├── docs ├── CNAME ├── images │ ├── Levi.png │ ├── Aedin.jpg │ ├── logo_bioconductor.gif │ └── sponsors │ │ ├── OICRlogo.png │ │ ├── genentech.jpg │ │ ├── rpci-logo_1.png │ │ ├── novartis_logo_pos_rgb.png │ │ └── RStudio-Logo-Blue-Gray-125.png ├── Gemfile ├── poster │ └── Bioc2018_Poster.pdf ├── assets │ ├── fonts │ │ ├── Noto-Sans-700 │ │ │ ├── Noto-Sans-700.eot │ │ │ ├── Noto-Sans-700.ttf │ │ │ ├── Noto-Sans-700.woff │ │ │ ├── Noto-Sans-700.woff2 │ │ │ └── Noto-Sans-700.svg │ │ ├── Noto-Sans-italic │ │ │ ├── Noto-Sans-italic.eot │ │ │ ├── Noto-Sans-italic.ttf │ │ │ ├── Noto-Sans-italic.woff │ │ │ └── Noto-Sans-italic.woff2 │ │ ├── Noto-Sans-regular │ │ │ ├── Noto-Sans-regular.eot │ │ │ ├── Noto-Sans-regular.ttf │ │ │ ├── Noto-Sans-regular.woff │ │ │ └── Noto-Sans-regular.woff2 │ │ └── Noto-Sans-700italic │ │ │ ├── Noto-Sans-700italic.eot │ │ │ ├── Noto-Sans-700italic.ttf │ │ │ ├── Noto-Sans-700italic.woff │ │ │ ├── Noto-Sans-700italic.woff2 │ │ │ └── Noto-Sans-700italic.svg │ ├── js 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7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | **Registration Cancelled** 14 | 15 | - [Start over][1]. 16 | - View the [schedule][2]. 17 | - Questions? Send [email][3]. 18 | 19 | [1]: ./registration 20 | [2]: ./schedule 21 | [3]: mailto:bioc2018@bioconductor.org?subject=BioC%202018%20registration 22 | -------------------------------------------------------------------------------- /docs/registration-finish.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day), 26, and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | **Registration successful** 14 | 15 | - Submit a [talk, poster, workshop, or birds-of-a-feather][2] abstract. 16 | - View the [schedule][1] for updated speakers and workshops. 17 | - Follow [#bioc2018][tweet] for late-breaking announcements. 18 | 19 | Remember to arrange your own lodging. See you in Toronto! 20 | 21 | [1]: ./schedule 22 | [2]: ./call-for-abstracts 23 | -------------------------------------------------------------------------------- /docs/assets/js/scale.fix.js: -------------------------------------------------------------------------------- 1 | (function(document) { 2 | var metas = document.getElementsByTagName('meta'), 3 | changeViewportContent = function(content) { 4 | for (var i = 0; i < metas.length; i++) { 5 | if (metas[i].name == "viewport") { 6 | metas[i].content = content; 7 | } 8 | } 9 | }, 10 | initialize = function() { 11 | changeViewportContent("width=device-width, minimum-scale=1.0, maximum-scale=1.0"); 12 | }, 13 | gestureStart = function() { 14 | changeViewportContent("width=device-width, minimum-scale=0.25, maximum-scale=1.6"); 15 | }, 16 | gestureEnd = function() { 17 | initialize(); 18 | }; 19 | 20 | 21 | if (navigator.userAgent.match(/iPhone/i)) { 22 | initialize(); 23 | 24 | document.addEventListener("touchstart", gestureStart, false); 25 | document.addEventListener("touchend", gestureEnd, false); 26 | } 27 | })(document); 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This repository contains material for the _Bioconductor_ annual 2 | conference. [View the conference web site][1]. 3 | 4 | Edit or add material as markdown files in the docs/ directory. Please 5 | wrap lines to 80 character width and aim for simple markdown rather 6 | than elaborate html or other content. 7 | 8 | Please follow best practices by previewing changes locally. 9 | 10 | 1. Make sure that ruby and bundler are installed, following the 11 | 'Requirements' section of [GitHub's documentation][2]. 12 | 13 | 2. Clone the repository and switch to the `docs/` directory 14 | 15 | cd BioC2018/docs 16 | 17 | 3. Install ruby pre-requisites. 18 | 19 | bundle install # once only; references Gemfile 20 | 21 | 4. Execute the jekyll server 22 | 23 | bundle exec jekyll serve 24 | 25 | and view the results at https://localhost:4000 26 | 27 | [1]: https://bioconductor.github.io/BioC2018 28 | [2]: https://help.github.com/articles/setting-up-your-github-pages-site-locally-with-jekyll/#requirements 29 | -------------------------------------------------------------------------------- /docs/scholarships.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day), 26, and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Scholarships 14 | 15 | Scholarships are available and targeted toward _Bioconductor_ package 16 | developers and contributors. Scholarships cover registration, travel 17 | (up to $500 per person), and accommodation. All scholarship recipients 18 | must present a talk or poster. To apply for a scholarship, complete 19 | the [Poster / Talk Submission Form][]. 20 | 21 | Key dates for scholarships: 22 | 23 | - Thursday March 15, 2018: Abstract Submissions opens. 24 | - Thursday May 17, 2018: Abstract Submission deadline. 25 | - Thursday May 31, 2018: Notification of talk, poster acceptance. 26 | 27 | [Poster / Talk Submission Form]: https://goo.gl/forms/meoGgX7eipL2ZHKD2 28 | -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day), 26, and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | This conference highlights current developments within and beyond 14 | _Bioconductor_. Morning scientific talks and afternoon workshops 15 | provide conference participants with insights and tools required for 16 | the analysis and comprehension of high-throughput genomic 17 | data. 'Developer Day' precedes the main conference on July 25, 18 | providing developers and would-be developers an opportunity to gain 19 | insights into project direction and software development best 20 | practices. 21 | 22 | The conference venue is at [Victoria University][uvic] on the downtown 23 | (St. George) campus of the University of Toronto on the north-east 24 | edge of Queen's Park. The specific building is Victoria College, 25 | indicated on the [map][ut]. 26 | 27 | More information: [workshop@bioconductor.org][contact] 28 | 29 | [ut]: http://map.utoronto.ca/utsg/building/501 30 | [uvic]: http://www.vicu.utoronto.ca/ 31 | [contact]: mailto:workshop@bioconductor.org?subject=BioC2018%20question 32 | -------------------------------------------------------------------------------- /docs/code_of_conduct.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day), 26, and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Code of Conduct 14 | 15 | _Bioconductor_ is built on the free and open exchange of scientific 16 | ideas from the contributions of our diverse user community. In this 17 | spirit, _BioC 2018_ strives to provide a harassment-free experience 18 | for everyone. Harassment of any form will not be tolerated in talks, 19 | workshops, poster sessions, social activities, or online. 20 | 21 | Reach out to conference organizers ([workshop@biocondutor.org][0]) or 22 | our on-site response team with any concerns 23 | 24 | --- | --- 25 | ![Aedin Culhane][1]
Aedin Culhane
[aedin@jimmy.harvard.edu][2] | ![Levi Waldron][3]
Levi Waldron
[lwaldron.research@gmail.com][4] 26 | 27 | 28 | [0]: mailto:workshop@bioconductor.org?subject=BioC2018%20code-of-conduct 29 | [1]: images/Aedin.jpg 30 | [2]: mailto:aedin@jimmy.harvard.edu?subject=BioC2018%20code-of-conduct 31 | [3]: images/Levi.png 32 | [4]: mailto:lwaldron.research@gmail.com?subject=BioC2018%20Code-of-conduct 33 | -------------------------------------------------------------------------------- /docs/sponsor.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day), 26, and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Sponsor Opportunities 14 | 15 | Interested in sponsoring this conference? Please contact 16 | Martin.Morgan@RoswellPark.org. 17 | 18 | Three levels of sponsorship are available: 19 | 20 | - Gold: $7,500 USD -- Up to 8 free registrations. Recognition in 21 | promotional and scheduling material. Sponsored session (e.g., 22 | 'Morning talks', 'Afternoon workshops'). 23 | 24 | - Silver: $5,000 USD -- Up to 5 free registrations. Recognition in 25 | promotional and scheduling material. Sponsored food or social event 26 | (e.g., 'breakfast', 'lunch', 'poster session', 'reception'). 27 | 28 | - Bronze: $2,500 USD. Up to 2 free registrations. Recognition in 29 | promotional and scheduling material. 30 | 31 | Approximate conference demographics: 32 | 33 | - 150+ participants from the global bioinformatics community. 34 | 35 | - Academic (50%), corporate (e.g., pharmaceutical; 30%); 36 | not-for-profit (e.g., Cancer Center; 15%), and government (5%) 37 | participants. 38 | 39 | - Lab / group leaders (10%), postdoctoral researchers (40%), 40 | bioinformatics core staff (20%), and graduate students (30%). 41 | 42 | - PhD (60%), MS (20%), and other degrees. 43 | 44 | - Thought leaders contributing to leading-edge methodological 45 | development, hands-on practitioners incorporating _R_ / 46 | _Bioconductor_ into robust work flows, and researchers developing 47 | bespoke solutions. 48 | -------------------------------------------------------------------------------- /docs/registration.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day), 26, and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Registration 14 | 15 | Includes: 16 | 17 | - Admission to conference facilities. 18 | - Continental breakfast, lunch, and light hors d'oeuvres, as well as 19 | morning and afternoon coffee breaks. 20 | 21 | ## Travel and accommodation 22 | 23 | Participants must arrange and pay for their own travel and 24 | accommodation; we have NOT arranged conference rates at nearby 25 | hotels. The conference venue is at [Victoria University][uvic] on the 26 | downtown (St. George) campus of the University of Toronto on the 27 | north-east edge of Queen's Park. The specific building is Victoria College, 28 | indicated on the [map][ut]. 29 | 30 | [uvic]: http://www.vicu.utoronto.ca/ 31 | [ut]: http://map.utoronto.ca/utsg/building/501 32 | 33 | ## Fees 34 | 35 | Developer Day (July 25) 36 | 37 | - Included with main conference registration. 38 | 39 | Main Conference (July 26, 27) 40 | 41 | - Before July 10: Academic and not-for-profit: $300. Commercial: $400. 42 | - After July 10: Academic and not-for-profit: $600. Commercial: $800. 43 | 44 | ## Register now! 45 | 46 |

47 |

48 | 49 | 50 | 51 | 55 |
Registration
56 | 57 | 58 | 59 |
60 |

