From 810bfbb987c60edf37ca8e8020bbb05c77b022e8 Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Tue, 27 Feb 2024 17:33:50 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- index.html | 120 +++--- prepare/prepare-11.html | 822 --------------------------------------- prepare/prepare-16.html | 827 --------------------------------------- prepare/prepare-19.html | 831 ---------------------------------------- prepare/prepare-23.html | 825 --------------------------------------- prepare/prepare-30.html | 819 --------------------------------------- prepare/prepare-31.html | 825 --------------------------------------- search.json | 72 +--- sitemap.xml | 54 +-- 10 files changed, 77 insertions(+), 5120 deletions(-) delete mode 100644 prepare/prepare-11.html delete mode 100644 prepare/prepare-16.html delete mode 100644 prepare/prepare-19.html delete mode 100644 prepare/prepare-23.html delete mode 100644 prepare/prepare-30.html delete mode 100644 prepare/prepare-31.html diff --git a/.nojekyll b/.nojekyll index 36e0c7d2..1fd3d8a5 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -83236ade \ No newline at end of file +25e9c6fa \ No newline at end of file diff --git a/index.html b/index.html index 9715cc3d..6be82519 100644 --- a/index.html +++ b/index.html @@ -243,24 +243,24 @@

MOLB 7950: Informatics and Statistics for Molecular Biology
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MOLB 7950: Informatics and Statistics for Molecular BiologyTaliaferro Intro to R & RStudio 📖 -📃 +📄 💪 🧠 @@ -857,7 +857,7 @@

MOLB 7950: Informatics and Statistics for Molecular BiologyStatistics Ramachandran Probability and descriptive stats -📖 + @@ -925,7 +925,7 @@

MOLB 7950: Informatics and Statistics for Molecular BiologyMapping chromatin structure and transactions Ramachandran Experimental overview -📖 + @@ -967,7 +967,7 @@

MOLB 7950: Informatics and Statistics for Molecular BiologyWhere do proteins bind in the genome? Ramachandran Experimental overview -📖 + @@ -1022,7 +1022,7 @@

MOLB 7950: Informatics and Statistics for Molecular BiologyDifferential Gene Expression Taliaferro - -📖 + @@ -1119,7 +1119,7 @@

MOLB 7950: Informatics and Statistics for Molecular BiologyLong-read sequencing Hesselberth - -📖 + @@ -1135,7 +1135,7 @@

MOLB 7950: Informatics and Statistics for Molecular BiologySingle-cell Riemondy - -📖 + diff --git a/prepare/prepare-11.html b/prepare/prepare-11.html deleted file mode 100644 index 3d4c1e70..00000000 --- a/prepare/prepare-11.html +++ /dev/null @@ -1,822 +0,0 @@ - - - - - - - - - -MOLB 7950 - Stats Bootcamp - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Stats Bootcamp

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Class 11

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Prepare

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Watch the following videos from StatQuest (it will take ~15 mins to watch them all):

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Histograms, Clearly Explained

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The Main Ideas behind Probability Distributions

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The Normal Distribution, Clearly Explained

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Preparation for the DNA Block

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Chromatin accessibility

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You will need to review this material before class 17.

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Papers we will discuss in the block

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We’ll use data from the following studies in chromatin accessibility section.

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Schep AN, Buenrostro JD, Denny SK, Schwartz K, Sherlock G, Greenleaf WJ. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Res. 2015 PMID: 26314830; PMCID: PMC4617971 [Link]

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Zentner GE, Henikoff S. Mot1 redistributes TBP from TATA-containing to TATA-less promoters. Mol Cell Biol. 2013 PMID: 24144978; PMCID: PMC3889552. [Link]

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New software we will use in the block

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GViz enables visualization of genomic signals in a “track” format. Review the GViz vignette, especially the “Basic Features” section, which provides an overview.

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valr is a tool set for genome interval manipulation with R. Read over the “Getting Started” to get a sense of the tools and the types of analysis they enable.

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ComplexHeatmap provides a flexible framework for generating heatmaps. Look over the “A Single Heatmap” section (section 2).

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Preparation for factor-centric chromatin analysis

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JH

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You will need to review this material before class 20.

