Mariela Cortés López's Avatar

Mariela Cortés López

@altspliced.bsky.social

Alternative splicing on alternative music. #SingleCell #LongReads #RNA 🇲🇽 📍 NYC https://mcortes-lopez.github.io/ https://www.songkick.com/users/alt_spliced

449 Followers  |  686 Following  |  7 Posts  |  Joined: 12.09.2023  |  1.9504

Latest posts by altspliced.bsky.social on Bluesky

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Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox - Nature Communications The majority of human genes can produce multiple isoforms, but studying their functional relevance requires tools to target specific isoforms. Here, the authors develop a CRISPR-based exon-exon juncti...

Excited for this to be out officially! It was a great team effort and has a lot of useful tidbits for studying isoform function. www.nature.com/articles/s41...

29.07.2025 16:34 — 👍 38    🔁 16    💬 2    📌 1
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Long-read RNA sequencing of transposable elements from single cells using CELLO-seq - Nature Protocols Single-cell long-read RNA sequencing enables the high-fidelity mapping of single-cell expression data from highly sequence-similar transposable elements to unique genomic loci by correcting errors fro...

Very happy to share our protocols paper for CELLO-seq. This will make single cell long read RNA-seq more accessible and provides analysis guidelines. We hope this helps the #transposon #TEsky community and folks working on #singleCell isoform and allelic #gene expression. doi.org/10.1038/s415...

16.07.2025 16:55 — 👍 103    🔁 38    💬 8    📌 1

A comparison of long-read single-cell transcriptomic approaches https://www.biorxiv.org/content/10.1101/2025.07.03.662955v1

06.07.2025 07:30 — 👍 4    🔁 1    💬 0    📌 0
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Perplexity as a Metric for Isoform Diversity in the Human Transcriptome Long-read sequencing (LRS) has revealed a far greater diversity of RNA isoforms than earlier technologies, increasing the critical need to determine which, and how many, isoforms per gene are biologic...

New work from the lab trying to wrap our heads around the massive complexity of the human transcriptome revealed by long-read RNA-seq! Fun collab with Gloria Sheynkman. www.biorxiv.org/content/10.1...

02.07.2025 23:46 — 👍 53    🔁 22    💬 2    📌 0
a, Early models built sequence motifs to describe the consensus sequences of individual core splicing elements, such as splice sites (SSs) and intronic and/or exonic enhancers and silencers. Statistical and machine-learning models were built to output the probability of a novel sequence acting as a core splicing element. The sequence logos shown for 5′SS and 3′SS were generated from Human hg38 RefSeq annotations (code available at https://www.github.com/ulelab/splicelogos). b, As our understanding of splicing mechanisms progressed, expert-selected features were extracted from sequences and used to train integrative models to predict splicing outcomes. c, With the advent of deep-learning, models could jointly learn features directly from raw sequence input. Although theoretically, sequence context could be as large as shown in part d, in practice smaller windows of up to 30 kb have been used. d, Supervised models with convolutional and transformer layers produce multimodal genome-wide data. These models use a much larger sequence context and can predict genome-wide data including RNA sequencing coverage, which can be further processed to evaluate splicing. e, By learning how to reconstruct partially masked genomic sequences across multiple species, self-supervised masked language models capture evolutionarily conserved sequence elements and their functional context in a very generic and flexible fashion. The informative numerical representations obtained by large language models can be used for splicing prediction tasks. Here 3′SS within different sequence contexts from multiple species are shown aligned for easier interpretation, but in practice sequences do not have to be aligned. Current masked language models with application to splicing use variable context windows from 1,000 to 1 million base pairs; however, it is currently unclear whether larger context windows confer better performance