61 | 62 | - Registering for someone else? Use the 'shipping address' to specify 63 | their name. 64 | - Unfortunately, we are not able to process purchase orders. 65 | -------------------------------------------------------------------------------- /docs/call-for-abstracts.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day), 26, and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Call for Posters 14 | 15 | Use the [Poster Submission Form][] to submit an abstract (up to 300 16 | words) for a poster up until July 19 for inclusion in the online 17 | programme. Include links to software where relevant. 18 | 19 | [Poster Submission Form]: https://goo.gl/forms/YwkZt6f9FCHYaMYn1 20 | 21 | ## Call for Birds of a Feather / Special Interest Groups 22 | 23 | We invite participants to form groups dedicated to discuss or work on 24 | topics of interest. We will make rooms available for groups to meet 25 | during the lunch breaks. We encourage students, postdocs and junior 26 | faculty to participant and form groups. 27 | 28 | To create a special interest group [open an issue][] describing the 29 | topic. The issue title should start with SIG: followed by a short but 30 | descriptive title. In the issue, include: 31 | 32 | - An introduction of yourself. 33 | - A description of the topic. 34 | - Whether it should be held during Developer Day (Wednesday July 25) 35 | or the conference days (July 26-27). 36 | - Desired outputs (could be code, a piece of documentation, or general 37 | discussion), and possible outcomes. 38 | 39 | Interested participants should use the issue to ask questions and/or 40 | express their interest in participating. Proposals with interest shown 41 | by at least 3 additional participants by July 12 will be announced on 42 | the conference web page, and will have a room reserved for their 43 | meeting. 44 | 45 | [open an issue]: https://github.com/Bioconductor/BioC2018/issues 46 | 47 | ## Call for F1000Research articles 48 | 49 | There is an [invitation][] to submit F1000Research articles to the 50 | [Bioconductor gateway][] which will be associated with Bioc2018. The 51 | deadline is June 18, 2018. See this 52 | support site thread for 53 | more details. 54 | 55 | [invitation]: https://support.bioconductor.org/p/107477 56 | [Bioconductor gateway]: https://f1000research.com/gateways/bioconductor 57 | 58 | ## Call for Talks 59 | 60 | The call for talks is closed. Talks will be peer-reviewed by the 61 | conference committee. 62 | 63 | ## Call for Workshops 64 | 65 | The call for workshops is closed. Workshop proposals will be 66 | peer-reviewed by the conference committee. Join the _Bioconductor_ 67 | [Community Slack][] and `#bioc2018-workshops` channel for up-to-date 68 | information. 69 | 70 | [Community Slack]: https://bioc-community.herokuapp.com/ 71 | 72 | ## Key dates 73 | 74 | | Notification of Acceptance for talks and reviewed posters | May 31 | 75 | | Deadline for poster abstracts and BoF proposals | July 12 | 76 | | BioC 2018 Meeting | July 25 (Developer Day), 26, and 27 | 77 | -------------------------------------------------------------------------------- /docs/travel-accommodations.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | 5 | # BioC 2018: Where Software and Biology Connect 6 | 7 | When: July 25 (Developer Day), 26, and 27, 2018
8 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
9 | Twitter: [#bioc2018][tweet] 10 | 11 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 12 | [venue]: ./travel-accommodations 13 | 14 | ## Venue 15 | 16 | The conference venue is at [Victoria University][uvic] on the downtown 17 | (St. George) campus of the University of Toronto on the north-east 18 | edge of Queen's Park. The specific building is Victoria College, 19 | indicated on the [map][ut]. 20 | 21 | [uvic]: http://www.vicu.utoronto.ca/ 22 | [ut]: http://map.utoronto.ca/utsg/building/501 23 | 24 | ## Accommodations 25 | 26 | Conference organizers and invited speakers will be staying at the 27 | [Chelsea Hotel][chelsea], which is a short walk to the conference at 28 | Victoria College. 29 | 30 | Here is a [list of hotels from booking.com][hotels] that are available 31 | July 24-27, sorted by distance from Victoria College. 32 | 33 | [chelsea]: http://www.chelseatoronto.com/en/ 34 | [hotels]: https://www.booking.com/searchresults.html?label=gen173nr-1FCAEoggJCAlhYSDNYBHIFdXNfbnmIAQGYATG4AQfIAQzYAQHoAQH4AQKSAgF5qAID&sid=0b1cb2185b9c5f9a23cf24f780ea5920&checkin_month=7&checkin_monthday=24&checkin_year=2018&checkout_month=7&checkout_monthday=27&checkout_year=2018&class_interval=1&dtdisc=0&from_sf=1&group_adults=2&group_children=0&inac=0&index_postcard=0&label_click=undef&no_rooms=1&place_id=ChIJWWeUhro0K4gRPfZbL-0v2EM&place_id_lat=43.6669317&place_id_lon=-79.39196349999997&postcard=0&room1=A%2CA&sb_price_type=total&src=searchresults&ss=Victoria%20College%2C%20Toronto%2C%20ON%2C%20Canada&ss_all=0&ssb=empty&sshis=0&ssne=Victoria%20College%2C%20Toronto%2C%20ON%2C%20Canada&ssne_untouched=Victoria%20College%2C%20Toronto%2C%20ON%2C%20Canada&=&=&nflt=oos%3D1%3B&rsf=oos-1&lsf=oos%7C1%7C-1 35 | 36 | ## Local transportation 37 | 38 | Toronto is a safe and walkable city, with many hotels within walking 39 | distance of Victoria College. Victoria College is also at the 40 | intersection of subway lines, near both the north-south 41 | Yonge-University line and the east-west Bloor line, making it fast to 42 | arrive from anywhere along those subway lines. It is near the Museum, 43 | Bay, and Wellesley stops ([subway map][subway]) 44 | 45 | [subway]: https://www.ttc.ca/Subway/interactive_map/interactive_map.jsp# 46 | 47 | ## Arriving by air 48 | 49 | Toronto is served by two airports. Pearson International Airport (YYZ) 50 | is the largest. It is served by the 51 | [Union Pearson Express Train][train] which takes 25 minutes to arrive 52 | downtown at Union Station where you can take a short subway or taxi 53 | ride to any downtown hotel. Otherwise, there is a flat fare of CDN$56 54 | to take a taxi directly to/from Pearson Airport to the University of 55 | Toronto downtown campus and surrounding hotels. Here is a 56 | [full list][pearson] of transportation options to/from Pearson 57 | Airport. 58 | 59 | The downtown [Billy Bishop Toronto City Airport][billy] (YTZ) is 60 | smaller and served only by propeller-driven flights from eastern 61 | Canada and the U.S. (Boston, New York-Newark, Chicago, 62 | Washington-Dulles). It's convenient if it serves where you are coming 63 | from. 64 | 65 | [train]: https://www.torontopearson.com/en/toandfrom/upexpress/# 66 | [pearson]: https://www.torontopearson.com/en/toandfrom/ground/# 67 | [billy]: https://www.billybishopairport.com/ 68 | 69 | ## Driving and parking 70 | 71 | For those driving to Toronto and staying at the Chelsea Hotel, the 72 | hotel offers parking for a fee: 73 | 74 | * Daily Parking is $16+applicable taxes 75 | * Self-Parking is $35+applicable taxes with in and out privileges 76 | * Valet Parking is $45+applicable taxes with in and out privileges 77 | 78 | For those wanting to park near Victoria College, there are a 79 | [few options listed on their website][parking]. 80 | 81 | [parking]: http://www.vicu.utoronto.ca/hospitality/resaccommodations/parking.htm 82 | 83 | ## Arriving by train 84 | 85 | Trains arrive at Union Station, which is a couple kilometers away from 86 | Victoria College and accessible by taxi or subway. 87 | 88 | -------------------------------------------------------------------------------- /docs/_layouts/default.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | {% seo %} 8 | 9 | 10 | 11 | 14 | 15 | 16 |
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34 | - Developer Day
35 | - Main Conference
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37 | Resources
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43 | Conference Poster (pdf)
44 | Code of Conduct
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48 | 49 | Novartis 54 | 55 | 56 | Genentech 61 | 62 |
63 | 64 | RStudio 69 | 70 |
71 | 72 | Ontario Institute for Cancer Research 77 | 78 |
79 | 80 | Roswell Park Cancer Institute 85 | 86 |
87 | 88 | University of Toronto 93 | 94 |

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109 | 110 | 111 | {% if site.google_analytics %} 112 | 121 | {% endif %} 122 | 123 | 124 | -------------------------------------------------------------------------------- /resources/workshop-syllabus.md: -------------------------------------------------------------------------------- 1 | # Insert Workshop Title 2 | 3 | # Instructor(s) name(s) and contact information 4 | 5 | Provide names and contact information for all instructors. 6 | 7 | # Workshop Description 8 | 9 | Along with the topic of your workshop, include how students can expect 10 | to spend their time. For the description may also include information 11 | about what type of workshop it is (e.g. instructor-led live demo, lab, 12 | lecture + lab, etc.). Instructors are strongly recommended to provide 13 | completely worked examples for lab sessions, and a set of stand-alone 14 | notes that can be read and understood outside of the workshop. 15 | 16 | ## Pre-requisites 17 | 18 | List any workshop prerequisites, for example: 19 | 20 | * Basic knowledge of R syntax 21 | * Familiarity with the GenomicRanges class 22 | * Familiarity with xyz vignette (provide link) 23 | 24 | List relevant background reading for the workshop, including any 25 | theoretical background you expect students to have. 26 | 27 | * List any textbooks, papers, or other reading that students should be 28 | familiar with. Include direct links where possible. 29 | 30 | ## Workshop Participation 31 | 32 | Describe how students will be expected to participate in the workshop. 33 | 34 | ## _R_ / _Bioconductor_ packages used 35 | 36 | List any _R_ / _Bioconductor_ packages that will be explicitly covered. 37 | 38 | ## Time outline 39 | 40 | An example for a 45-minute workshop: 41 | 42 | | Activity | Time | 43 | |------------------------------|------| 44 | | Packages | 15m | 45 | | Package Development | 15m | 46 | | Contributing to Bioconductor | 5m | 47 | | Best Practices | 10m | 48 | 49 | # Workshop goals and objectives 50 | 51 | List "big picture" student-centered workshop goals and learning 52 | objectives. Learning goals and objectives are related, but not the 53 | same thing. These goals and objectives will help some people to decide 54 | whether to attend the conference for training purposes, so please make 55 | these as precise and accurate as possible. 56 | 57 | *Learning goals* are high-level descriptions of what 58 | participants will learn and be able to do after the workshop is 59 | over. *Learning objectives*, on the other hand, describe in very 60 | specific and measurable terms specific skills or knowledge 61 | attained. The [Bloom's Taxonomy](#bloom) may be a useful framework 62 | for defining and describing your goals and objectives, although there 63 | are others. 64 | 65 | ## Learning goals 66 | 67 | Some examples: 68 | 69 | * describe how to... 70 | * identify methods for... 71 | * understand the difference between... 72 | 73 | ## Learning objectives 74 | 75 | * analyze xyz data to produce... 76 | * create xyz plots 77 | * evaluate xyz data for artifacts 78 | 79 | ### A note about learning goals and objectives (#bloom) 80 | 81 | While not a new or modern system for thinking about learning, 82 | [Bloom's taxonomy][1] is one useful framework for understanding the 83 | cognitive processes involved in learning. From lowest to highest 84 | cognitive requirements: 85 | 86 | 1. Knowledge: Learners must be able to recall or remember the 87 | information. 88 | 2. Comprehension: Learners must be able to understand the information. 89 | 3. Application: Learners must be able to use the information they have 90 | learned at the same or different contexts. 91 | 4. Analysis: Learners must be able to analyze the information, by 92 | identifying its different components. 93 | 5. Synthesis: Learners must be able to create something new using 94 | different chunks of the information they have already mastered. 