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Experimental methods

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Skene PJ, Henikoff S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife. 2017 PMID: 28079019; PMCID: PMC5310842. [Link]

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Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG, Ahmad K, Henikoff S. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun. 2019 PMID: 31036827; PMCID: PMC6488672. [Link]

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Software tools

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MACS is the gold-standard in peak calling. It models read coverage as a Poisson process, enabling identification of regions of higher than expected coverage (i.e., peaks) to be identified using a single parmaeter (lambda) that captures the mean and variance of read coverage. Read over the paper to get a sense of how it works.

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We’ll use the motifRG R library, which implements a discriminative (i.e., foreground / background) approach for motif discovery and answer the question, “Which sequences drive factor association to DNA?”.

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Preparation for RNA-seq analysis

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Papers we will discuss in the block

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We’ll use data from the following studies in the RNA-seq section.

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Hubbard KS, Gut IM, Lyman ME, McNutt PM. Longitudinal RNA sequencing of the deep transcriptome during neurogenesis of cortical glutamatergic neurons from murine ESCs. F1000Res. 2013 PMID: 24358889; PMCID: PMC3829120. [Link]

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Preparation for long-read squencing

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These recent papers provide insights that could only be made with the information gleaned by long-read sequencing.

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Long-read RNA sequencing

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Alfonso-Gonzalez C, Legnini I, Holec S, Arrigoni L, Ozbulut HC, Mateos F, Koppstein D, Rybak-Wolf A, Bönisch U, Rajewsky N, Hilgers V. Sites of transcription initiation drive mRNA isoform selection. Cell. 2023 PMID: 37178687; PMCID: PMC10228280. [Link]

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Choquet K, Baxter-Koenigs AR, Dülk SL, Smalec BM, Rouskin S, Churchman LS. Pre-mRNA splicing order is predetermined and maintains splicing fidelity across multi-intronic transcripts. Nat Struct Mol Biol. 2023 Aug;30(8):1064-1076. doi: 10.1038/s41594-023-01035-2. Epub 2023 Jul 13. PMID: 37443198.

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Long-read DNA sequencing

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Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV, Mikheenko A, Vollger MR, Altemose N, Uralsky L, Gershman A, Aganezov S, Hoyt SJ, Diekhans M, Logsdon GA, Alonge M, Antonarakis SE, Borchers M, Bouffard GG, Brooks SY, Caldas GV, Chen NC, Cheng H, Chin CS, Chow W, de Lima LG, Dishuck PC, Durbin R, Dvorkina T, Fiddes IT, Formenti G, Fulton RS, Fungtammasan A, Garrison E, Grady PGS, Graves-Lindsay TA, Hall IM, Hansen NF, Hartley GA, Haukness M, Howe K, Hunkapiller MW, Jain C, Jain M, Jarvis ED, Kerpedjiev P, Kirsche M, Kolmogorov M, Korlach J, Kremitzki M, Li H, Maduro VV, Marschall T, McCartney AM, McDaniel J, Miller DE, Mullikin JC, Myers EW, Olson ND, Paten B, Peluso P, Pevzner PA, Porubsky D, Potapova T, Rogaev EI, Rosenfeld JA, Salzberg SL, Schneider VA, Sedlazeck FJ, Shafin K, Shew CJ, Shumate A, Sims Y, Smit AFA, Soto DC, Sović I, Storer JM, Streets A, Sullivan BA, Thibaud-Nissen F, Torrance J, Wagner J, Walenz BP, Wenger A, Wood JMD, Xiao C, Yan SM, Young AC, Zarate S, Surti U, McCoy RC, Dennis MY, Alexandrov IA, Gerton JL, O’Neill RJ, Timp W, Zook JM, Schatz MC, Eichler EE, Miga KH, Phillippy AM. The complete sequence of a human genome. Science. 2022 PMID: 35357919; PMCID: PMC9186530. [Link]

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Stergachis AB, Debo BM, Haugen E, Churchman LS, Stamatoyannopoulos JA. Single-molecule regulatory architectures captured by chromatin fiber sequencing. Science. 2020 Jun 26;368(6498):1449-1454. doi: 10.1126/science.aaz1646. PMID: 32587015.

