Discover and read the best of Twitter Threads about #MultiOmics
Most recents (11)
We all think we're one of a kind.
But sometimes, we come across someone who looks just like us!
A @CellReports study tested the DNA of "fake twins".
Guess what:
They also share 🧬DNA variants related to facial features & behavior 🤯
Surprised or not really? Let’s dig in🧵👇
But sometimes, we come across someone who looks just like us!
A @CellReports study tested the DNA of "fake twins".
Guess what:
They also share 🧬DNA variants related to facial features & behavior 🤯
Surprised or not really? Let’s dig in🧵👇
First, let’s see why this study might NOT surprise you.
Monozygotic twins share almost identical facial traits & the same DNA sequence. Therefore, looking-alike strangers could follow a similar pattern.
Still, looking-alike strangers are not twins! So we can’t know for sure if:
Monozygotic twins share almost identical facial traits & the same DNA sequence. Therefore, looking-alike strangers could follow a similar pattern.
Still, looking-alike strangers are not twins! So we can’t know for sure if:
a. they share more of their genome than random people
b. if yes to (a), how much they share & what would be the functional role of the genes on which such SNVs are
c. how about #multiomics similarity, such as DNA methylation or microbiome (different in monozygotic twins)?
b. if yes to (a), how much they share & what would be the functional role of the genes on which such SNVs are
c. how about #multiomics similarity, such as DNA methylation or microbiome (different in monozygotic twins)?
We all know that babies inherit their microbiome from mum👩‍👧(vertical gene transfer).
First time ever, a brand new🔥study finds another novel mechanism for microbiome sharing between mothers & their infants: horizontal gene 🧬 transfer.
Why is this totally crazy?
Let's unpack🧵
First time ever, a brand new🔥study finds another novel mechanism for microbiome sharing between mothers & their infants: horizontal gene 🧬 transfer.
Why is this totally crazy?
Let's unpack🧵
Let's first clarify why these findings might seem shocking:
This paper found hundreds of mother-to-infant gene bacterial transmission events WITHOUT transmission of the full bacterial genomes themselves.
This is called horizontal gene transfer.
cell.com/cell/fulltext/…
This paper found hundreds of mother-to-infant gene bacterial transmission events WITHOUT transmission of the full bacterial genomes themselves.
This is called horizontal gene transfer.
cell.com/cell/fulltext/…
This paper, a #multiomics longitudinal study just out in @CellCellPress, tracked the co-development of microbiomes & metabolomes from late pregnancy to 1 year of age in 70 mother-infant pairs.
Top 5 main findings, explained👇
Top 5 main findings, explained👇
Its always difficult to take the first step, its always challenging to dive into a new domain. Happy that all the hard works brought the fruit eventually. Read our work from @RajeshPandeyLab @IGIBSocial @AcSIR_India on #singlecell #multiomics
The story highlights how important it is to understand the effect of #SARSCoV2 even after recovered from #COVID19 and how strength of #MachineLearning can be used for developing #disease #Biomarkers
This was indeed a challenging task, both on #Biology and #Bioinformatics front, but learnt a lot apart from the science part of it, especially #teamwork and #timemanagement.
#AcademicTwitter #AcademicChatter #phdlife
#AcademicTwitter #AcademicChatter #phdlife
A LinkedIn post by Sarah Hartley-Kane on the UK becoming the 'Silicon Valley of Global Genomics' linkedin.com/posts/sarahhk_…
The cost of a whole genome is probably already in the $100 mark, but not widely available: the only companies that have shown realistic goals towards the $100 genome are: MGI Tech with their DNBSEQ-T7 and T10x4 large instruments, together with @UltimaGenomics with their U100 ...
which is expected to be commercially available in 2023 and produces $1/Gb reads (albeit short-reads with homopolymer noise), and $ONT.L Oxford @nanopore, which is currently closing into the $2/Gb mark of Q20+ single pass reads on the PromethION flowcells
Really chuffed to share our latest work from Sanjana Lab out in @nature today (nature.com/articles/s4158…).
We tested >12,000 genes to find positive regulators of T cell proliferation to be used for next-gen #immunotherapies.
A thread...
We tested >12,000 genes to find positive regulators of T cell proliferation to be used for next-gen #immunotherapies.
A thread...
(2/28) T cell therapies have shown the potential to cure patients from #cancer – but even in B cell cancers, relapses outnumber cures… One of the problems is limited T cell persistence. #celltherapy #CARTcell
(3/28) We wanted to find new genes (including genes never expressed in T cells) that could improve T cell persistence. Now, the question was: should we use #crispr activation or directly deliver target genes on a #lentivirus?
Daily Bookmarks to GAVNet 06/23/2021 greeneracresvaluenetwork.wordpress.com/2021/06/23/dai…
Community-level evidence for SARS-CoV-2 vaccine protection of unvaccinated individuals
nature.com/articles/s4159…
#COVID19 #VaccinationRate #transmissibility #protection #unvaccinated
nature.com/articles/s4159…
#COVID19 #VaccinationRate #transmissibility #protection #unvaccinated
New research into the spreading of infections reveals need for greater collaboration between biology and physics
phys.org/news/2021-06-i…
#biology #physics #research #infections #spreading #collaboration
phys.org/news/2021-06-i…
#biology #physics #research #infections #spreading #collaboration
Did you know #lipids control cell identity? Yes, they do! Happy to share our first preprint on #singlecell #lipidomics in collaboration with @gio_dangelo and a fabulous team led by @CapolupoLaura and Irina Khven. biorxiv.org/content/10.110…
We used #MALDI imaging mass spectrometry to measure the single-cell lipidomes of hundreds of individual human dermal #fibroblasts. We identify specific lipid metabolic pathways that display cell-to-cell variability. Unexpectedly, single cells clustered by lipid composition!
