| 04:52:04 | Ying Wang: | @Brenda Great work on sheep. Sorry, probably I didn’t hear clear. How many tissue currently have all 4 histone mark chip-seq datasets for sheep? |
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| 04:52:59 | michael sussman: | ISO 20691 covers the formats for FAIR in the life sciences. ISO 23092 covers MPEG-G encoding for the sequences and the metadata. |
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| 04:55:10 | Dominique ROCHA: | @Jessica: is there still some "orphan" tissues (not adopted yet)? |
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| 04:56:01 | Jessica Petersen: | @Dominique - yes! We have some orphans waiting for adoption. If you are interested, I would be happy to let you know what are available. |
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| 04:56:30 | Brenda Murdoch: | @Ying we have 47 tissues with all four histone marks. |
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| 04:57:17 | Jessica Petersen: | Might not have made that last comment available to all: |
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| 04:59:19 | Ted Kalbfleisch: | @Dailu. We’ve loaded about 8 datasets before, but yes, if you are looking at a region with lots of expression, it will slow down. |
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| 05:20:02 | Doreen Becker: | @Wesley: How many marker genes for inferring cell type do you usually use? |
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| 05:20:25 | Dailu Guan: | Do you consider doing deconvolution using your scRNAseq data? |
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| 05:20:38 | Michèle Tixier-Boichard: | Many thanks Wes. One practical question: are your protocols for nuclei isolation from specific tissues available on the FAANG data portal ? |
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| 05:21:02 | Dominique ROCHA: | @Wes: as you start with nuclei, will you do scRNA/scATAC seq for the same cell preps as well, to connect genes expressed and regulatory regions? |
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| 05:26:26 | Wesley Warren: | We typically use the top 20 DEGs per cluster to identify cell type. Once we have robust cell type markers we plan to test some deconvolution algorithms with our cell type specific training set. We can place our nuclei prep protocols in FAANG but will also publish these as part of an initial immune cell atlas. We don’t plan to generate scATACseq data at this point on these samples. |
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| 05:30:09 | Dominique ROCHA: | @Lingzhao: how did you compare genetic variants/RNA-seq expression diversity (across breeds)? Did you use only one tissue (i.e. blood or muscle) or did you combine several? |
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| 05:36:39 | Dominique ROCHA: | @Lingzhao: will you make public the RNA-seq data (count) like you did for cattle with the Cattle Gene Atlas? |
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| 05:39:50 | Andreas Pfenning: | @Lingzhao, Amazing resource! Did you compare orthologous genes or loci to the human GTEX project? Do observe/expect some similarity across that evolutionary distance? |
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| 05:41:55 | Andreas Pfenning: | @Lingzhao, very cool, thanks! |
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| 05:44:15 | Dailu Guan: | @Lingzhao, Great talk!! |
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| 05:47:55 | FANG Lingzhao: | @Dominique: Thank you very much for the question. when comparing eQTLs/gene expression, we only focused on one tissue at a time. Yes, all the data from FarmGTEx will be publically available later |
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| 05:48:24 | Dominique ROCHA: | @Lingzhao: super. :) |
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| 05:52:14 | FANG Lingzhao: | @ Andreas, Yes, we compared the orthologous genes and loci between humans and pigs. We did observe certain conservation of eGenes and eVariants between these two species. In the future, we will systemically compare gene expression and eQTLs across all the farm animals analysed in FarmGTEx and humans. |
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| 05:52:50 | FANG Lingzhao: | @Dailu, Thank you very much |
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| 05:54:44 | Dailu Guan: | @RuiDong, I am wondering what the conserved SNPs mean in your context? If it means a SNP in cattle is still a SNP in human, for instance? |
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| 05:56:31 | David Hawkins: | Unfortunately, I’ll miss the round table discussion. I have to teach. Great talks everyone! |
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| 06:02:49 | Dominique ROCHA: | @Ruidong: will the 50K functionaly enriched variants will work for beef cattle too? |
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| 06:03:58 | Robert Mukiibi: | @Ruidong How would such a functional panel perform for traits that do not have major QTLs eg Feed efficiency? |
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| 06:03:59 | Dominique ROCHA: | @Ruidong: overlap between QTL/eQTL is up to 10% but what about splicing QTLs. Looks like that mQTLs and sQTLs were better than eQTLs in your initial work (PNAS)? |