a, Early models built sequence motifs to describe the consensus sequences of individual core splicing elements, such as splice sites (SSs) and intronic and/or exonic enhancers and silencers. Statistical and machine-learning models were built to output the probability of a novel sequence acting as a core splicing element. The sequence logos shown for 5′SS and 3′SS were generated from Human hg38 RefSeq annotations (code available at https://www.github.com/ulelab/splicelogos). b, As our understanding of splicing mechanisms progressed, expert-selected features were extracted from sequences and used to train integrative models to predict splicing outcomes. c, With the advent of deep-learning, models could jointly learn features directly from raw sequence input. Although theoretically, sequence context could be as large as shown in part d, in practice smaller windows of up to 30 kb have been used. d, Supervised models with convolutional and transformer layers produce multimodal genome-wide data. These models use a much larger sequence context and can predict genome-wide data including RNA sequencing coverage, which can be further processed to evaluate splicing. e, By learning how to reconstruct partially masked genomic sequences across multiple species, self-supervised masked language models capture evolutionarily conserved sequence elements and their functional context in a very generic and flexible fashion. The informative numerical representations obtained by large language models can be used for splicing prediction tasks. Here 3′SS within different sequence contexts from multiple species are shown aligned for easier interpretation, but in practice sequences do not have to be aligned. Current masked language models with application to splicing use variable context windows from 1,000 to 1 million base pairs; however, it is currently unclear whether larger context windows confer better performance

Evolution of splicing model architectures go.nature.com/4eweliE
Figure from our recent Review: From computational models of the splicing code to regulatory mechanisms and therapeutic implications (free to read here: rdcu.be/dVNV4)

02.07.2025 10:21 — 👍 12    🔁 4    💬 0    📌 0
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International researchers are fighting for stability in troubled times We are a group of international scholars at Weill Cornell Medicine (WCM) who moved to New York City to conduct cutting edge research that…

Great day to share this op-ed we co-authored with other international postdocs, highlighting the struggle of doing science under an increasingly hostile political climate, while also fighting for fairer working conditions at WCM: medium.com/@alt_spliced...

19.06.2025 22:34 — 👍 4    🔁 0    💬 0    📌 0
Home | Nycrnasymposium Register for the inaugural NYCRNASymposium. Come share your RNA research in NYC!

Save the Date: 2025 NYC RNA Symposium — Tuesday, October 21, 2025
more @ www.nycrnasymposium.com

20.05.2025 15:31 — 👍 2    🔁 4    💬 0    📌 1
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Treatment of acute myeloid leukemia models by targeting a cell surface RNA-binding protein - Nature Biotechnology An RNA-binding protein on leukemia cells provides an effective target in mouse models.

Treatment of acute myeloid leukemia models by targeting a cell surface RNA-binding protein - @raflynn5.bsky.social go.nature.com/3YbT1In

23.04.2025 15:10 — 👍 24    🔁 9    💬 2    📌 1
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mRNA export factors store nascent transcripts within nuclear speckles as an adaptive response to transient global inhibition of transcription Transcription inhibitors also disrupt nuclear export. Here, Williams et al. reveal that mRNA export factors sense transcription inhibition and adapt by storing mature export-competent mRNA in nuclear speckles. This enables rapid release when transcription resumes and ensures retention of cellular identity and viability during a transient global transcription insult.

Why can a human tolerate a drug that globally inhibits transcription? Why do transcription inhibitors not cure cancer? Our first paper of 2025 may help explain (some) of this!

So incredibly proud of @tobiaswilliams.bsky.social & Ewa Michalak who led the work!

www.cell.com/molecular-ce...

02.01.2025 20:02 — 👍 202    🔁 71    💬 10    📌 10
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Very proud to share this work just out in NAR: spearheaded by @jamieauxillos.bsky.social and Arnauld Stigliani: TLDR-seq, a method for 5’ to 3’ end long-read sequencing of capped RNAs regardless of 3’ end polyadenylation, based on the @nanoporetech.com platform. (1/4) tinyurl.com/3c2ksdmr

07.04.2025 08:15 — 👍 28    🔁 11    💬 1    📌 1
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Beyond the Gene in Genetics: How Isoform-Resolved Analysis Empowers the Study of Both Common and Rare Genetic Variation Genetics is rapidly deepening our understanding of human health and disease by investigating common and rare genetic variants and their influence on gene expression1,2. Alternative splicing is a molec...