95 | 6. Evaluation: Learners must be able to present opinions, justify 96 | decisions, and make judgments about the information presented, 97 | based on previously acquired knowledge. 98 | 99 | To use Bloom's taxonomy, consider the following sets of verbs and 100 | descriptions for learning objectives: 101 | 102 | 1. Remember: Memorize, show, pick, spell, list, quote, recall, repeat, 103 | catalogue, cite, state, relate, record, name. 104 | 2. Understand: Explain, restate, alter, outline, discuss, expand, 105 | identify, locate, report, express, recognize, discuss, qualify, 106 | covert, review, infer. 107 | 3. Apply: Translate, interpret, explain, practice, illustrate, 108 | operate, demonstrate, dramatize, sketch, put into action, complete, 109 | model, utilize, experiment, schedule, use. 110 | 4. Analyze: Distinguish, differentiate, separate, take apart, 111 | appraise, calculate, criticize, compare, contrast, examine, test, 112 | relate, search, classify, experiment. 113 | 5. Evaluate: Decide, appraise, revise, score, recommend, select, 114 | measure, argue, value, estimate, choose, discuss, rate, assess, 115 | think. 116 | 6. Create: Compose, plan, propose, produce, predict, design, assemble, 117 | prepare, formulate, organize, manage, construct, generate, imagine, 118 | set-up. 119 | 120 | [1]: https://cft.vanderbilt.edu/guides-sub-pages/blooms-taxonomy/ "Bloom's Taxonomy" 121 | -------------------------------------------------------------------------------- /docs/schedule-developer-day.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day) 26 and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Developer Day Schedule (Tentative) 14 | 15 | Logistics: 16 | 17 | - Lightning talks -- [sign up][]! Include authors, affiliation, title. 18 | - Start your [course AMI][] 19 | - Join the [bioc-community slack][] 20 | - Provide interactive feedback on [sli.do][] `#bioc2018` 21 | 22 | [sign up]: mailto:stvjc@channing.harvard.edu?subject=lightning talk 23 | [course AMI]: https://courses.bioconductor.org 24 | [bioc-community slack]: https://bioc-community.herokuapp.com/ 25 | [sli.do]: https://sli.do 26 | 27 | 8:30 - 9:00 -- Registration and breakfast, VC second floor foyer 28 | 29 | 9:00 - 9:40 -- Welcome 30 | : Orientation & project updates -- [slides][1] -- VC 213 Chapel 31 | 32 | 9:40 - 10:00 -- Peter F. Hickey 33 | : Lessons from switching to on-disk storage using DelayedArray 34 | containers -- [slides][2] -- VC 213 Chapel 35 | 36 | 10:00 - 10:30 -- Lightning talks I 37 | : Lightning talk platforms will be available to all 38 | Developer Day attendees -- VC 213 Chapel 39 | 40 | - Qian Liu\*, Hervé Pagès, Martin Morgan, Roswell Park Comprehensive 41 | Cancer Center. DelayedDataFrame: an on-disk data representation in 42 | DataFrame metaphor. 43 | - Aedin Culhane\*, Dana Farber Cancer Institute. TCGA immune 44 | subtypes. 45 | - Wolfgang Huber\*, Susan Holmes, EMBL. Short preview of our new 46 | book "Modern Statistics for Modern Biology". 47 | - Lori Shepherd\*, Roswell Park Comprehensive Cancer 48 | Center. Submitting packages to Bioconductor 49 | - Martin Morgan\*, Roswell Park Comprehensive Cancer Center. Ad hoc 50 | workshops -- alternative activities during workshop / BoF 51 | sessions. 52 | 53 | 10:30 - 11:00 -- Break 54 | : 55 | 56 | 11:00 - 12:00 -- Birds-of-a-feather I / Workshops I 57 | : - BoF: Stephanie Hicks, [Statistical Analysis and Comprehension of 58 | the Human Cell Atlas in R/Bioconductor][hca] -- VC 206 59 | - Workshop: Nitesh Turaga, 60 | [Maintaining your Bioconductor package][510] -- VC 212 61 | - Impromptu: tidyverse and _Bioconductor_ -- VC 211 62 | 63 | 68 | 69 | 12:00 - 1:00 -- Lunch -- VC Foyer & Alumni Hall 70 | : 71 | 72 | 1:00 - 1:30 -- Lightning talks II 73 | : VC 213 Chapel 74 | 75 | - Aedin Culhane\*, Dana Farber Cancer Institute. Update in Bioconductor meet ups. 76 | - Lori Shepherd\*, Roswell Park Comprehensive Cancer Center. BiocFileCache 77 | - Qian Liu\*, Martin Morgan, Roswell Park Comprehensive Cancer 78 | Center. VariantExperiment: A RangedSummarizedExperiment container 79 | for VCF/GDS data with GDS back-end. 80 | - Nitesh Turaga\*, Bioconductor Core Team. Scalable Computing in 81 | Bioconductor: from cores to clusters. 82 | - Levi Waldron\*, CUNY. Creating the Bioc2018 workshop book and AMIs. 83 | - Daniel Van Twisk\*, RPCI. Organism.dplyr: A dplyr compatible 84 | Annotation Resource Interface. 85 | 86 | 1:30 - 2:30 -- Birds-of-a-feather II / Workshops II 87 | : - BoF: Levi Waldron, [New Data Structures for Bioconductor][structures] -- VC 88 | 206 89 | - Workshop: Peter Hickey, 90 | [Effectively using the DelayedArray framework to support the analysis of large datasets][500] -- VC 212 91 | - Impromptu: unit tests -- VC 211 92 | 93 | 2:30 - 3:00 -- Break 94 | : 95 | 96 | 3:00 - 3:30 -- Lightning talks III 97 | : VC 213 Chapel 98 | 99 | - Michael Steinbaugh\*, HSPH. Bioconductor tricks for dealing with 100 | genome annotations. [slides][3.1] 101 | - Charlotte Soneson\*, UZH; Kevin Rue-Albrecht, Uni Oxford; Federico 102 | Marini, Uni Mainz; Aaron Lun, CRUK: iSEE: Interactive 103 | SummarizedExperiment Explorer 104 | - Nathan Sheffield\*, UVA, Managing genomic project metadata 105 | - Shian Su\*, WEHI: BiocExplorer 106 | - Vincent Carey*, S Gopaulakrishnan, S Pollack, BJ Stubbs, A 107 | Culhane. Harvard Medical School/Harvard School of Public 108 | Health. Recent cloud-scale innovations in Bioconductor. 109 | - Tim Triche, USC: Sesame: a simple way/to analyze a methylation 110 | array 111 | 112 | [3.1]: https://github.com/steinbaugh/presentations/raw/master/2018-07-25/bioc2018.pdf 113 | 114 | 3:30 - 4:30 -- Community activities 115 | : VC 213 Chapel 116 | 117 | + Tracking down a bug: rownames and `SummarizedExperiment`. [issue][4.1] 118 | + Toward a better support site. [notes][4.2] 119 | + Designing PharmaGX data representations. [demo code][4.3] 120 | + Collaborative opportunities: Biocverse package discovery. [slides][4.4] 121 | 122 | [4.1]: https://github.com/Bioconductor/SummarizedExperiment/issues/13 123 | [4.2]: https://docs.google.com/document/d/1TPKn5sdBLrSuMEiXtdAyGipd7p2XNXghbHGYzmH_Xpk/edit?usp=sharing 124 | [4.3]: https://github.com/bhklab/longArray 125 | [4.4]: https://docs.google.com/presentation/d/1_SkOfeLT7j7wdQCwxfwmuChjuB7wtW-2evrbYKMWnIg/edit#slide=id.p 126 | 127 | 4:30 - 5:00 -- Panel discussion: project directions and opportunities 128 | : VC 213 Chapel 129 | 130 | [hca]: https://github.com/Bioconductor/BioC2018/issues/5 131 | [structures]: https://github.com/Bioconductor/BioC2018/issues/8 132 | 133 | [1]: https://docs.google.com/presentation/d/1QamlkH7H6B9hY8iCtDBA1qr7qSPXKc_y5ze2dH67C5s/edit?usp=sharing 134 | [2]: https://docs.google.com/presentation/d/1MsmcvCZz_k-0Xw0GEai0x61_-D1YzefnBB84zT3hnpQ/edit?usp=sharing 135 | [500]: http://bioconductor.github.io/BiocWorkshops/effectively-using-the-delayedarray-framework-to-support-the-analysis-of-large-datasets.html 136 | [510]: http://bioconductor.github.io/BiocWorkshops/maintaining-your-bioconductor-package.html 137 | -------------------------------------------------------------------------------- /docs/posters.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day) 26 and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Posters 14 | 15 | Ba-Alawi W\*, Haibe-Kains B. Enhancing Drug Sensitivity Prediction using Logical Models. Keywords: GeneExpression, Pharmacogenomics, StatisticalMethod, BiomarkerDiscovery. 16 | 17 | Burton D\*, McCall M. Incorporating Uncertainty in a Ternary Gene Regulatory Network Model. Keywords: CellBiology, GraphAndNetwork, Software. 18 | 19 | Daniela Cassol\* and Thomas Girke. systemPipeR: NGS Workflow Environment with Command-Line Interface. Keywords: Generic Workflow Environment, NGS Data Analysis, Command-line Interface, Visualization, Reporting Infrastructure. 20 | 21 | Cheng PF\*, Dummer R, Robinson M, Morgan M, Levesque MP. TCGAbrowser. Keywords: Software, WorkflowStep, Visualization. 22 | 23 | Clark O\*, Safikhani Z, Smirnov P, and Haibe-Kains B. Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Keywords: PharmacoGx, Biomarkers, Isoforms, Splicing, Drug Response. 24 | 25 | Daniel Giguere\*, Jean Macklaim, Greg Gloor. omicplotR: A Shiny app for exploring omic datasets as compositions software. Keywords: RNASeq, Metagenomics, Visualization, GUI. 26 | 27 | Diya Das\*, Levi Gadye, Michael A. Sanchez, Kelly Street, Ariane Baudhuin, Allon Wagner, Michael B. Cole, Yoon Gi Choi, Nir Yosef, Elizabeth Purdom, Sandrine Dudoit, Davide Risso, John Ngai and Russell B. Fletcher. Unraveling Tissue Regeneration With Single-Cell RNA-Sequencing. Keywords: Normalization, Clustering, RNASeq, SingleCell, Software. 28 | 29 | Drnevich J\*, Tseng M\*, Dalling J, Heath K, and Ferrer A. Transcriptome assembly problems found using Bioconductor packages. Keywords: Transcription, RNASeq, Clustering, Visualization. 30 | 31 | Ho C\*, Kofia V, Smirnov P, Ba-alawi W, Safikhani Z, Haibe-Kains B. PharmacoDB: an integrative web application for mining multiple pharmacogenomic datasets. Keywords: pharmacogenetics, pharmacogenomics, visualization. 32 | 33 | Chiaowen Joyce Hsiao\*, PoYuan Tung, John Blischak, Jonathan Burnett, Kenneth Barr, Yoav Gilad and Matthew Stephens. The identification and assessment of cell cycle signatures in single-cell gene expression data. Keywords: SingleCell, RNASeq, Transcriptomics, GeneRegulation, Classification. 34 | 35 | Chiaowen Joyce Hsiao\*, PoYuan Tung, John Blischak, Jonathan Burnett, Kenneth Barr, Yoav Gilad and Matthew Stephens. Identification and assessment of cell cycle signatures in single-cell gene expression data. Keywords: SingleCell, RNASeq, Transcriptomics, GeneRegulation, Classification. 36 | 37 | Chang Cao, Davide Chicco\*, Michael M. Hoffman. The MCC-F1 curve: a performance evaluation technique for binary classification. Keywords: Matthews Correlation Coefficient, Confusion Matrix, F1 Score, Accuracy, Applied Statistics. 38 | 39 | Innes BT\* and Bader GD. scClustViz - Single-cell RNAseq Cluster Assessment and Interactive Visualisation. Keywords: Visualization, SingleCell, Transcriptomics, GUI. 40 | 41 | Benjamin K Johnson\*, Asif Zubair, Timothy J Triche Jr. ATACseeker: a toolkit for ATAC- and scATAC-seq analysis. Keywords: ATACSeq, QualityControl, SingleCell, DifferentialPeakCalling, Visualization. 42 | 43 | Bo Li\*, Wail Ba-Alawi, Benjamin Haibe-Kains. R-LOBICO: an R package for building logical models. Keywords: Software, Classification, Pharmacogenomics. 44 | 45 | Tamara Mahbubani\*, Chantal Ho, Wail Ba-Alawi, Petr Smirnov, Benjamin Haibe-Kains, Arvind Singh Mer. BioHeat: an R Package for Interactive Heat Map Visualization. Keywords: Visualization, Pathways, Software, MultipleComparison. 46 | 47 | Ann Meyer\*, Michelle D Brazas, and B.F.F. Ouellette. Tracking Workshop Development and Online User Access. Keywords: Education. 48 | 49 | Zhun Miao\*, Ke Deng, Xiaowo Wang, Xuegong Zhang. DEsingle for detecting three types of differential expression in single-cell RNA-seq data. Keywords: DifferentialExpression, GeneExpression, RNASeq, SingleCell, Transcriptomics. 50 | 51 | Nagraj VP, Magee M, and Sheffield NC\*. LOLAweb: A containerized web application for interactive genomic locus overlap enrichment analysis. Keywords: ChIPSeq, FunctionalGenomics, GeneRegulation, GeneSetEnrichment, GenomeAnnotation. 