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Preparation for scRNA-seq analysis

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KR

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Experimental methods

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In this section we will analyze data generated by the 10x Genomics Chromium scRNA-seq platform. The following paper introduces the technology:

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Zheng GXY, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Communications. 2017;8:ncomms14049. https://doi.org/10.1038/ncomms14049 [Link].

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- - - - - - \ No newline at end of file diff --git a/search.json b/search.json index f5000b17..c66a3f61 100644 --- a/search.json +++ b/search.json @@ -77,54 +77,12 @@ "section": "Submit", "text": "Submit\nBe sure to click the “Render” button to render the HTML output.\nThen paste the URL of this Posit Cloud project into the problem set on Canvas." }, - { - "objectID": "prepare/prepare-30.html", - "href": "prepare/prepare-30.html", - "title": "Preparation for long-read squencing", - "section": "", - "text": "These recent papers provide insights that could only be made with the information gleaned by long-read sequencing.\n\nLong-read RNA sequencing\nAlfonso-Gonzalez C, Legnini I, Holec S, Arrigoni L, Ozbulut HC, Mateos F, Koppstein D, Rybak-Wolf A, Bönisch U, Rajewsky N, Hilgers V. Sites of transcription initiation drive mRNA isoform selection. Cell. 2023 PMID: 37178687; PMCID: PMC10228280. [Link]\nChoquet K, Baxter-Koenigs AR, Dülk SL, Smalec BM, Rouskin S, Churchman LS. Pre-mRNA splicing order is predetermined and maintains splicing fidelity across multi-intronic transcripts. Nat Struct Mol Biol. 2023 Aug;30(8):1064-1076. doi: 10.1038/s41594-023-01035-2. Epub 2023 Jul 13. 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The complete sequence of a human genome. Science. 2022 PMID: 35357919; PMCID: PMC9186530. [Link]\nStergachis AB, Debo BM, Haugen E, Churchman LS, Stamatoyannopoulos JA. Single-molecule regulatory architectures captured by chromatin fiber sequencing. Science. 2020 Jun 26;368(6498):1449-1454. doi: 10.1126/science.aaz1646. PMID: 32587015." - }, - { - "objectID": "prepare/prepare-19.html", - "href": "prepare/prepare-19.html", - "title": "Preparation for factor-centric chromatin analysis", - "section": "", - "text": "Important\n\n\n\nYou will need to review this material before class 20." - }, - { - "objectID": "prepare/prepare-19.html#experimental-methods", - "href": "prepare/prepare-19.html#experimental-methods", - "title": "Preparation for factor-centric chromatin analysis", - "section": "Experimental methods", - "text": "Experimental methods\nSkene PJ, Henikoff S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. 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Read over the paper to get a sense of how it works.\nWe’ll use the motifRG R library, which implements a discriminative (i.e., foreground / background) approach for motif discovery and answer the question, “Which sequences drive factor association to DNA?”