Jafar Jabbari
@jafarjabbari
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Nanopore, PacBio, Illumina and Single Cell Genomics Platforms Technologies Developer, Microbiomics, Meta-omics, Genomics Innovation and Development Scientist
Melbourne, Victoria
Joined May 2011
Excited to share our teamwork on spatially profiling human bone marrow trephines is now online! 🔬🗺️🦴 https://t.co/85kSWG6PTE More high-res atlases to come - liquid/solid cancer in bone, cellular therapies settings..💪 #Xenium #myeloma #spatialomics
ashpublications.org
Key PointsThe first map of human bone marrow was generated using single-cell spatial transcriptomics profiling of 5001 genes.Malignant plasma cells form sp
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Are you using separate assays to measure transduction efficiency, vector copy number, and integration sites? Our high-throughput, droplet-based single-cell method streamlines all three analyses into a single assay. Visit poster #159 today at #LorneGenome25 to learn more!
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Tired of tedious RNA extractions for your transcriptomic studies? Introducing TIRE-seq, a novel protocol that streamlines bulk RNA-seq by integrating sample extraction with library preparation. The whole process can be completed in a day and a half. (1/6)
biorxiv.org
RNA sequencing (RNA-seq) is widely used in biomedical research, advancing our understanding of gene expression across biological systems. Traditional methods require upstream RNA extraction from...
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1 pION flow cell, 1 load, no wash, 2 runs because pores looked good after completion of 1st run and around 200GB estimated output. I want more flow cells like this! @nanopore
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Our long read benchmarking work is finally published🥳
Long-read sequencing tools can capture the full picture of genomes and transcriptomes. An Analysis assesses the performance of long-read RNA sequencing analysis tools including isoform detection and differential expression analysis workflows. https://t.co/jbvKS7NX2b
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James! Your impressive multidisciplinary approach, integrating metabolomics, single-cell genomics, and more, is outstanding!
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Thanks for invitation and audience engagement.
Great @SA_genomics event last night with @nanopore @UniofAdelaide. A huge THANK YOU to our speakers (@CristobalOnetto, @jafarjabbari and Dr. Kevin Fenix) for telling us about their research. And thank you to everyone who attended.
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all made possible thanks to great collaborators @julienisking @greg_tram @jafarjabbari and many others from across the globe
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Read my latest @nanopore blog how we from @WEHI_research developed new single-cell long-read methods to apply to primary #leukemia patient samples to fully uncover #venetoclax resistance.
In this blog, @Rachel_Thijssen discussed performing whole transcriptome nanopore sequencing on 10,000 single cells. She used full-length transcripts to identify novel mutations & isoform, linking cancer cell state to transcriptomics & alternative splicing:
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I will share an overview of our established and in progress methods using @10xGenomics and @naopore platforms in single cell genomics.
10 Nov #SCRUM meeting. Senior Researcher @jafarjabbari from @WEHI_research presents: High throughput single-cell genomics utilizing long-read sequencing technologies. In person or online. Register: https://t.co/12WyzMTjMp
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Our review on Spatial Transcriptomics is out @NatureBiotech! With @insitubiology and @macosko we reviewed the technology advance in sequencing and imaging-based ST and envisioned the future of tissue genomic where dynamics and perturbation measured at spatial levels.
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I do not know the secret for this healthy @nanopore flow cell! Could it be the low translocation speed of R10.4 or timely activation of a coded health pack? 48 hours run, 25M reads, 95 Gb data, good Q score and still going strong. https://t.co/thVNaJZSEi
Things that @nanopore never told me. Over 2,500 pores sequencing at the same time using Q20EA kit and R10.4 flow cell. This number would go up but I wo'nt be in the lab to see it.
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Things that @nanopore never told me. Over 2,500 pores sequencing at the same time using Q20EA kit and R10.4 flow cell. This number would go up but I wo'nt be in the lab to see it.
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Great initiative by a pioneer.
OK, I think it's time we started a "Clonal Biology" interest group in Australia. If you're into clonal fate (patterns, regulators) in any field (cancer, development, immunology) using various tech (barcoding, single cell tracking) let's get a list of names and see what we can do.
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Excellent use of @10xGenomics single cell cDNA to reveal acquired Venetoclax resistance in CLL patients by capturing BCL2 family isoforms from thousands of cells and @nanopore sequencing. @Rachel_Thijssen, @Luyi_T
https://t.co/rlwEMOZtbC
https://t.co/4alVasx2HW
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Our paper on Glimma v2 is out at NARGAB https://t.co/L1jBHGlPB5. Congrats Has Kariyawasam @shian_su Oliver Voogd & Charity Law. Next time you're analysing gene expression data & want to share results with collaborators, give Glimma a go! #rstats #bioconductor #interactivedataviz
academic.oup.com
Abstract. Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update
Glimma v2 allows interactive plots to be embedded in #Rmarkdown, includes new dash-board-style controls for changing plot features & options for saving figures and results. Give Glimma v2 a go next time you have gene expression analysis results to share! #bioconductor (2/2)
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Happy to share my first first author paper! It is finally online! We benchmarked in total 10 #scRNAseq preprocessing workflows and explored how they influence the downstream results.
Latest paper in our lab's #scRNAseq benchmarking series looks at preprocessing tools (i.e. comparing methods that create cell x gene count matrices) is out👇 https://t.co/dIdGrYugoV. Epic study led by @YueYOU9 & collaborators @Luyi_T @shian_su @DongXueyi @jafarjabbari @PeteHaitch
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Great experience working with @Luyi_T , @mritchieau and the team.
We had a lot of fun playing with scRNAseq and long-read sequencing together. Thanks to all coauthors for their help and support. especially @jafarjabbari
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Amazed at throughput of @nanopore #PromethION, sequencing @10xGenomics Multiome single nuclei cDNA. Over 130M reads!
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