it will have been impacted.\nThe interaction of the biological material with its environment,\nas well as the sampling carried out, already constitute a first source of variance.\n","title":"Next generation sequencing specificities","titles":["Experimental design in the context of next generation sequencing"]},"3":{"href":"/blog/expdesign#methodology","html":"
\n
    \n
  1. Clearly define the question posed and the technology chosen to answer it.
    \nMany confounding factors will depend on the technology chosen. They will have a greater or lesser impact depending on the question asked.
    2. \n
  2. Control the experimental variables
    \nVariables to control can include sample preparation techniques, sequencing parameters and environmental conditions. Ensure that the impact of this factors\non your data is minimized and controled to isolate the impact of the specific variables of interest, enhancing ythe reliability of the results.
    3. \n
  3. Randomization techniques
    \nApplying randomization techniques will prevent your experiment to capture systemic bias in your data.
    4. \n
  4. Replication Strategies
    \nReplicating the experiment with different samples or under different conditions enhances the robustness of the results.\nIt allows for the identification of consistent patterns and aids in generalizing findings to a broader context.
    5. \n
  5. Factorial design
    \nFactorial design is a statistical experimental method that explores the effects of multiple independent variables simultaneously.\nIt allows researchers to assess the individual effects of each factor and any potential interactions between them, providing a more comprehensive understanding\nof experimental outcomes.
    6. \n
\n
","isPage":false,"text":"\n\nClearly define the question posed and the technology chosen to answer it. \nMany confounding factors will depend on the technology chosen. They will have a greater or lesser impact depending on the question asked.\nControl the experimental variables \nVariables to control can include sample preparation techniques, sequencing parameters and environmental conditions. Ensure that the impact of this factors\non your data is minimized and controled to isolate the impact of the specific variables of interest, enhancing ythe reliability of the results.\nRandomization techniques \nApplying randomization techniques will prevent your experiment to capture systemic bias in your data.\nReplication Strategies\nReplicating the experiment with different samples or under different conditions enhances the robustness of the results.\nIt allows for the identification of consistent patterns and aids in generalizing findings to a broader context.\nFactorial design\nFactorial design is a statistical experimental method that explores the effects of multiple independent variables simultaneously.\nIt allows researchers to assess the individual effects of each factor and any potential interactions between them, providing a more comprehensive understanding\nof experimental outcomes.\n\n","title":"Methodology","titles":[]},"4":{"href":"/blog/expdesign#conclusion","html":"
\n

Experimental design is not so complicated in terms of methodology, and is essential to get the most out of sequencing your biological samples.\nHowever, a thorough knowledge of the different technologies available and of experimental design techniques and strategies is required to apply it effectively...

\n
","isPage":false,"text":"\nExperimental design is not so complicated in terms of methodology, and is essential to get the most out of sequencing your biological samples.\nHowever, a thorough knowledge of the different technologies available and of experimental design techniques and strategies is required to apply it effectively...\n","title":"Conclusion","titles":[]},"5":{"href":"/blog/expdesign#to-go-further","html":"
\n\n","isPage":false,"text":"\n\n","title":"To go further","titles":[]},"6":{"href":"/blog/gsea#a-quick-overview-of-gsea-analysis","html":"\n","isPage":true,"text":"\n","title":"A quick overview of GSEA analysis","titles":[]},"7":{"href":"/blog/gsea#why-gsea-analysis-","html":"\n

Genome wide expression analysis has become become a mainstay of genomics research. However, there is still a wide range of tools for interpreting these gene expression profiles.\nThey all have pros, cons, and are still evolving. This, coupled with the fact that these studies rely on the testing of a large number of hypotheses and relatively small sample sizes, lead that whole-genome expression studies in particular, to be often not reproducible.\nThis is why reproducibility is one of the major challenges facing studies involving whole-genome expression data. 1, 2, 3

\n

Finally, interpreting lists of thousands of differentially expressed genes is a tedious exercise for the biologist.

\n

The GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies, while facilitating their interpetation.

\n","isPage":false,"text":"\nGenome wide expression analysis has become become a mainstay of genomics research. However, there is still a wide range of tools for interpreting these gene expression profiles.\nThey all have pros, cons, and are still evolving. This, coupled with the fact that these studies rely on the testing of a large number of hypotheses and relatively small sample sizes, lead that whole-genome expression studies in particular, to be often not reproducible.\nThis is why reproducibility is one of the major challenges facing studies involving whole-genome expression data. 1, 2, 3\nFinally, interpreting lists of thousands of differentially expressed genes is a tedious exercise for the biologist.\nThe GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies, while facilitating their interpetation.\n","title":"Why GSEA Analysis ?","titles":["A quick overview of GSEA analysis"]},"8":{"href":"/blog/gsea#principles","html":"\n","isPage":false,"text":"\n","title":"Principles","titles":["A quick overview of GSEA analysis"]},"9":{"href":"/blog/gsea#what-is-the-question-","html":"\n

Let's says you have ranked a gene list L according to your favourite metric. The GSEA tries to answer the following question :

\n

Given a gene set S : Does the genes belonging to S tends to occur toward the top (or the bottom) of the list L,\nin which case the gene set is correlated with the phenotypic class distinction.

\n

Of course we will do as many independant tests as we have genes sets to try.\nA multiple testing correction should then be considered.

\n","isPage":false,"text":"\nLet's says you have ranked a gene list L according to your favourite metric. The GSEA tries to answer the following question : \nGiven a gene set S : Does the genes belonging to S tends to occur toward the top (or the bottom) of the list L,\nin which case the gene set is correlated with the phenotypic class distinction.\nOf course we will do as many independant tests as we have genes sets to try.\nA multiple testing correction should then be considered.\n","title":"What is the question ?","titles":["A quick overview of GSEA analysis","Principles"]},"10":{"href":"/blog/gsea#the-method","html":"\n

Step 1 : Compute an enrichment score (ES)
\nThis score reflects the degree to which the set S is overrepresented at the extremes top (ES > 0) or bottom (ES < 0) of the ranked list R. It is calculated by walking down the list L,\nincreasing the running sum when we encounter a gene in S and decreasing it when we encounter genes not in S. Finally the maximum deviation from zero encountered during the random walk is kept as ES.

\nStep 2 : Estimation of significance level of ES
\nThe nominal P-Value is estimates the statistical significance of the ES by using an empirical phenotype-based permutation test that\npreserves the correlation structure of the gene expression data. Phenotype labels are permuted and the ES recomputed to generate a null distribution for the ES. The empirical nominal P Value of the observed ES is then calculated relative to\nthis null distribution. Permutation of class labels preserves gene-gene correlations and thus, provide a more biologically reasonable assessment of significance than would be obtained by permuting genes.