." - }, - { - "objectID": "prepare/prepare-11.html", - "href": "prepare/prepare-11.html", - "title": "Stats Bootcamp", - "section": "", - "text": "Watch the following videos from StatQuest (it will take ~15 mins to watch them all):" - }, - { - "objectID": "prepare/prepare-11.html#prepare", - "href": "prepare/prepare-11.html#prepare", - "title": "Stats Bootcamp", - "section": "", - "text": "Watch the following videos from StatQuest (it will take ~15 mins to watch them all):" - }, { "objectID": "index.html", "href": "index.html", "title": "MOLB 7950: Informatics and Statistics for Molecular Biology", "section": "", - "text": "This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses, with all changes documented here.\n\n\n\n\n\n\n\n\n\nMOLB 7950 - Fall 2023 Schedule\n\n\nClasses held in-person in TBD, 9:00-10:30am\n\n\n\nDate\nBlock\nTopic\nInstructor\nTitle\nLinks\n\n\nPrepare\nSlides\nExercises\nHW\nKey\n\n\n\n\nWeek 1\n\n\n01\nMon, Aug 26, 2024\nBootcamp\nR\nTaliaferro\nIntro to R & RStudio\n📖\n📃\n💪\n🧠\n\n\n\n02\nTue, Aug 27, 2024\nBootcamp\nR\nTaliaferro\nTidy data & tidyr\n\n\n\n\n\n\n\n03\nWed, Aug 28, 2024\nBootcamp\nR\nTaliaferro\ndplyr\n\n\n\n\n\n\n\n04\nThu, Aug 29, 2024\nBootcamp\nR\nTaliaferro\nggplot2\n\n\n\n\n\n\n\n05\nFri, Aug 30, 2024\nBootcamp\nR\nTaliaferro\nggplot2\n\n\n\n\n\n\n\nWeek 2\n\n\n06\nMon, Sep 2, 2024\n-\n-\n-\nNO CLASS: LABOR DAY\n\n\n\n\n\n\n\n07\nTue, Sep 3, 2024\nBootcamp\nR\nTaliaferro\ntidyverse odds & ends\n\n\n\n\n\n\n\n08\nWed, Sep 4, 2024\nBootcamp\nR\nTaliaferro\nputting it all together\n\n\n\n\n\n\n\n09\nThu, Sep 5, 2024\nBootcamp\nR\nTaliaferro\nputting it all together\n\n\n\n\n\n\n\n10\nFri, Sep 6, 2024\nBootcamp\nStatistics\nRamachandran\nStats intro and history\n\n\n\n\n\n\n\nWeek 3\n\n\n11\nMon, Sep 9, 2024\nBootcamp\nStatistics\nRamachandran\nProbability and descriptive stats\n📖\n\n\n\n\n\n\n12\nTue, Sep 10, 2024\nBootcamp\nStatistics\nRamachandran\nHypothesis testing\n\n\n\n\n\n\n\n13\nWed, Sep 11, 2024\nBootcamp\nStatistics\nRamachandran\nHypothesis testing\n\n\n\n\n\n\n\n14\nThu, Sep 12, 2024\nBootcamp\nStatistics\nRamachandran\nExploratory data analysis\n\n\n\n\n\n\n\n15\nFri, Sep 13, 2024\nBootcamp\nStatistics\nRamachandran\nBig data concerns\n\n\n\n\n\n\n\nWeek 4\n\n\n16\nMon, Sep 16, 2024\nDNA\nMapping chromatin structure and transactions\nRamachandran\nExperimental overview\n📖\n\n\n\n\n\n\n17\nWed, Sep 18, 2024\nDNA\nChromatin-centric methods\nRamachandran\nInformation from fragment length distributions\n\n\n\n\n\n\n\n18\nFri, Sep 20, 2024\nDNA\nChromatin-centric methods\nRamachandran\nMeta-plots and heatmaps\n\n\n\n\n\n\n\nWeek 5\n\n\n19\nMon, Sep 23, 2024\nDNA\nWhere do proteins bind in the genome?\nRamachandran\nExperimental overview\n📖\n\n\n\n\n\n\n20\nWed, Sep 25, 2024\nDNA\nFactor-centric methods\nRamachandran\nPeak calling\n\n\n\n\n\n\n\n21\nFri, Sep 27, 2024\nDNA\nFactor-centric methods\nRamachandran\nSequence motif analysis\n\n\n\n\n\n\n\nWeek 6\n\n\n22\nMon, Sep 30, 2024\nRNA\nRNA-seq Overview\nTaliaferro\n-\n\n\n\n\n\n\n\n23\nWed, Oct 2, 2024\nRNA\nDifferential Gene Expression\nTaliaferro\n-\n📖\n\n\n\n\n\n\n24\nFri, Oct 4, 2024\nRNA\nDifferential Gene