It took a lot of experiments to convince us, but now we can say it confidently: there is such a thing as a #lipotype!! First of all, toxin-based lipid staining validated the existence of the different populations of dermal fibroblasts both in vitro and in vivo.
My first piece on #multiomics #IBD as part of my thesis is out as a preprint!
I'll summarize it on this thread
medrxiv.org/content/10.110…
I'll summarize it on this thread
medrxiv.org/content/10.110…
First, we check how the many parameters of RGCCA #rstats influence and work in their original dataset. We check that different designs select different variables, we discard the superblock design.
2/x
2/x
Then we look our own dataset of #IBD samples undergoing a treatment and some controls. We use a method to find good models of the data. The method is available as #rstats package, with its own #pkgdown website llrs.github.io/inteRmodel/
3/x
3/x
PathME unsupervised #multiomics
1) genes space → pathways space
2) for each pathway: collapse pathways from multiple omics to one per patient
3) sparse NMF biclustering
âś“compared against SNF and iCluster
âś“TCGA x 4
âś“hyperparameters
âś“source code
âś“5-fold CV
#SundayMultiOmics
1) genes space → pathways space
2) for each pathway: collapse pathways from multiple omics to one per patient
3) sparse NMF biclustering
âś“compared against SNF and iCluster
âś“TCGA x 4
âś“hyperparameters
âś“source code
âś“5-fold CV
#SundayMultiOmics
Article: doi.org/10.1186/s12859…
GitHub: github.com/AminaLEM/PathME
Figure © by authors, reused under CC-BY 4.0:
creativecommons.org/licenses/by/4.0
#omics #NeuralNetworks
GitHub: github.com/AminaLEM/PathME
Figure © by authors, reused under CC-BY 4.0:
creativecommons.org/licenses/by/4.0
#omics #NeuralNetworks
Worth noting:
- authors use sNMF consensus from 500 runs (cophenetic correlation + permutation testing to choose # of clusters)
- the autoencoders are denoising
- worth praise is the effort into interpretability (of both features/omics & clinical associations) - see supplement!
- authors use sNMF consensus from 500 runs (cophenetic correlation + permutation testing to choose # of clusters)
- the autoencoders are denoising
- worth praise is the effort into interpretability (of both features/omics & clinical associations) - see supplement!
Multi-Omic inTegrative Analysis (MOTA): an application of differential network analysis to #multiomics.
âś“3 non-TCGA datasets (HCC vs CIRR)
âś“explanations for parameters & rgCCA choice
âś“good at known cancer drivers recovery
âś“consistent across cohorts
#SundayMultiOmics 1/n
âś“3 non-TCGA datasets (HCC vs CIRR)
âś“explanations for parameters & rgCCA choice
âś“good at known cancer drivers recovery
âś“consistent across cohorts
#SundayMultiOmics 1/n
[[ Detailed description & comments follow ]]
Link: doi.org/10.3390/metabo…
Figures © by authors, reused under CC-BY 4.0
creativecommons.org/licenses/by/4.…
Developed @LombardiCancer @gumedcenter (sorry I could not to find authors on Twitter)
Link: doi.org/10.3390/metabo…
Figures © by authors, reused under CC-BY 4.0
creativecommons.org/licenses/by/4.…
Developed @LombardiCancer @gumedcenter (sorry I could not to find authors on Twitter)
["Layman" introduction]: many studies focus on the differences in the abundance of specific biomolecules; while very useful to highlight the obvious #biomarkers, it may not explain complex disease mechanisms & can be very sensitive to cohort differences (e.g. in #metabolomics)
A true gem among #multiomics preprints: Integrative Network Fusion by @MarcoChierici, @nicole_bussola, @viperale, et al:
âś“ 3 TCGA cancers & simulated data
âś“ cross-validation described in detail
âś“ flow diagram
âś“ source code & data shared
âś“ packages w/ version, cited
/n
âś“ 3 TCGA cancers & simulated data
âś“ cross-validation described in detail
âś“ flow diagram
âś“ source code & data shared
âś“ packages w/ version, cited
/n
- [the method description & comments follows]
- link: biorxiv.org/content/10.110…
- licence the above figures/tables: CC BY-NC-ND 4.0
- an earlier version of INF was previously presented in 2018: doi.org/10.1186/s13062…
- this is the first tweet in #SundayMultiOmics series
- link: biorxiv.org/content/10.110…
- licence the above figures/tables: CC BY-NC-ND 4.0
- an earlier version of INF was previously presented in 2018: doi.org/10.1186/s13062…
- this is the first tweet in #SundayMultiOmics series
[[Introduction]]: Similarity network fusion (SNF, doi.org/10.1038/nmeth.…) is a popular technique (600+ citations, a lot for multi-omics!) for getting a sort of consensus signal from multiple omics; it requires the same patients (less commonly - observations) in each omic.