The Genetics research community has a problem. Most recent articles do not consider #splicing/isoforms.

Here, we analyze how important this opportunity gap is - and spoiler warning - we find it is essential for both analysis of common and rare variants

More info👇

www.medrxiv.org/content/10.1...

02.04.2025 07:45 — 👍 29    🔁 10    💬 3    📌 0
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U2-2 snRNA Mutations Alter the Transcriptome Intron removal from pre-mRNA is catalyzed by the spliceosome, which comprises 5 snRNPs containing small nuclear RNAs (snRNAs). U2 snRNA makes critical RNA-RNA and RNA-protein contacts throughout the s...

New work on human U2 snRNA variants (incl. mutations associated with cancer) from the Query lab!

www.biorxiv.org/content/10.1...

01.04.2025 18:49 — 👍 6    🔁 2    💬 0    📌 0

I am so happy to see this manuscript finally out!!! We review and discuss all analysis steps in long reads transcriptomics. Hope the community finds this useful! Hugo thanks to @carolinamonzo.bsky.social and @tianyuanliu.bsky.social for the huge work!!! @longtrec.bsky.social @hitseq.bsky.social

28.03.2025 12:52 — 👍 27    🔁 11    💬 2    📌 2

Intriguing indeed. The Al'Khafaji lab recently compared PIPseq and 10x for isoform sequencing. Although PIPseq v4, diff. gene capture between platforms is evident. They suggest that some of it might be due to high RNAse content after more cell stress in the PIPseq protocol. bsky.app/profile/aziz...

15.03.2025 16:45 — 👍 1    🔁 0    💬 0    📌 0

With the attacks on science and academia by the current administration, if those of us who have tenure don't speak up, it's really hard to continue justifying the tenure system based on academic freedom.

14.03.2025 17:42 — 👍 1679    🔁 376    💬 23    📌 21

Love this!

26.02.2025 16:17 — 👍 0    🔁 0    💬 0    📌 0
Stand Up for Science 2025 - NYC Stand up for science with us on March 7th, 2025, because science is for everyone! More info at www.standupforscience2025.org

All those asking about a Stand Up For Science event in NYC, here you go!

www.eventbrite.com/e/stand-up-f...

26.02.2025 02:41 — 👍 65    🔁 46    💬 2    📌 2

Still a bit more than a week left to apply! PhD opportunity in the Beusch lab. Please share to anyone interested in RNA biology 🧪 #RNAsky #RNAbiology

07.02.2025 17:00 — 👍 10    🔁 15    💬 0    📌 1
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Deep learning to decode sites of RNA translation in normal and cancerous tissues - Nature Communications RNA translation is a core cell process that is deregulated in cancer. Here, the authors show that a machine learning approach, RiboTIE, can reconstruct RNA translation in cancer and non-cancer cells. ...

Looking to see how #RiboSeq can improve your cancer research?

Check out how we've been developing new methods to study #medulloblastoma and other forms of #childhoodcancer.

Out in @naturecomms.bsky.social now. Thanks to Jim Clauwaert and Gerben Menschaert as well!

www.nature.com/articles/s41...

02.02.2025 20:01 — 👍 30    🔁 9    💬 2    📌 1
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Join us to connect with the vibrant #singlecell community.
📢Register for the #ISCO'25 Conference "Innovations in #SingleCell #OMICS" in Berlin!
🗓️ 12-13 May 2025
🎤 Fantastic Keynote and Invited Speakers
🫵🏿 Many slots for talks: submit your abstract
🔗http://isco-conference.eu
Please spread the word!