\nStep 3 : Adjustment of Multiple Hypothesis Testing.
\nThe enrichment scores for each set are normalized and a false discovery rate is calculated

\n

It is usefull to keep in mind that

\n","isPage":false,"text":"\nStep 1 : Compute an enrichment score (ES)\nThis score reflects the degree to which the set S is overrepresented at the extremes top (ES > 0) or bottom (ES < 0) of the ranked list R. It is calculated by walking down the list L,\nincreasing the running sum when we encounter a gene in S and decreasing it when we encounter genes not in S. Finally the maximum deviation from zero encountered during the random walk is kept as ES. \nStep 2 : Estimation of significance level of ES\nThe nominal P-Value is estimates the statistical significance of the ES by using an empirical phenotype-based permutation test that\npreserves the correlation structure of the gene expression data. Phenotype labels are permuted and the ES recomputed to generate a null distribution for the ES. The empirical nominal P Value of the observed ES is then calculated relative to\nthis null distribution. Permutation of class labels preserves gene-gene correlations and thus, provide a more biologically reasonable assessment of significance than would be obtained by permuting genes.\nStep 3 : Adjustment of Multiple Hypothesis Testing.\nThe enrichment scores for each set are normalized and a false discovery rate is calculated\nIt is usefull to keep in mind that\nThat the GSEA official software will not complain if you provide them with raw gene expression data. However your results will get totally incorrect results.\nYou can perform a pre ranked GSEA, which can be very helpful for performing gene set enrichment analysis on data that do not conform to the typical GSEA scenario. For example, it can be used when the ranking metric choices provided by GSEA are not appropriate for the data, or when a ranked list of genomic features deviates from traditional gene expression data (e.g., GWAS results, ChIP seq, etc.). Also if you lack computing power and have access to pre-ranked list, this solution can be your best option.\nClearly define the question your are trying to address and choose the appropriate ranking metrics.\nGenes sets curation could be useful as a pre-processing step. Indeed you do not need to perform test on genes set you are not interested in. It will increase your need for computing resources and create supplementary noise for the Adjustment of Multiple Hypothesis Testing procedure.\n\n","title":"The Method","titles":["A quick overview of GSEA analysis","Principles"]},"11":{"href":"/blog/gsea#to-go-further-with-theory","html":"\n

There exists a variant of GSEA called FGSEA for Fast Gene Set Enrichment Analysis.

\n

Another common approach to perform pathways analysis is the Gene Ontology Enrichment analysis.

\n","isPage":false,"text":"\nThere exists a variant of GSEA called FGSEA for Fast Gene Set Enrichment Analysis.\nAnother common approach to perform pathways analysis is the Gene Ontology Enrichment analysis.\n","title":"To go further with theory","titles":["A quick overview of GSEA analysis","Principles"]},"12":{"href":"/blog/gsea#available-programs-for-practice","html":"\n\n\n
","isPage":false,"text":"\n\nOfficial Broad institute tools\nWEB-based GEne SeT AnaLysis Toolkit\nGUI\nR package\nFast Gene Set Enrichment Analysis (Pre-ranked only)\n\n\n","title":"Available programs for practice","titles":["A quick overview of GSEA analysis"]},"13":{"href":"/blog/gsea#footnote-label","html":"\n
    \n
  1. \n

    Timothy E. Sweeney , Winston A. Haynes , Francesco Vallania , John P. Ioannidis\nand Purvesh Khatri. (2017). Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Research, Volume 45(Issue 1), Page Range. DOI ↩

    \n
    2. \n
  2. \n

    Steven N Goodman , Daniele Fanelli , John P A Ioannidis. (2016). *What does research reproducibility mean? *. Sci Transl Med, 8(341),12. DOI ↩

    \n
    3. \n
  3. \n

    Lianbo Yu. (2020). RNA-Seq Reproducibility Assessment of the Sequencing Quality Control Project. Cancer Inform, Volume 19 DOI ↩

    \n
    4. \n
\n
","isPage":false,"text":"\n\n\nTimothy E. Sweeney , Winston A. Haynes , Francesco Vallania , John P. Ioannidis\nand Purvesh Khatri. (2017). Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Research, Volume 45(Issue 1), Page Range. DOI ↩\n\n\nSteven N Goodman , Daniele Fanelli , John P A Ioannidis. (2016). *What does research reproducibility mean? *. Sci Transl Med, 8(341),12. DOI ↩\n\n\nLianbo Yu. (2020). RNA-Seq Reproducibility Assessment of the Sequencing Quality Control Project. Cancer Inform, Volume 19 DOI ↩\n\n\n","title":"Footnotes","titles":["A quick overview of GSEA analysis"]},"14":{"href":"/blog#blog","html":"\n
","isPage":true,"text":"\n","title":"Blog","titles":[]},"15":{"href":"/projects/germlinevardb#germlinevardb","html":"\n

A graphical interface to explore your germline genomic variation database.


\n\"\"/

\n","isPage":true,"text":"\n A graphical interface to explore your germline genomic variation database.\n\n","title":"GermlineVarDB","titles":[]},"16":{"href":"/projects/germlinevardb#why-this-app","html":"\n

Few hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:

\n\n

Turning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:

\n\n

With this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.

\n

However, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.

\n

SomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.

\n","isPage":false,"text":"\nFew hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:\n\nLack of resources and investment capacity to anticipate rapid technological change.\nThe data professions, which are still in their infancy, have not yet fully penetrated these structures.\nCumbersome legal and administrative constraints on the accreditation of IT solutions for clinical use.\n\nTurning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:\n\nThe cost of the outsourced service is ultimately much higher than in-house.\nUsers (clinical biologists) work free of charge to sort the databases built by these external service providers from the raw data sent to them by the hospitals to which these clinicians belong.\nThe hospital will therefore once again pay for access to the Ias that have been trained thanks to its work.\n\nWith this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.\nHowever, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.\nSomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.\n","title":"Why this app","titles":["GermlineVarDB"]},"17":{"href":"/projects/germlinevardb#open-source","html":"\n\n","isPage":false,"text":"\n\nLive demo\nSource code\n\n","title":"Open-source","titles":["GermlineVarDB"]},"18":{"href":"/projects/germlinevardb#can-i-use-it-for-clinical-purposes-","html":"\n

Unfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time.

\nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.

\n
","isPage":false,"text":"\nUnfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time. \nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.\n","title":"Can I use it for clinical purposes ?","titles":["GermlineVarDB"]},"19":{"href":"/projects/germlinevardb#footnote-label","html":"\n
    \n
  1. \n

    Tuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩

    \n
    2. \n
  2. \n

    Cyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩

    \n
    3. \n
  3. \n

    Małgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL ↩

    \n
    4. \n
\n
","isPage":false,"text":"\n\n\nTuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩\n\n\nCyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩\n\n\nMałgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL ↩\n\n\n","title":"Footnotes","titles":["GermlineVarDB"]},"20":{"href":"/projects#here-are-my-projects","html":"\n","isPage":true,"text":"\n","title":"Here are my projects","titles":[]},"21":{"href":"/projects#omicsverse","html":"\n

The initial aim of the OmicsVerse portal is to host demo versions of the various data applications I create.

\n

Application source code and documentation for administrators can be found on my github.

\n

The following sections & pages will host detailed documentation of users.

\n

If you have a genomics-oriented data application to host, don't hesitate to contact me. provided there are resources available.

\n","isPage":false,"text":"\nThe initial aim of the OmicsVerse portal is to host demo versions of the various data applications I create.\nApplication source code and documentation for administrators can be found on my github.\nThe following sections & pages will host detailed documentation of users.\nIf you have a genomics-oriented data application to host, don't hesitate to contact me. provided there are resources available.\n","title":"OmicsVerse","titles":["Here are my projects"]},"22":{"href":"/projects/somavardb#somavardb","html":"\n

A graphical interface to explore your somatic genomic variation database.

\n\"\"/

\n","isPage":true,"text":"\n A graphical interface to explore your somatic genomic variation database.\n\n","title":"SomaVarDB","titles":[]},"23":{"href":"/projects/somavardb#why-this-app","html":"\n

Few hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:

\n\n

Turning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:

\n\n

With this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.

\n

However, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.

\n

SomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.