52 | 53 | Vandana Sandhu\*, Knut Jorgen Labori, Ayelet Borgida, Ilinca Lungu, John Bartlett, Sara Hafezi-Bakhtiari, Rob Denroche, Gun Ho Jang, Danielle Pasternack, Faridah Mbaabali, Matthew Watson, Julie Wilson, Elin H. Kure, Steven Gallinger, Benjamin Haibe-Kains. Meta-analysis of transcriptomic profiles identifies prognostic model for pancreatic ductal adenocarcinoma patients. Keywords: Meta-analysis, One Sample Classifier, Top Scoring Pairs, Overall Survival Predictive Model. 54 | 55 | Heewon Seo\* and Benjamin Haibe-Kains. Pharmacogenomics of Gemcitabine in Pancreatic Cancer Cell Lines. Keywords: Pharmacogenomics, Gemcitabine, Pancreatic Cancer, Cell Lines. 56 | 57 | Shorser S\*, Reactome Team, Wu G, Hermajob H, D’Eustachio P and Stein L. The Reactome Knowledgebase. 58 | 59 | Mer, Arvind Singh\*, Brew Ben Ortmann, Janosch Goldenberg Anna Haibe-Kains Benjamin. Xeva: an R package for patient derived xenograft data management and analysis. Keywords: Pharmacogenomics, Biomarker, Xenografts, Drug Response. 60 | 61 | Smirnov P\*, Kofia V, Zhu K, Safikhani K, Ho C, Pugh T, Haibe-Kains B. Leveraging Preclinical Pharmacogenomics Studies to Inform Precision Medicine Research. Keywords: Pharmacogenomics, Visualization, GeneExpression, Regression, Software. 62 | 63 | Smith I\*, Haibe-Kains B. Identifying Biomarkers For Drug Sensitivity in Cancer With DNA Methylation Data. Keywords: Pharmacogenomics, DifferentialMethylation, DNAMethylation, DimensionReduction, CancerData. 64 | 65 | Sohail N\*, Johnson B, Zubair A, Triche T. MTseeker: mitochondrial variant analysis tools for Bioconductor. Keywords: MitochondrialVariation, VariantAnnotation, FunctionalPrediction, DataRepresentation, Visualization. 66 | 67 | Luyi Tian, Shian Su\*, Xueyi Dong, Daniela Amann-Zalcenstein, Christine Biben, Shalin H Naik, Matthew E Ritchie. Tools for preprocessing and benchmarking single cell RNA-sequencing data. Keywords: SingleCell, Preprocessing, Sequencing, QualityControl. 68 | 69 | Seyed Ali Madani Tonekaboni\*, Parisa Mazrooei, Victor Kofia, Benjamin Haibe-Kains, Mathieu Lupien. CREAM as an open source R package for calling clusters of genomic regions. Keywords: Cis-Regulatory Element, Clustering, CORE, Super-enhancer. 70 | -------------------------------------------------------------------------------- /docs/Gemfile.lock: -------------------------------------------------------------------------------- 1 | GEM 2 | remote: https://rubygems.org/ 3 | specs: 4 | actionpack (4.2.9) 5 | actionview (= 4.2.9) 6 | activesupport (= 4.2.9) 7 | rack (~> 1.6) 8 | rack-test (~> 0.6.2) 9 | rails-dom-testing (~> 1.0, >= 1.0.5) 10 | rails-html-sanitizer (~> 1.0, >= 1.0.2) 11 | actionview (4.2.9) 12 | activesupport (= 4.2.9) 13 | builder (~> 3.1) 14 | erubis (~> 2.7.0) 15 | rails-dom-testing (~> 1.0, >= 1.0.5) 16 | rails-html-sanitizer (~> 1.0, >= 1.0.3) 17 | activesupport (4.2.9) 18 | i18n (~> 0.7) 19 | minitest (~> 5.1) 20 | thread_safe (~> 0.3, >= 0.3.4) 21 | tzinfo (~> 1.1) 22 | addressable (2.5.2) 23 | public_suffix (>= 2.0.2, < 4.0) 24 | builder (3.2.3) 25 | coffee-script (2.4.1) 26 | coffee-script-source 27 | execjs 28 | coffee-script-source 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-------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day) 26 and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Schedule 14 | 15 | * [Developer Day](schedule-developer-day): Wednesday, July 25. 16 | * [Day 1](#day-1-thursday-july-26): Thursday July 26. 17 | * [Day 2](#day-2-friday-july-27): Friday July 27. 18 | 19 | [1]: http://sites.utoronto.ca/andrewslab/ 20 | [2]: https://www.pmgenomics.ca/bhklab/ 21 | [3]: https://www.rits.onc.jhmi.edu/DBB/members/?members=Faculty&member=efertig1 22 | [4]: https://csoneson.github.io/ 23 | [5]: https://hoffmanlab.org/ 24 | [6]: http://hugheslab.med.utoronto.ca/ 25 | 26 | ### Developer Day: Wednesday July 25 27 | 28 | See the [Developer Day](schedule-developer-day) schedule. 29 | 30 | ### Day 1: Thursday July 26 31 | 32 | Logistics: 33 | 34 | - Start your [course AMI][] 35 | - Join the [bioc-community slack][] 36 | 37 | [course AMI]: https://courses.bioconductor.org 38 | [bioc-community slack]: https://bioc-community.herokuapp.com/ 39 | 40 | 8:00 - 8:45 -- Registration and breakfast, VC second floor foyer 41 | : 42 | 43 | 8:45 - 9:00 -- Welcoming remarks -- [slides][1.0] (Martin Morgan) 44 | : VC 213 Chapel 45 | 46 | 9:00 - 9:30 -- [Michael Hoffman][5] University of Toronto. 47 | : Virtual ChIP-seq: predicting transcription factor binding by 48 | learning from the transcriptome -- VC 213 Chapel 49 | 50 | 9:30 - 10:00 -- [Elana Fertig][3] John Hopkins University. 51 | : Enter the Matrix: Interpreting omics through matrix factorization -- VC 213 Chapel 52 | 53 | 10:00 - 10:30 -- Break 54 | : 55 | 56 | 10:30 - 11:00 [Brenda Andrews][1] University of Toronto. 57 | : Analysis of high content microscopy data generated through automated 58 | yeast genetics -- VC 213 Chapel 59 | 60 | 11:00 - 12:00 -- Contributed talks -- VC 213 Chapel 61 | : + Innes BT\* and Bader GD. scClustViz - Single-cell RNAseq Cluster 62 | Assessment and Interactive Visualisation. 63 | + Righelli D\*, Koberstein J, Gomes B, Zhang N, Angelini C, 64 | Peixoto L, Risso D. Differential Enriched Scan 2 (DEScan2): a 65 | fast pipeline for broad peak analysis. 66 | + Adithya M, Bhargava A, Wright E\*. Improving the accuracy of 67 | taxonomic classification for identifying taxa in microbiome 68 | samples. 69 | + Zhun Miao\*, Ke Deng, Xiaowo Wang, Xuegong Zhang. DEsingle for 70 | detecting three types of differential expression in single-cell 71 | RNA-seq data. 72 | + Ludwig Geistlinger\*, Gergely Csaba, Mara Santarelli, Lucas 73 | Schiffer, Marcel Ramos, Ralf Zimmer, and Levi Waldron. Towards a 74 | gold standard for benchmarking gene set enrichment analysis. 75 | 76 | 12:00 - 1:00 -- Lunch / Birds-of-a-feather -- VC Foyer and Alumni Hall 77 | : 78 | + Organizing and coordinating R / Bioconductor meetups -- VC 211 79 | 80 | 1:00 - 2:45 -- Workshop Session 1a 81 | : + Martin Morgan\*. [Bioconductor for Everyone][100] -- VC 212 82 | + Ludwig Geistlinger\* and Levi Waldron. [Functional enrichment 83 | analysis of high-throughput omics data][210] -- VC 215 84 | 85 | 1:45 - 2:45 -- Workshops Session 1b 86 | : + Zhaleh Safikhani\*, Petr Smirnov, Benjamin 87 | Haibe-Kains. [Biomarker discovery from large pharmacogenomics 88 | datasets][260] -- VC 206 89 | 90 | 2:45 - 3:15 -- Break 91 | : 92 | 93 | 3:15 - 5:00 -- Workshops Session 2a 94 | : + MacDonald J, Shepherd L. [Introduction to Bioconductor annotation 95 | resources][101] -- VC 206 96 | + Love MI. [RNA-seq data analysis with DESeq2][201] -- VC 215 97 | 98 | 4:00 - 5:00 -- Workshops Session 2b 99 | : + Coetzee SG\* and Hazelett DJ. Variant Functional Annotation 100 | using StatePaintR, FunciVar and MotifBreakR 101 | + Isserlin R\*, Innes B, Bader 102 | GD. [Cytoscape Automation in R using Rcy3][230] -- **SET UP** your 103 | laptop for this workshop by arriving at 3:30. For the workshop 104 | [download and install cytoscape 3.6.1][cyto] and [workshop files][cytowork] -- VC 212 105 | 106 | 5:30 - 7:30 -- Contributed posters -- VC Foyer and Alumni Hall 107 | : 108 | 109 | [1.0]: https://docs.google.com/presentation/d/1W0tzadEgIKSq6J5Bcq7iBgcEIgRlSlbdtMRpDaRQ9m8/edit?usp=sharing 110 | [cyto]: http://cytoscape.org/download.php 111 | [cytowork]: http://download.baderlab.org/Bioc2018/ 112 | 113 | ### Day 2: Friday July 27 114 | 115 | 8:00 - 8:30 -- Breakfast, VC Second floor foyer 116 | 117 | 8:30 - 9:00 -- [Tim Hughes][6], University of Toronto. 118 | : Binding motifs for DNA and RNA binding proteins -- VC 213 Chapel 119 | 120 | 9:00 - 9:30 -- [Benjamin Haibe-Kains][2], Princesss Margaret Cancer 121 | Center, Toronto. 122 | : Cancer Biomarker Discovery: Building a Bridge Between Preclinical 123 | and Clinical Research -- VC 213 Chapel 124 | 125 | 9:30 - 10:00 -- Contributed talks -- VC 213 Chapel 126 | : + Lee S\*, Cook D, Lawrence M. plyranges: a fluent interface to 127 | Bioconductor's Ranges infrastructure 128 | + Love MI\*, Hickey P, Soneson, C, and Patro R. Automatic metadata 129 | propagation for RNA-seq 130 | 131 | 132 | 10:00 - 10:30 -- Break 133 | : 134 | 135 | 10:30 - 11:00 -- [Charlotte Soneson][4], University of Zurich, Switzerland. 136 | : A junction coverage compatibility score to quantify the reliability 137 | of transcript abundance estimates and annotation catalogs -- VC 213 Chapel 138 | 139 | 11:00 - 12:00 -- Contributed talks -- VC 213 Chapel 140 | : + Albert Y Zhang, Shian Su, Matthew E Ritchie, and Charity W 141 | Law\*. Unpacking signal from RNA-seq intron reads using Rsubread 142 | and limma packages. 143 | + Steinbaugh MJ\*, Kirchner RD, Ho Sui S. bcbioSingleCell: R 144 | package for bcbio single-cell analysis. 145 | + Abbas Rizvi\*, Ezgi Karaesmen\*, Leah Preus, Michael Sovic, 146 | Junke Wang, Lara Sucheston-Campbell. gwasurvivr: an R package to 147 | perform survival association testing on imputed genetic data. 148 | + Nima Hejazi\*, Alan Hubbard, Mark van der Laan. Data-Adaptive 149 | Estimation and Inference for Differential Methylation Analysis. 150 | + Rachael V Phillips\*, Alan Hubbard. Data Adaptive Evaluation of 151 | Preprocessing Methods using Ensemble Machine Learning. 152 | 153 | 12:00 - 1:00 -- Lunch / Birds-of-a-feather -- VC Foyer and Alumni Hall 154 | : 155 | 156 | 1:00 - 2:45 -- Workshops Session 3a 157 | : + Michael Lawrence\*, Martin Morgan. [Solving common bioinformatic 158 | challenges using GenomicRanges][102] -- VC 215 159 | + Ramos M, Geistlinger L, Waldron L\*. [Workflow for Multi-omics 160 | Analysis with MultiAssayExperiment][220] -- VC 212 161 | 162 | 1:45 - 2:45 -- Workshops Session 3b 163 | : + Charity Law\* , Monther Alhamdoosh, Shian Su, Gordon Smyth and 164 | Matthew Ritchie. [RNA-seq analysis is easy as 1-2-3 with limma, 165 | Glimma and edgeR][200] -- VC 206 166 | 167 | 2:45 - 3:15 -- Break 168 | : 169 | 170 | 3:15 - 5:00 -- Workshops Session 4a 171 | : + Das D\*, Street K\*, Risso D\*. [Analysis of single-cell RNA-seq 172 | data: Dimensionality reduction, clustering, and lineage 173 | inference][202] -- VC 215 174 | + Nicholas Cooley\*, Erik Wright. [Working with Genomic Data in R 175 | with the DECIPHER package][250] -- VC 212 176 | + Levi Waldron, Benjamin Haibe-Kains, Sean Davis. [Public Data 177 | Resources and Bioconductor][103] -- VC 211 178 | 179 | 4:00 - 5:00 -- Workshops Session 4b 180 | : + Lee S\*, Lawrence M. [Fluent genomic data analysis with plyranges][240] 181 | -- VC 206 182 | 183 | 5:30 - 7:30 -- Contributed posters -- VC Foyer and Alumni Hall 184 | : 185 | 186 | 187 | [100]: http://bioconductor.github.io/BiocWorkshops/r-and-bioconductor-for-everyone-an-introduction.html 188 | [101]: http://bioconductor.github.io/BiocWorkshops/introduction-to-bioconductor-annotation-resources.html 189 | [102]: http://bioconductor.github.io/BiocWorkshops/solving-common-bioinformatic-challenges-using-genomicranges.html 190 | [103]: http://bioconductor.github.io/BiocWorkshops/public-data-resources-and-bioconductor.html 191 | 192 | [200]: http://bioconductor.github.io/BiocWorkshops/rna-seq-analysis-is-easy-as-1-2-3-with-limma-glimma-and-edger.html 193 | [201]: http://bioconductor.github.io/BiocWorkshops/rna-seq-data-analysis-with-deseq2.html 194 | [202]: http://bioconductor.github.io/BiocWorkshops/analysis-of-single-cell-rna-seq-data-dimensionality-reduction-clustering-and-lineage-inference.html 195 | [210]: http://bioconductor.github.io/BiocWorkshops/functional-enrichment-analysis-of-high-throughput-omics-data.html 196 | [220]: http://bioconductor.github.io/BiocWorkshops/workflow-for-multi-omics-analysis-with-multiassayexperiment.html 197 | [230]: http://bioconductor.github.io/BiocWorkshops/cytoscape-automation-in-r-using-rcy3.html 198 | [240]: http://bioconductor.github.io/BiocWorkshops/fluent-genomic-data-analysis-with-plyranges.html 199 | [250]: http://bioconductor.github.io/BiocWorkshops/working-with-genomic-data-in-r-with-the-decipher-package.html 200 | [260]: http://bioconductor.github.io/BiocWorkshops/biomarker-discovery-from-large-pharmacogenomics-datasets.html 201 | -------------------------------------------------------------------------------- /docs/assets/css/style.css: -------------------------------------------------------------------------------- 1 | @font-face { 2 | font-family: 'Noto Sans'; 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394 | bottom: 50px; 395 | -webkit-font-smoothing: subpixel-antialiased; } 396 | 397 | @media print, screen and (max-width: 960px) { 398 | div.wrapper { 399 | width: auto; 400 | margin: 0; } 401 | 402 | header, section, footer { 403 | float: none; 404 | position: static; 405 | width: auto; } 406 | 407 | header { 408 | padding-right: 320px; } 409 | 410 | section { 411 | border: 1px solid #e5e5e5; 412 | border-width: 1px 0; 413 | padding: 20px 0; 414 | margin: 0 0 20px; } 415 | 416 | header a small { 417 | display: inline; } 418 | 419 | header ul { 420 | position: absolute; 421 | right: 50px; 422 | top: 52px; } } 423 | @media print, screen and (max-width: 720px) { 424 | body { 425 | word-wrap: break-word; } 426 | 427 | header { 428 | padding: 0; } 429 | 430 | header ul, header p.view { 431 | position: static; } 432 | 433 | pre, code { 434 | word-wrap: normal; } } 435 | @media print, screen and (max-width: 480px) { 436 | body { 437 | padding: 15px; } 438 | 439 | header ul { 440 | width: 99%; } 441 | 442 | header li, header ul li + li + li { 443 | width: 33%; } } 444 | @media print { 445 | body { 446 | padding: 0.4in; 447 | font-size: 12pt; 448 | color: #444; } } 449 | -------------------------------------------------------------------------------- /docs/workshops.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day) 26 and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Workshops 14 | 15 | Join the _[Bioconductor][]_ Community Slack and #bioc2018-workshops 16 | channel for up-to-date information. 17 | 18 | - [Main Conference](#main-conference) 19 | - [Developer Day](#developer-day) 20 | 21 | ### Main Conference 22 | 23 | - Lee S*, Lawrence M. _Fluent genomic data analysis with 24 | [plyranges][]_. [Syllabus & Materials][Lee_Plyranges]. 25 | 26 | In this workshop, we will give an overview of how to 27 | perform low-level analyses of genomic data using the grammar of 28 | genomic data transformation defined in the plyranges package. We 29 | will cover: 30 | 31 | - introduction to GRanges 32 | - overview of the core verbs for arithmetic, restriction, and 33 | aggregation of GRanges objects 34 | - performing joins between GRanges objects 35 | - designing pipelines to quickly explore data from AnnotationHub 36 | - reading BAM and other file types as GRanges objects 37 | 38 | The workshop will be a computer lab, in which the participants will 39 | be able to ask questions and interact with the instructors." 40 | 41 | - MacDonald J, Shepherd L. _Introduction to [Bioconductor][] 42 | annotation resources_. [Syllabus & Materials][MacDonald_Annotation]. 43 | 44 | There are various annotation packages provided by the 45 | _[Bioconductor][]_ project that can be used to incorporate 46 | additional information to results from high-throughput 47 | experiments. This can be as simple as mapping Ensembl IDs to 48 | corresponding HUGO gene symbols, to much more complex queries 49 | involving multiple data sources. In this workshop we will cover the 50 | various classes of annotation packages, what they contain, and how 51 | to use them efficiently. 52 | 53 | - Levi Waldron, Benjamin Haibe-Kains, Sean Davis. _Public Data 54 | Resources and [Bioconductor][]_. [Syllabus & Materials][Waldron_PublicData]. 55 | 56 | The goal of this workshop is to introduce _[Bioconductor][]_ 57 | packages for finding, accessing, and using large-scale public data 58 | resources including the Gene Expression Omnibus 59 | [GEO](https://www.ncbi.nlm.nih.gov/geo), Sequence Read Archive 60 | [SRA](https://www.ncbi.nlm.nih.gov/sra), the Genomic Data Commons 61 | [GDC](https://portal.gdc.cancer.gov/), and _[Bioconductor][]_-hosted 62 | curated data resources for metagenomics, pharmacogenomics, and The 63 | Cancer Genome Atlas. 64 | 65 | - Love MI. _RNA-seq data analysis with [DESeq2][]_. [Syllabus & Materials][Love_DESeq2]. 66 | 67 | In this workshop, we will give a quick overview of the most useful 68 | functions in the [DESeq2][] package, and a basic RNA-seq 69 | analysis. We will cover: how to quantify transcript expression from 70 | FASTQ files using Salmon, import quantification from Salmon with 71 | [tximport][] and [tximeta][], generate plots for quality control and 72 | exploratory data analysis EDA (also using [MultiQC][]), perform 73 | differential expression (DE) (also using [apeglm][]), overlap with 74 | other experimental data (using [AnnotationHub][]), and build reports 75 | (using [ReportingTools][] and [Glimma][]). We will give a short 76 | example of integration of [DESeq2][] with the zinbwave package for 77 | single-cell RNA-seq differential expression. The workshop is 78 | designed to be a lab with plenty of time for questions throughout 79 | the lab. 80 | 81 | - Charity Law , Monther Alhamdoosh, Shian Su, Gordon Smyth and Matthew 82 | Ritchie. _RNA-seq analysis is easy as 1-2-3 with [limma][], 83 | [Glimma][] and [edgeR][]_. [Syllabus & Materials][Law_RNAseq123]. 84 | 85 | In this workshop, we analyse RNA-sequencing data from the mouse 86 | mammary gland, demonstrating use of the popular edgeR package to 87 | import, organise, filter and normalise the data, followed by the 88 | [limma][] package with its voom method, linear modelling and 89 | empirical Bayes moderation to assess differential expression. This 90 | pipeline is further enhanced by the [Glimma][] package which enables 91 | interactive exploration of the results so that individual samples 92 | and genes can be examined by the user. The complete analysis offered 93 | by these three packages highlights the ease with which researchers 94 | can turn the raw counts from an RNA-sequencing experiment into 95 | biological insights using _[Bioconductor][]_. 96 | 97 | 98 | - Michael Lawrence, Martin Morgan. _Solving common bioinformatic 99 | challenges using [GenomicRanges][]_. [Syllabus & Materials][Lawrence_GenomicRanges]. 100 | 101 | We will introduce the fundamental concepts underlying the 102 | [GenomicRanges][] package and related infrastructure. After a 103 | structured introduction, we will follow a realistic workflow, along 104 | the way exploring the central data structures, including `GRanges` 105 | and `SummarizedExperiment`, and useful operations in the ranges 106 | algebra. Topics will include data import/export, computing and 107 | summarizing data on genomic features, overlap detection, integration 108 | with reference annotations, scaling strategies, and 109 | visualization. Students can follow along, and there will be plenty 110 | of time for students to ask questions about how to apply the 111 | infrastructure to their particular use case. 112 | 113 | - Zhaleh Safikhani, Petr Smirnov, Benjamin Haibe-Kains. _Biomarker 114 | discovery from large pharmacogenomics datasets_. [Syllabus & Materials][Safikhani_Pharmacogenomics]. 115 | 116 | This workshop will focus on the challenges encountered when applying 117 | machine learning techniques in complex, high dimensional biological 118 | data. In particular, we will focus on biomarker discovery from 119 | pharmacogenomic data, which consists of developing predictors of 120 | response of cancer cell lines to chemical compounds based on their 121 | genomic features. From a methodological viewpoint, biomarker 122 | discovery is strongly linked to variable selection, through methods 123 | such as Supervised Learning with sparsity inducing norms (e.g., 124 | ElasticNet) or techniques accounting for the complex correlation 125 | structure of biological features (e.g., mRMR). Yet, the main focus 126 | of this talk will be on sound use of such methods in a 127 | pharmacogenomics context, their validation and correct 128 | interpretation of the produced results. We will discuss how to 129 | assess the quality of both the input and output data. We will 130 | illustrate the importance of unified analytical platforms, data and 131 | code sharing in bioinformatics and biomedical research, as the data 132 | generation process becomes increasingly complex and requires high 133 | level of replication to achieve robust results. This is particularly 134 | relevant as our portfolio of machine learning techniques is ever 135 | enlarging, with its set of hyperparameters that can be tuning in a 136 | multitude of ways, increasing the risk of overfitting when 137 | developing multivariate predictors of drug response. 138 | 139 | - Ludwig Geistlinger and Levi Waldron. _Functional enrichment analysis 140 | of high-throughput omics 141 | data_. [Syllabus & Materials][Geistlinger_Omics]. 142 | 143 | This workshop gives an in-depth overview of existing methods for 144 | enrichment analysis of gene expression data with regard to 145 | functional gene sets, pathways, and networks. 146 | 147 | The workshop will help participants understand the distinctions 148 | between assumptions and hypotheses of existing methods as well as 149 | the differences in objectives and interpretation of results. It will 150 | provide code and hands-on practice of all necessary steps for 151 | differential expression analysis, gene set- and network-based 152 | enrichment analysis, and identification of enriched genomic regions 153 | and regulatory elements, along with visualization and exploration of 154 | results. 155 | 156 | 157 | - Ramos M, Geistlinger L, Waldron L. _Workflow for Multi-omics 158 | Analysis with [MultiAssayExperiment][]_. [Syllabus & Materials][Ramos_MultiAssayExperiment]. 159 | 160 | This workshop demonstrates data management and analyses of multiple 161 | assays associated with a single set of biological specimens, using 162 | the `MultiAssayExperiment` data class and methods. It introduces the 163 | `RaggedExperiment` data class, which provides efficient and powerful 164 | operations for representation of copy number and mutation and 165 | variant data that are represented by different genomic ranges for 166 | each specimen. " 167 | 168 | - Das D*, Street K*, Risso D. _Analysis of single-cell RNA-seq data: 169 | Dimensionality reduction, clustering, and lineage inference_. 