31.01.2025 10:29 — 👍 13    🔁 9    💬 1    📌 1
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Splicing accuracy varies across human introns, tissues, age and disease - Nature Communications Inaccuracies in RNA splicing may play a significant role in aging and disease. Here, the authors present a comprehensive characterization of splicing accuracy across over 14,000 human samples, offerin...

New in @naturecomms.bsky.social: I'm thrilled to share with you our latest work that applies #srRNAseq to understand splicing accuracy across human introns, tissues and in the context of ageing and neurodegeneration www.nature.com/articles/s41...

27.01.2025 21:53 — 👍 20    🔁 12    💬 6    📌 1
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Let's hope that the new administration doesn't ban #RNA research from their agenda. In the meantime 2 things: 1) There are some organized actions regarding funding restrictions like form.jotform.com/250226137228... 2) And grants are great but improved postdoc conditions are better #WCMPU-UAW ✊

29.01.2025 03:59 — 👍 3    🔁 0    💬 0    📌 1

I am very thankful to my current and past mentors, amazing colleagues and specially all my peers who took the time to share their insights on the application process.

29.01.2025 03:59 — 👍 1    🔁 0    💬 1    📌 0

I am happy to share that today I got my NOA for the #NCI Early K99/R00! This will support our ongoing efforts to understand RNA splicing impact in cancer phenotypes using single cell multi-omics.
Some good news around all this chaos 💫

29.01.2025 03:59 — 👍 79    🔁 7    💬 5    📌 0
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Transcriptome-wide outlier approach identifies individuals with minor spliceopathies RNA-sequencing has improved the diagnostic yield of individuals with rare diseases. Current analyses predominantly focus on identifying outliers in single genes that can be attributed to cis-acting va...

What if one variant can cause splicing outliers transcriptome-wide? In our preprint, we show how examining transcriptome-wide patterns of splicing outliers can both diagnose individuals with rare spliceopathies and uncover novel disease-gene relationships! (www.medrxiv.org/content/10.1...)

07.01.2025 21:15 — 👍 28    🔁 11    💬 1    📌 4

My attempts to use the term "immunospliceosome" in a grant went over like a lead balloon, but maybe "immunoribosome" will gain some traction. This is a SUPER exciting finding, kudos to the authors:

www.cell.com/action/showP...

04.12.2024 19:36 — 👍 10    🔁 3    💬 1    📌 0
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Cell-type specific prediction of RNA stability from RNA-protein interactions RNA-binding proteins (RBPs) are important contributors to post-transcriptional regulatory processes. The combinatorial action of expressed RBPs and non-coding factors bound to the same transcript dete...

#paperalert!
In our new study, we ask the question: Can we predict RNA stability across conditions and protocols, based solely on experimental and/or computationally predicted RNA binding protein target sites?
Read the paper to find out !

www.biorxiv.org/content/10.1...

04.12.2024 12:48 — 👍 51    🔁 13    💬 1    📌 1
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High content of nuclei-free low-quality cells in reference single-cell atlases: a call for more stringent quality control using nuclear fraction - BMC Genomics The advent of droplet-based single-cell RNA-sequencing (scRNA-seq) has dramatically increased data throughput, enabling the release of a diverse array of tissue cell atlases to the public. However, we...


Not checking nuclear markers like MALAT1 or intronic reads in your scRNA-seq data?🚨
We show their power to flag low-quality cells—even in top public datasets. It’s time to prioritize better QC for cleaner, more reliable genomics research!
Read more: bmcgenomics.biomedcentral.com/articles/10....
1/8

03.12.2024 08:38 — 👍 241    🔁 126    💬 4    📌 9
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m6A sites in the coding region trigger translation-dependent mRNA decay Zhou et al. discovered a specific role of adenosine modifications in the coding region of mRNAs. These chemical alterations slow down the movement of the ribosome during translation and trigger degrad...

Interesting findings by the Zarnack and König labs:
www.cell.com/molecular-ce...
#RNAbiology

21.11.2024 17:32 — 👍 113    🔁 37    💬 0    📌 1

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