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| 06:04:12 | Christopher Tuggle: | @Ruidong- how does this use of FANAG type data compare with your earlier work showing evolutionarily conserved positions add to the prediction accuracy? |
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| 06:05:01 | Mazdak Salavati: | @Ruidong What are your thoughts on milk focused costume chips and selecting high producing animals at the expense of loss of longevity or welfare estate of the dairy cattle? |
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| 06:10:25 | Christopher Tuggle: | AG2PI could be interested in bringing these groups together to help USDA understand how to organize future RFAs in G2P |
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| 06:10:33 | RuidongXiang: | @Dominique: yes we do see 'better' results from sQTL, this is because sQTL mapping is more powerful, so they deliver more informative variants to be used in genomic mapping/selection; mQTL is also good, but at the moment we don't have very large sample size to increase the power of detecting them, we are still working on evaluating the merit of them. |
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| 06:12:07 | Dominique ROCHA: | @Ruidong: thanks for your reply. Is your list of finally mapped pleiotropic variants available? |
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| 06:13:15 | Christa Kühn: | The challenge for the global FAANG will be to harmonize across the major funding agencies USDA, EU etc., particularly for larger Projects beyond travel etc. |
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| 06:13:56 | RuidongXiang: | @Christopher: there are a lot of types and large volume of new FAANG type data coming and we are in the process of assessing these datasets again. The conserved regions are still very competitively enriched in heritability, but we want to see if new FAANG datasets can change this picture. |
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| 06:16:59 | James Koltes: | @Emily Clark: If folks are interested in the AG2PI data reuse meeting that Chris and I lead, a summary of that meeting is at our website: https://www.ag2pi.org/workshops-and-activities/community-workshop-2022-02-09/ There are recordings available from our speakers and we will add more information overtime. Unfortunately, I need to run, but I wanted to share this for those who may be interested. |
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| 06:17:02 | RuidongXiang: | @Mazdak: that's a very good question. In fact the customised chip is based on 35 traits, so they included non-milk traits like fertility and health. However the benefit of the customised chip has biases towards milk traits because they have much larger sample size and this is true for many organisations. so to improve this will probably need international collaborations to increase the sample size of these traits with fewer records |
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| 06:18:59 | Christopher Tuggle: | @Ruidong- thanks! It would be interesting to see if functional annotation WITHIN species can overcome the evolutionary conservation, which is so effective. |
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| 06:19:06 | RuidongXiang: | @Dominique: yes! here are they: https://figshare.com/s/93bd992a42786f9466b7 . the coordinates are based on UMD3.1 but we have tested liftovering to ARS and it works. |
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| 06:19:21 | Mazdak Salavati: | @Ruidong Thanks for your answer. Indeed focus on the dairy journey of the animal is a very difficult task but surely going forward we should try to bring more longevity phenotype recording on board chip designs. Otherwise we will end up repeating the history for the black and white cow. Very nice talk and thanks for elaborating. |
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| 06:19:26 | Robert Mukiibi: | Will there be special sessions dedicated to FAANG projects at the WCGALP? |
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| 06:19:59 | Dominique ROCHA: | @Ruidong: thanks |
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| 06:20:35 | Robert Mukiibi: | Thank you. |
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| 06:20:35 | Christa Kühn: | There is a compendium of talks in 3 subsessions, taking About 4 Hours at the WCGALP |
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| 06:21:23 | Michèle Tixier-Boichard: | Regarding bioinformatics structure, it seems that there are several parallel initiatives, when do they merge (GTex for example, what Ted presented?) |
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| 06:23:06 | RuidongXiang: | @Christopher: we hypothesise that the across-species conserved and within-species annotation may be both important but function a bit differently, it would be good to line them up to do direct comparisons. |
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| 06:25:36 | RuidongXiang: | @Mazdak: yes agreed. I think some of the presentations talking about DNA methylation related to longevity are really cool. They may be other angles from functional genomics to improve non-production activities. |
