Rebekah Loving retweeted
This is legit.
Preprint on "BWT construction and search at the terabase scale". We can compress 100 human genomes to 11GB in 21 hours, find SMEMs with it, do affine-gap alignment and retrieve similar local haplotypes. 7.3Tb commonly sequenced bacterial genomes ⇒ 30GB arxiv.org/abs/2409.00613
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Rebekah Loving retweeted
Watch the talk from Joseph Rich, grad student in @lpachter's lab, from BioC2024 timestamp ~12:30 youtu.be/T6cOyKHlxpE?si…
The impact of package selection and versioning on single-cell RNA-seq analysis biorxiv.org/cgi/content/sh… #biorxiv_bioinfo
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Rebekah Loving retweeted
☛ scRNA-seq is solved, we're moving on to multiomics and ML
☛ forgot to mention, DE for sc is just DE for bulk, we don't have a way to use cell-level info
☛ also DE for bulk requires normalization and every method gives different results; try a bunch and see if they agree
☛ forgot to mention, DE for sc is just DE for bulk, we don't have a way to use cell-level info
☛ also DE for bulk requires normalization and every method gives different results; try a bunch and see if they agree
Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data.
#science #publication #research #publications
lifescience.net/publications/9…
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Rebekah Loving retweeted
We are excited to welcome Dr. Lambda Moses, our new Postdoctoral Research Scientist! She specializes in spatial-omics data analysis and software development. In the @bidumit group, Lambda focuses on computational biology, single-cell genomics, and statistical bioinformatics.
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Rebekah Loving retweeted
I'm giving a talk this Thursday at 5p at Harvard Medical School in the Dept. of Biomedical Informatics. If you are in the area and would like to attend in person- please DM me.
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Rebekah Loving retweeted
The mantra that spatial transcriptomics is about location, location, location is catchy, but what does it really mean? We have just posted biorxiv.org/content/10.110…, work of Kayla Jackson et al., that describes the concordex method for identifying spatial homogeneous regions. 1/🧵
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Rebekah Loving retweeted
biVI models the biophysical processes generating nascent and mature single-cell transcriptomes using variational autoencoders.
@MariaCarilli @GorinGennady @funion10 @lpachter
nature.com/articles/s4159…
@MariaCarilli @GorinGennady @funion10 @lpachter
nature.com/articles/s4159…
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Rebekah Loving retweeted
Code to reproduce results is available at github.com/pachterlab/LSR… 13/13
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Rebekah Loving retweeted
Many thanks to Barbara Wold, who also supervised the work, and the team from @calizavi lab team from @UCIrvine who did exceptional work generating the matched ONT and Illumina data. An incredible accomplishment by @bound_to_love! x.com/MarissaKawehi/… 12/
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Rebekah Loving retweeted
The preprint has detailed analyses not just demonstrating performance but also examining why lr-kallisto performs well and how it can be optimized. Tl;dr, longer k-mers in the transcriptome de Bruijn graph can be helpful. 11/
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Rebekah Loving retweeted
lr-kallisto also performed well on a @PacBio dataset which was Illumina sequenced as well, albeit with less performance gain because the long-read data has a very high error rate (12.4%). 10/
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Rebekah Loving retweeted
We also tested lr-kallisto on the main dataset used in the recent Oarfish preprint (the HCT116 cell line w/ Illumina and ONT), and succeeded in replicating the Oarfish results while showing that lr-kallisto outperforms Oarfish. 9/
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Rebekah Loving retweeted
... and we performed numerous benchmarks on simulated data. These benchmarks corroborated our results on experimental data, and provided information on performance limits. In particular, lr-kallisto performance starts to degrade at very high error rates. 8/
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Rebekah Loving retweeted
We also performed extensive benchmarking on the amazing #LRGASP resource that was recently published... x.com/anaconesa/stat… 7/
I´m thrilled to see our #LRGASP paper published in @naturemethods today. This is the work of many to benchmark long-read methods for transcriptomics, and a must-read paper for those using lrRNA-seq. Special thnks to @FJPardoPalacios and @TheBrooksLab (1/6)
rdcu.be/dJ9ev
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Rebekah Loving retweeted
We benchmarked some other tools that have recently been developed for long-read quantification, including Bambu, Oarfish, and IsoQuant. lr-kallisto is faster and more accurate. 6/
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Rebekah Loving retweeted
The main result (Fig. 1) is that long-read quantification with lr-kallisto is consistent with kallisto short-read quantification. The CCC (concordance correlation coefficients) are extremely high, demonstrating both high quality data and high accuracy quantification 5/
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Rebekah Loving retweeted
We decided to adapt kallisto, which @DelaneyKSull has recently updated and improved in many ways, for long-read quantification. @bound_to_love figured out that several modifications and improvement, especially w.r.t fragment lengths were needed. x.com/lpachter/statu… 3/
Recent changes to #scRNAseq quantification tools can leave one dizzy. In biorxiv.org/content/10.110… w/ @DelaneyKSull, @kreldjarn, @NikhilaSwarna, @GuillaumOleSan & @pmelsted we clarify what kallisto, Cell Ranger, salmon/alevin and STARsolo are doing and how they're performing. 1/🧵
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Rebekah Loving retweeted
Long-read sequencing has been transformative for genome sequencing and transcript (isoform) discovery, but read depth, constrained by cost, has limited its use for quantification. That has changed in the past few years. And error rates have dropped. 2/ x.com/geertvangeest/…
Based on the table of @AlbertVilella I created this graph of (minimum) sequencing cost vs (maximum) yield. Pretty strong correlation. Long read sequencing methods from @PacBio and @nanopore doing remarkably well #NGS #Bioinformatics.
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Rebekah Loving retweeted
In a new work led by @bound_to_love, joint with @calizavi and his lab, we introduce a method based on kallisto for accurate quantification of @nanopore or @PacBio long-read RNA-seq, and demonstrate its accuracy with numerous comparisons and benchmarks 1/🧵 biorxiv.org/content/10.110…
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