\n","isPage":false,"text":"\nFew hospitals do not outsource their bioinformatics analysis to private partners (at least in France). There are many reasons for this:\n\nLack of resources and investment capacity to anticipate rapid technological change.\nThe data professions, which are still in their infancy, have not yet fully penetrated these structures.\nCumbersome legal and administrative constraints on the accreditation of IT solutions for clinical use.\n\nTurning to an external service provider is therefore a short-term solution. In the long term, however, outsourcing poses two major problems:\n\nThe cost of the outsourced service is ultimately much higher than in-house.\nUsers (clinical biologists) work free of charge to sort the databases built by these external service providers from the raw data sent to them by the hospitals to which these clinicians belong.\nThe hospital will therefore once again pay for access to the Ias that have been trained thanks to its work.\n\nWith this in mind, I turned my attention to the question of re-internalising this activity at CHUGA.\nThe analytical part will be the subject of another article, but briefly,\nthe nf-core community is, in my opinion, the best candidate on which to base this objective.\nHowever, there is another blocking point. Bioinformatics providers provide, along with a pipeline.\nGraphical interfaces enabling biologists to efficiently process genomic variation data for diagnostic rendering. This being the case, even if you internalize an accredited clinical-quality pipeline, it will be difficult to convince them to go back to a much less attractive Excel spreadsheet. This will be perceived as a step backwards.\nSomaVarDB therefore aims to plug into the output of a nf-core pipeline, clinically accredited if possible.\nIt's a package that contains both the tools for building the local database from nf-core output,\nand the interface for interacting with this database.\n","title":"Why this app","titles":["SomaVarDB"]},"24":{"href":"/projects/somavardb#open-source","html":"\n\n","isPage":false,"text":"\n\nLive demo\nSource code\n\n","title":"Open-source","titles":["SomaVarDB"]},"25":{"href":"/projects/somavardb#can-i-use-it-for-clinical-purposes-","html":"\n

Unfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time.

\nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.

\n
","isPage":false,"text":"\nUnfortunately the answer is no at the moment. To do so it would first need to meet the compliance and\nRegulations in healthcare Software Development1,2,3.\nAs you can imagine, this is an unattainable goal for just one person in their spare time. \nSo if you feel like contributing to the project. Don't hesitate to contact me :\nbenoitclement.data@gmail.com.\n","title":"Can I use it for clinical purposes ?","titles":["SomaVarDB"]},"26":{"href":"/projects/somavardb#footnote-label","html":"\n
    \n
  1. \n

    Tuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩

    \n
    2. \n
  2. \n

    Cyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩

    \n
    3. \n
  3. \n

    Małgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL ↩

    \n
    4. \n
\n
","isPage":false,"text":"\n\n\nTuomas Granlund; Tommi Mikkonen; Vlad Stirbu (2020). On Medical Device Software CE Compliance and Conformity Assessment. IEEE, DOI ↩\n\n\nCyrille Michaud (2020). How to qualify, classify and CE mark software. MD101, URL ↩\n\n\nMałgorzata Kruszynska (2021). CE marking for Medical Device Software: a step-by-step guide. Spyrosoft, URL 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diff --git a/assets/cutonestrand-mMqmQ38Y.js b/assets/cutonestrand-mMqmQ38Y.js new file mode 100644 index 0000000..807cbff --- /dev/null +++ b/assets/cutonestrand-mMqmQ38Y.js @@ -0,0 +1,71 @@ +import{d as o,j as e}from"./index-DqZhg8K9.js";const i={layout:"minimal",authors:["[cbenoit](www.linkedin.com/in/clement-benoit)"],date:"2024-08-01",title:"How data analysis can help to fix genetic disorders",description:"undefined"};function a(t){const n={a:"a",div:"div",h1:"h1",h2:"h2",header:"header",li:"li",ol:"ol",p:"p",section:"section",strong:"strong",sup:"sup",...o(),...t.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"how-data-analysis-can-help-to-fix-genetic-disorders",children:["How data analysis can help to fix genetic disorders",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#how-data-analysis-can-help-to-fix-genetic-disorders",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` +`,e.jsxs(n.h2,{id:"introduction",children:["Introduction",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#introduction",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` +`,e.jsxs(n.p,{children:["Gene therapy as seen a major breakthrough with the development of ",e.jsx(n.strong,{children:"CRISPR-Cas9"}),` technology. +This revolutionary tool allows scientists to precisely edit genes, offering new hope for +treating genetic disorders and diseases. `,e.jsx(n.strong,{children:`With the potential to correct genetic mutations at +the source, CRISPR-Cas9 opens up a world of possibilities for personalized medicine and targeted therapies.`}),` +The future of gene therapy looks brighter than ever, +with the promise of improved treatments and even potential cures for a wide range of conditions.`]}),` +`,e.jsxs(n.p,{children:[e.jsx(n.a,{href:"https://www.genome.gov/genetics-glossary/Autosomal-Dominant-Disorder",children:"Autosomal-dominant disorders"}),` are among the diseases that could see gene treatments in the future. +As the name dominant implies, the presence of a single pathogenic mutated allele is sufficient for +the disease to appear, so some researchers are counting on crispr-cas9 technology to break the mutated allele. +Only the wild-type allele remains, and the disease is thus cured. +Although the effectiveness of this approach looks promising `,e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-1",id:"user-content-fnref-1","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"1"})})," ",e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-2",id:"user-content-fnref-2","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"2"})})," ",e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-3",id:"user-content-fnref-3","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"3"})}),`, a number of issues still need +to be addressed, two of which we will try to address in this article :`]}),` +`,e.jsx("p",{className:"popacitydanger",children:e.jsx("div",{style:{textAlign:"center"},children:e.jsx("strong",{children:e.jsxs(n.p,{children:["How can the design of these personalized medicine treatments can be effective and quick for each patient ? ",e.jsx("br",{}),e.jsx("br",{}),` +How can we specifically target the mutated allele without breaking the functional allele or another part of +the genome ?`]})})})}),` +`,e.jsxs(n.h2,{id:"data-analysis-can-be-use-to-create-a-list-of-interesting-genomic-regions-for-gene-therapy",children:["Data analysis can be use to create a list of interesting genomic regions for gene therapy",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#data-analysis-can-be-use-to-create-a-list-of-interesting-genomic-regions-for-gene-therapy",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` +`,e.jsxs(n.p,{children:[`The targeted genome cleavage is achieved by targeting sequence-specific cleavage of S. pyogenes Cas9 (spCas9) +endonuclease with a gRNA. In order for the gRNA to successfully direct Cas9 cleavage, +the corresponding target DNA sequence in the genome must be found next to a PAM site, +also known as a Protospacer Adjacent Motif. The canonical PAM is associated with the spCas9 nuclease is `,e.jsx(n.strong,{children:"5'-NGG-3'"}),`. +We are therefore going to try to draw up an exhaustive list of all the genomic regions that could be used for this +gene therapy.`]}),` +`,e.jsxs(n.ol,{children:[` +`,e.jsxs(n.li,{children:[`We start by selecting all the SNPs that are frequent in the population (> 5%), for which we can +use the gnomAD database `,e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-4",id:"user-content-fnref-4","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"4"})}),`. We want the list created to be usable to treat as many +patients as possible, so we avoid SNPs that are too rare.`]}),` +`,e.jsxs(n.li,{children:["Only SNPs that induce the disappearance or appearance of the ",e.jsx(n.strong,{children:"5‘-NGG-3’"}),` +motif will allow us to target only the mutated allele while preserving the WT. To do this, we wrote an in-house script in Python.`]}),` +`,e.jsxs(n.li,{children:["We used the ",e.jsx(n.a,{href:"https://github.com/lindenb/jvarkit",children:"jvarkit tools suite"}),` to reconstitute the genomic context of these SNPs, i.e. +to add the flanking sequences to the left and right of our SNPs of interest, according to the human reference genome.`]}),` +`,e.jsxs(n.li,{children:["Finally, we used the ",e.jsx(n.a,{href:"https://github.com/mckennalab/FlashFry",children:"FlashFry"}),` tool to calculate and predict efficiency and specificity +scores for each of the positions we selected. We wanted to cut the diseased gene efficiently, +without altering other regions of the genome.`]}),` +`]}),` +`,e.jsxs(n.p,{children:[`Using this method, we were able to draw up a list of genomic positions of interest in the treatment of Ryanodine receptor +type 1-related myopathies (RYR1-RM) of the ‘Autosomal-Dominant-Disorder’ type. `,e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-5",id:"user-content-fnref-5","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"5"})}),` +Thanks to next-generation sequencing, it is possible to obtain both genomic sequences of a patient +at a reasonable cost. All the positions on our list for which the patient is heterozygous are therefore +candidates for gene therapy!`]}),` +`,e.jsx(n.p,{children:e.jsx(n.a,{href:"https://github.com/clbenoit/CutOneStrand",children:"Check out the analysis code here !"})}),` +`,e.jsxs(n.h2,{id:"generalization",children:["Generalization",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#generalization",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` +`,e.jsx(n.p,{children:`Of course, the implementation of gene therapy has to deal with other obstacles and questions, +but this approach can be generalised to other Autosomal-Dominant-Disorders and enable carers to +screen the genome extensively in order to create a short list of regions +of interest for this type of gene therapy !