Amit Rupani

Bioinformatician helping enable genome-informed precision medicine for cancers and rare diseases. RTs are stuff I read. Sometimes.

[Ming "Tommy" Tang @tangming2005]

1/ Even in the era of single-cell RNAseq, bulk-RNAseq data are still very valuable. 10 tools/papers related to bulk-RNAseq deconvolution.🧵

[Simona Cristea @simocristea]

Incredible resource: analysis of Whole Genome Sequencing data on 10,478 cancer patients spanning 35 types identifies 330 candidate driver genes, including 74 new drivers.

TP53 is a driver in 29‼️tumor types, followed by PIK3CA(18), ARID1A(16) & PTEN (14)
https://t.co/Q6Y00l4F4f

[Riyue Sunny Bao, PhD @RiyueSunnyBao]

Evolution of delayed resistance to #immunotherapy in a #melanoma #responder: "37 tumor samples over 9 years from a patient with metastatic melanoma with complete clinical response to ICB followed by delayed recurrence and death"

https://t.co/tFR9mRDJwE

[Mo Lotfollahi @mo_lotfollahi]

Our multimodal model to combine TCR sequences with scRNA-seq is finally out. The most interesting part for me was observing a clear case where the combination of TCR sequence and cell state (RNA) preserved both clonotype and cell type information, leading to a more holistic cell representation that captures phenotype and functionality. We also decompose the contribution and importance of each modality. This was a really fun project spearheaded by @DrostFelix, whose amazing perseverance during a long review period, and collaboration with @schubert_benni and other fantastic collaborators, made it possible.

Read the paper :https://t.co/UtrYcb5EyY

[Steven Salzberg 💙💛 @StevenSalzberg1]

The human chromosomes are numbered incorrectly (The fourth in an irregular series of threads about #Bioinformatics.) As most people know, 22 of our chromosomes are numbered from longest to shortest, plus we have X and Y... 1/6

[Eric Topol @EricTopol]

A major problem with cell-free tumor DNA to detect cancer (Liquid biopsy) is its very low quantity. Today @NatureMedicine use of #AI to overcome that issue with impressive results for radical signal-to-noise enrichment
https://t.co/zMWaq3F8jP
free access https://t.co/VPLpESbw3a

[antisense. @razoralign]

SeqCAT: Sequence Conversion and Analysis Toolbox https://t.co/SDWUQRhrcm

[Simona Cristea @simocristea]

“NK cells are well-positioned to steal the immunotherapy spotlight away from T cells”

https://t.co/h7Tzz7PV7z

[antisense. @razoralign]

VILOCA: Sequencing quality-aware haplotype reconstruction and mutation calling for short- and long-read data https://t.co/UWJWpnkJnb

[Javier Santoyo @jsantoyo]

Figeno: multi-region genomic figures with long-read support. #GenomicsData #LongReads #FigureGenerator #PublicationGradeFigures #Bioinformatics
https://t.co/uE1mwEUrX3

[Ming "Tommy" Tang @tangming2005]

MultiRNAflow: integrated analysis of temporal RNA-seq data with multiple biological conditions https://t.co/flsM7CSa6l

[Javier Santoyo @jsantoyo]

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing. #RareDiseases @Solve_RD #HiFi #LongReads @PacBio
@medrxivpreprint @ahoischen https://t.co/IyuU0TdeaV

[Ming "Tommy" Tang @tangming2005]

Precise identification of cell states altered in disease using healthy single-cell references | Nature Genetics
https://t.co/GEOunIZ6bF

[antisense. @razoralign]

LotOfCells: data visualization and statistics of single cell metadata https://t.co/b13ixn3Cz2

[Steven Salzberg 💙💛 @StevenSalzberg1]

We analyzed all 5734 of the available WGS samples from The Cancer Genome Atlas, and find far, far smaller numbers of microbes than previous reports that appeared (with great fanfare) in @Nature and @CellCellPress. With @YuchenGe1 et al https://t.co/qTUvkbCuPC

[Ming "Tommy" Tang @tangming2005]

The future of rapid and automated single-cell data analysis using reference mapping https://t.co/VBL5zVXnDl

[Ming "Tommy" Tang @tangming2005]

chatomics! new blog post: Common mistakes when analyzing single-cell RNAseq data https://t.co/QREPCzAI0N

[bioRxiv Bioinfo @biorxiv_bioinfo]

Empowering bioinformatics communities with Nextflow and nf-core https://t.co/ZTOfCITmdu #biorxiv_bioinfo

[Samuel Hume @DrSamuelBHume]

Cancer therapy with antibodies: many approved and many more to come

https://t.co/HDfo5DVTsG

[antisense. @razoralign]

Highly Effective Batch Effect Correction Method for RNA-seq Count Data https://t.co/TllEDGaxo3