170 | [Workshop Syllabus & Materials][Das_SingleCellRNAseq]. 171 | 172 | This workshop will be presented as a lab session (brief introduction 173 | followed by hands-on coding) that instructs participants in a 174 | _[Bioconductor][]_ workflow for the analysis of single-cell 175 | RNA-sequencing data, in three parts: 176 | 177 | 1. dimensionality reduction that accounts for zero inflation, 178 | over-dispersion, and batch effects 179 | 2. cell clustering that employs a resampling-based approach 180 | resulting in robust and stable clusters 181 | 3. lineage trajectory analysis that uncovers continuous, branching 182 | developmental processes 183 | 184 | We will provide worked examples for lab sessions, and a set of 185 | stand-alone notes in this repository. 186 | 187 | - Isserlin R,Innes B, Bader GD. _[Cytoscape][] Automation in R using 188 | [Rcy3][]_. [Workshop Syllabus & Materials][Isserlin_Rcy3]. 189 | 190 | [Cytoscape][] is one of the most popular applications for 191 | network analysis and visualization. In this workshop, we will 192 | demonstrate new capabilities to integrate Cytoscape into 193 | programmatic workflows and pipelines using R. We will begin with an 194 | overview of network biology themes and concepts, and then we will 195 | translate these into Cytoscape terms for practical applications. The 196 | bulk of the workshop will be a hands-on demonstration of accessing 197 | and controlling Cytoscape from R to perform a network analysis of 198 | tumor expression data. 199 | 200 | - Coetzee SG and Hazelett DJ. _Variant Functional Annotation using 201 | [StatePaintR][], [FunciVar][] and 202 | [MotifBreakR][]_ 203 | 204 | - Cooley N and Wright E. _Working with Genomic Data in R with the 205 | DECIPHER package_. [Syllabus & Materials][Cooley_DECIPHER]. 206 | 207 | [DECIPHER][] is a multifaceted package that includes many tools for 208 | working with genome-scale sequence data. Genomic sequences undergo a 209 | variety of large-scale mutational processes, including 210 | rearrangements, inversions, duplications, insertions, and 211 | deletions. Since genomes are often not collinear, it is often useful 212 | to map syntenic regions between genomes to facilitate 213 | analyses. DECIPHER contains a synteny mapping function that locates 214 | syntenic regions among genomes and can be used to identify 215 | orthologous genes. Additional functions allow for alignment and 216 | downstream analyses of these syntenic regions. This workshop will 217 | walk through a complete workflow for analyzing a set of genomes 218 | using DECIPHER, starting with importing genomes from local files or 219 | external repositories. Synteny will be mapped among multiple genomes 220 | and used as the basis for ortholog prediction. We will show how to 221 | use these sets of orthologous genes to construct phylogenetic trees 222 | representing the evolutionary history of the core-genome and 223 | pan-genome. 224 | 225 | ### Developer Day 226 | 227 | - Hickey P.F.* _Effectively using the [DelayedArray][] framework to 228 | support the analysis of large 229 | datasets_. [Syllabus & Materials][Hickey_DelayedArray]. 230 | 231 | This workshop will teach the fundamental concepts underlying the 232 | DelayedArray framework and related infrastructure. It is intended 233 | for package developers who want to learn how to use the 234 | [DelayedArray][] framework to support the analysis of large 235 | datasets, particularly through the use of on-disk data storage. The 236 | first part of the workshop will provide an overview of the 237 | [DelayedArray][] infrastructure and introduce computing on 238 | DelayedArray objects using delayed operations and 239 | block-processing. The second part of the workshop will present 240 | strategies for adding support for [DelayedArray][] to an existing 241 | package and extending the [DelayedArray][] framework. Students can 242 | expect a mixture of lecture and question-and-answer session to teach 243 | the fundamental concepts. There will be plenty of examples to 244 | illustrate common design patterns for writing performant code, 245 | although we will not be writing much code during the workshop. 246 | 247 | - Nitesh Turaga. Roswell Park Comprehensive Cancer 248 | Center. _Maintaining your [Bioconductor][] 249 | package_. [Syllabus & Materials][Turaga_MaintainBioc]. 250 | 251 | Once an R package is accepted into _[Bioconductor][]_, maintaining 252 | it is an active responsibility undertaken by the package developers 253 | and maintainers. In this short workshop, we will cover how to 254 | maintain a _[Bioconductor][]_ package using existing 255 | infrastructure. _[Bioconductor][]_ hosts a range of tools which 256 | maintainers can use such as daily build reports, landing page 257 | badges, RSS feeds, download stats, support site questions, and the 258 | bioc-devel mailing list. Some packages have their own continuous 259 | integration hook setup on their github pages which would be an 260 | additional tool maintainers can use to monitor their package. We 261 | will also highlight one particular git practice which need to be 262 | done while updating and maintaining your package on out git system. 263 | 264 | [AnnotationHub]: https://bioconductor.org/packages/AnnotationHub 265 | [Bioconductor]: https://bioconductor.org/developers 266 | [DESeq2]: https://bioconductor.org/packages/DESeq2 267 | [DECIPHER]: https://bioconductor.org/packages/DECIPHER 268 | [DelayedArray]: https://bioconductor.org/packages/DelayedArray 269 | [FunciVar]: https://bioconductor.org/packages/FunciVar 270 | [GenomicRanges]: https://bioconductor.org/packages/GenomicRanges 271 | [Glimma]: https://bioconductor.org/packages/Glimma 272 | [MotifBreakR]: https://bioconductor.org/packages/MotifBreakR 273 | [MultiAssayExperiment]: https://bioconductor.org/packages/MultiAssayExperiment 274 | [MultiQC]: https://bioconductor.org/packages/MultiQC 275 | [Rcy3]: https://bioconductor.org/packages/Rcy3 276 | [ReportingTools]: https://bioconductor.org/packages/ReportingTools 277 | [StatePaintR]: https://bioconductor.org/packages/StatePaintR 278 | [apeglm]: https://bioconductor.org/packages/apeglm 279 | [edgeR]: https://bioconductor.org/packages/edgeR 280 | [limma]: https://bioconductor.org/packages/limma 281 | [plyranges]: https://bioconductor.org/packages/plyranges 282 | [tximport]: https://bioconductor.org/packages/tximport 283 | [txmeta]: https://bioconductor.org/packages/txmetta 284 | 285 | [Cytoscape]: http://www.cytoscape.org/ 286 | 287 | [Coetzee_StatePaintR]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Coetzee_StatePaintR.Rmd 288 | [Cooley_DECIPHER]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Cooley_DECIPHER.Rmd 289 | [Das_SingleCellRNAseq]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Das_SingleCellRNAseq.Rmd 290 | [Geistlinger_Omics]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Geistlinger_Omics.Rmd 291 | [Hickey_DelayedArray]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Hickey_DelayedArray.Rmd 292 | [Isserlin_Rcy3]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Isserlin_Rcy3.Rmd 293 | [Law_RNAseq123]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Law_RNAseq123.Rmd 294 | [Lawrence_GenomicRanges]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Lawrence_GenomicRanges.Rmd 295 | [Lee_Plyranges]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Lee_Plyranges.Rmd 296 | [Love_DESeq2]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Love_DESeq2.Rmd 297 | [MacDonald_Annotation]: https://github.com/Bioconductor/BiocWorkshops/blob/master/MacDonald_Annotation.Rmd 298 | [Ramos_MultiAssayExperiment]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Ramos_MultiAssayExperiment.Rmd 299 | [Safikhani_Pharmacogenomics]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Safikhani_Pharmacogenomics.Rmd 300 | [Turaga_MaintainBioc]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Turaga_MaintainBioc.Rmd 301 | [Waldron_PublicData]: https://github.com/Bioconductor/BiocWorkshops/blob/master/Waldron_PublicData.Rmd 302 | -------------------------------------------------------------------------------- /docs/talks-community.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | # BioC 2018: Where Software and Biology Connect 5 | 6 | When: July 25 (Developer Day) 26 and 27, 2018
7 | Where: [Victoria University][venue], University of Toronto, Toronto, Canada
8 | Twitter: [#bioc2018][tweet] 9 | 10 | [tweet]: https://twitter.com/hashtag/bioc2018?f=tweets 11 | [venue]: ./travel-accommodations 12 | 13 | ## Community-Contributed Talks (Tentative) 14 | 15 | - See the Conference [Schedule](schedule.md) for invited talks. 16 | 17 | - Lee S\*, Cook D, Lawrence M. _[plyranges][]: a fluent interface to 18 | Bioconductor's Ranges infrastructure_. The Bioconductor project has 19 | created many powerful S4 classes for reasoning about genomics data, 20 | such as the SummarizedExperiment class for representing experimental 21 | assays and the Ranges family of classes for representing genomic 22 | intervals. For new users of Bioconductor who are unfamiliar with 23 | object oriented programming or genomic data analysis, the learning 24 | curve for these classes is steep. New users often find themselves 25 | asking: how do I get my data into an appropriate class for my 26 | analysis? What are accessors and why do I have to use them? Why does 27 | this function return an object of class that I have not seen before? 28 | This results in analysts trying to solve problems that have been 29 | efficiently dealt with in other Bioconductor packages or writing 30 | code they may not fully understand. 31 | 32 | The goal of a fluent interface is to enable users to write 33 | human-readable code via method chaining and consistent function 34 | returns. Fluent interfaces fit naturally in the context of 35 | Bioinformatics workflows because they enable writing succinct 36 | pipelines. We have developed plyranges a package that attempts to 37 | construct a fluent interface to the Ranges classes defined in the 38 | IRanges and [GenomicRanges][] packages. This package is inspired by 39 | [dplyr][] and implements and extends its grammar of data 40 | manipulation to Ranges. We have defined methods for constructing, 41 | grouping, mutating, filtering, and summarising Ranges. We have also 42 | defined an algebra for reasoning about actions on Ranges and 43 | relationships between Ranges. By having an expansive grammar, we 44 | hope to cover the majority of analysis tasks a new user may face and 45 | thereby enable users to write clearer and more reproducible code. 46 | 47 | - Zhun Miao\*, Ke Deng, Xiaowo Wang, Xuegong Zhang. _[DEsingle][] for 48 | detecting three types of differential expression in single-cell 49 | RNA-seq data_. The excessive amount of zeros in single-cell RNA-seq 50 | (scRNA-seq) data includes ‘real’ zeros due to the on-off nature of 51 | gene transcription in single cells and ‘dropout’ zeros due to 52 | technical reasons. Existing differential expression (DE) analysis 53 | methods cannot distinguish these two types of zeros. 