Expression\nTaliaferro\n-\n\n\n\n\n\n\n\nWeek 7\n\n\n25\nMon, Oct 7, 2024\nRNA\nAlternative Splicing\nTaliaferro\n-\n\n\n\n\n\n\n\n26\nWed, Oct 9, 2024\nRNA\nVignette\nTaliaferro\n-\n\n\n\n\n\n\n\n27\nFri, Oct 11, 2024\n-\n-\n-\nNO CLASS: CSDV RETREAT\n\n\n\n\n\n\n\nWeek 8\n\n\n28\nMon, Oct 14, 2024\nRNA\nRBP\nTaliaferro\n-\n\n\n\n\n\n\n\n29\nWed, Oct 16, 2024\nRNA\nRBP\nTaliaferro\n-\n\n\n\n\n\n\n\n30\nFri, Oct 18, 2024\nRNA\nLong-read sequencing\nHesselberth\n-\n📖\n\n\n\n\n\n\nWeek 9\n\n\n31\nMon, Oct 21, 2024\nRNA\nSingle-cell\nRiemondy\n-\n📖\n\n\n\n\n\n\n32\nWed, Oct 23, 2024\nRNA\nSingle-cell\nRiemondy\n-\n\n\n\n\n\n\n\n33\nFri, Oct 25, 2024\n-\n-\n-\nNO CLASS: MOLB RETREAT\n\n\n\n\n\n\n\nWeek 10\n\n\n34\nMon, Oct 28, 2024\nFinal\n-\n-\nFinal project presentations\n\n\n\n\n\n\n\n35\nWed, Oct 30, 2024\nFinal\n-\n-\nFinal project presentations", + "text": "This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses, with all changes documented here.\n\n\n\n\n\n\n\n\n\nMOLB 7950 - Fall 2023 Schedule\n\n\nClasses held in-person in TBD, 9:00-10:30am\n\n\n\nDate\nBlock\nTopic\nInstructor\nTitle\nLinks\n\n\nPrepare\nSlides\nExercises\nHW\nKey\n\n\n\n\nWeek 1\n\n\n01\nMon, Aug 26, 2024\nBootcamp\nR\nTaliaferro\nIntro to R & RStudio\n📖\n📄\n💪\n🧠\n\n\n\n02\nTue, Aug 27, 2024\nBootcamp\nR\nTaliaferro\nTidy data & tidyr\n\n\n\n\n\n\n\n03\nWed, Aug 28, 2024\nBootcamp\nR\nTaliaferro\ndplyr\n\n\n\n\n\n\n\n04\nThu, Aug 29, 2024\nBootcamp\nR\nTaliaferro\nggplot2\n\n\n\n\n\n\n\n05\nFri, Aug 30, 2024\nBootcamp\nR\nTaliaferro\nggplot2\n\n\n\n\n\n\n\nWeek 2\n\n\n06\nMon, Sep 2, 2024\n-\n-\n-\nNO CLASS: LABOR DAY\n\n\n\n\n\n\n\n07\nTue, Sep 3, 2024\nBootcamp\nR\nTaliaferro\ntidyverse odds & ends\n\n\n\n\n\n\n\n08\nWed, Sep 4, 2024\nBootcamp\nR\nTaliaferro\nputting it all together\n\n\n\n\n\n\n\n09\nThu, Sep 5, 2024\nBootcamp\nR\nTaliaferro\nputting it all together\n\n\n\n\n\n\n\n10\nFri, Sep 6, 2024\nBootcamp\nStatistics\nRamachandran\nStats intro and history\n\n\n\n\n\n\n\nWeek 3\n\n\n11\nMon, Sep 9, 2024\nBootcamp\nStatistics\nRamachandran\nProbability and descriptive stats\n\n\n\n\n\n\n\n12\nTue, Sep 10, 2024\nBootcamp\nStatistics\nRamachandran\nHypothesis testing\n\n\n\n\n\n\n\n13\nWed, Sep 11, 2024\nBootcamp\nStatistics\nRamachandran\nHypothesis testing\n\n\n\n\n\n\n\n14\nThu, Sep 12, 2024\nBootcamp\nStatistics\nRamachandran\nExploratory data analysis\n\n\n\n\n\n\n\n15\nFri, Sep 13, 2024\nBootcamp\nStatistics\nRamachandran\nBig data concerns\n\n\n\n\n\n\n\nWeek 4\n\n\n16\nMon, Sep 16, 2024\nDNA\nMapping chromatin structure and transactions\nRamachandran\nExperimental overview\n\n\n\n\n\n\n\n17\nWed, Sep 18, 2024\nDNA\nChromatin-centric methods\nRamachandran\nInformation from fragment length distributions\n\n\n\n\n\n\n\n18\nFri, Sep 20, 2024\nDNA\nChromatin-centric methods\nRamachandran\nMeta-plots and heatmaps\n\n\n\n\n\n\n\nWeek 5\n\n\n19\nMon, Sep 23, 2024\nDNA\nWhere do proteins bind in the genome?