`}),` +`,e.jsxs(n.section,{"data-footnotes":!0,className:"footnotes",children:[e.jsxs(n.h2,{className:"sr-only",id:"footnote-label",children:["Footnotes",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#footnote-label",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` +`,e.jsxs(n.ol,{children:[` +`,e.jsxs(n.li,{id:"user-content-fn-1",children:[` +`,e.jsxs(n.p,{children:["Anzalone A.V, Koblan L.W and Liu D.R . ",e.jsx(n.strong,{children:"Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors"})," ",e.jsx(n.a,{href:"https://www.nature.com/articles/s41587-020-0561-9",children:"DOI"})," ",e.jsx(n.a,{href:"#user-content-fnref-1","data-footnote-backref":"","aria-label":"Back to reference 1",className:"data-footnote-backref",children:"↩"})]}),` +`]}),` +`,e.jsxs(n.li,{id:"user-content-fn-2",children:[` +`,e.jsxs(n.p,{children:[`F Chemello, A.C Chai, H Li, C Rodriguez-Caycedo, E Sanchez-Ortiz, A Atmanli, A.A Mireault, N Liu, +R Bassel-Duby, E.N Olson. `,e.jsx(n.strong,{children:`Precise correction of Duchenne muscular dystrophy exon +deletion mutations by base and prime editing`})," ",e.jsx(n.a,{href:"https://pubmed.ncbi.nlm.nih.gov/33931459/",children:"DOI"})," ",e.jsx(n.a,{href:"#user-content-fnref-2","data-footnote-backref":"","aria-label":"Back to reference 2",className:"data-footnote-backref",children:"↩"})]}),` +`]}),` +`,e.jsxs(n.li,{id:"user-content-fn-3",children:[` +`,e.jsxs(n.p,{children:["Kelly Godbout, Joël Rousseau, Jacques P Tremblay. ",e.jsx(n.strong,{children:`Successful Correction by Prime Editing of a +Mutation in the RYR1 Gene Responsible for a Myopathy`})," ",e.jsx(n.a,{href:"https://www.mdpi.com/2073-4409/13/1/31",children:"DOI"})," ",e.jsx(n.a,{href:"#user-content-fnref-3","data-footnote-backref":"","aria-label":"Back to reference 3",className:"data-footnote-backref",children:"↩"})]}),` +`]}),` +`,e.jsxs(n.li,{id:"user-content-fn-4",children:[` +`,e.jsxs(n.p,{children:[e.jsx(n.a,{href:"https://gnomad.broadinstitute.org/about",children:"The Genome Aggregation Database (gnomAD)"})," ",e.jsx(n.a,{href:"#user-content-fnref-4","data-footnote-backref":"","aria-label":"Back to reference 4",className:"data-footnote-backref",children:"↩"})]}),` +`]}),` +`,e.jsxs(n.li,{id:"user-content-fn-5",children:[` +`,e.jsxs(n.p,{children:[`Mathilde Beaufils, Margaux Melka, Julie Brocard, Clement Benoit, Nagi Debbah, Kamel Mamchaoui, +Norma B. Romero, Anne Frédérique Dalmas-Laurent, Susana Quijano-Roy, Julien Fauré, John Rendu +and Isabelle Marty. `,e.jsx(n.strong,{children:"Functional benefit of CRISPR-Cas9-induced allele deletion for RYR1 dominant mutation"})," ",e.jsx(n.a,{href:"#user-content-fnref-5","data-footnote-backref":"","aria-label":"Back to reference 5",className:"data-footnote-backref",children:"↩"})]}),` +`]}),` +`]}),` +`]})]})}function r(t={}){const{wrapper:n}={...o(),...t.components};return n?e.jsx(n,{...t,children:e.jsx(a,{...t})}):a(t)}export{r as default,i as frontmatter}; diff --git a/assets/expdesign-5536fBV5.js b/assets/expdesign-BV4_cdNM.js similarity index 98% rename from assets/expdesign-5536fBV5.js rename to assets/expdesign-BV4_cdNM.js index a469a57..9e3c732 100644 --- a/assets/expdesign-5536fBV5.js +++ b/assets/expdesign-BV4_cdNM.js @@ -1,4 +1,4 @@ -import{d as o,j as e}from"./index-DDTn0JQN.js";const r={layout:"minimal",authors:["[cbenoit](www.linkedin.com/in/clement-benoit)"],date:"2024-02-15",title:"Experimental design in the context of next generation sequencing",description:"undefined"};function t(i){const n={a:"a",div:"div",h1:"h1",h2:"h2",header:"header",li:"li",ol:"ol",p:"p",strong:"strong",...o(),...i.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"experimental-design-in-the-context-of-next-generation-sequencing",children:["Experimental design in the context of next generation sequencing",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#experimental-design-in-the-context-of-next-generation-sequencing",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` +import{d as o,j as e}from"./index-DqZhg8K9.js";const r={layout:"minimal",authors:["[cbenoit](www.linkedin.com/in/clement-benoit)"],date:"2024-02-15",title:"Experimental design in the context of next generation sequencing",description:"undefined"};function t(i){const n={a:"a",div:"div",h1:"h1",h2:"h2",header:"header",li:"li",ol:"ol",p:"p",strong:"strong",...o(),...i.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"experimental-design-in-the-context-of-next-generation-sequencing",children:["Experimental design in the context of next generation sequencing",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#experimental-design-in-the-context-of-next-generation-sequencing",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` `,e.jsxs(n.h2,{id:"introduction",children:["Introduction",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#introduction",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` `,e.jsx(n.p,{children:"Coming from a biotechnology background, I was sensitized to the problems of experimental design. I then spent a few years as a bioinformatician, leaving this task to my platform colleagues."}),` `,e.jsxs(n.p,{children:[`As a bioinformatician, you focus on producing quality pipelines and algorithms for processing sequencer output. diff --git a/assets/germlinevardb-BAxt0Ab1.js b/assets/germlinevardb-XcNTVQb9.js similarity index 99% rename from assets/germlinevardb-BAxt0Ab1.js rename to assets/germlinevardb-XcNTVQb9.js index a528794..da2cf31 100644 --- a/assets/germlinevardb-BAxt0Ab1.js +++ b/assets/germlinevardb-XcNTVQb9.js @@ -1,4 +1,4 @@ -import{d as a,j as e}from"./index-DDTn0JQN.js";const o={layout:"deafult",title:"GermlineVarDB",description:"undefined"};function i(t){const n={a:"a",div:"div",em:"em",h1:"h1",h2:"h2",header:"header",img:"img",li:"li",ol:"ol",p:"p",section:"section",strong:"strong",sup:"sup",ul:"ul",...a(),...t.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"germlinevardb",children:["GermlineVarDB",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#germlinevardb",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` +import{d as a,j as e}from"./index-DqZhg8K9.js";const o={layout:"deafult",title:"GermlineVarDB",description:"undefined"};function i(t){const n={a:"a",div:"div",em:"em",h1:"h1",h2:"h2",header:"header",img:"img",li:"li",ol:"ol",p:"p",section:"section",strong:"strong",sup:"sup",ul:"ul",...a(),...t.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"germlinevardb",children:["GermlineVarDB",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#germlinevardb",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` `,e.jsxs(n.p,{children:[e.jsxs("div",{children:[" ",e.jsx("h3",{children:e.jsx("i",{children:"A graphical interface to explore your germline genomic variation database."})})]}),e.jsx("br",{}),` `,e.jsx(n.img,{src:"/portfolio/somavardb.gif",alt:""})]}),` `,e.jsxs(n.h2,{id:"why-this-app",children:["Why this app",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#why-this-app",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` diff --git a/assets/gsea-CNheLLXB.js b/assets/gsea-kOLEqhgC.js similarity index 92% rename from assets/gsea-CNheLLXB.js rename to assets/gsea-kOLEqhgC.js index 8ff2ecb..75dc8e2 100644 --- a/assets/gsea-CNheLLXB.js +++ b/assets/gsea-kOLEqhgC.js @@ -1,10 +1,11 @@ -import{d as i,j as e}from"./index-DDTn0JQN.js";const a={layout:"minimal",authors:["[cbenoit](www.linkedin.com/in/clement-benoit)"],date:"2024-02-15",title:"A quick overview of GSEA analysis",description:"undefined"};function s(t){const n={a:"a",div:"div",em:"em",h1:"h1",h2:"h2",h3:"h3",header:"header",li:"li",ol:"ol",p:"p",section:"section",strong:"strong",sup:"sup",ul:"ul",...i(),...t.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"a-quick-overview-of-gsea-analysis",children:["A quick overview of GSEA analysis",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#a-quick-overview-of-gsea-analysis",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` +import{d as i,j as e}from"./index-DqZhg8K9.js";const a={layout:"minimal",authors:["[cbenoit](www.linkedin.com/in/clement-benoit)"],date:"2024-02-15",title:"A quick overview of Gene sets enrichment analysis",description:"undefined"};function s(t){const n={a:"a",div:"div",em:"em",h1:"h1",h2:"h2",h3:"h3",header:"header",li:"li",ol:"ol",p:"p",section:"section",strong:"strong",sup:"sup",ul:"ul",...i(),...t.