54 | 55 | We developed an R package [DEsingle][] which employed Zero-Inflated 56 | Negative Binomial model to estimate the proportion of real and 57 | dropout zeros and to define and detect three types of DE genes in 58 | scRNA-seq data, with regard to different expression status (DEs), 59 | differential expression abundance (DEa), and general differential 60 | expression (DEg). 61 | 62 | Results showed that [DEsingle][] outperforms existing methods for 63 | scRNA-seq DE analysis, and can reveal different types of DE genes 64 | that are enriched in different biological functions. 65 | 66 | - Abbas Rizvi\*, Ezgi Karaesmen\*, Leah Preus, Michael Sovic, Junke 67 | Wang, Lara Sucheston-Campbell. _[gwasurvivr][]: an R package to 68 | perform survival association testing on imputed genetic data_. 69 | Increasingly researchers have become interested in time-to-event 70 | outcomes in the context of genetic variation. Existing software for 71 | performing survival analyses across millions of SNPs are 72 | limited. Recently, comprehensive stand-alone software packages, 73 | genipe and SurvivalGWAS_SV, were developed, however, they require 74 | user interaction with the raw output after imputation opening room 75 | for error during analysis. GWASTools, while available in R and can 76 | implement survival, is primarily for storing large SNP datasets and 77 | rigorous QC/QA. To address this unmet need, we developed an 78 | R/Bioconductor package to conduct fast and efficient genome wide 79 | survival analyses for on imputed genetic data generated using 80 | IMPUTE2 and VCF data generated from Michigan or Sanger imputation 81 | servers. 82 | 83 | [gwasurvivr][] implements Cox proportional hazards models to test 84 | SNP association with outcome; the package allows for covariates and 85 | SNP-covariate interaction. To potentially decrease the number of 86 | iterations needed for convergence when optimizing the parameters 87 | estimates in the Cox model, we modified survival::coxph.fit, to fit 88 | covariates without the SNP and use those parameter estimates as 89 | initial starting points. For models without covariates the parameter 90 | estimation optimization begins with null initial value. Users can 91 | internally subset the data by providing sample IDs and pre-filter 92 | SNPs by info score and MAF. Output for each SNP includes parameter 93 | estimates, p-values, MAFs, INFO scores, number of events and total 94 | sample N. [gwasurvivr][] is well-suited for multi-core processors 95 | and users can specify node preferences used during computation. To 96 | overcome R memory limitations [gwasurvivr][] iteratively performs 97 | survival on subsets of the entire data. 98 | 99 | We benchmarked our package with genipe, SurvivalGWAS_SV, and 100 | GWASTools using IMPUTE2 data for varying sample sizes (n=100, 101 | n=1000, n=5000) and SNPs (p=1000, p=10000, p=100000) including two 102 | non-genetic covariates. All packages showed excellent agreement 103 | across MAF estimates, coefficient estimates, and p-values, with 104 | [gwasurvivr][] outperforming the other packages in time to 105 | completion for all simulations. 106 | 107 | - Ludwig Geistlinger, Gergely Csaba, Mara Santarelli, Lucas Schiffer, 108 | Marcel Ramos, Ralf Zimmer, and Levi Waldron. _Towards a gold 109 | standard for benchmarking gene set enrichment analysis._ Although 110 | gene set enrichment analysis has become an integral part of 111 | high-throughput gene expression data analysis, the assessment of 112 | enrichment methods remains rudimentary and ad hoc. In the absence of 113 | suitable gold standards, the evaluation is commonly restricted to 114 | selected data sets and biological reasoning on the relevance of 115 | resulting enriched gene sets. However, this is typically incomplete 116 | and biased towards the individual investigation goals. In this 117 | article, we present a curated compendium of 50 expression data sets 118 | investigating 34 different human diseases. The compendium features 119 | microarray and RNA-seq measurements, and each data set is associated 120 | with a precompiled GO/KEGG relevance ranking for the corresponding 121 | disease under investigation. We perform a comprehensive assessment 122 | of 20 major enrichment methods based on the benchmark set, thereby 123 | identifying methods that accurately recover the a priori defined 124 | relevance rankings. The compendium is embedded in a directly 125 | executable benchmark system, the _R / Bioconductor_ 126 | [GSEABenchmarkeR][] package, allowing straightforward execution on 127 | additional enrichment methods. 128 | 129 | - Nima Hejazi\*, Alan Hubbard, Mark van der Laan. _Data-Adaptive 130 | Estimation and Inference for Differential Methylation Analysis_. DNA 131 | methylation is amongst the best studied of epigenetic mechanisms 132 | impacting gene expression. While much attention has been paid to the 133 | proper normalization of bioinformatical data produced by DNA 134 | methylation assays, linear models remain the current standard for 135 | analyzing post-processed methylation data, for the ease they afford 136 | for both statistical inference and scientific interpretation. We 137 | present a new, general statistical algorithm for the model-free 138 | estimation of the differential methylation of DNA CpG sites, 139 | complete with straightforward and interpretable statistical 140 | inference for such estimates. The new approach leverages variable 141 | importance measures, a class of parameters arising in causal 142 | inference, in a manner that facilitates their use in obtaining 143 | targeted estimates of the importance of each CpG site. The proposed 144 | procedure is computationally efficient and self-contained, 145 | incorporating techniques to isolate a subset of candidate CpG sites 146 | based on cursory evidence of differential methylation and providing 147 | a multiple testing correction that appropriately controls the False 148 | Discovery Rate in such multi-stage analysis settings. The 149 | effectiveness of the new methodology is demonstrated by way of data 150 | analysis with real DNA methylation data, and a recently developed R 151 | package (methyvim; available via Bioconductor) that provides support 152 | for data analysis with this methodology is introduced. 153 | 154 | - Albert Y Zhang, Shian Su, Matthew E Ritchie, and Charity W 155 | Law\*. _Unpacking signal from RNA-seq intron reads using Rsubread and 156 | limma packages_. RNA-seq datasets contain up to millions of intron 157 | reads per library. These reads are typically removed from downstream 158 | analysis without even considering the proportion at which they 159 | contribute to total reads. By default only reads overlapping 160 | annotated exons are thought to be informative since mature mRNA is 161 | assumed to be the major component sequenced, especially when 162 | examining poly(A) RNA samples. Using Bioconductor packages, Rsubread 163 | and limma, we show that intron reads contain signal that is 164 | biologically relevant. Multi-dimensional scaling plots show that 165 | samples separate into biological and experimental groups using 166 | conservative intron counts, where the degree of separation is 167 | similar to that of exon counts despite there being far fewer intron 168 | reads in comparison to exon reads. The coverage of exon and intron 169 | regions are assessed for thousands of genes, showing a tendency for 170 | an increase in read coverage from 3' to 5' in poly(A) RNA 171 | samples. Coverage in Total RNA samples tend to be more uniform in 172 | appearance. We show that intron signal is prevalent across multiple 173 | datasets and discuss the possibility of its origin from pre-mRNA and 174 | intron retention. Results presented here can be used in the 175 | development of future Bioconductor packages to interrogate different 176 | biological aspects relating to intron signal, and inspire 177 | modifications to existing methods for RNA-seq analysis. 178 | 179 | - Rachael V Phillips\*, Alan Hubbard. _Data Adaptive Evaluation of 180 | Preprocessing Methods using Ensemble Machine Learning_. For many 181 | types of biological data generated by high-throughput technologies, 182 | there is no single gold-standard for converting the raw data into a 183 | form that can be analyzed for relationships of the relevant 184 | biomarkers to exposures and disease. For example, much of the 185 | variation in the raw data generated by Illumina HumanMethylationEPIC 186 | and 450K arrays is due to the technicalities of the experimental 187 | design (comprising two different assay methods, two different color 188 | channels, and batch effects) and potentially less so due to the 189 | biological factor(s) of interest. Accordingly, several preprocessing 190 | methods have been developed, however it is unclear which combination 191 | should be retained in downstream analysis. To address this issue, we 192 | have developed a data adaptive methodology that incorporates 193 | ensemble machine learning to assess which preprocessing streams 194 | generate better signal-to-noise ratio, according to the prediction 195 | of positive and negative control variables. We employ this method to 196 | select normalizations for EPIC and 450K arrays in a principled 197 | way. The results suggest 1) differences in the relative performance 198 | of the possible preprocessing choices and 2) that such machine 199 | learning approaches can be practically applied to complex omics data 200 | to choose among the growing number of choices for preprocessing. 201 | 202 | - Steinbaugh MJ\*, Kirchner RD, Ho Sui S. _bcbioSingleCell: R package 203 | for bcbio single-cell analysis_. Single-cell RNA sequencing 204 | (scRNA-seq) has ushered in a new era of genomics research, enabling 205 | researchers to visualize dynamic changes in cell populations, and 206 | quantify changes in gene expression at an unprecedented new level of 207 | resolution. While this new technology is extremely exciting and 208 | empowering, it remains overly challenging for the vast majority of 209 | biologists to import and analyze their results with confidence. The 210 | bcbio toolkit addresses this problem, offering native best-practice 211 | support for quantification of multiple barcoded droplet platforms, 212 | including inDrops, Drop-seq, 10X Genomics Chromium, and Illumina 213 | SureCell. The corresponding R package, _bcbioSingleCell_, provides 214 | support for easy loading of results into the SingleCellExperiment 215 | container class. Using the standardized SingleCellExperiment 216 | container enables researchers to efficiently pass their data to 217 | other single-cell packages available on Bioconductor. Additionally, 218 | the package offers a suite of quality control functions optimized 219 | for low quality cellular barcode removal, along with clustering and 220 | visualization functions that integrate with the Seurat 221 | toolkit. Differential expression analysis is supported and utilizes 222 | the zero-inflated negative binomial model provided by zinbwave, thus 223 | unlocking downstream testing with the robust DESeq2 or edgeR RNA-seq 224 | packages. 