\nRamachandran\nExperimental overview\n\n\n\n\n\n\n\n20\nWed, Sep 25, 2024\nDNA\nFactor-centric methods\nRamachandran\nPeak calling\n\n\n\n\n\n\n\n21\nFri, Sep 27, 2024\nDNA\nFactor-centric methods\nRamachandran\nSequence motif analysis\n\n\n\n\n\n\n\nWeek 6\n\n\n22\nMon, Sep 30, 2024\nRNA\nRNA-seq Overview\nTaliaferro\n-\n\n\n\n\n\n\n\n23\nWed, Oct 2, 2024\nRNA\nDifferential Gene Expression\nTaliaferro\n-\n\n\n\n\n\n\n\n24\nFri, Oct 4, 2024\nRNA\nDifferential Gene Expression\nTaliaferro\n-\n\n\n\n\n\n\n\nWeek 7\n\n\n25\nMon, Oct 7, 2024\nRNA\nAlternative Splicing\nTaliaferro\n-\n\n\n\n\n\n\n\n26\nWed, Oct 9, 2024\nRNA\nVignette\nTaliaferro\n-\n\n\n\n\n\n\n\n27\nFri, Oct 11, 2024\n-\n-\n-\nNO CLASS: CSDV RETREAT\n\n\n\n\n\n\n\nWeek 8\n\n\n28\nMon, Oct 14, 2024\nRNA\nRBP\nTaliaferro\n-\n\n\n\n\n\n\n\n29\nWed, Oct 16, 2024\nRNA\nRBP\nTaliaferro\n-\n\n\n\n\n\n\n\n30\nFri, Oct 18, 2024\nRNA\nLong-read sequencing\nHesselberth\n-\n\n\n\n\n\n\n\nWeek 9\n\n\n31\nMon, Oct 21, 2024\nRNA\nSingle-cell\nRiemondy\n-\n\n\n\n\n\n\n\n32\nWed, Oct 23, 2024\nRNA\nSingle-cell\nRiemondy\n-\n\n\n\n\n\n\n\n33\nFri, Oct 25, 2024\n-\n-\n-\nNO CLASS: MOLB RETREAT\n\n\n\n\n\n\n\nWeek 10\n\n\n34\nMon, Oct 28, 2024\nFinal\n-\n-\nFinal project presentations\n\n\n\n\n\n\n\n35\nWed, Oct 30, 2024\nFinal\n-\n-\nFinal project presentations", "crumbs": [ "Course information", "Schedule" @@ -459,34 +417,6 @@ "section": "Prepare", "text": "Prepare\n📖 Read the syllabus\n📖 Read the support resources\n📖 Look over the RStudio cheatsheet" }, - { - "objectID": "prepare/prepare-16.html", - "href": "prepare/prepare-16.html", - "title": "Preparation for the DNA Block", - "section": "", - "text": "Important\n\n\n\nYou will need to review this material before class 17.\n\n\n\nPapers we will discuss in the block\nWe’ll use data from the following studies in chromatin accessibility section.\nSchep AN, Buenrostro JD, Denny SK, Schwartz K, Sherlock G, Greenleaf WJ. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Res. 2015 PMID: 26314830; PMCID: PMC4617971 [Link]\nZentner GE, Henikoff S. Mot1 redistributes TBP from TATA-containing to TATA-less promoters. Mol Cell Biol. 2013 PMID: 24144978; PMCID: PMC3889552. [Link]\n\n\nNew software we will use in the block\nGViz enables visualization of genomic signals in a “track” format. Review the GViz vignette, especially the “Basic Features” section, which provides an overview.\nvalr is a tool set for genome interval manipulation with R. Read over the “Getting Started” to get a sense of the tools and the types of analysis they enable.\nComplexHeatmap provides a flexible framework for generating heatmaps. Look over the “A Single Heatmap” section (section 2)." - }, - { - "objectID": "prepare/prepare-23.html", - "href": "prepare/prepare-23.html", - "title": "Preparation for RNA-seq analysis", - "section": "", - "text": "Important\n\n\n\nYou will need to review this material before class 23.\n\n\n\nPapers we will discuss in the block\nWe’ll use data from the following studies in the RNA-seq section.