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"a-quick-overview-of-gene-sets-enrichment-analysis",children:["A quick overview of Gene sets enrichment analysis",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#a-quick-overview-of-gene-sets-enrichment-analysis",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` `,e.jsxs(n.h2,{id:"why-gsea-analysis-",children:["Why GSEA Analysis ?",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#why-gsea-analysis-",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` `,e.jsxs(n.p,{children:[`Genome wide expression analysis has become become a mainstay of genomics research. However, there is still a wide range of tools for interpreting these gene expression profiles. They all have pros, cons, and are still evolving. This, coupled with the fact that these studies rely on the testing of a large number of hypotheses and relatively small sample sizes, lead that whole-genome expression studies in particular, to be often not reproducible. This is why reproducibility is one of the major challenges facing studies involving whole-genome expression data. `,e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-1",id:"user-content-fnref-1","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"1"})}),", ",e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-2",id:"user-content-fnref-2","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"2"})}),", ",e.jsx(n.sup,{children:e.jsx(n.a,{href:"#user-content-fn-3",id:"user-content-fnref-3","data-footnote-ref":!0,"aria-describedby":"footnote-label",children:"3"})})]}),` `,e.jsx(n.p,{children:"Finally, interpreting lists of thousands of differentially expressed genes is a tedious exercise for the biologist."}),` -`,e.jsx(n.p,{children:"The GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies, while facilitating their interpetation."}),` +`,e.jsx(n.p,{children:`The GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies, +while facilitating their interpetation.`}),` `,e.jsxs(n.h2,{id:"principles",children:["Principles",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#principles",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` `,e.jsxs(n.h3,{id:"what-is-the-question-",children:["What is the question ?",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#what-is-the-question-",children:e.jsx(n.div,{"data-autolink-icon":!0})})]}),` `,e.jsxs(n.p,{children:["Let's says you have ranked a gene list ",e.jsx("b",{children:e.jsx("i",{children:"L"})})," according to your favourite metric. The GSEA tries to answer the following question : ",e.jsx("br",{})]}),` diff --git a/assets/index-C7UbjirX.js b/assets/index-7hivevq9.js similarity index 89% rename from assets/index-C7UbjirX.js rename to assets/index-7hivevq9.js index cf1692d..9925cac 100644 --- a/assets/index-C7UbjirX.js +++ b/assets/index-7hivevq9.js @@ -1,2 +1,2 @@ -import{d as a,j as e}from"./index-DDTn0JQN.js";const d={layout:"minimal",title:"Blog",description:"undefined"};function o(t){const n={a:"a",div:"div",h1:"h1",header:"header",...a(),...t.components};return e.jsxs(e.Fragment,{children:[e.jsx(n.header,{children:e.jsxs(n.h1,{id:"blog",children:["Blog",e.jsx(n.a,{"aria-hidden":"true",tabIndex:"-1",href:"#blog",children:e.jsx(n.div,{"data-autolink-icon":!0})})]})}),` +import{d as a,j as e}from"./index-DqZhg8K9.js";const d={layout:"minimal",title:"Blog",description:"undefined"};function o(t){const n={a:"a",div:"div",h1:"h1",header:"header",...a(),...t.components};return 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t.flags&256?(t.flags&=-257,r=ks(Error(M(422))),ca(e,t,a,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(i=r.fallback,o=t.mode,r=Il({mode:"visible",children:r.children},o,0,null),i=pr(i,o,a,null),i.flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,t.mode&1&&ho(t,e.child,null,a),t.child.memoizedState=Vc(a),t.memoizedState=Uc,i);if(!(t.mode&1))return ca(e,t,a,null);if(o.data==="$!"){if(r=o.nextSibling&&o.nextSibling.dataset,r)var l=r.dgst;return r=l,i=Error(M(419)),r=ks(i,r,void 0),ca(e,t,a,r)}if(l=(a&e.childLanes)!==0,pt||l){if(r=Ge,r!==null){switch(a&-a){case 4:o=2;break;case 16:o=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:o=32;break;case 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n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Hc=function(){};Zm=function(e,t,n,r){var o=e.memoizedProps;if(o!==r){e=t.stateNode,cr(tn.current);var i=null;switch(n){case"input":o=dc(e,o),r=dc(e,r),i=[];break;case"select":o=ke({},o,{value:void 0}),r=ke({},r,{value:void 0}),i=[];break;case"textarea":o=vc(e,o),r=vc(e,r),i=[];break;default:typeof o.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=Ja)}gc(n,r);var a;n=null;for(c in o)if(!r.hasOwnProperty(c)&&o.hasOwnProperty(c)&&o[c]!=null)if(c==="style"){var l=o[c];for(a in l)l.hasOwnProperty(a)&&(n||(n={}),n[a]="")}else 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r._enqueuedVacuumConditions=pu,r.performVacuuming(t,o)}),this._enqueuedVacuum)):this.vacuumConditionsMet(n)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(t),this._currentVacuum)},e.prototype.performVacuuming=function(t,n){return z4(this,void 0,void 0,function(){var r,o,i,a,l,s,c,f,u,d,p,y,g,C,v,m,x,E,_,b,S,$,k,P,j;return B4(this,function(L){switch(L.label){case 0:if(r=this._dirtCount,!this.vacuumConditionsMet(n))return[3,10];o=t.batchSize||hu.batchSize,i=t.batchWait||hu.batchWait,a=1,L.label=1;case 1:L.trys.push([1,7,8,9]),l=ne(this._index),s=l.next(),L.label=2;case 2:if(s.done)return[3,6];c=Le(s.value,2),f=c[0],u=c[1];try{for(d=($=void 0,ne(u)),p=d.next();!p.done;p=d.next()){y=Le(p.value,2),g=y[0],C=y[1];try{for(v=(P=void 0,ne(C)),m=v.next();!m.done;m=v.next())x=Le(m.value,1),E=x[0],!this._documentIds.has(E)&&(C.size<=1?u.delete(g):C.delete(E))}catch(D){P={error:D}}finally{try{m&&!m.done&&(j=v.return)&&j.call(v)}finally{if(P)throw 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var(--vocs-twoslash_borderColor);background:var(--vocs-twoslash_popupBackground);transform:rotate(-45deg);width:6px;height:6px;pointer-events:none}.twoslash .twoslash-popup-scroll-container::-webkit-scrollbar{display:none}.twoslash .twoslash-popup-jsdoc{border-top:1px solid var(--vocs-color_border2);color:var(--vocs-color_text);font-family:sans-serif;font-weight:500;margin-top:4px;padding:4px 10px 0}.twoslash-tag-line+.twoslash-tag-line{margin-top:-.2em}.twoslash-query-persisted .twoslash-popup-info{z-index:9;transform:translateY(1.5em)}.twoslash-hover:hover .twoslash-popup-info,.twoslash-query-persisted .twoslash-popup-info{opacity:1;pointer-events:auto}.twoslash-popup-info:hover,.twoslash-popup-info-hover:hover{-webkit-user-select:auto;-moz-user-select:auto;user-select:auto}.twoslash-error-line{position:relative;background-color:var(--vocs-twoslash_errorBackground);border-left:2px solid var(--vocs-twoslash_errorColor);color:var(--vocs-twoslash_errorColor);margin:.2em 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cd-bounce-2-inverse{0%{opacity:0;-webkit-transform:translateX(100px)}60%{opacity:1;-webkit-transform:translateX(-20px)}to{-webkit-transform:translateX(0)}}@keyframes cd-bounce-2-inverse{0%{opacity:0;-webkit-transform:translateX(100px);transform:translate(100px)}60%{opacity:1;-webkit-transform:translateX(-20px);transform:translate(-20px)}to{-webkit-transform:translateX(0);transform:translate(0)}} diff --git a/assets/style-JPChhUFf.css b/assets/style-JPChhUFf.css new file mode 100644 index 0000000..a50aba7 --- /dev/null +++ b/assets/style-JPChhUFf.css @@ -0,0 +1 @@ +@layer vocs_preflight;:root{--vocs-color_white: rgba(255 255 255 / 100%);--vocs-color_black: rgba(0 0 0 / 100%);--vocs-color_background: rgba(255 255 255 / 100%);--vocs-color_background2: #f9f9f9;--vocs-color_background3: #f6f6f6;--vocs-color_background4: #f0f0f0;--vocs-color_background5: #e8e8e8;--vocs-color_backgroundAccent: #5b5bd6;--vocs-color_backgroundAccentHover: #5151cd;--vocs-color_backgroundAccentText: rgba(255 255 255 / 100%);--vocs-color_backgroundBlueTint: #008cff0b;--vocs-color_backgroundDark: #f9f9f9;--vocs-color_backgroundGreenTint: #00a32f0b;--vocs-color_backgroundGreenTint2: #00a43319;--vocs-color_backgroundIrisTint: #0000ff07;--vocs-color_backgroundRedTint: #ff000008;--vocs-color_backgroundRedTint2: #f3000d14;--vocs-color_backgroundYellowTint: #f4dd0016;--vocs-color_border: #ececec;--vocs-color_border2: #cecece;--vocs-color_borderAccent: #5753c6;--vocs-color_borderBlue: #009eff2a;--vocs-color_borderGreen: #019c393b;--vocs-color_borderIris: #dadcff;--vocs-color_borderRed: #ff000824;--vocs-color_borderYellow: #ffd5008f;--vocs-color_heading: #202020;--vocs-color_inverted: rgba(0 0 0 / 100%);--vocs-color_shadow: #0000000f;--vocs-color_shadow2: #00000006;--vocs-color_text: #4c4c4c;--vocs-color_text2: #646464;--vocs-color_text3: #838383;--vocs-color_text4: #bbbbbb;--vocs-color_textAccent: #5753c6;--vocs-color_textAccentHover: #272962;--vocs-color_textBlue: #0d74ce;--vocs-color_textBlueHover: #113264;--vocs-color_textGreen: #218358;--vocs-color_textGreenHover: #193b2d;--vocs-color_textIris: #5753c6;--vocs-color_textIrisHover: #272962;--vocs-color_textRed: #ce2c31;--vocs-color_textRedHover: #641723;--vocs-color_textYellow: #9e6c00;--vocs-color_textYellowHover: #473b1f;--vocs-color_title: #202020}:root.dark{--vocs-color_white: rgba(255 255 255 / 100%);--vocs-color_black: rgba(0 0 0 / 100%);--vocs-color_background: #232225;--vocs-color_background2: #2b292d;--vocs-color_background3: #2e2c31;--vocs-color_background4: #323035;--vocs-color_background5: #3c393f;--vocs-color_backgroundAccent: #5b5bd6;--vocs-color_backgroundAccentHover: #5753c6;--vocs-color_backgroundAccentText: rgba(255 255 255 / 100%);--vocs-color_backgroundBlueTint: #008ff519;--vocs-color_backgroundDark: #1e1d1f;--vocs-color_backgroundGreenTint: #00a43319;--vocs-color_backgroundGreenTint2: #00a83829;--vocs-color_backgroundIrisTint: #000bff19;--vocs-color_backgroundRedTint: #f3000d14;--vocs-color_backgroundRedTint2: #ff000824;--vocs-color_backgroundYellowTint: #f4dd0016;--vocs-color_border: #3c393f;--vocs-color_border2: #6f6d78;--vocs-color_borderAccent: #6e6ade;--vocs-color_borderBlue: #009eff2a;--vocs-color_borderGreen: #019c393b;--vocs-color_borderIris: #303374;--vocs-color_borderRed: #ff000824;--vocs-color_borderYellow: #f4dd0016;--vocs-color_heading: #e9e9ea;--vocs-color_inverted: rgba(255 255 255 / 100%);--vocs-color_shadow: #00000000;--vocs-color_shadow2: rgba(0, 0, 0, .05);--vocs-color_text: #cfcfcf;--vocs-color_text2: #bdbdbe;--vocs-color_text3: #a7a7a8;--vocs-color_text4: #656567;--vocs-color_textAccent: #b1a9ff;--vocs-color_textAccentHover: #6e6ade;--vocs-color_textBlue: #70b8ff;--vocs-color_textBlueHover: #3b9eff;--vocs-color_textGreen: #3dd68c;--vocs-color_textGreenHover: #33b074;--vocs-color_textIris: #b1a9ff;--vocs-color_textIrisHover: #6e6ade;--vocs-color_textRed: #ff9592;--vocs-color_textRedHover: #ec5d5e;--vocs-color_textYellow: #f5e147;--vocs-color_textYellowHover: #e2a336;--vocs-color_title: rgba(255 255 255 / 100%)}:root{--vocs-color_blockquoteBorder: var(--vocs-color_border);--vocs-color_blockquoteText: var(--vocs-color_text3);--vocs-color_dangerBackground: var(--vocs-color_backgroundRedTint);--vocs-color_dangerBorder: var(--vocs-color_borderRed);--vocs-color_dangerText: var(--vocs-color_textRed);--vocs-color_dangerTextHover: var(--vocs-color_textRedHover);--vocs-color_infoBackground: var(--vocs-color_backgroundBlueTint);--vocs-color_infoBorder: var(--vocs-color_borderBlue);--vocs-color_infoText: var(--vocs-color_textBlue);--vocs-color_infoTextHover: var(--vocs-color_textBlueHover);--vocs-color_noteBackground: var(--vocs-color_background2);--vocs-color_noteBorder: var(--vocs-color_border);--vocs-color_noteText: var(--vocs-color_text2);--vocs-color_successBackground: var(--vocs-color_backgroundGreenTint);--vocs-color_successBorder: var(--vocs-color_borderGreen);--vocs-color_successText: var(--vocs-color_textGreen);--vocs-color_successTextHover: var(--vocs-color_textGreenHover);--vocs-color_tipBackground: var(--vocs-color_backgroundIrisTint);--vocs-color_tipBorder: var(--vocs-color_borderIris);--vocs-color_tipText: var(--vocs-color_textIris);--vocs-color_tipTextHover: var(--vocs-color_textIrisHover);--vocs-color_warningBackground: var(--vocs-color_backgroundYellowTint);--vocs-color_warningBorder: var(--vocs-color_borderYellow);--vocs-color_warningText: var(--vocs-color_textYellow);--vocs-color_warningTextHover: var(--vocs-color_textYellowHover);--vocs-color_codeBlockBackground: var(--vocs-color_background2);--vocs-color_codeCharacterHighlightBackground: var(--vocs-color_background5);--vocs-color_codeHighlightBackground: var(--vocs-color_background4);--vocs-color_codeHighlightBorder: var(--vocs-color_border2);--vocs-color_codeInlineBackground: var(--vocs-color_background4);--vocs-color_codeInlineBorder: var(--vocs-color_border);--vocs-color_codeInlineText: var(--vocs-color_textAccent);--vocs-color_codeTitleBackground: var(--vocs-color_background4);--vocs-color_lineNumber: var(--vocs-color_text4);--vocs-color_hr: var(--vocs-color_border);--vocs-color_link: var(--vocs-color_textAccent);--vocs-color_linkHover: var(--vocs-color_textAccentHover);--vocs-color_searchHighlightBackground: var(--vocs-color_borderAccent);--vocs-color_searchHighlightText: var(--vocs-color_background);--vocs-color_tableBorder: var(--vocs-color_border);--vocs-color_tableHeaderBackground: var(--vocs-color_background2);--vocs-color_tableHeaderText: var(--vocs-color_text2)}:root{--vocs-borderRadius_0: 0;--vocs-borderRadius_2: 2px;--vocs-borderRadius_3: 3px;--vocs-borderRadius_4: 4px;--vocs-borderRadius_6: 6px;--vocs-borderRadius_8: 8px}:root{--vocs-fontFamily_default: system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif;--vocs-fontFamily_mono: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace}:root{--vocs-fontSize_9: .5625rem;--vocs-fontSize_11: .6875rem;--vocs-fontSize_12: .75rem;--vocs-fontSize_13: .8125rem;--vocs-fontSize_14: .875rem;--vocs-fontSize_15: .9375rem;--vocs-fontSize_16: 1rem;--vocs-fontSize_18: 1.125rem;--vocs-fontSize_20: 1.25rem;--vocs-fontSize_24: 1.5rem;--vocs-fontSize_32: 2rem;--vocs-fontSize_64: 3rem;--vocs-fontSize_root: 16px;--vocs-fontSize_h1: var(--vocs-fontSize_32);--vocs-fontSize_h2: var(--vocs-fontSize_24);--vocs-fontSize_h3: var(--vocs-fontSize_20);--vocs-fontSize_h4: var(--vocs-fontSize_18);--vocs-fontSize_h5: var(--vocs-fontSize_16);--vocs-fontSize_h6: var(--vocs-fontSize_16);--vocs-fontSize_calloutCodeBlock: .8em;--vocs-fontSize_code: .875em;--vocs-fontSize_codeBlock: var(--vocs-fontSize_14);--vocs-fontSize_lineNumber: var(--vocs-fontSize_15);--vocs-fontSize_subtitle: var(--vocs-fontSize_20);--vocs-fontSize_th: var(--vocs-fontSize_14);--vocs-fontSize_td: var(--vocs-fontSize_14)}:root{--vocs-fontWeight_regular: 300;--vocs-fontWeight_medium: 400;--vocs-fontWeight_semibold: 500}:root{--vocs-lineHeight_code: 1.75em;--vocs-lineHeight_heading: 1.5em;--vocs-lineHeight_listItem: 1.5em;--vocs-lineHeight_outlineItem: 1em;--vocs-lineHeight_paragraph: 1.75em;--vocs-lineHeight_sidebarItem: 1.375em}:root{--vocs-space_0: 0px;--vocs-space_1: 1px;--vocs-space_2: .125rem;--vocs-space_3: .1875rem;--vocs-space_4: .25rem;--vocs-space_6: .375rem;--vocs-space_8: .5rem;--vocs-space_12: .75rem;--vocs-space_14: .875rem;--vocs-space_16: 1rem;--vocs-space_18: 1.125rem;--vocs-space_20: 1.25rem;--vocs-space_22: 1.375rem;--vocs-space_24: 1.5rem;--vocs-space_28: 1.75rem;--vocs-space_32: 2rem;--vocs-space_36: 2.25rem;--vocs-space_40: 2.5rem;--vocs-space_44: 2.75rem;--vocs-space_48: 3rem;--vocs-space_56: 3.5rem;--vocs-space_64: 4rem;--vocs-space_72: 4.5rem;--vocs-space_80: 5rem}:root{--vocs-zIndex_backdrop: 69420;--vocs-zIndex_drawer: 69421;--vocs-zIndex_gutterRight: 11;--vocs-zIndex_gutterLeft: 14;--vocs-zIndex_gutterTop: 13;--vocs-zIndex_gutterTopCurtain: 12;--vocs-zIndex_popover: 69422;--vocs-zIndex_surface: 10}:root{--vocs-content_horizontalPadding: var(--vocs-space_48);--vocs-content_verticalPadding: var(--vocs-space_32);--vocs-content_width: calc(70ch + (var(--vocs-content_horizontalPadding) * 2))}:root{--vocs-outline_width: 280px}:root{--vocs-sidebar_horizontalPadding: var(--vocs-space_24);--vocs-sidebar_verticalPadding: var(--vocs-space_0);--vocs-sidebar_width: 300px}:root{--vocs-topNav_height: 60px;--vocs-topNav_horizontalPadding: var(--vocs-content_horizontalPadding);--vocs-topNav_curtainHeight: 40px}@media screen and (width <= 1080px){:root{--vocs-content_verticalPadding: var(--vocs-space_48);--vocs-content_horizontalPadding: var(--vocs-space_24);--vocs-sidebar_horizontalPadding: var(--vocs-space_16);--vocs-sidebar_verticalPadding: var(--vocs-space_16);--vocs-sidebar_width: 300px;--vocs-topNav_height: 48px}}@media screen and (width <= 720px){:root{--vocs-content_horizontalPadding: var(--vocs-space_16);--vocs-content_verticalPadding: var(--vocs-space_32)}}.vocs_Banner{background-color:var(--vocs_Banner_bannerBackgroundColor, var(--vocs-color_backgroundAccent));border-bottom:1px solid var(--vocs_Banner_bannerBackgroundColor, var(--vocs-color_borderAccent));color:var(--vocs_Banner_bannerTextColor, var(--vocs-color_backgroundAccentText));height:var(--vocs_Banner_bannerHeight, 36px);position:fixed;top:0;width:100%;z-index:var(--vocs-zIndex_gutterTop)}.vocs_Banner_content{font-size:var(--vocs-fontSize_14);overflow-x:scroll;padding-left:var(--vocs-space_8);padding-right:var(--vocs-space_8);margin-right:var(--vocs-space_24);-ms-overflow-style:none;scrollbar-width:none;white-space:pre}.vocs_Banner_content::-webkit-scrollbar{display:none}.vocs_Banner_inner{align-items:center;display:flex;height:100%;justify-content:center;position:relative;width:100%}.vocs_Banner_closeButton{align-items:center;background-color:var(--vocs_Banner_bannerBackgroundColor, var(--vocs-color_backgroundAccent));display:flex;justify-content:center;height:100%;position:absolute;right:0;width:var(--vocs-space_24)}.vocs_Banner_content a{font-weight:400;text-underline-offset:2px;text-decoration:underline}@media screen and (width <= 1080px){.vocs_Banner{position:initial}}.vocs_DocsLayout{--vocs_DocsLayout_leftGutterWidth: max(calc((100vw - var(--vocs-content_width)) / 2), var(--vocs-sidebar_width))}.vocs_DocsLayout_content{background-color:var(--vocs-color_background);margin-left:auto;margin-right:auto;max-width:var(--vocs-content_width);min-height:100vh}.vocs_DocsLayout_content_withSidebar{margin-left:var(--vocs_DocsLayout_leftGutterWidth);margin-right:unset}.vocs_DocsLayout_gutterLeft{background-color:var(--vocs-color_backgroundDark);justify-content:flex-end;display:flex;height:100vh;position:fixed;top:var(--vocs_Banner_bannerHeight, 0px);width:var(--vocs_DocsLayout_leftGutterWidth);z-index:var(--vocs-zIndex_gutterLeft)}.vocs_DocsLayout_gutterTop{align-items:center;background-color:color-mix(in srgb,var(--vocs-color_background) 98%,transparent);height:var(--vocs-topNav_height);width:100vw;z-index:var(--vocs-zIndex_gutterTop)}.vocs_DocsLayout_gutterTopCurtain{display:flex;height:var(--vocs-topNav_curtainHeight);width:100vw;z-index:var(--vocs-zIndex_gutterTopCurtain)}.vocs_DocsLayout_gutterTopCurtain_hidden{background:unset;display:none}.vocs_DocsLayout_gutterRight{display:flex;height:100vh;overflow-y:auto;padding:calc(var(--vocs-content_verticalPadding) + var(--vocs-topNav_height) + var(--vocs-space_8)) var(--vocs-space_24) 0 0;position:fixed;top:var(--vocs_Banner_bannerHeight, 0px);right:0;width:calc((100vw - var(--vocs-content_width)) / 2);z-index:var(--vocs-zIndex_gutterRight)}.vocs_DocsLayout_gutterRight::-webkit-scrollbar{display:none}.vocs_DocsLayout_gutterRight_withSidebar{width:calc(100vw - var(--vocs-content_width) - var(--vocs_DocsLayout_leftGutterWidth))}.vocs_DocsLayout_outlinePopover{display:none;overflow-y:auto;height:calc(100vh - var(--vocs-topNav_height) - var(--vocs-topNav_curtainHeight))}.vocs_DocsLayout_sidebar{padding:var(--vocs-space_0) var(--vocs-sidebar_horizontalPadding) var(--vocs-space_24) var(--vocs-sidebar_horizontalPadding)}.vocs_DocsLayout_sidebarDrawer{display:none}@media screen and (width <= 720px){.vocs_DocsLayout_content{overflow-x:hidden}}@media screen and (width > 1080px){.vocs_DocsLayout_content_withTopNav{padding-top:calc(var(--vocs-topNav_height) + var(--vocs_Banner_bannerHeight, 0px))}.vocs_DocsLayout_gutterTop{padding-left:calc(var(--vocs_DocsLayout_leftGutterWidth) - var(--vocs-sidebar_width));padding-right:calc(var(--vocs_DocsLayout_leftGutterWidth) - var(--vocs-sidebar_width));position:fixed;top:var(--vocs_Banner_bannerHeight, 0px)}.vocs_DocsLayout_gutterTop_offsetLeftGutter{padding-left:var(--vocs_DocsLayout_leftGutterWidth)}.vocs_DocsLayout_gutterTopCurtain{position:fixed;top:calc(var(--vocs-topNav_height) + var(--vocs_Banner_bannerHeight, 0px))}.vocs_DocsLayout_gutterTopCurtain_withSidebar{margin-left:var(--vocs_DocsLayout_leftGutterWidth)}}@media screen and (width <= 1080px){.vocs_DocsLayout_content{margin-left:auto;margin-right:auto}.vocs_DocsLayout_gutterLeft{display:none}.vocs_DocsLayout_gutterTop{position:initial}.vocs_DocsLayout_gutterTop_sticky,.vocs_DocsLayout_gutterTopCurtain{position:sticky;top:0}.vocs_DocsLayout_outlinePopover,.vocs_DocsLayout_sidebarDrawer{display:block}}@media screen and (width <= 1280px){.vocs_DocsLayout_gutterRight{display:none}}@layer vocs_reset_reset;html,body,.vocs_DocsLayout{font-family:var(--vocs-fontFamily_default);font-feature-settings:"rlig" 1,"calt" 1;font-size:var(--vocs-fontSize_root)}button,select{text-transform:none}button,select{-webkit-appearance:button;-moz-appearance:button;appearance:button;background-color:transparent;background-image:none}:-moz-focusring{outline:auto}:-moz-ui-invalid{outline:auto}progress{vertical-align:baseline}::-webkit-inner-spin-button,::-webkit-outer-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;-moz-appearance:textfield;appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit-appearance:none;-moz-appearance:none;appearance:none}::-webkit-file-upload-button{-webkit-appearance:button;-moz-appearance:button;appearance:button;font:inherit}summary{display:list-item}blockquote,dl,dd,h1,h2,h3,h4,h5,h6,hr,figure,p,pre{margin:0}fieldset{margin:0;padding:0}legend{padding:0}ol,ul,menu{list-style:none;margin:0;padding:0}dialog{padding:0}textarea{resize:vertical}input::-moz-placeholder,textarea::-moz-placeholder{opacity:1}input::placeholder,textarea::placeholder{opacity:1}button,[role=button]{cursor:pointer}:disabled{overflow:default}img,svg,video,canvas,audio,iframe,embed,object{display:block;vertical-align:middle}img,video{max-width:100%;height:auto}[hidden]{display:none}@layer vocs_reset_reset{*,:before,:after{box-sizing:border-box;border-width:0;border-style:solid}*:focus-visible{outline:2px solid var(--vocs-color_borderAccent);outline-offset:2px;outline-style:dashed}html,body{-webkit-text-size-adjust:100%;-moz-text-size-adjust:100%;text-size-adjust:100%;-moz-tab-size:4;-o-tab-size:4;tab-size:4;line-height:inherit;margin:0;padding:0;border:0;text-rendering:optimizeLegibility}hr{height:0;color:inherit;border-top-width:1px}abbr:where([title]){-webkit-text-decoration:underline dotted;text-decoration:underline dotted}h1,h2,h3,h4,h5,h6{font-size:inherit;font-weight:inherit;text-wrap:balance}a{color:inherit;text-decoration:inherit}b,strong{font-weight:bolder}code,kbd,samp,pre{font-family:var(--vocs-fontFamily_mono);font-size:var(--vocs-fontSize_root)}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}table{border-color:inherit;border-collapse:collapse;text-indent:0}button,input,optgroup,select,textarea{font-family:inherit;font-feature-settings:inherit;font-variation-settings:inherit;font-size:100%;font-weight:inherit;line-height:inherit;color:inherit;margin:0;padding:0}}.vocs_Tabs{background-color:var(--vocs-color_codeBlockBackground);border:1px solid var(--vocs-color_codeInlineBorder);border-radius:var(--vocs-borderRadius_4)}.vocs_Tabs_list{background-color:var(--vocs-color_codeTitleBackground);border-bottom:1px solid 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