225 | 226 | - Righelli D\*, Koberstein J, Gomes B, Zhang N, Angelini C, Peixoto L, 227 | Risso D. _Differential Enriched Scan 2 ([DEScan2][]): a fast 228 | pipeline for broad peak analysis_. We present DEScan2 a novel 229 | bioconductor package for the analysis of Sono-Seq/Atac-Seq data, 230 | with the aim to facilitate the investigation of broad peak regions 231 | data. 232 | 233 | The method consists of three main steps: 1) a peak caller, 2) a peak 234 | filtering and 3) a method to efficiently compute a count matrix of 235 | the filtered peaks. 236 | 237 | The peak caller in step 1) is a standard moving window scan that 238 | compares the counts within a sliding window to the counts in a 239 | larger region outside the window, using a simple Poisson likelihood, 240 | providing a final z-score for each peak. However, the package can 241 | work with any external peak caller returning results in terms of bed 242 | files, indeed the package provides additional functionalities to 243 | load bed files of peaks and handle them as [GenomicRanges][] 244 | structures. 245 | 246 | The filtering step 2 is aimed to determine if a peak is a “true 247 | peak” on the basis of its replicability in other samples. Basing on 248 | this idea, we developed the filtering step to filter out those peaks 249 | not present in at least a user given number of samples. A further 250 | threshold can be used over the peak score. 251 | 252 | Finally, the third step produces a count matrix where each column is 253 | a sample and each row a filtered peak computed in the filtering 254 | step. The value of the matrix cell is the number of reads for the 255 | peak in the sample. 256 | 257 | Furthermore, our package provides several functionalities for 258 | [GenomicRanges][] data structure handling. One over the others gives 259 | the possibility to split a [GenomicRanges][] over the chromosomes to 260 | speed-up the computations parallelizing them over the chromosomes. 261 | 262 | - Love MI\*, Hickey P, Soneson, C, and Patro R. _Automatic metadata 263 | propagation for RNA-seq_. tximeta performs numerous annotation and 264 | metadata gathering tasks on behalf of users during the import of 265 | transcript quantifications from Salmon into _R / Bioconductor_. The 266 | key idea within tximeta is to store a signature of the transcriptome 267 | sequence, computed and stored by the index and quant functions of 268 | Salmon. This signature acts as the identifying information for later 269 | building out rich annotations and metadata in the background, on 270 | behalf of the user. This should greatly facilitate genomic 271 | workflows, where the user can immediately begin overlapping their 272 | transcriptomic data with other genomic datasets, e.g. epigenetic 273 | tracks such as ChIP or methylation, as the data has been embedded 274 | within an organism and genome context, including the proper genome 275 | version. We seek to reduce wasted time of bioinformatic analysts, 276 | prevent costly bioinformatic mistakes, and promote computational 277 | reproducibility by avoiding situations of annotation and metadata 278 | ambiguity, when files are shared publicly or among collaborators but 279 | critical details go missing. 280 | 281 | - Adithya M, Bhargava A, Wright E\*. _Improving the accuracy of 282 | taxonomic classification for identifying taxa in microbiome 283 | samples_. It has become increasingly clear that the microbiome is an 284 | essential component of human and ecosystem health. Microbiome 285 | studies frequently involve sequencing a taxonomic marker, such as 286 | the 16S rRNA or ITS, to identify the microorganisms that are present 287 | in a sample of interest. Here I will describe a new method, named 288 | IDTAXA, for taxonomic classification of marker gene sequences that 289 | exhibits a substantially lower error rate than previous 290 | approaches. In particular, IDTAXA avoids misclassifying sequences 291 | belonging to novel taxonomic groups that are not represented in 292 | existing taxonomic databases, which is the predominant type of error 293 | made by current classifiers. For example, the popular RDP Classifier 294 | incorrectly assigns 26.0% of novel 16S rRNA sequences to an existing 295 | taxonomic group when the organism actually belongs to a novel 296 | taxonomic group. In contrast, IDTAXA only incorrectly classifies 297 | 13.6% of such sequences, while correspondingly improving on the 298 | fraction of sequences correctly classified to known taxonomic 299 | groups. This has a major impact on the interpretation of microbiome 300 | data because many microbial communities contain a large fraction of 301 | previously undescribed microorganisms that are not yet represented 302 | in taxonomic databases. Furthermore, we find that many taxonomic 303 | databases contain a considerable number of misclassified sequences 304 | that can corrupt the classification process. IDTAXA is able to 305 | automatically identify errors in the training taxonomy so that users 306 | are able to take corrective action. This enables us to 307 | systematically contrast existing taxonomic databases and make 308 | recommendations for their use. Collectively, these improvements 309 | often lead to substantially different classifications on real 310 | microbiome data, which may considerably alter its 311 | interpretation. IDTAXA is available as part of the [DECIPHER][] 312 | package in R (http://DECIPHER.codes). 313 | 314 | - Innes BT\* and Bader GD. _scClustViz - Single-cell RNAseq Cluster 315 | Assessment and Interactive Visualisation_. Single-cell RNA 316 | sequencing is becoming an increasingly popular technology, used both 317 | in large multi-centre projects, and for more directed biological 318 | experiments. A common purpose for applying this technology is the 319 | in silico classification of cell types in a tissue. This is 320 | generally done using one of a myriad of clustering algorithms, based 321 | on the assumption that cells within a cell type are share similar 322 | transcriptomes, which are distinct from other cell types in the 323 | tissue. However, nearly all clustering algorithms have tunable 324 | parameters which affect, either directly or indirectly, the number 325 | of clusters they will return from the data. 326 | 327 | The R Shiny software tool outlined here provides a simple 328 | interactive interface for assessing the biological relevance of 329 | clustering results. Given that cell types are expected to have 330 | distinct gene expression patterns, it uses differential gene 331 | expression between clusters as a metric for assessing overfitting of 332 | clustering (Yuzwa et al., Cell 333 | Reports 2017. DOI:10.1016/j.celrep.2017.12.017). Along with this, 334 | it also provides interactive visualisation of: cluster-specific 335 | distributions of technical factors and other metadata; cluster-wise 336 | gene expression statistics to simplify annotation of cell types and 337 | identification of marker genes; and gene expression distributions 338 | over all cells. 339 | 340 | Interactive user interfaces for single-cell RNAseq analysis already 341 | exist, but this tool fills a distinct niche, as it is explicitly 342 | designed to assist in the biological interpretation of clustering 343 | solutions. Since it is meant to be included in analysis workflows, 344 | it has intentionally been built to be easily customized by the 345 | bioinformatician, as well as used by the non-technical biologist. 346 | 347 | This tool provides an interactive interface for visualisation, 348 | assessment, and biological interpretation of cell-type 349 | classifications in single-cell RNAseq experiments that can be easily 350 | added to existing analysis pipelines, allowing non-technical 351 | biologists easier access to their data. 352 | 353 | [DECIPHER]: https://bioconductor.org/packages/DECIPHER 354 | [DEScan2]: https://bioconductor.org/packages/DEScan2 355 | [DEsingle]: https://bioconductor.org/packages/DEsingle 356 | [GSEABenchmarkeR]: https://bioconductor.org/packages/GSEABenchmarkeR 357 | [GenomicRanges]: https://bioconductor.org/packages/GenomicRanges 358 | [dplyr]: https://bioconductor.org/packages/dplyr 359 | [gwasurvivr]: https://bioconductor.org/packages/gwasurvivr 360 | [plyranges]: https://bioconductor.org/packages/plyranges 361 | -------------------------------------------------------------------------------- /docs/assets/fonts/Noto-Sans-700/Noto-Sans-700.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 12 | 13 | 14 | 15 | 16 | 18 | 21 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 35 | 36 | 38 | 41 | 42 | 44 | 47 | 48 | 51 | 54 | 56 | 58 | 59 | 60 | 61 | 64 | 67 | 68 | 70 | 71 | 72 | 73 | 74 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 85 | 86 | 88 | 89 | 91 | 92 | 93 | 94 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 107 | 109 | 110 | 112 | 114 | 115 | 117 | 118 | 119 | 120 | 121 | 122 | 124 | 125 | 127 | 129 | 131 | 132 | 134 | 135 | 136 | 137 | 139 | 140 | 141 | 142 | 144 | 145 | 147 | 149 | 150 | 151 | 153 | 154 | 156 | 157 | 158 | 161 | 163 | 166 | 168 | 169 | 170 | 171 | 174 | 175 | 177 | 178 | 179 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 189 | 190 | 192 | 194 | 197 | 199 | 200 | 201 | 203 | 205 | 207 | 209 | 210 | 212 | 213 | 214 | 215 | 217 | 218 | 219 | 220 | 222 | 223 | 225 | 227 | 229 | 231 | 234 | 237 | 238 | 240 | 242 | 244 | 246 | 248 | 249 | 250 | 253 | 255 | 257 | 259 | 262 | 265 | 268 | 271 | 273 | 275 | 277 | 279 | 282 | 283 | 284 | 285 | 287 | 289 | 291 | 293 | 295 | 297 | 300 | 303 | 305 | 307 | 309 | 311 | 313 | 315 | 317 | 319 | 321 | 322 | 323 | 324 | 325 | 326 | 328 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | -------------------------------------------------------------------------------- /docs/assets/fonts/Noto-Sans-700italic/Noto-Sans-700italic.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 12 | 13 | 14 | 15 | 17 | 19 | 22 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 35 | 36 | 38 | 40 | 41 | 43 | 45 | 46 | 48 | 50 | 51 | 52 | 53 | 54 | 55 | 57 | 60 | 61 | 63 | 65 | 66 | 67 | 68 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 79 | 80 | 82 | 83 | 85 | 86 | 87 | 88 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 101 | 103 | 105 | 107 | 109 | 111 | 113 | 114 | 115 | 117 | 118 | 119 | 121 | 122 | 124 | 126 | 128 | 129 | 131 | 132 | 133 | 134 | 136 | 137 | 138 | 139 | 141 | 142 | 144 | 145 | 146 | 147 | 149 | 151 | 153 | 154 | 155 | 158 | 159 | 162 | 164 | 165 | 166 | 167 | 170 | 171 | 173 | 174 | 176 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 186 | 187 | 189 | 191 | 194 | 196 | 197 | 198 | 200 | 202 | 204 | 206 | 207 | 209 | 210 | 211 | 213 | 215 | 216 | 217 | 219 | 221 | 223 | 225 | 227 | 229 | 231 | 234 | 237 | 238 | 240 | 242 | 244 | 246 | 248 | 249 | 250 | 253 | 255 | 257 | 259 | 262 | 265 | 268 | 271 | 273 | 275 | 277 | 279 | 282 | 283 | 284 | 285 | 287 | 289 | 291 | 293 | 295 | 297 | 300 | 303 | 305 | 307 | 309 | 311 | 313 | 315 | 317 | 319 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | --------------------------------------------------------------------------------