\nHubbard KS, Gut IM, Lyman ME, McNutt PM. Longitudinal RNA sequencing of the deep transcriptome during neurogenesis of cortical glutamatergic neurons from murine ESCs. F1000Res. 2013 PMID: 24358889; PMCID: PMC3829120. [Link]" - }, - { - "objectID": "prepare/prepare-31.html", - "href": "prepare/prepare-31.html", - "title": "Preparation for scRNA-seq analysis", - "section": "", - "text": "Important\n\n\n\nYou will need to review this material before class 31." - }, - { - "objectID": "prepare/prepare-31.html#experimental-methods", - "href": "prepare/prepare-31.html#experimental-methods", - "title": "Preparation for scRNA-seq analysis", - "section": "Experimental methods", - "text": "Experimental methods\nIn this section we will analyze data generated by the 10x Genomics Chromium scRNA-seq platform. The following paper introduces the technology:\nZheng GXY, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Communications. 2017;8:ncomms14049. https://doi.org/10.1038/ncomms14049 [Link]." - }, { "objectID": "resources/block-dna-resources.html", "href": "resources/block-dna-resources.html", diff --git a/sitemap.xml b/sitemap.xml index b8ce80b6..80c1f874 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,86 +2,62 @@ https://rnabioco.github.io/molb-7950/zzz.html - 2024-02-27T15:49:10.663Z + 2024-02-27T17:31:36.362Z https://rnabioco.github.io/molb-7950/resources/plot-competition.html - 2024-02-27T15:49:10.659Z + 2024-02-27T17:31:36.358Z https://rnabioco.github.io/molb-7950/resources/block-rna-resources.html - 2024-02-27T15:49:10.659Z + 2024-02-27T17:31:36.358Z https://rnabioco.github.io/molb-7950/problem-sets/ps-01.html - 2024-02-27T15:49:10.659Z - - - https://rnabioco.github.io/molb-7950/prepare/prepare-30.html - 2024-02-27T15:49:10.655Z - - - https://rnabioco.github.io/molb-7950/prepare/prepare-19.html - 2024-02-27T15:49:10.655Z - - - https://rnabioco.github.io/molb-7950/prepare/prepare-11.html - 2024-02-27T15:49:10.655Z + 2024-02-27T17:31:36.358Z https://rnabioco.github.io/molb-7950/index.html - 2024-02-27T15:49:10.655Z + 2024-02-27T17:31:36.354Z https://rnabioco.github.io/molb-7950/course-info/team.html - 2024-02-27T15:49:09.555Z + 2024-02-27T17:31:35.254Z https://rnabioco.github.io/molb-7950/course-info/support.html - 2024-02-27T15:49:09.555Z + 2024-02-27T17:31:35.254Z https://rnabioco.github.io/molb-7950/course-info/final-projects.html - 2024-02-27T15:49:09.551Z + 2024-02-27T17:31:35.250Z https://rnabioco.github.io/molb-7950/course-info/problem-sets.html - 2024-02-27T15:49:09.551Z + 2024-02-27T17:31:35.254Z https://rnabioco.github.io/molb-7950/course-info/syllabus.html - 2024-02-27T15:49:09.555Z + 2024-02-27T17:31:35.254Z https://rnabioco.github.io/molb-7950/exercises/ex-01.html - 2024-02-27T15:49:10.303Z + 2024-02-27T17:31:36.002Z https://rnabioco.github.io/molb-7950/prepare/prepare-01.html - 2024-02-27T15:49:10.655Z - - - https://rnabioco.github.io/molb-7950/prepare/prepare-16.html - 2024-02-27T15:49:10.655Z - - - https://rnabioco.github.io/molb-7950/prepare/prepare-23.html - 2024-02-27T15:49:10.655Z - - - https://rnabioco.github.io/molb-7950/prepare/prepare-31.html - 2024-02-27T15:49:10.655Z + 2024-02-27T17:31:36.354Z https://rnabioco.github.io/molb-7950/resources/block-dna-resources.html - 2024-02-27T15:49:10.659Z + 2024-02-27T17:31:36.358Z https://rnabioco.github.io/molb-7950/resources/bootcamp-resources.html - 2024-02-27T15:49:10.659Z + 2024-02-27T17:31:36.358Z https://rnabioco.github.io/molb-7950/slides/slides-01.html - 2024-02-27T15:49:10.663Z + 2024-02-27T17:31:36.362Z