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| 06:25:49 | Meenu Bhati: | @Ruidong, have you compared mammalian conserved score vs conserved elements in 100 vertebrate ? Considering more similar function evolution than whole vertebrate. |
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| 06:27:58 | Fiona McCarthy: | I think it is important to have specific pipelines and that does not stop people from having/running their own pipelines. Standard pipelines/workflows will allow benchmarking. |
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| 06:29:53 | Christopher Tuggle: | I agree with Fiona (and Ted). Who wants to volunteer to organize a discussion |
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| 06:30:16 | Ted Kalbfleisch: | I’m happy to volunteer. |
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| 06:30:31 | Fiona McCarthy: | Me too |
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| 06:30:49 | Christa Kühn: | nf-core could be a great starting Point with the DSL2 modulation options |
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| 06:31:40 | Fiona McCarthy: | @KristaKuhn Agreed! Would like to see discussion before additional/new workflows are added. |
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| 06:32:11 | Fiona McCarthy: | Have the subgroups (e.g., bioinformatics) been meeting? |
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| 06:35:59 | Christopher Tuggle: | I think Mick as Chair of Bioinformatics has not been calling meetings, but I don’t know who he invites to such meetings. |
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| 06:37:26 | Ole Madsen: | As far as know there have not been meetings for years |
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| 06:37:56 | Peter Harrison: | @Fiona. The pandemic did really slow many of the global subgroups. As this survey highlights, time to relaunch and refresh the scope of existing subgroups and launch new ones. |
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| 06:38:15 | RuidongXiang: | @Neenu: yes we have compared conserved across 32 mammals VS 100 vertebrates in terms of heritability enrichment. conserved 100way is slightly more enriched than the conserved 30way, but they are both very strongly enriched. Our previous work (https://www.pnas.org/content/116/39/19398.short, in the supplementary data) has showed this. |
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| 06:38:52 | Mazdak Salavati: | A bioinformatics focused subgroup meeting is definitely needed. Lots has happend that can benefit from practitioners feedback. |
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| 06:41:10 | Amanda Chamberlain: | There is a lot of data now so maybe a large combined data or meta analysis would be a good way to get groups working together and producing high impact papers |
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| 06:42:16 | Fiona McCarthy: | Not than I am aware |
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| 06:42:16 | Amanda Chamberlain: | No subcommittee meetigns for ages |
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| 06:43:16 | Christa Kühn: | Maybe, we refresh the subcommittees with input from bioinformaticians AND data producers |
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| 06:43:23 | Amanda Chamberlain: | There wasn't any data to work on so it was speculative pipeline talk |
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| 06:43:38 | Peter Harrison: | A lot of the meetings were before their time, we now have a lot of highly active data and people funded to work on pipelines and other aspects that can therefore contribute more. |
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| 06:43:45 | Fiona McCarthy: | @AmandaChamberlain Agreed! |
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| 06:44:58 | Emily Clark: | When I was an early career researcher the sub-committees, particularly the metadata one, were the way I became really involved in FAANG 🙂 |
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| 06:47:41 | Amanda Chamberlain: | Will the posters and talks be available after? |
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| 06:48:33 | Mazdak Salavati: | 👏🏼 Thanks for organising the workshop. |
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| 06:49:30 | FANG Lingzhao: | Thanks a lot for organizing this exciting workshop! |
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| 06:49:36 | Dominique ROCHA: | Thanks. Interesting talks and posters. Many thanks to all presenting. How a good day/night/lunch/dinner. |
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| 06:49:38 | Ryan Corbett: | Thanks everyone! Great workshop |
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| 06:49:47 | Androniki Psifidi: | Thanks everyone :-) |
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| 06:49:56 | RuidongXiang: | excellent session! thanks everyone! |
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| 06:49:59 | Oladipupo Bello: | Thanks |
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| 06:50:13 | Smahane CHALABI: | thanks! |
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